WorldWideScience

Sample records for herg classification model

  1. Modeling of the hERG K+ Channel Blockage Using Online Chemical Database and Modeling Environment (OCHEM).

    Science.gov (United States)

    Li, Xiao; Zhang, Yuan; Li, Huanhuan; Zhao, Yong

    2017-12-01

    Human ether-a-go-go related gene (hERG) K+ channel plays an important role in cardiac action potential. Blockage of hERG channel may result in long QT syndrome (LQTS), even cause sudden cardiac death. Many drugs have been withdrawn from the market because of the serious hERG-related cardiotoxicity. Therefore, it is quite essential to estimate the chemical blockage of hERG in the early stage of drug discovery. In this study, a diverse set of 3721 compounds with hERG inhibition data was assembled from literature. Then, we make full use of the Online Chemical Modeling Environment (OCHEM), which supplies rich machine learning methods and descriptor sets, to build a series of classification models for hERG blockage. We also generated two consensus models based on the top-performing individual models. The consensus models performed much better than the individual models both on 5-fold cross validation and external validation. Especially, consensus model II yielded the prediction accuracy of 89.5 % and MCC of 0.670 on external validation. This result indicated that the predictive power of consensus model II should be stronger than most of the previously reported models. The 17 top-performing individual models and the consensus models and the data sets used for model development are available at https://ochem.eu/article/103592. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  2. hERG classification model based on a combination of support vector machine method and GRIND descriptors

    DEFF Research Database (Denmark)

    Li, Qiyuan; Jorgensen, Flemming Steen; Oprea, Tudor

    2008-01-01

    and diverse library of 495 compounds. The models combine pharmacophore-based GRIND descriptors with a support vector machine (SVM) classifier in order to discriminate between hERG blockers and nonblockers. Our models were applied at different thresholds from 1 to 40 mu m and achieved an overall accuracy up...

  3. Dynamics of hERG closure allow novel insights into hERG blocking by small molecules.

    Science.gov (United States)

    Schmidtke, Peter; Ciantar, Marine; Theret, Isabelle; Ducrot, Pierre

    2014-08-25

    Today, drug discovery routinely uses experimental assays to determine very early if a lead compound can yield certain types of off-target activity. Among such off targets is hERG. The ion channel plays a primordial role in membrane repolarization and altering its activity can cause severe heart arrhythmia and sudden death. Despite routine tests for hERG activity, rather little information is available for helping medicinal chemists and molecular modelers to rationally circumvent hERG activity. In this article novel insights into the dynamics of hERG channel closure are described. Notably, helical pairwise closure movements have been observed. Implications and relations to hERG inactivation are presented. Based on these dynamics novel insights on hERG blocker placement are presented, compared to literature, and discussed. Last, new evidence for horizontal ligand positioning is shown in light of former studies on hERG blockers.

  4. Indexing molecules for their hERG liability.

    Science.gov (United States)

    Rayan, Anwar; Falah, Mizied; Raiyn, Jamal; Da'adoosh, Beny; Kadan, Sleman; Zaid, Hilal; Goldblum, Amiram

    2013-07-01

    The human Ether-a-go-go-Related-Gene (hERG) potassium (K(+)) channel is liable to drug-inducing blockage that prolongs the QT interval of the cardiac action potential, triggers arrhythmia and possibly causes sudden cardiac death. Early prediction of drug liability to hERG K(+) channel is therefore highly important and preferably obligatory at earlier stages of any drug discovery process. In vitro assessment of drug binding affinity to hERG K(+) channel involves substantial expenses, time, and labor; and therefore computational models for predicting liabilities of drug candidates for hERG toxicity is of much importance. In the present study, we apply the Iterative Stochastic Elimination (ISE) algorithm to construct a large number of rule-based models (filters) and exploit their combination for developing the concept of hERG Toxicity Index (ETI). ETI estimates the molecular risk to be a blocker of hERG potassium channel. The area under the curve (AUC) of the attained model is 0.94. The averaged ETI of hERG binders, drugs from CMC, clinical-MDDR, endogenous molecules, ACD and ZINC, were found to be 9.17, 2.53, 3.3, -1.98, -2.49 and -3.86 respectively. Applying the proposed hERG Toxicity Index Model on external test set composed of more than 1300 hERG blockers picked from chEMBL shows excellent performance (Matthews Correlation Coefficient of 0.89). The proposed strategy could be implemented for the evaluation of chemicals in the hit/lead optimization stages of the drug discovery process, improve the selection of drug candidates as well as the development of safe pharmaceutical products. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

  5. Characterization of hERG1a and hERG1b potassium channels-a possible role for hERG1b in the I (Kr) current

    DEFF Research Database (Denmark)

    Larsen, Anders Peter; Olesen, Søren-Peter; Grunnet, Morten

    2008-01-01

    I (Kr) is the fast component of the delayed rectifier potassium currents responsible for the repolarization of the cardiac muscle. The molecular correlate underlying the I (Kr) current has been identified as the hERG1 channel. Recently, two splice variants of the hERG1 alpha-subunit, hERG1a and hERG......1b, have been shown to be co-expressed in human cardiomyocytes. In this paper, we present the electrophysiological characterization of hERG1a, hERG1b, and co-expressed hERG1a/b channels in a mammalian expression system using the whole-cell patch clamp technique. We also quantified the messenger RNA...... (mRNA) levels of hERG1a and hERG1b in human cardiac tissue, and based on the expressed ratios, we evaluated the resulting currents in Xenopus laevis oocytes. Compared to hERG1a channels, activation was faster for both hERG1b and hERG1a/b channels. The deactivation kinetics was greatly accelerated...

  6. A novel hypothesis for the binding mode of HERG channel blockers

    International Nuclear Information System (INIS)

    Choe, Han; Nah, Kwang Hoon; Lee, Soo Nam; Lee, Han Sam; Lee, Hui Sun; Jo, Su Hyun; Leem, Chae Hun; Jang, Yeon Jin

    2006-01-01

    We present a new docking model for HERG channel blockade. Our new model suggests three key interactions such that (1) a protonated nitrogen of the channel blocker forms a hydrogen bond with the carbonyl oxygen of HERG residue T623; (2) an aromatic moiety of the channel blocker makes a π-π interaction with the aromatic ring of HERG residue Y652; and (3) a hydrophobic group of the channel blocker forms a hydrophobic interaction with the benzene ring of HERG residue F656. The previous model assumes two interactions such that (1) a protonated nitrogen of the channel blocker forms a cation-π interaction with the aromatic ring of HERG residue Y652; and (2) a hydrophobic group of the channel blocker forms a hydrophobic interaction with the benzene ring of HERG residue F656. To test these models, we classified 69 known HERG channel blockers into eight binding types based on their plausible binding modes, and further categorized them into two groups based on the number of interactions our model would predict with the HERG channel (two or three). We then compared the pIC 5 value distributions between these two groups. If the old hypothesis is correct, the distributions should not differ between the two groups (i.e., both groups show only two binding interactions). If our novel hypothesis is correct, the distributions should differ between Groups 1 and 2. Consistent with our hypothesis, the two groups differed with regard to pIC 5 , and the group having more predicted interactions with the HERG channel had a higher mean pIC 5 value. Although additional work will be required to further validate our hypothesis, this improved understanding of the HERG channel blocker binding mode may help promote the development of in silico predictions methods for identifying potential HERG channel blockers

  7. Escitalopram block of hERG potassium channels.

    Science.gov (United States)

    Chae, Yun Ju; Jeon, Ji Hyun; Lee, Hong Joon; Kim, In-Beom; Choi, Jin-Sung; Sung, Ki-Wug; Hahn, Sang June

    2014-01-01

    Escitalopram, a selective serotonin reuptake inhibitor, is the pharmacologically active S-enantiomer of the racemic mixture of RS-citalopram and is widely used in the treatment of depression. The effects of escitalopram and citalopram on the human ether-a-go-go-related gene (hERG) channels expressed in human embryonic kidney cells were investigated using voltage-clamp and Western blot analyses. Both drugs blocked hERG currents in a concentration-dependent manner with an IC50 value of 2.6 μM for escitalopram and an IC50 value of 3.2 μM for citalopram. The blocking of hERG by escitalopram was voltage-dependent, with a steep increase across the voltage range of channel activation. However, voltage independence was observed over the full range of activation. The blocking by escitalopram was frequency dependent. A rapid application of escitalopram induced a rapid and reversible blocking of the tail current of hERG. The extent of the blocking by escitalopram during the depolarizing pulse was less than that during the repolarizing pulse, suggesting that escitalopram has a high affinity for the open state of the hERG channel, with a relatively lower affinity for the inactivated state. Both escitalopram and citalopram produced a reduction of hERG channel protein trafficking to the plasma membrane but did not affect the short-term internalization of the hERG channel. These results suggest that escitalopram blocked hERG currents at a supratherapeutic concentration and that it did so by preferentially binding to both the open and the inactivated states of the channels and by inhibiting the trafficking of hERG channel protein to the plasma membrane.

  8. Determinants of Isoform-Specific Gating Kinetics of hERG1 Channel: Combined Experimental and Simulation Study

    Directory of Open Access Journals (Sweden)

    Laura L. Perissinotti

    2018-04-01

    Full Text Available IKr is the rapidly activating component of the delayed rectifier potassium current, the ion current largely responsible for the repolarization of the cardiac action potential. Inherited forms of long QT syndrome (LQTS (Lees-Miller et al., 1997 in humans are linked to functional modifications in the Kv11.1 (hERG ion channel and potentially life threatening arrhythmias. There is little doubt now that hERG-related component of IKr in the heart depends on the tetrameric (homo- or hetero- channels formed by two alternatively processed isoforms of hERG, termed hERG1a and hERG1b. Isoform composition (hERG1a- vs. the b-isoform has recently been reported to alter pharmacologic responses to some hERG blockers and was proposed to be an essential factor pre-disposing patients for drug-induced QT prolongation. Very little is known about the gating and pharmacological properties of two isoforms in heart membranes. For example, how gating mechanisms of the hERG1a channels differ from that of hERG1b is still unknown. The mechanisms by which hERG 1a/1b hetero-tetramers contribute to function in the heart, or what role hERG1b might play in disease are all questions to be answered. Structurally, the two isoforms differ only in the N-terminal region located in the cytoplasm: hERG1b is 340 residues shorter than hERG1a and the initial 36 residues of hERG1b are unique to this isoform. In this study, we combined electrophysiological measurements for HEK cells, kinetics and structural modeling to tease out the individual contributions of each isoform to Action Potential formation and then make predictions about the effects of having various mixture ratios of the two isoforms. By coupling electrophysiological data with computational kinetic modeling, two proposed mechanisms of hERG gating in two homo-tetramers were examined. Sets of data from various experimental stimulation protocols (HEK cells were analyzed simultaneously and fitted to Markov-chain models (M-models

  9. Voltage-Dependent Gating of hERG Potassium Channels

    Science.gov (United States)

    Cheng, Yen May; Claydon, Tom W.

    2012-01-01

    The mechanisms by which voltage-gated channels sense changes in membrane voltage and energetically couple this with opening of the ion conducting pore has been the source of significant interest. In voltage-gated potassium (Kv) channels, much of our knowledge in this area comes from Shaker-type channels, for which voltage-dependent gating is quite rapid. In these channels, activation and deactivation are associated with rapid reconfiguration of the voltage-sensing domain unit that is electromechanically coupled, via the S4–S5 linker helix, to the rate-limiting opening of an intracellular pore gate. However, fast voltage-dependent gating kinetics are not typical of all Kv channels, such as Kv11.1 (human ether-à-go-go related gene, hERG), which activates and deactivates very slowly. Compared to Shaker channels, our understanding of the mechanisms underlying slow hERG gating is much poorer. Here, we present a comparative review of the structure–function relationships underlying activation and deactivation gating in Shaker and hERG channels, with a focus on the roles of the voltage-sensing domain and the S4–S5 linker that couples voltage sensor movements to the pore. Measurements of gating current kinetics and fluorimetric analysis of voltage sensor movement are consistent with models suggesting that the hERG activation pathway contains a voltage independent step, which limits voltage sensor transitions. Constraints upon hERG voltage sensor movement may result from loose packing of the S4 helices and additional intra-voltage sensor counter-charge interactions. More recent data suggest that key amino acid differences in the hERG voltage-sensing unit and S4–S5 linker, relative to fast activating Shaker-type Kv channels, may also contribute to the increased stability of the resting state of the voltage sensor. PMID:22586397

  10. Voltage-dependent gating of hERG potassium channels

    Directory of Open Access Journals (Sweden)

    Yen May eCheng

    2012-05-01

    Full Text Available The mechanisms by which voltage-gated channels sense changes in membrane voltage and energetically couple this with opening of the ion conducting pore has been the source of significant interest. In voltage-gated potassium (Kv channels, much of our knowledge in this area comes from Shaker-type channels, for which voltage-dependent gating is quite rapid. In these channels, activation and deactivation are associated with rapid reconfiguration of the voltage-sensing domain unit that is electromechanically coupled, via the S4-S5 linker helix, to the rate-limiting opening of an intracellular pore gate. However, fast voltage-dependent gating kinetics are not typical of all Kv channels, such as Kv11.1 (human ether-a-go-go related gene, hERG, which activates and deactivates very slowly. Compared to Shaker channels, our understanding of the mechanisms underlying slow hERG gating is much poorer. Here, we present a comparative review of the structure-function relationships underlying voltage-dependent gating in Shaker and hERG channels, with a focus on the roles of the voltage sensing domain and the S4-S5 linker that couples voltage sensor movements to the pore. Measurements of gating current kinetics and fluorimetric analysis of voltage sensor movement are consistent with models suggesting that the hERG activation pathway contains a voltage independent step, which limits voltage sensor transitions. Constraints upon hERG voltage sensor movement may result from loose packing of the S4 helices and additional intra-voltage sensor counter charge interactions. More recent data suggest that key amino acid differences in the hERG voltage sensing unit and S4-S5 linker, relative to fast activating Shaker-type Kv channels, may also contribute to the increased stability of the resting state of the voltage sensor.

  11. In vitro chronic effects on hERG channel caused by the marine biotoxin Yessotoxin.

    Directory of Open Access Journals (Sweden)

    Sara Fernández Ferreiro

    2014-06-01

    Currently, published evidence indicates that hERG channel dysfunction can be due to more than one mechanism for many drugs (Guth, 2007. Alterations of hERG channel trafficking are considered an important factor in hERG-related cardiotoxicity. Actually, a screening study revealed that almost 40% of the drugs that block Ikr have also trafficking effects (Wible et al., 2005. Although YTX does not block hERG channels, it has been historically described as cardiotoxic due to in vivo damage to cardiomyocytes. Our results show that YTX induces a significant increase of hERG channel levels on the extracellular side of the plasma membrane in vitro. YTX causes cell death in many cell lines (Korsnes and Espenes, 2011 and the alterations of surface hERG levels might be related to the apoptotic process. However, annexin-V, a relatively early marker of apoptosis (Vermes et al., 1995, occurs later than the increase of surface hERG. Additionally, staurosporine triggered apoptosis without a simultaneous increase of surface hERG, so events are not necessarily related. Therefore YTX-induced elevated hERG in the plasma membrane seem to be independent of apoptosis. Functional implications of hERG currents have been described after alterations of cell surface hERG density (Guth, 2007. YTX did not cause significant alterations of hERG currents. Furthermore the hERG levels after YTX treatment were duplicated, so the effect on currents should be clearly evidenced if these channels were functional. The hERG channels on the cell surface are regulated by its production, translocation to the plasma membrane and degradation. The increase of extracellular channel could be a consequence of a higher production and externalization or a slower degradation. Higher synthesis in our cell model would not be physiologically relevant but our results demonstrated that the amount of immature hERG is reduced instead of increased. Fully glycosylated hERG seems slightly increased in these conditions but it is

  12. High Glucose Represses hERG K+ Channel Expression through Trafficking Inhibition

    Directory of Open Access Journals (Sweden)

    Yuan-Qi Shi

    2015-08-01

    Full Text Available Background/Aims: Abnormal QT prolongation is the most prominent cardiac electrical disturbance in patients with diabetes mellitus (DM. It is well known that the human ether-ago-go-related gene (hERG controls the rapid delayed rectifier K+ current (IKr in cardiac cells. The expression of the hERG channel is severely down-regulated in diabetic hearts, and this down-regulation is a critical contributor to the slowing of repolarization and QT prolongation. However, the intracellular mechanisms underlying the diabetes-induced hERG deficiency remain unknown. Methods: The expression of the hERG channel was assessed via western blot analysis, and the hERG current was detected with a patch-clamp technique. Results: The results of our study revealed that the expression of the hERG protein and the hERG current were substantially decreased in high-glucose-treated hERG-HEK cells. Moreover, we demonstrated that the high-glucose-mediated damage to the hERG channel depended on the down-regulation of protein levels but not the alteration of channel kinetics. These discoveries indicated that high glucose likely disrupted hERG channel trafficking. From the western blot and immunoprecipitation analyses, we found that high glucose induced trafficking inhibition through an effect on the expression of Hsp90 and its interaction with hERG. Furthermore, the high-glucose-induced inhibition of hERG channel trafficking could activate the unfolded protein response (UPR by up-regulating the expression levels of activating transcription factor-6 (ATF-6 and the ER chaperone protein calnexin. In addition, we demonstrated that 100 nM insulin up-regulated the expression of the hERG channel and rescued the hERG channel repression caused by high glucose. Conclusion: The results of our study provide the first evidence of a high-glucose-induced hERG channel deficiency resulting from the inhibition of channel trafficking. Furthermore, insulin promotes the expression of the hERG channel

  13. hERG trafficking inhibition in drug-induced lethal cardiac arrhythmia.

    Science.gov (United States)

    Nogawa, Hisashi; Kawai, Tomoyuki

    2014-10-15

    Acquired long QT syndrome induced by non-cardiovascular drugs can cause lethal cardiac arrhythmia called torsades de points and is a significant problem in drug development. The prolongation of QT interval and cardiac action potential duration are mainly due to reduced physiological function of the rapidly activating voltage-dependent potassium channels encoded by human ether-a-go-go-related gene (hERG). Structurally diverse groups of drugs are known to directly inhibit hERG channel conductance. Therefore, the ability of acute hERG inhibition is routinely assessed at the preclinical stages in pharmaceutical testing. Recent findings indicated that chronic treatment with various drugs not only inhibits hERG channels but also decreases hERG channel expression in the plasma membrane of cardiomyocytes, which has become another concern in safety pharmacology. The mechanisms involve the disruption of hERG trafficking to the surface membrane or the acceleration of hERG protein degradation. From this perspective, we present a brief overview of mechanisms of drug-induced trafficking inhibition and pathological regulation. Understanding of drug-induced hERG trafficking inhibition may provide new strategies for predicting drug-induced QT prolongation and lethal cardiac arrhythmia in pharmaceutical drug development. Copyright © 2014 Elsevier B.V. All rights reserved.

  14. Mechanism and pharmacological rescue of berberine-induced hERG channel deficiency

    Science.gov (United States)

    Yan, Meng; Zhang, Kaiping; Shi, Yanhui; Feng, Lifang; Lv, Lin; Li, Baoxin

    2015-01-01

    Berberine (BBR), an isoquinoline alkaloid mainly isolated from plants of Berberidaceae family, is extensively used to treat gastrointestinal infections in clinics. It has been reported that BBR can block human ether-a-go-go-related gene (hERG) potassium channel and inhibit its membrane expression. The hERG channel plays crucial role in cardiac repolarization and is the target of diverse proarrhythmic drugs. Dysfunction of hERG channel can cause long QT syndrome. However, the regulatory mechanisms of BBR effects on hERG at cell membrane level remain unknown. This study was designed to investigate in detail how BBR decreased hERG expression on cell surface and further explore its pharmacological rescue strategies. In this study, BBR decreases caveolin-1 expression in a concentration-dependent manner in human embryonic kidney 293 (HEK293) cells stably expressing hERG channel. Knocking down the basal expression of caveolin-1 alleviates BBR-induced hERG reduction. In addition, we found that aromatic tyrosine (Tyr652) and phenylalanine (Phe656) in S6 domain mediate the long-term effect of BBR on hERG by using mutation techniques. Considering both our previous and present work, we propose that BBR reduces hERG membrane stability with multiple mechanisms. Furthermore, we found that fexofenadine and resveratrol shorten action potential duration prolongated by BBR, thus having the potential effects of alleviating the cardiotoxicity of BBR. PMID:26543354

  15. Inhibition of HERG potassium channels by celecoxib and its mechanism.

    Directory of Open Access Journals (Sweden)

    Roman V Frolov

    Full Text Available Celecoxib (Celebrex, a widely prescribed selective inhibitor of cyclooxygenase-2, can modulate ion channels independently of cyclooxygenase inhibition. Clinically relevant concentrations of celecoxib can affect ionic currents and alter functioning of neurons and myocytes. In particular, inhibition of Kv2.1 channels by celecoxib leads to arrhythmic beating of Drosophila heart and of rat heart cells in culture. However, the spectrum of ion channels involved in human cardiac excitability differs from that in animal models, including mammalian models, making it difficult to evaluate the relevance of these observations to humans. Our aim was to examine the effects of celecoxib on hERG and other human channels critically involved in regulating human cardiac rhythm, and to explore the mechanisms of any observed effect on the hERG channels.Celecoxib inhibited the hERG, SCN5A, KCNQ1 and KCNQ1/MinK channels expressed in HEK-293 cells with IC(50s of 6.0 µM, 7.5 µM, 3.5 µM and 3.7 µM respectively, and the KCND3/KChiP2 channels expressed in CHO cells with an IC(50 of 10.6 µM. Analysis of celecoxib's effects on hERG channels suggested gating modification as the mechanism of drug action.The above channels play a significant role in drug-induced long QT syndrome (LQTS and short QT syndrome (SQTS. Regulatory guidelines require that all new drugs under development be tested for effects on the hERG channel prior to first administration in humans. Our observations raise the question of celecoxib's potential to induce cardiac arrhythmias or other channel related adverse effects, and make a case for examining such possibilities.

  16. Mechanism and pharmacological rescue of berberine-induced hERG channel deficiency

    Directory of Open Access Journals (Sweden)

    Yan M

    2015-10-01

    Full Text Available Meng Yan,1 Kaiping Zhang,1 Yanhui Shi,1 Lifang Feng,1 Lin Lv,1 Baoxin Li1,2 1Department of Pharmacology, Harbin Medical University, 2State-Province Key Laboratory of Biopharmaceutical Engineering, Harbin, Heilongjiang, People’s Republic of China Abstract: Berberine (BBR, an isoquinoline alkaloid mainly isolated from plants of Berberidaceae family, is extensively used to treat gastrointestinal infections in clinics. It has been reported that BBR can block human ether-a-go-go-related gene (hERG potassium channel and inhibit its membrane expression. The hERG channel plays crucial role in cardiac repolarization and is the target of diverse proarrhythmic drugs. Dysfunction of hERG channel can cause long QT syndrome. However, the regulatory mechanisms of BBR effects on hERG at cell membrane level remain unknown. This study was designed to investigate in detail how BBR decreased hERG expression on cell surface and further explore its pharmacological rescue strategies. In this study, BBR decreases caveolin-1 expression in a concentration-dependent manner in human embryonic kidney 293 (HEK293 cells stably expressing hERG channel. Knocking down the basal expression of caveolin-1 alleviates BBR-induced hERG reduction. In addition, we found that aromatic tyrosine (Tyr652 and phenylalanine (Phe656 in S6 domain mediate the long-term effect of BBR on hERG by using mutation techniques. Considering both our previous and present work, we propose that BBR reduces hERG membrane stability with multiple mechanisms. Furthermore, we found that fexofenadine and resveratrol shorten action potential duration prolongated by BBR, thus having the potential effects of alleviating the cardiotoxicity of BBR. Keywords: berberine, hERG, cavoline-1, cardiotoxicity, LQTS, pharmacological rescue

  17. hERG1 channels are overexpressed in glioblastoma multiforme and modulate VEGF secretion in glioblastoma cell lines

    Science.gov (United States)

    Masi, A; Becchetti, A; Restano-Cassulini, R; Polvani, S; Hofmann, G; Buccoliero, A M; Paglierani, M; Pollo, B; Taddei, G L; Gallina, P; Di Lorenzo, N; Franceschetti, S; Wanke, E; Arcangeli, A

    2005-01-01

    Recent studies have led to considerable advancement in our understanding of the molecular mechanisms that underlie the relentless cell growth and invasiveness of human gliomas. Partial understanding of these mechanisms has (1) improved the classification for gliomas, by identifying prognostic subgroups, and (2) pointed to novel potential therapeutic targets. Some classes of ion channels have turned out to be involved in the pathogenesis and malignancy of gliomas. We studied the expression and properties of K+ channels in primary cultures obtained from surgical specimens: human ether a gò-gò related (hERG)1 voltage-dependent K+ channels, which have been found to be overexpressed in various human cancers, and human ether a gò-gò-like 2 channels, that share many of hERG1's biophysical features. The expression pattern of these two channels was compared to that of the classical inward rectifying K+ channels, IRK, that are widely expressed in astrocytic cells and classically considered a marker of astrocytic differentiation. In our study, hERG1 was found to be specifically overexpressed in high-grade astrocytomas, that is, glioblastoma multiforme (GBM). In addition, we present evidence that, in GBM cell lines, hERG1 channel activity actively contributes to malignancy by promoting vascular endothelial growth factor secretion, thus stimulating the neoangiogenesis typical of high-grade gliomas. Our data provide important confirmation for studies proposing the hERG1 channel as a molecular marker of tumour progression and a possible target for novel anticancer therapies. PMID:16175187

  18. Inhibitory effects and mechanism of dihydroberberine on hERG channels expressed in HEK293 cells.

    Directory of Open Access Journals (Sweden)

    Dahai Yu

    Full Text Available The human ether-a-go-go-related gene (hERG potassium channel conducts rapid delayed rectifier potassium currents (IKr and contributes to phase III cardiac action potential repolarization. Drugs inhibit hERG channels by binding to aromatic residues in hERG helixes. Berberine (BBR has multiple actions, and its hydrogenated derivative dihydroberberine (DHB is a potential candidate for developing new drugs. Previous studies have demonstrated that BBR blocks hERG channels and prolongs action potential duration (APD. Our present study aimed to investigate the effects and mechanism of DHB on hERG channels. Protein expression and the hERG current were analyzed using western blotting and patch-clamp, respectively. DHB inhibited the hERG current concentration-dependently after instantaneous perfusion, accelerated channel inactivation by directly binding tyrosine (Tyr652 and phenylalanine (Phe656, and decreased mature (155-kDa and simultaneously increased immature (135-kDa hERG expression, respectively. This suggests disruption of forward trafficking of hERG channels. Besides, DHB remarkably reduced heat shock protein 90 (Hsp90 expression and its interaction with hERG, indicating that DHB disrupted hERG trafficking by impairing channel folding. Meanwhie, DHB enhanced the expression of cleaved activating transcription factor-6 (ATF-6, a biomarker of unfolded protein response (UPR. Expression of calnexin and calreticulin, chaperones activated by ATF-6 to facilitate channel folding, were also increased, which indicating UPR activation. Additionally, the degradation rate of mature 155-kDa hERG increased following DHB exposure. In conclusion, we demonstrated that DHB acutely blocked hERG channels by binding the aromatic Tyr652 and Phe656. DHB may decrease hERG plasma membrane expression through two pathways involving disruption of forward trafficking of immature hERG channels and enhanced degradation of mature hERG channels. Furthermore, forward trafficking was

  19. Overcoming hERG affinity in the discovery of maraviroc; a CCR5 antagonist for the treatment of HIV.

    Science.gov (United States)

    Price, David A; Armour, Duncan; de Groot, Marcel; Leishman, Derek; Napier, Carolyn; Perros, Manos; Stammen, Blanda L; Wood, Anthony

    2008-01-01

    Avoiding cardiac liability associated with blockade of hERG (human ether a go-go) is key for successful drug discovery and development. This paper describes the work undertaken in the discovery of a potent CCR5 antagonist, maraviroc 34, for the treatment of HIV. In particular the use of a pharmacophore model of the hERG channel and a high throughput binding assay for the hERG channel are described that were critical to elucidate SAR to overcome hERG liabilities. The key SAR involves the introduction of polar substituents into regions of the molecule where it is postulated to undergo hydrophobic interactions with the ion channel. Within the CCR5 project there appeared to be no strong correlation between hERG affinity and physiochemical parameters such as pKa or lipophilicity. It is believed that chemists could apply these same strategies early in drug discovery to remove hERG interactions associated with lead compounds while retaining potency at the primary target.

  20. Homozygous premature truncation of the HERG protein : the human HERG knockout

    NARCIS (Netherlands)

    Hoorntje, T.; Alders, M.; van Tintelen, P.; van der Lip, K.; Sreeram, N.; van der Wal, A.; Mannens, M.; Wilde, A.

    1999-01-01

    Background-In long-QT syndrome (LQTS), heterozygosity for a mutation in 1 of the K(+) channel genes leads to prolongation of the cardiac action potential, because the aberrant protein exhibits "loss of function." HERG, which is involved in LQT2, is the gene encoding the rapid component of the

  1. Channel sialic acids limit hERG channel activity during the ventricular action potential.

    Science.gov (United States)

    Norring, Sarah A; Ednie, Andrew R; Schwetz, Tara A; Du, Dongping; Yang, Hui; Bennett, Eric S

    2013-02-01

    Activity of human ether-a-go-go-related gene (hERG) 1 voltage-gated K(+) channels is responsible for portions of phase 2 and phase 3 repolarization of the human ventricular action potential. Here, we questioned whether and how physiologically and pathophysiologically relevant changes in surface N-glycosylation modified hERG channel function. Voltage-dependent hERG channel gating and activity were evaluated as expressed in a set of Chinese hamster ovary (CHO) cell lines under conditions of full glycosylation, no sialylation, no complex N-glycans, and following enzymatic deglycosylation of surface N-glycans. For each condition of reduced glycosylation, hERG channel steady-state activation and inactivation relationships were shifted linearly by significant depolarizing ∼9 and ∼18 mV, respectively. The hERG window current increased significantly by 50-150%, and the peak shifted by a depolarizing ∼10 mV. There was no significant change in maximum hERG current density. Deglycosylated channels were significantly more active (20-80%) than glycosylated controls during phases 2 and 3 of action potential clamp protocols. Simulations of hERG current and ventricular action potentials corroborated experimental data and predicted reduced sialylation leads to a 50-70-ms decrease in action potential duration. The data describe a novel mechanism by which hERG channel gating is modulated through physiologically and pathophysiologically relevant changes in N-glycosylation; reduced channel sialylation increases hERG channel activity during the action potential, thereby increasing the rate of action potential repolarization.

  2. Anti-HERG activity and the risk of drug-induced arrhythmias and sudden death

    DEFF Research Database (Denmark)

    De Bruin, M L; Pettersson, M; Meyboom, R H B

    2005-01-01

    AIMS: Drug-induced QTc-prolongation, resulting from inhibition of HERG potassium channels may lead to serious ventricular arrhythmias and sudden death. We studied the quantitative anti-HERG activity of pro-arrhythmic drugs as a risk factor for this outcome in day-to-day practice. METHODS...... defined as reports of cardiac arrest, sudden death, torsade de pointes, ventricular fibrillation, and ventricular tachycardia (n = 5591), and compared with non-cases regarding the anti-HERG activity, defined as the effective therapeutic plasma concentration (ETCPunbound) divided by the HERG IC50 value......, of suspected drugs. We identified a significant association of 1.93 (95% CI: 1.89-1.98) between the anti-HERG activity of drugs, measured as log10 (ETCPunbound/IC50), and reporting of serious ventricular arrhythmias and sudden death to the WHO-UMC database. CONCLUSION: Anti-HERG activity is associated...

  3. Data on the construction of a recombinant HEK293 cell line overexpressing hERG potassium channel and examining the presence of hERG mRNA and protein expression

    Directory of Open Access Journals (Sweden)

    Yi Fan Teah

    2017-10-01

    Full Text Available The data presented in this article are related to the research article entitled “The effects of deoxyelephantopin on the cardiac delayed rectifier potassium channel current (IKr and human ether-a-go-go-related gene (hERG expression” (Y.F. Teah, M.A. Abduraman, A. Amanah, M.I. Adenan, S.F. Sulaiman, M.L. Tan [1], which the possible hERG blocking properties of deoxyelephantopin were investigated. This article describes the construction of human embryonic kidney 293 (HEK293 cells overexpressing HERG potassium channel and verification of the presence of hERG mRNA and protein expression in this recombinant cell line.

  4. Role of the pH in state-dependent blockade of hERG currents

    Science.gov (United States)

    Wang, Yibo; Guo, Jiqing; Perissinotti, Laura L.; Lees-Miller, James; Teng, Guoqi; Durdagi, Serdar; Duff, Henry J.; Noskov, Sergei Yu.

    2016-10-01

    Mutations that reduce inactivation of the voltage-gated Kv11.1 potassium channel (hERG) reduce binding for a number of blockers. State specific block of the inactivated state of hERG block may increase risks of drug-induced Torsade de pointes. In this study, molecular simulations of dofetilide binding to the previously developed and experimentally validated models of the hERG channel in open and open-inactivated states were combined with voltage-clamp experiments to unravel the mechanism(s) of state-dependent blockade. The computations of the free energy profiles associated with the drug block to its binding pocket in the intra-cavitary site display startling differences in the open and open-inactivated states of the channel. It was also found that drug ionization may play a crucial role in preferential targeting to the open-inactivated state of the pore domain. pH-dependent hERG blockade by dofetilie was studied with patch-clamp recordings. The results show that low pH increases the extent and speed of drug-induced block. Both experimental and computational findings indicate that binding to the open-inactivated state is of key importance to our understanding of the dofetilide’s mode of action.

  5. Rab11-dependent Recycling of the Human Ether-a-go-go-related Gene (hERG) Channel*

    Science.gov (United States)

    Chen, Jeffery; Guo, Jun; Yang, Tonghua; Li, Wentao; Lamothe, Shawn M.; Kang, Yudi; Szendrey, John A.; Zhang, Shetuan

    2015-01-01

    The human ether-a-go-go-related gene (hERG) encodes the pore-forming subunit of the rapidly activating delayed rectifier potassium channel (IKr). A reduction in the hERG current causes long QT syndrome, which predisposes affected individuals to ventricular arrhythmias and sudden death. We reported previously that hERG channels in the plasma membrane undergo vigorous internalization under low K+ conditions. In the present study, we addressed whether hERG internalization occurs under normal K+ conditions and whether/how internalized channels are recycled back to the plasma membrane. Using patch clamp, Western blot, and confocal imaging analyses, we demonstrated that internalized hERG channels can effectively recycle back to the plasma membrane. Low K+-enhanced hERG internalization is accompanied by an increased rate of hERG recovery in the plasma membrane upon reculture following proteinase K-mediated clearance of cell-surface proteins. The increased recovery rate is not due to enhanced protein synthesis, as hERG mRNA expression was not altered by low K+ exposure, and the increased recovery was observed in the presence of the protein biosynthesis inhibitor cycloheximide. GTPase Rab11, but not Rab4, is involved in the recycling of hERG channels. Interfering with Rab11 function not only delayed hERG recovery in cells after exposure to low K+ medium but also decreased hERG expression and function in cells under normal culture conditions. We concluded that the recycling pathway plays an important role in the homeostasis of plasma membrane-bound hERG channels. PMID:26152716

  6. Cholesterol regulates HERG K+ channel activation by increasing phospholipase C β1 expression.

    Science.gov (United States)

    Chun, Yoon Sun; Oh, Hyun Geun; Park, Myoung Kyu; Cho, Hana; Chung, Sungkwon

    2013-01-01

    Human ether-a-go-go-related gene (HERG) K(+) channel underlies the rapidly activating delayed rectifier K(+) conductance (IKr) during normal cardiac repolarization. Also, it may regulate excitability in many neuronal cells. Recently, we showed that enrichment of cell membrane with cholesterol inhibits HERG channels by reducing the levels of phosphatidylinositol 4,5-bisphosphate [PtdIns(4,5)P2] due to the activation of phospholipase C (PLC). In this study, we further explored the effect of cholesterol enrichment on HERG channel kinetics. When membrane cholesterol level was mildly increased in human embryonic kidney (HEK) 293 cells expressing HERG channel, the inactivation and deactivation kinetics of HERG current were not affected, but the activation rate was significantly decelerated at all voltages tested. The application of PtdIns(4,5)P2 or inhibitor for PLC prevented the effect of cholesterol enrichment, while the presence of antibody against PtdIns(4,5)P2 in pipette solution mimicked the effect of cholesterol enrichment. These results indicate that the effect of cholesterol enrichment on HERG channel is due to the depletion of PtdIns(4,5)P2. We also found that cholesterol enrichment significantly increases the expression of β1 and β3 isoforms of PLC (PLCβ1, PLCβ3) in the membrane. Since the effects of cholesterol enrichment on HERG channel were prevented by inhibiting transcription or by inhibiting PLCβ1 expression, we conclude that increased PLCβ1 expression leads to the deceleration of HERG channel activation rate via downregulation of PtdIns(4,5)P2. These results confirm a crosstalk between two plasma membrane-enriched lipids, cholesterol and PtdIns(4,5)P2, in the regulation of HERG channels.

  7. Endocytosis of HERG is clathrin-independent and involves arf6.

    Directory of Open Access Journals (Sweden)

    Rucha Karnik

    Full Text Available The hERG potassium channel is critical for repolarisation of the cardiac action potential. Reduced expression of hERG at the plasma membrane, whether caused by hereditary mutations or drugs, results in long QT syndrome and increases the risk of ventricular arrhythmias. Thus, it is of fundamental importance to understand how the density of this channel at the plasma membrane is regulated. We used antibodies to an extracellular native or engineered epitope, in conjunction with immunofluorescence and ELISA, to investigate the mechanism of hERG endocytosis in recombinant cells and validated the findings in rat neonatal cardiac myocytes. The data reveal that this channel undergoes rapid internalisation, which is inhibited by neither dynasore, an inhibitor of dynamin, nor a dominant negative construct of Rab5a, into endosomes that are largely devoid of the transferrin receptor. These results support a clathrin-independent mechanism of endocytosis and exclude involvement of dynamin-dependent caveolin and RhoA mechanisms. In agreement, internalised hERG displayed marked overlap with glycosylphosphatidylinositol-anchored GFP, a clathrin-independent cargo. Endocytosis was significantly affected by cholesterol extraction with methyl-β-cyclodextrin and inhibition of Arf6 function with dominant negative Arf6-T27N-eGFP. Taken together, we conclude that hERG undergoes clathrin-independent endocytosis via a mechanism involving Arf6.

  8. High yield purification of full-length functional hERG K+ channels produced in Saccharomyces cerevisiae

    DEFF Research Database (Denmark)

    Molbaek, Karen; Scharff-Poulsen, Peter; Hélix-Nielsen, Claus

    2015-01-01

    knowledge this is the first reported high-yield production and purification of full length, tetrameric and functional hERG. This significant breakthrough will be paramount in obtaining hERG crystal structures, and in establishment of new high-throughput hERG drug safety screening assays....

  9. A molecular switch driving inactivation in the cardiac K+ channel HERG.

    Directory of Open Access Journals (Sweden)

    David A Köpfer

    Full Text Available K(+ channels control transmembrane action potentials by gating open or closed in response to external stimuli. Inactivation gating, involving a conformational change at the K(+ selectivity filter, has recently been recognized as a major K(+ channel regulatory mechanism. In the K(+ channel hERG, inactivation controls the length of the human cardiac action potential. Mutations impairing hERG inactivation cause life-threatening cardiac arrhythmia, which also occur as undesired side effects of drugs. In this paper, we report atomistic molecular dynamics simulations, complemented by mutational and electrophysiological studies, which suggest that the selectivity filter adopts a collapsed conformation in the inactivated state of hERG. The selectivity filter is gated by an intricate hydrogen bond network around residues S620 and N629. Mutations of this hydrogen bond network are shown to cause inactivation deficiency in electrophysiological measurements. In addition, drug-related conformational changes around the central cavity and pore helix provide a functional mechanism for newly discovered hERG activators.

  10. New aspects of HERG K⁺ channel function depending upon cardiac spatial heterogeneity.

    Directory of Open Access Journals (Sweden)

    Pen Zhang

    Full Text Available HERG K(+ channel, the genetic counterpart of rapid delayed rectifier K(+ current in cardiac cells, is responsible for many cases of inherited and drug-induced long QT syndromes. HERG has unusual biophysical properties distinct from those of other K(+ channels. While the conventional pulse protocols in patch-clamp studies have helped us elucidate these properties, their limitations in assessing HERG function have also been progressively noticed. We employed AP-clamp techniques using physiological action potential waveforms recorded from various regions of canine heart to study HERG function in HEK293 cells and identified several novel aspects of HERG function. We showed that under AP-clamp IHERG increased gradually with membrane repolarization, peaked at potentials around 20-30 mV more negative than revealed by pulse protocols and at action potential duration (APD to 60%-70% full repolarization, and fell rapidly at the terminal phase of repolarization. We found that the rising phase of IHERG was conferred by removal of inactivation and the decaying phase resulted from a fall in driving force, which were all determined by the rate of membrane repolarization. We identified regional heterogeneity and transmural gradient of IHERG when quantified with the area covered by IHERG trace. In addition, we observed regional and transmural differences of IHERG in response to dofetilide blockade. Finally, we characterized the influence of HERG function by selective inhibition of other ion currents. Based on our results, we conclude that the distinct biophysical properties of HERG reported by AP-clamp confer its unique function in cardiac repolarization thereby in antiarrhythmia and arrhythmogenesis.

  11. Towards a Structural View of Drug Binding to hERG K+ Channels.

    Science.gov (United States)

    Vandenberg, Jamie I; Perozo, Eduardo; Allen, Toby W

    2017-10-01

    The human ether-a-go-go-related gene (hERG) K + channel is of great medical and pharmaceutical relevance. Inherited mutations in hERG result in congenital long-QT syndrome which is associated with a markedly increased risk of cardiac arrhythmia and sudden death. hERG K + channels are also remarkably susceptible to block by a wide range of drugs, which in turn can cause drug-induced long-QT syndrome and an increased risk of sudden death. The recent determination of the near-atomic resolution structure of the hERG K + channel, using single-particle cryo-electron microscopy (cryo-EM), provides tremendous insights into how these channels work. It also suggests a way forward in our quest to understand why these channels are so promiscuous with respect to drug binding. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. Endocytosis of hERG Is Clathrin-Independent and Involves Arf6

    Science.gov (United States)

    Abuarab, Nada; Smith, Andrew J.; Hardy, Matthew E. L.; Elliott, David J. S.; Sivaprasadarao, Asipu

    2013-01-01

    The hERG potassium channel is critical for repolarisation of the cardiac action potential. Reduced expression of hERG at the plasma membrane, whether caused by hereditary mutations or drugs, results in long QT syndrome and increases the risk of ventricular arrhythmias. Thus, it is of fundamental importance to understand how the density of this channel at the plasma membrane is regulated. We used antibodies to an extracellular native or engineered epitope, in conjunction with immunofluorescence and ELISA, to investigate the mechanism of hERG endocytosis in recombinant cells and validated the findings in rat neonatal cardiac myocytes. The data reveal that this channel undergoes rapid internalisation, which is inhibited by neither dynasore, an inhibitor of dynamin, nor a dominant negative construct of Rab5a, into endosomes that are largely devoid of the transferrin receptor. These results support a clathrin-independent mechanism of endocytosis and exclude involvement of dynamin-dependent caveolin and RhoA mechanisms. In agreement, internalised hERG displayed marked overlap with glycosylphosphatidylinositol-anchored GFP, a clathrin-independent cargo. Endocytosis was significantly affected by cholesterol extraction with methyl-β-cyclodextrin and inhibition of Arf6 function with dominant negative Arf6-T27N-eGFP. Taken together, we conclude that hERG undergoes clathrin-independent endocytosis via a mechanism involving Arf6. PMID:24392021

  13. A novel assessment of nefazodone-induced hERG inhibition by electrophysiological and stereochemical method

    Energy Technology Data Exchange (ETDEWEB)

    Shin, Dae-Seop; Park, Myoung Joo [Drug Discovery Platform Technology Research Group, Korea Research Institute of Chemical Technology, Daejeon (Korea, Republic of); Lee, Hyang-Ae [Korea Institute of Toxicology, Korea Research Institute of Chemical Technology, Daejeon (Korea, Republic of); Lee, Joo Yun; Chung, Hee-Chung; Yoo, Dae Seok; Chae, Chong Hak [Drug Discovery Platform Technology Research Group, Korea Research Institute of Chemical Technology, Daejeon (Korea, Republic of); Park, Sang-Joon [College of Veterinary Medicine, Kyungpook National University, Daegu (Korea, Republic of); Kim, Ki-Suk [Korea Institute of Toxicology, Korea Research Institute of Chemical Technology, Daejeon (Korea, Republic of); Bae, Myung Ae, E-mail: mbae@krict.re.kr [Drug Discovery Platform Technology Research Group, Korea Research Institute of Chemical Technology, Daejeon (Korea, Republic of)

    2014-02-01

    Nefazodone was used widely as an antidepressant until it was withdrawn from the U.S. market in 2004 due to hepatotoxicity. We have investigated methods to predict various toxic effects of drug candidates to reduce the failure rate of drug discovery. An electrophysiological method was used to assess the cardiotoxicity of drug candidates. Small molecules, including withdrawn drugs, were evaluated using a patch-clamp method to establish a database of hERG inhibition. Nefazodone inhibited hERG channel activity in our system. However, nefazodone-induced hERG inhibition indicated only a theoretical risk of cardiotoxicity. Nefazodone inhibited the hERG channel in a concentration-dependent manner with an IC{sub 50} of 45.3 nM in HEK-293 cells. Nefazodone accelerated both the recovery from inactivation and its onset. Nefazodone also accelerated steady-state inactivation, although it did not modify the voltage-dependent character. Alanine mutants of hERG S6 and pore region residues were used to identify the nefazodone-binding site on hERG. The hERG S6 point mutants Y652A and F656A largely abolished the inhibition by nefazodone. The pore region mutant S624A mildly reduced the inhibition by nefazodone but T623A had little effect. A docking study showed that the aromatic rings of nefazodone interact with Y652 and F656 via π–π interactions, while an amine interacted with the S624 residue in the pore region. In conclusion, Y652 and F656 in the S6 domain play critical roles in nefazodone binding. - Highlights: • Nefazodone inhibits hERG channels with an IC{sub 50} of 45.3 nM in HEK-293 cells. • Nefazodone blocks hERG channels by binding to the open channels. • Y652 and F656 are important for binding of nefazodone. • The aromatic rings of nefazodone interact with Y652 and F656 via π–π interactions.

  14. Mechanism of HERG potassium channel inhibition by tetra-n-octylammonium bromide and benzethonium chloride

    International Nuclear Information System (INIS)

    Long, Yan; Lin, Zuoxian; Xia, Menghang; Zheng, Wei; Li, Zhiyuan

    2013-01-01

    Tetra-n-octylammonium bromide and benzethonium chloride are synthetic quaternary ammonium salts that are widely used in hospitals and industries for the disinfection and surface treatment and as the preservative agent. Recently, the activities of HERG channel inhibition by these compounds have been found to have potential risks to induce the long QT syndrome and cardiac arrhythmia, although the mechanism of action is still elusive. This study was conducted to investigate the mechanism of HERG channel inhibition by these compounds by using whole-cell patch clamp experiments in a CHO cell line stably expressing HERG channels. Tetra-n-octylammonium bromide and benzethonium chloride exhibited concentration-dependent inhibitions of HERG channel currents with IC 50 values of 4 nM and 17 nM, respectively, which were also voltage-dependent and use-dependent. Both compounds shifted the channel activation I–V curves in a hyperpolarized direction for 10–15 mV and accelerated channel activation and inactivation processes by 2-fold. In addition, tetra-n-octylammonium bromide shifted the inactivation I–V curve in a hyperpolarized direction for 24.4 mV and slowed the rate of channel deactivation by 2-fold, whereas benzethonium chloride did not. The results indicate that tetra-n-octylammonium bromide and benzethonium chloride are open-channel blockers that inhibit HERG channels in the voltage-dependent, use-dependent and state-dependent manners. - Highlights: ► Tetra-n-octylammonium and benzethonium are potent HERG channel inhibitors. ► Channel activation and inactivation processes are accelerated by the two compounds. ► Both compounds are the open-channel blockers to HERG channels. ► HERG channel inhibition by both compounds is use-, voltage- and state dependent. ► The in vivo risk of QT prolongation needs to be studied for the two compounds

  15. Mechanism of HERG potassium channel inhibition by tetra-n-octylammonium bromide and benzethonium chloride

    Energy Technology Data Exchange (ETDEWEB)

    Long, Yan; Lin, Zuoxian [Key Laboratory of Regenerative Biology, Guangzhou Institute of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530 (China); Xia, Menghang; Zheng, Wei [National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD 20892 (United States); Li, Zhiyuan, E-mail: li_zhiyuan@gibh.ac.cn [Key Laboratory of Regenerative Biology, Guangzhou Institute of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530 (China)

    2013-03-01

    Tetra-n-octylammonium bromide and benzethonium chloride are synthetic quaternary ammonium salts that are widely used in hospitals and industries for the disinfection and surface treatment and as the preservative agent. Recently, the activities of HERG channel inhibition by these compounds have been found to have potential risks to induce the long QT syndrome and cardiac arrhythmia, although the mechanism of action is still elusive. This study was conducted to investigate the mechanism of HERG channel inhibition by these compounds by using whole-cell patch clamp experiments in a CHO cell line stably expressing HERG channels. Tetra-n-octylammonium bromide and benzethonium chloride exhibited concentration-dependent inhibitions of HERG channel currents with IC{sub 50} values of 4 nM and 17 nM, respectively, which were also voltage-dependent and use-dependent. Both compounds shifted the channel activation I–V curves in a hyperpolarized direction for 10–15 mV and accelerated channel activation and inactivation processes by 2-fold. In addition, tetra-n-octylammonium bromide shifted the inactivation I–V curve in a hyperpolarized direction for 24.4 mV and slowed the rate of channel deactivation by 2-fold, whereas benzethonium chloride did not. The results indicate that tetra-n-octylammonium bromide and benzethonium chloride are open-channel blockers that inhibit HERG channels in the voltage-dependent, use-dependent and state-dependent manners. - Highlights: ► Tetra-n-octylammonium and benzethonium are potent HERG channel inhibitors. ► Channel activation and inactivation processes are accelerated by the two compounds. ► Both compounds are the open-channel blockers to HERG channels. ► HERG channel inhibition by both compounds is use-, voltage- and state dependent. ► The in vivo risk of QT prolongation needs to be studied for the two compounds.

  16. Role of the activation gate in determining the extracellular potassium dependency of block of HERG by trapped drugs.

    Science.gov (United States)

    Pareja, Kristeen; Chu, Elaine; Dodyk, Katrina; Richter, Kristofer; Miller, Alan

    2013-01-01

    Drug induced long QT syndrome (diLQTS) results primarily from block of the cardiac potassium channel HERG (human-ether-a-go-go related gene). In some cases long QT syndrome can result in the lethal arrhythmia torsade de pointes, an arrhythmia characterized by a rapid heart rate and severely compromised cardiac output. Many patients requiring medication present with serum potassium abnormalities due to a variety of conditions including gastrointestinal dysfunction, renal and endocrine disorders, diuretic use, and aging. Extracellular potassium influences HERG channel inactivation and can alter block of HERG by some drugs. However, block of HERG by a number of drugs is not sensitive to extracellular potassium. In this study, we show that block of WT HERG by bepridil and terfenadine, two drugs previously shown to be trapped inside the HERG channel after the channel closes, is insensitive to extracellular potassium over the range of 0 mM to 20 mM. We also show that bepridil block of the HERG mutant D540K, a mutant channel that is unable to trap drugs, is dependent on extracellular potassium, correlates with the permeant ion, and is independent of HERG inactivation. These results suggest that the lack of extracellular potassium dependency of block of HERG by some drugs may in part be related to the ability of these drugs to be trapped inside the channel after the channel closes.

  17. Overcoming HERG affinity in the discovery of the CCR5 antagonist maraviroc.

    Science.gov (United States)

    Price, David A; Armour, Duncan; de Groot, Marcel; Leishman, Derek; Napier, Carolyn; Perros, Manos; Stammen, Blanda L; Wood, Anthony

    2006-09-01

    The discovery of maraviroc 17 is described with particular reference to the generation of high selectivity over affinity for the HERG potassium channel. This was achieved through the use of a high throughput binding assay for the HERG channel that is known to show an excellent correlation with functional effects.

  18. Stereoselective inhibition of the hERG1 potassium channel

    Directory of Open Access Journals (Sweden)

    Liliana eSintra Grilo

    2010-11-01

    Full Text Available A growing number of drugs have been shown to prolong cardiac repolarization, predisposing individuals to life-threatening ventricular arrhythmias known as Torsades de Pointes. Most of these drugs are known to interfere with the human ether à-gogo related gene 1 (hERG1 channel, whose current is one of the main determinants of action potential duration. Prolonged repolarization is reflected by lengthening of the QT interval of the electrocardiogram, as seen in the suitably named drug-induced long QT syndrome. Chirality (presence of an asymmetric atom is a common feature of marketed drugs, which can therefore exist in at least two enantiomers with distinct three-dimensional structures and possibly distinct biological fates. Both the pharmacokinetic and pharmacodynamic properties can differ between enantiomers, as well as also between individuals who take the drug due to metabolic polymorphisms. Despite the large number of reports about drugs reducing the hERG1 current, potential stereoselective contributions have only been scarcely investigated. In this review, we present a non-exhaustive list of clinically important molecules which display chiral toxicity that may be related to hERG1-blocking properties. We particularly focus on methadone cardiotoxicity, which illustrates the importance of the stereoselective effect of drug chirality as well as individual variations resulting from pharmacogenetics. Furthermore, it seems likely that, during drug development, consideration of chirality in lead optimization and systematic assessment of the hERG1 current block with all enantiomers could contribute to the reduction of the risk of drug-induced LQTS.

  19. Early identification of hERG liability in drug discovery programs by automated patch clamp

    Directory of Open Access Journals (Sweden)

    Timm eDanker

    2014-09-01

    Full Text Available Blockade of the cardiac ion channel coded by hERG can lead to cardiac arrhythmia, which has become a major concern in drug discovery and development. Automated electrophysiological patch clamp allows assessment of hERG channel effects early in drug development to aid medicinal chemistry programs and has become routine in pharmaceutical companies. However, a number of potential sources of errors in setting up hERG channel assays by automated patch clamp can lead to misinterpretation of data or false effects being reported. This article describes protocols for automated electrophysiology screening of compound effects on the hERG channel current. Protocol details and the translation of criteria known from manual patch clamp experiments to automated patch clamp experiments to achieve good quality data are emphasized. Typical pitfalls and artifacts that may lead to misinterpretation of data are discussed. While this article focuses on hERG channel recordings using the QPatch (Sophion A/S, Copenhagen, Denmark technology, many of the assay and protocol details given in this article can be transferred for setting up different ion channel assays by automated patch clamp and are similar on other planar patch clamp platforms.

  20. Structural refinement of the hERG1 pore and voltage-sensing domains with ROSETTA-membrane and molecular dynamics simulations.

    Science.gov (United States)

    Subbotina, Julia; Yarov-Yarovoy, Vladimir; Lees-Miller, James; Durdagi, Serdar; Guo, Jiqing; Duff, Henry J; Noskov, Sergei Yu

    2010-11-01

    The hERG1 gene (Kv11.1) encodes a voltage-gated potassium channel. Mutations in this gene lead to one form of the Long QT Syndrome (LQTS) in humans. Promiscuous binding of drugs to hERG1 is known to alter the structure/function of the channel leading to an acquired form of the LQTS. Expectably, creation and validation of reliable 3D model of the channel have been a key target in molecular cardiology and pharmacology for the last decade. Although many models were built, they all were limited to pore domain. In this work, a full model of the hERG1 channel is developed which includes all transmembrane segments. We tested a template-driven de-novo design with ROSETTA-membrane modeling using side-chain placements optimized by subsequent molecular dynamics (MD) simulations. Although backbone templates for the homology modeled parts of the pore and voltage sensors were based on the available structures of KvAP, Kv1.2 and Kv1.2-Kv2.1 chimera channels, the missing parts are modeled de-novo. The impact of several alignments on the structure of the S4 helix in the voltage-sensing domain was also tested. Herein, final models are evaluated for consistency to the reported structural elements discovered mainly on the basis of mutagenesis and electrophysiology. These structural elements include salt bridges and close contacts in the voltage-sensor domain; and the topology of the extracellular S5-pore linker compared with that established by toxin foot-printing and nuclear magnetic resonance studies. Implications of the refined hERG1 model to binding of blockers and channels activators (potent new ligands for channel activations) are discussed. © 2010 Wiley-Liss, Inc.

  1. Structural implications of hERG K+ channel block by a high-affinity minimally structured blocker

    Science.gov (United States)

    Helliwell, Matthew V.; Zhang, Yihong; El Harchi, Aziza; Du, Chunyun; Hancox, Jules C.; Dempsey, Christopher E.

    2018-01-01

    Cardiac potassium channels encoded by human ether-à-go-go–related gene (hERG) are major targets for structurally diverse drugs associated with acquired long QT syndrome. This study characterized hERG channel inhibition by a minimally structured high-affinity hERG inhibitor, Cavalli-2, composed of three phenyl groups linked by polymethylene spacers around a central amino group, chosen to probe the spatial arrangement of side chain groups in the high-affinity drug-binding site of the hERG pore. hERG current (IhERG) recorded at physiological temperature from HEK293 cells was inhibited with an IC50 of 35.6 nm with time and voltage dependence characteristic of blockade contingent upon channel gating. Potency of Cavalli-2 action was markedly reduced for attenuated inactivation mutants located near (S620T; 54-fold) and remote from (N588K; 15-fold) the channel pore. The S6 Y652A and F656A mutations decreased inhibitory potency 17- and 75-fold, respectively, whereas T623A and S624A at the base of the selectivity filter also decreased potency (16- and 7-fold, respectively). The S5 helix F557L mutation decreased potency 10-fold, and both F557L and Y652A mutations eliminated voltage dependence of inhibition. Computational docking using the recent cryo-EM structure of an open channel hERG construct could only partially recapitulate experimental data, and the high dependence of Cavalli-2 block on Phe-656 is not readily explainable in that structure. A small clockwise rotation of the inner (S6) helix of the hERG pore from its configuration in the cryo-EM structure may be required to optimize Phe-656 side chain orientations compatible with high-affinity block. PMID:29545312

  2. Effect of beta-adrenoceptor blockers on human ether-a-go-go-related gene (HERG) potassium channels

    DEFF Research Database (Denmark)

    Dupuis, Delphine S; Klaerke, Dan A; Olesen, Søren-Peter

    2005-01-01

    Patients with congenital long QT syndrome may develop arrhythmias under conditions of increased sympathetic tone. We have addressed whether some of the beta-adrenoceptor blockers commonly used to prevent the development of these arrhythmias could per se block the cardiac HERG (Human Ether....... These data showed that HERG blockade by beta-adrenoceptor blockers occurred only at high micromolar concentrations, which are significantly above the recently established safe margin of 100 (Redfern et al., 2003).......-1H-inden-4-yl)oxy]-3-[(1-methylethyl)amino]-2-butanol hydrochloride) blocked the HERG channel with similar affinity, whereas the beta1-receptor antagonists metoprolol and atenolol showed weak effects. Further, the four compounds blocked HERG channels expressed in a mammalian HEK293 cell line...

  3. Effects of Tannic Acid, Green Tea and Red Wine on hERG Channels Expressed in HEK293 Cells.

    Directory of Open Access Journals (Sweden)

    Xi Chu

    Full Text Available Tannic acid presents in varying concentrations in plant foods, and in relatively high concentrations in green teas and red wines. Human ether-à-go-go-related gene (hERG channels expressed in multiple tissues (e.g. heart, neurons, smooth muscle and cancer cells, and play important roles in modulating cardiac action potential repolarization and tumor cell biology. The present study investigated the effects of tannic acid, green teas and red wines on hERG currents. The effects of tannic acid, teas and red wines on hERG currents stably transfected in HEK293 cells were studied with a perforated patch clamp technique. In this study, we demonstrated that tannic acid inhibited hERG currents with an IC50 of 3.4 μM and ~100% inhibition at higher concentrations, and significantly shifted the voltage dependent activation to more positive potentials (Δ23.2 mV. Remarkably, a 100-fold dilution of multiple types of tea (green tea, oolong tea and black tea or red wine inhibited hERG currents by ~90%, and significantly shifted the voltage dependent activation to more positive potentials (Δ30.8 mV and Δ26.0 mV, respectively. Green tea Lung Ching and red wine inhibited hERG currents, with IC50 of 0.04% and 0.19%, respectively. The effects of tannic acid, teas and red wine on hERG currents were irreversible. These results suggest tannic acid is a novel hERG channel blocker and consequently provide a new mechanistic evidence for understanding the effects of tannic acid. They also revealed the potential pharmacological basis of tea- and red wine-induced biology activities.

  4. Effects of Tannic Acid, Green Tea and Red Wine on hERG Channels Expressed in HEK293 Cells

    Science.gov (United States)

    Xu, Bingyuan; Li, Wenya; Lin, Yue; Sun, Xiaorun; Ding, Chunhua; Zhang, Xuan

    2015-01-01

    Tannic acid presents in varying concentrations in plant foods, and in relatively high concentrations in green teas and red wines. Human ether-à-go-go-related gene (hERG) channels expressed in multiple tissues (e.g. heart, neurons, smooth muscle and cancer cells), and play important roles in modulating cardiac action potential repolarization and tumor cell biology. The present study investigated the effects of tannic acid, green teas and red wines on hERG currents. The effects of tannic acid, teas and red wines on hERG currents stably transfected in HEK293 cells were studied with a perforated patch clamp technique. In this study, we demonstrated that tannic acid inhibited hERG currents with an IC50 of 3.4 μM and ~100% inhibition at higher concentrations, and significantly shifted the voltage dependent activation to more positive potentials (Δ23.2 mV). Remarkably, a 100-fold dilution of multiple types of tea (green tea, oolong tea and black tea) or red wine inhibited hERG currents by ~90%, and significantly shifted the voltage dependent activation to more positive potentials (Δ30.8 mV and Δ26.0 mV, respectively). Green tea Lung Ching and red wine inhibited hERG currents, with IC50 of 0.04% and 0.19%, respectively. The effects of tannic acid, teas and red wine on hERG currents were irreversible. These results suggest tannic acid is a novel hERG channel blocker and consequently provide a new mechanistic evidence for understanding the effects of tannic acid. They also revealed the potential pharmacological basis of tea- and red wine-induced biology activities. PMID:26625122

  5. A radiolabeled peptide ligand of the hERG channel, [125I]-BeKm-1

    DEFF Research Database (Denmark)

    Angelo, Kamilla; Korolkova, Yuliya V; Grunnet, Morten

    2003-01-01

    The wild-type scorpion toxin BeKm-1, which selectively blocks human ether-a-go-go related (hERG) channels, was radiolabeled with iodine at tyrosine 11. Both the mono- and di-iodinated derivatives were found to be biologically active. In electrophysiological patch-clamp recordings mono-[127I]-BeKm-1...... had a concentration of half-maximal inhibition (IC50 value) of 27 nM, while wild-type BeKm-1 inhibited hERG channels with an IC50 value of 7 nM. Mono-[125I]-BeKm-1 was found to bind in a concentration-dependent manner and with picomolar affinity to hERG channel protein in purified membrane vesicles...... of [125I]-BeKm-1 to the hERG channel to an IC50 of 7 nM. In autoradiographic studies on rat hearts, binding of [125I]-BeKm-1 was dose-dependent and could partially be displaced by the addition of excess amounts of non-radioactive BeKm-1. The density of the radioactive signal was equally distributed...

  6. High potency inhibition of hERG potassium channels by the sodium–calcium exchange inhibitor KB-R7943

    Science.gov (United States)

    Cheng, Hongwei; Zhang, Yihong; Du, Chunyun; Dempsey, Christopher E; Hancox, Jules C

    2012-01-01

    BACKGROUND AND PURPOSE KB-R7943 is an isothiourea derivative that is used widely as a pharmacological inhibitor of sodium–calcium exchange (NCX) in experiments on cardiac and other tissue types. This study investigated KB-R7943 inhibition of hERG (human ether-à-go-go-related gene) K+ channels that underpin the cardiac rapid delayed rectifier potassium current, IKr. EXPERIMENTAL APPROACH Whole-cell patch-clamp measurements were made of hERG current (IhERG) carried by wild-type or mutant hERG channels and of native rabbit ventricular IKr. Docking simulations utilized a hERG homology model built on a MthK-based template. KEY RESULTS KB-R7943 inhibited both IhERG and native IKr rapidly on membrane depolarization with IC50 values of ∼89 and ∼120 nM, respectively, for current tails at −40 mV following depolarizing voltage commands to +20 mV. Marked IhERG inhibition also occurred under ventricular action potential voltage clamp. IhERG inhibition by KB-R7943 exhibited both time- and voltage-dependence but showed no preference for inactivated over activated channels. Results of alanine mutagenesis and docking simulations indicate that KB-R7943 can bind to a pocket formed of the side chains of aromatic residues Y652 and F656, with the compound's nitrobenzyl group orientated towards the cytoplasmic side of the channel pore. The structurally related NCX inhibitor SN-6 also inhibited IhERG, but with a markedly reduced potency. CONCLUSIONS AND IMPLICATIONS KB-R7943 inhibits IhERG/IKr with a potency that exceeds that reported previously for acute cardiac NCX inhibition. Our results also support the feasibility of benzyloxyphenyl-containing NCX inhibitors with reduced potential, in comparison with KB-R7943, to inhibit hERG. PMID:21950687

  7. Interaction between the cardiac rapidly (IKr) and slowly (IKs) activating delayed rectifier potassium channels revealed by low K+-induced hERG endocytic degradation.

    Science.gov (United States)

    Guo, Jun; Wang, Tingzhong; Yang, Tonghua; Xu, Jianmin; Li, Wentao; Fridman, Michael D; Fisher, John T; Zhang, Shetuan

    2011-10-07

    Cardiac repolarization is controlled by the rapidly (I(Kr)) and slowly (I(Ks)) activating delayed rectifier potassium channels. The human ether-a-go-go-related gene (hERG) encodes I(Kr), whereas KCNQ1 and KCNE1 together encode I(Ks). Decreases in I(Kr) or I(Ks) cause long QT syndrome (LQTS), a cardiac disorder with a high risk of sudden death. A reduction in extracellular K(+) concentration ([K(+)](o)) induces LQTS and selectively causes endocytic degradation of mature hERG channels from the plasma membrane. In the present study, we investigated whether I(Ks) compensates for the reduced I(Kr) under low K(+) conditions. Our data show that when hERG and KCNQ1 were expressed separately in human embryonic kidney (HEK) cells, exposure to 0 mM K(+) for 6 h completely eliminated the mature hERG channel expression but had no effect on KCNQ1. When hERG and KCNQ1 were co-expressed, KCNQ1 significantly delayed 0 mM K(+)-induced hERG reduction. Also, hERG degradation led to a significant reduction in KCNQ1 in 0 mM K(+) conditions. An interaction between hERG and KCNQ1 was identified in hERG+KCNQ1-expressing HEK cells. Furthermore, KCNQ1 preferentially co-immunoprecipitated with mature hERG channels that are localized in the plasma membrane. Biophysical and pharmacological analyses indicate that although hERG and KCNQ1 closely interact with each other, they form distinct hERG and KCNQ1 channels. These data extend our understanding of delayed rectifier potassium channel trafficking and regulation, as well as the pathology of LQTS.

  8. Improved functional expression of recombinant human ether-a-go-go (hERG K+ channels by cultivation at reduced temperature

    Directory of Open Access Journals (Sweden)

    Hamilton Bruce

    2007-12-01

    Full Text Available Abstract Background HERG potassium channel blockade is the major cause for drug-induced long QT syndrome, which sometimes cause cardiac disrhythmias and sudden death. There is a strong interest in the pharmaceutical industry to develop high quality medium to high-throughput assays for detecting compounds with potential cardiac liability at the earliest stages of drug development. Cultivation of cells at lower temperature has been used to improve the folding and membrane localization of trafficking defective hERG mutant proteins. The objective of this study was to investigate the effect of lower temperature maintenance on wild type hERG expression and assay performance. Results Wild type hERG was stably expressed in CHO-K1 cells, with the majority of channel protein being located in the cytoplasm, but relatively little on the cell surface. Expression at both locations was increased several-fold by cultivation at lower growth temperatures. Intracellular hERG protein levels were highest at 27°C and this correlated with maximal 3H-dofetilide binding activity. In contrast, the expression of functionally active cell surface-associated hERG measured by patch clamp electrophysiology was optimal at 30°C. The majority of the cytoplasmic hERG protein was associated with the membranes of cytoplasmic vesicles, which markedly increased in quantity and size at lower temperatures or in the presence of the Ca2+-ATPase inhibitor, thapsigargin. Incubation with the endocytic trafficking blocker, nocodazole, led to an increase in hERG activity at 37°C, but not at 30°C. Conclusion Our results are consistent with the concept that maintenance of cells at reduced temperature can be used to boost the functional expression of difficult-to-express membrane proteins and improve the quality of assays for medium to high-throughput compound screening. In addition, these results shed some light on the trafficking of hERG protein under these growth conditions.

  9. Performance of Machine Learning Algorithms for Qualitative and Quantitative Prediction Drug Blockade of hERG1 channel.

    Science.gov (United States)

    Wacker, Soren; Noskov, Sergei Yu

    2018-05-01

    Drug-induced abnormal heart rhythm known as Torsades de Pointes (TdP) is a potential lethal ventricular tachycardia found in many patients. Even newly released anti-arrhythmic drugs, like ivabradine with HCN channel as a primary target, block the hERG potassium current in overlapping concentration interval. Promiscuous drug block to hERG channel may potentially lead to perturbation of the action potential duration (APD) and TdP, especially when with combined with polypharmacy and/or electrolyte disturbances. The example of novel anti-arrhythmic ivabradine illustrates clinically important and ongoing deficit in drug design and warrants for better screening methods. There is an urgent need to develop new approaches for rapid and accurate assessment of how drugs with complex interactions and multiple subcellular targets can predispose or protect from drug-induced TdP. One of the unexpected outcomes of compulsory hERG screening implemented in USA and European Union resulted in large datasets of IC 50 values for various molecules entering the market. The abundant data allows now to construct predictive machine-learning (ML) models. Novel ML algorithms and techniques promise better accuracy in determining IC 50 values of hERG blockade that is comparable or surpassing that of the earlier QSAR or molecular modeling technique. To test the performance of modern ML techniques, we have developed a computational platform integrating various workflows for quantitative structure activity relationship (QSAR) models using data from the ChEMBL database. To establish predictive powers of ML-based algorithms we computed IC 50 values for large dataset of molecules and compared it to automated patch clamp system for a large dataset of hERG blocking and non-blocking drugs, an industry gold standard in studies of cardiotoxicity. The optimal protocol with high sensitivity and predictive power is based on the novel eXtreme gradient boosting (XGBoost) algorithm. The ML-platform with XGBoost

  10. The human ether-a-go-go-related gene (hERG) current inhibition selectively prolongs action potential of midmyocardial cells to augment transmural dispersion.

    Science.gov (United States)

    Yasuda, C; Yasuda, S; Yamashita, H; Okada, J; Hisada, T; Sugiura, S

    2015-08-01

    The majority of drug induced arrhythmias are related to the prolongation of action potential duration following inhibition of rapidly activating delayed rectifier potassium current (I(Kr)) mediated by the hERG channel. However, for arrhythmias to develop and be sustained, not only the prolongation of action potential duration but also its transmural dispersion are required. Herein, we evaluated the effect of hERG inhibition on transmural dispersion of action potential duration using the action potential clamp technique that combined an in silico myocyte model with the actual I(Kr) measurement. Whole cell I(Kr) current was measured in Chinese hamster ovary cells stably expressing the hERG channel. The measured current was coupled with models of ventricular endocardial, M-, and epicardial cells to calculate the action potentials. Action potentials were evaluated under control condition and in the presence of 1, 10, or 100 μM disopyramide, an hERG inhibitor. Disopyramide dose-dependently increased the action potential durations of the three cell types. However, action potential duration of M-cells increased disproportionately at higher doses, and was significantly different from that of epicardial and endocardial cells (dispersion of repolarization). By contrast, the effects of disopyramide on peak I(Kr) and instantaneous current-voltage relation were similar in all cell types. Simulation study suggested that the reduced repolarization reserve of M-cell with smaller amount of slowly activating delayed rectifier potassium current levels off at longer action potential duration to make such differences. The action potential clamp technique is useful for studying the mechanism of arrhythmogenesis by hERG inhibition through the transmural dispersion of repolarization.

  11. Bag1 Co-chaperone Promotes TRC8 E3 Ligase-dependent Degradation of Misfolded Human Ether a Go-Go-related Gene (hERG) Potassium Channels.

    Science.gov (United States)

    Hantouche, Christine; Williamson, Brittany; Valinsky, William C; Solomon, Joshua; Shrier, Alvin; Young, Jason C

    2017-02-10

    Cardiac long QT syndrome type 2 is caused by mutations in the human ether a go-go-related gene (hERG) potassium channel, many of which cause misfolding and degradation at the endoplasmic reticulum instead of normal trafficking to the cell surface. The Hsc70/Hsp70 chaperones assist the folding of the hERG cytosolic domains. Here, we demonstrate that the Hsp70 nucleotide exchange factor Bag1 promotes hERG degradation by the ubiquitin-proteasome system at the endoplasmic reticulum to regulate hERG levels and channel activity. Dissociation of hERG complexes containing Hsp70 and the E3 ubiquitin ligase CHIP requires the interaction of Bag1 with Hsp70, but this does not involve the Bag1 ubiquitin-like domain. The interaction with Bag1 then shifts hERG degradation to the membrane-anchored E3 ligase TRC8 and its E2-conjugating enzyme Ube2g2, as determined by siRNA screening. TRC8 interacts through the transmembrane region with hERG and decreases hERG functional expression. TRC8 also mediates degradation of the misfolded hERG-G601S disease mutant, but pharmacological stabilization of the mutant structure prevents degradation. Our results identify TRC8 as a previously unknown Hsp70-independent quality control E3 ligase for hERG. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

  12. The S4-S5 linker acts as a signal integrator for HERG K+ channel activation and deactivation gating.

    Directory of Open Access Journals (Sweden)

    Chai Ann Ng

    Full Text Available Human ether-à-go-go-related gene (hERG K(+ channels have unusual gating kinetics. Characterised by slow activation/deactivation but rapid inactivation/recovery from inactivation, the unique gating kinetics underlie the central role hERG channels play in cardiac repolarisation. The slow activation and deactivation kinetics are regulated in part by the S4-S5 linker, which couples movement of the voltage sensor domain to opening of the activation gate at the distal end of the inner helix of the pore domain. It has also been suggested that cytosolic domains may interact with the S4-S5 linker to regulate activation and deactivation kinetics. Here, we show that the solution structure of a peptide corresponding to the S4-S5 linker of hERG contains an amphipathic helix. The effects of mutations at the majority of residues in the S4-S5 linker of hERG were consistent with the previously identified role in coupling voltage sensor movement to the activation gate. However, mutations to Ser543, Tyr545, Gly546 and Ala548 had more complex phenotypes indicating that these residues are involved in additional interactions. We propose a model in which the S4-S5 linker, in addition to coupling VSD movement to the activation gate, also contributes to interactions that stabilise the closed state and a separate set of interactions that stabilise the open state. The S4-S5 linker therefore acts as a signal integrator and plays a crucial role in the slow deactivation kinetics of the channel.

  13. MiR-17-5p impairs trafficking of H-ERG K+ channel protein by targeting multiple er stress-related chaperones during chronic oxidative stress.

    Directory of Open Access Journals (Sweden)

    Qi Wang

    Full Text Available BACKGROUND: To investigate if microRNAs (miRNAs play a role in regulating h-ERG trafficking in the setting of chronic oxidative stress as a common deleterious factor for many cardiac disorders. METHODS: We treated neonatal rat ventricular myocytes and HEK293 cells with stable expression of h-ERG with H2O2 for 12 h and 48 h. Expression of miR-17-5p seed miRNAs was quantified by real-time RT-PCR. Protein levels of chaperones and h-ERG trafficking were measured by Western blot analysis. Luciferase reporter gene assay was used to study miRNA and target interactions. Whole-cell patch-clamp techniques were employed to record h-ERG K(+ current. RESULTS: H-ERG trafficking was impaired by H2O2 after 48 h treatment, accompanied by reciprocal changes of expression between miR-17-5p seed miRNAs and several chaperones (Hsp70, Hsc70, CANX, and Golga2, with the former upregulated and the latter downregulated. We established these chaperones as targets for miR-17-5p. Application miR-17-5p inhibitor rescued H2O2-induced impairment of h-ERG trafficking. Upregulation of endogenous by H2O2 or forced miR-17-5p expression either reduced h-ERG current. Sequestration of AP1 by its decoy molecule eliminated the upregulation of miR-17-5p, and ameliorated impairment of h-ERG trafficking. CONCLUSIONS: Collectively, deregulation of the miR-17-5p seed family miRNAs can cause severe impairment of h-ERG trafficking through targeting multiple ER stress-related chaperones, and activation of AP1 likely accounts for the deleterious upregulation of these miRNAs, in the setting of prolonged duration of oxidative stress. These findings revealed the role of miRNAs in h-ERG trafficking, which may contribute to the cardiac electrical disturbances associated with oxidative stress.

  14. MiR-17-5p impairs trafficking of H-ERG K+ channel protein by targeting multiple er stress-related chaperones during chronic oxidative stress.

    Science.gov (United States)

    Wang, Qi; Hu, Weina; Lei, Mingming; Wang, Yong; Yan, Bing; Liu, Jun; Zhang, Ren; Jin, Yuanzhe

    2013-01-01

    To investigate if microRNAs (miRNAs) play a role in regulating h-ERG trafficking in the setting of chronic oxidative stress as a common deleterious factor for many cardiac disorders. We treated neonatal rat ventricular myocytes and HEK293 cells with stable expression of h-ERG with H2O2 for 12 h and 48 h. Expression of miR-17-5p seed miRNAs was quantified by real-time RT-PCR. Protein levels of chaperones and h-ERG trafficking were measured by Western blot analysis. Luciferase reporter gene assay was used to study miRNA and target interactions. Whole-cell patch-clamp techniques were employed to record h-ERG K(+) current. H-ERG trafficking was impaired by H2O2 after 48 h treatment, accompanied by reciprocal changes of expression between miR-17-5p seed miRNAs and several chaperones (Hsp70, Hsc70, CANX, and Golga2), with the former upregulated and the latter downregulated. We established these chaperones as targets for miR-17-5p. Application miR-17-5p inhibitor rescued H2O2-induced impairment of h-ERG trafficking. Upregulation of endogenous by H2O2 or forced miR-17-5p expression either reduced h-ERG current. Sequestration of AP1 by its decoy molecule eliminated the upregulation of miR-17-5p, and ameliorated impairment of h-ERG trafficking. Collectively, deregulation of the miR-17-5p seed family miRNAs can cause severe impairment of h-ERG trafficking through targeting multiple ER stress-related chaperones, and activation of AP1 likely accounts for the deleterious upregulation of these miRNAs, in the setting of prolonged duration of oxidative stress. These findings revealed the role of miRNAs in h-ERG trafficking, which may contribute to the cardiac electrical disturbances associated with oxidative stress.

  15. Computational analysis of the effects of the hERG channel opener NS1643 in a human ventricular cell model

    DEFF Research Database (Denmark)

    Peitersen, Torben; Grunnet, Morten; Benson, Alan P

    2008-01-01

    BACKGROUND: Dysfunction or pharmacologic inhibition of repolarizing cardiac ionic currents can lead to fatal arrhythmias. The hERG potassium channel underlies the repolarizing current I(Kr), and mutations therein can produce both long and short QT syndromes (LQT2 and SQT1). We previously reported...

  16. The effects of deoxyelephantopin on the cardiac delayed rectifier potassium channel current (IKr) and human ether-a-go-go-related gene (hERG) expression.

    Science.gov (United States)

    Teah, Yi Fan; Abduraman, Muhammad Asyraf; Amanah, Azimah; Adenan, Mohd Ilham; Sulaiman, Shaida Fariza; Tan, Mei Lan

    2017-09-01

    Elephantopus scaber Linn and its major bioactive component, deoxyelephantopin are known for their medicinal properties and are often reported to have various cytotoxic and antitumor activities. This plant is widely used as folk medicine for a plethora of indications although its safety profile remains unknown. Human ether-a-go-go-related gene (hERG) encodes the cardiac I Kr current which is a determinant of the duration of ventricular action potentials and QT interval. The hERG potassium channel is an important antitarget in cardiotoxicity evaluation. This study investigated the effects of deoxyelephantopin on the current, mRNA and protein expression of hERG channel in hERG-transfected HEK293 cells. The hERG tail currents following depolarization pulses were insignificantly affected by deoxyelephantopin in the transfected cell line. Current reduction was less than 40% as compared with baseline at the highest concentration of 50 μM. The results were consistent with the molecular docking simulation and hERG surface protein expression. Interestingly, it does not affect the hERG expression at both transcriptional and translational level at most concentrations, although higher concentration at 10 μM caused protein accumulation. In conclusion, deoxyelephantopin is unlikely a clinically significant hERG channel and I kr blocker. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Irresponsiveness of two retinoblastoma cases to conservative therapy correlates with up- regulation of hERG1 channels and of the VEGF-A pathway

    Directory of Open Access Journals (Sweden)

    La Torre Agostino

    2010-09-01

    Full Text Available Abstract Background Treatment strategies for Retinoblastoma (RB, the most common primary intraocular tumor in children, have evolved over the past few decades and chemoreduction is currently the most popular treatment strategy. Despite success, systemic chemotherapeutic treatment has relevant toxicity, especially in the pediatric population. Antiangiogenic therapy has thus been proposed as a valuable alternative for pediatric malignancies, in particolar RB. Indeed, it has been shown that vessel density correlates with both local invasive growth and presence of metastases in RB, suggesting that angiogenesis could play a pivotal role for both local and systemic invasive growth in RB. We present here two cases of sporadic, bilateral RB that did not benefit from the conservative treatment and we provide evidence that the VEGF-A pathway is significantly up-regulated in both RB cases along with an over expression of hERG1 K+ channels. Case presentation Two patients showed a sporadic, bilateral RB, classified at Stage II of the Reese-Elsworth Classification. Neither of them got benefits from conservative treatment, and the two eyes were enucleated. In samples from both RB cases we studied the VEGF-A pathway: VEGF-A showed high levels in the vitreous, the vegf-a, flt-1, kdr, and hif1-α transcripts were over-expressed. Moreover, both the transcripts and proteins of the hERG1 K+ channels turned out to be up-regulated in the two RB cases compared to the non cancerous retinal tissue. Conclusions We provide evidence that the VEGF-A pathway is up-regulated in two particular aggressive cases of bilateral RB, which did not experience any benefit from conservative treatment, showing the overexpression of the vegf-a, flt-1, kdr and hif1-α transcripts and the high secretion of VEGF-A. Moreover we also show for the first time that the herg1 gene transcripts and protein are over expressed in RB, as occurs in several aggressive tumors. These results further stress

  18. Common variants in the hERG (KCNH2) voltage-gated potassium channel are associated with altered fasting and glucose-stimulated plasma incretin and glucagon responses

    DEFF Research Database (Denmark)

    Engelbrechtsen, Line; Mahendran, Yuvaraj; Jonsson, Anna

    2018-01-01

    BACKGROUND: Patients with long QT syndrome due to rare loss-of-function mutations in the human ether-á-go-go-related gene (hERG) have prolonged QT interval, risk of arrhythmias, increased secretion of insulin and incretins and impaired glucagon response to hypoglycemia. This is caused by a dysfun......BACKGROUND: Patients with long QT syndrome due to rare loss-of-function mutations in the human ether-á-go-go-related gene (hERG) have prolonged QT interval, risk of arrhythmias, increased secretion of insulin and incretins and impaired glucagon response to hypoglycemia. This is caused...... by a dysfunctional Kv11.1 voltage-gated potassium channel. Based on these findings in patients with rare variants in hERG, we hypothesized that common variants in hERG may also lead to alterations in glucose homeostasis. Subsequently, we aimed to evaluate the effect of two common gain-of-function variants in hERG...... in hERG on QT-interval and circulation levels of incretins, insulin and glucagon. The Danish population-based Inter99 cohort (n = 5895) was used to assess the effect of common variants on QT-interval. The Danish ADDITION-PRO cohort was used (n = 1329) to study genetic associations with levels of GLP-1...

  19. In Silico Predictions of hERG Channel Blockers in Drug Discovery

    DEFF Research Database (Denmark)

    Taboureau, Olivier; Sørensen, Flemming Steen

    2011-01-01

    The risk for cardiotoxic side effects represents a major problem in clinical studies of drug candidates and regulatory agencies have explicitly recommended that all new drug candidates should be tested for blockage of the human Ether-a-go-go Related-Gene (hERG) potassium channel. Indeed, several ...

  20. Voltage-sensing domain mode shift is coupled to the activation gate by the N-terminal tail of hERG channels.

    Science.gov (United States)

    Tan, Peter S; Perry, Matthew D; Ng, Chai Ann; Vandenberg, Jamie I; Hill, Adam P

    2012-09-01

    Human ether-a-go-go-related gene (hERG) potassium channels exhibit unique gating kinetics characterized by unusually slow activation and deactivation. The N terminus of the channel, which contains an amphipathic helix and an unstructured tail, has been shown to be involved in regulation of this slow deactivation. However, the mechanism of how this occurs and the connection between voltage-sensing domain (VSD) return and closing of the gate are unclear. To examine this relationship, we have used voltage-clamp fluorometry to simultaneously measure VSD motion and gate closure in N-terminally truncated constructs. We report that mode shifting of the hERG VSD results in a corresponding shift in the voltage-dependent equilibrium of channel closing and that at negative potentials, coupling of the mode-shifted VSD to the gate defines the rate of channel closure. Deletion of the first 25 aa from the N terminus of hERG does not alter mode shifting of the VSD but uncouples the shift from closure of the cytoplasmic gate. Based on these observations, we propose the N-terminal tail as an adaptor that couples voltage sensor return to gate closure to define slow deactivation gating in hERG channels. Furthermore, because the mode shift occurs on a time scale relevant to the cardiac action potential, we suggest a physiological role for this phenomenon in maximizing current flow through hERG channels during repolarization.

  1. An NMR investigation of the structure, function and role of the hERG channel selectivity filter in the long QT syndrome.

    Science.gov (United States)

    Gravel, Andrée E; Arnold, Alexandre A; Dufourc, Erick J; Marcotte, Isabelle

    2013-06-01

    The human ether-a-go-go-related gene (hERG) voltage-gated K(+) channels are located in heart cell membranes and hold a unique selectivity filter (SF) amino acid sequence (SVGFG) as compared to other K(+) channels (TVGYG). The hERG provokes the acquired long QT syndrome (ALQTS) when blocked, as a side effect of drugs, leading to arrhythmia or heart failure. Its pore domain - including the SF - is believed to be a cardiotoxic drug target. In this study combining solution and solid-state NMR experiments we examine the structure and function of hERG's L(622)-K(638) segment which comprises the SF, as well as its role in the ALQTS using reported active drugs. We first show that the SF segment is unstructured in solution with and without K(+) ions in its surroundings, consistent with the expected flexibility required for the change between the different channel conductive states predicted by computational studies. We also show that the SF segment has the potential to perturb the membrane, but that the presence of K(+) ions cancels this interaction. The SF moiety appears to be a possible target for promethazine in the ALQTS mechanism, but not as much for bepridil, cetirizine, diphenhydramine and fluvoxamine. The membrane affinity of the SF is also affected by the presence of drugs which also perturb model DMPC-based membranes. These results thus suggest that the membrane could play a role in the ALQTS by promoting the access to transmembrane or intracellular targets on the hERG channel, or perturbing the lipid-protein synergy. Copyright © 2013 Elsevier B.V. All rights reserved.

  2. The role of hERG1 ion channels in epithelial-mesenchymal transition and the capacity of riluzole to reduce cisplatin resistance in colorectal cancer cells.

    Science.gov (United States)

    Fortunato, Angelo

    2017-08-01

    The transition of cells from the epithelial to the mesenchymal state (EMT) plays an important role in tumor progression. EMT allows cells to acquire mobility, stem-like behavior and resistance to apoptosis and drug treatment. These features turn EMT into a central process in tumor biology. Ion channels are attractive targets for the treatment of cancer since they play critical roles in controlling a wide range of physiological processes that are frequently deregulated in cancer. Here, we investigated the role of ether-a-go-go-related 1 (hERG1) ion channels in the EMT of colorectal cancer cells. We studied the epithelial-mesenchymal profile of different colorectal cancer-derived cell lines and the expression of hERG1 potassium channels in these cell lines using real-time PCR. Next, we knocked down hERG1 expression in HCT116 cells using lentivirus mediated RNA interference and characterized the hERG1 silenced cells in vitro and in vivo. Finally, we investigated the capacity of riluzole, an ion channel-modulating drug used in humans to treat amyotrophic lateral sclerosis, to reduce the resistance of the respective colorectal cancer cells to the chemotherapeutic drug cisplatin. We found that of the colorectal cancer-derived cell lines tested, HCT116 showed the highest mesenchymal profile and a high hERG1 expression. Subsequent hERG1 expression knockdown induced a change in cell morphology, which was accompanied by a reduction in the proliferative and tumorigenic capacities of the cells. Notably, we found that hERG1expression knockdown elicited a reversion of the EMT profile in HCT116 cells with a reacquisition of the epithelial-like profile. We also found that riluzole increased the sensitivity of HCT116 cisplatin-resistant cells to cisplatin. Our data indicate that hERG1 plays a role in the EMT of colorectal cancer cells and that its knockdown reduces the proliferative and tumorigenic capacities of these cells. In addition, we conclude that riluzole may be used in

  3. MiR-17-5p Impairs Trafficking of H-ERG K+ Channel Protein by Targeting Multiple ER Stress-Related Chaperones during Chronic Oxidative Stress

    OpenAIRE

    Wang, Qi; Hu, Weina; Lei, Mingming; Wang, Yong; Yan, Bing; Liu, Jun; Zhang, Ren; Jin, Yuanzhe

    2013-01-01

    BACKGROUND: To investigate if microRNAs (miRNAs) play a role in regulating h-ERG trafficking in the setting of chronic oxidative stress as a common deleterious factor for many cardiac disorders. METHODS: We treated neonatal rat ventricular myocytes and HEK293 cells with stable expression of h-ERG with H2O2 for 12 h and 48 h. Expression of miR-17-5p seed miRNAs was quantified by real-time RT-PCR. Protein levels of chaperones and h-ERG trafficking were measured by Western blot analysis. Lucifer...

  4. Validation and Clinical Utility of the hERG IC50:Cmax Ratio to Determine the Risk of Drug-Induced Torsades de Pointes: A Meta-Analysis.

    Science.gov (United States)

    Lehmann, David F; Eggleston, William D; Wang, Dongliang

    2018-03-01

    Use of the QT interval corrected for heart rate (QTc) on the electrocardiogram (ECG) to predict torsades de pointes (TdP) risk from culprit drugs is neither sensitive nor specific. The ratio of the half-maximum inhibitory concentration of the hERG channel (hERG IC50) to the peak serum concentration of unbound drug (C max ) is used during drug development to screen out chemical entities likely to cause TdP. To validate the use of the hERG IC50:C max ratio to predict TdP risk from a culprit drug by its correlation with TdP incidence. Medline (between 1966 and March 2017) was accessed for hERG IC50 and C max values from the antihistamine, fluoroquinolone, and antipsychotic classes to identify cases of drug-induced TdP. Exposure to a culprit drug was estimated from annual revenues reported by the manufacturer. Inclusion criteria for TdP cases were provision of an ECG tracing that demonstrated QTc prolongation with TdP and normal serum values of potassium, calcium, and magnesium. Cases reported in patients with a prior rhythm disturbance and those involving a drug interaction were excluded. The Meta-Analysis of Observational Studies in Epidemiology checklist was used for epidemiological data extraction by two authors. Negligible risk drugs were defined by an hERG IC50:C max ratio that correlated with less than a 5% chance of one TdP event for every 100 million exposures (relative risk [RR] 1.0). The hERG IC50:C max ratio correlated with TdP risk (0.312; 95% confidence interval 0.205-0.476, pratio of 80 (RR 1.0). The RR from olanzapine is on par with loratadine; ziprasidone is comparable with ciprofloxacin. Drugs with an RR greater than 50 include astemizole, risperidone, haloperidol, and thioridazine. The hERG IC50:C max ratio was correlated with TdP incidence for culprit drugs. This validation provides support for the potential use of the hERG IC50:C max ratio for clinical decision making in instances of drug selection where TdP risk is a concern. © 2018

  5. Kuifjes katholieke jeugd. De katholieke achtergrond van Hergé. Deel 1 van 2

    NARCIS (Netherlands)

    de Groot, C.N.

    2013-01-01

    Following the launch of Steven Spielberg’s ‘Tintin and the Secret of the Unicorn’, l’Osservore Romano hailed Tintin as a ‘catholic hero’. This article demonstrates that the comic originates, more specifically, in conservative and reactionary milieus in Belgian Catholicism. Hergé (ps. for Georges

  6. Oxycodone is associated with dose-dependent QTc prolongation in patients and low-affinity inhibiting of hERG activity in vitro

    DEFF Research Database (Denmark)

    Fanoe, Søren; Jensen, Gorm Boje; Sjøgren, Per

    2008-01-01

    with the use of these drugs. WHAT THIS PAPER ADDS: This study is the first to show that oxycodone dose is associated with QT prolongation and in vitro blockade of hERG channels expressed in HEK293. Neither morphine nor tramadol doses are associated with the QT interval length. AIMS: During recent years some...... and TdP could be a more general problem associated with the use of these drugs. The aims of this study were to evaluate the association between different opioids and the QTc among patients and measure hERG activity under influence by opioids in vitro. METHODS: One hundred chronic nonmalignant pain...... patients treated with methadone, oxycodone, morphine or tramadol were recruited in a cross-sectional study. The QTc was estimated from a 12-lead ECG. To examine hERG activity in the presence of oxycodone, electrophysiological testing was conducted using Xenopus laevis oocytes and HEK293 cells expressing h...

  7. hERG blocking potential of acids and zwitterions characterized by three thresholds for acidity, size and reactivity

    DEFF Research Database (Denmark)

    Nikolov, Nikolai Georgiev; Dybdahl, Marianne; Jonsdottir, Svava Osk

    2014-01-01

    with a concordance of 91% by a decision tree based on the rule. Two external validations were performed with sets of 35 and 48 observations, respectively, both showing concordances of 91%. In addition, a global QSAR model of hERG blocking was constructed based on a large diverse training set of 1374 chemicals...... covering all ionization classes, externally validated showing high predictivity and compared to the decision tree. The decision tree was found to be superior for the acids and zwitterionic ampholytes classes....

  8. Local anesthetic interaction with human ether-a-go-go-related gene (HERG) channels: role of aromatic amino acids Y652 and F656

    DEFF Research Database (Denmark)

    Siebrands, Cornelia C; Schmitt, Nicole; Friederich, Patrick

    2005-01-01

    was to determine the effect of the mutations Y652A and F656A in the putative drug binding region of HERG on the inhibition by bupivacaine, ropivacaine, and mepivacaine. METHODS: The authors examined the inhibition of wild-type and mutant HERG channels, transiently expressed in Chinese hamster ovary cells...... by bupivacaine, ropivacaine, and mepivacaine. Whole cell patch clamp recordings were performed at room temperature. RESULTS: Inhibition of HERG wild-type and mutant channels by the different local anesthetics was concentration dependent, stereoselective, and reversible. The sensitivity decreased in the order...... bupivacaine > ropivacaine > mepivacaine for wild-type and mutant channels. The mutant channels were approximately 4-30 times less sensitive to the inhibitory action of the different local anesthetics than the wild-type channel. The concentration-response data were described by Hill functions (bupivacaine...

  9. Latent classification models

    DEFF Research Database (Denmark)

    Langseth, Helge; Nielsen, Thomas Dyhre

    2005-01-01

    parametric family ofdistributions.  In this paper we propose a new set of models forclassification in continuous domains, termed latent classificationmodels. The latent classification model can roughly be seen ascombining the \\NB model with a mixture of factor analyzers,thereby relaxing the assumptions...... classification model, and wedemonstrate empirically that the accuracy of the proposed model issignificantly higher than the accuracy of other probabilisticclassifiers....

  10. Cluster Based Text Classification Model

    DEFF Research Database (Denmark)

    Nizamani, Sarwat; Memon, Nasrullah; Wiil, Uffe Kock

    2011-01-01

    We propose a cluster based classification model for suspicious email detection and other text classification tasks. The text classification tasks comprise many training examples that require a complex classification model. Using clusters for classification makes the model simpler and increases...... the accuracy at the same time. The test example is classified using simpler and smaller model. The training examples in a particular cluster share the common vocabulary. At the time of clustering, we do not take into account the labels of the training examples. After the clusters have been created......, the classifier is trained on each cluster having reduced dimensionality and less number of examples. The experimental results show that the proposed model outperforms the existing classification models for the task of suspicious email detection and topic categorization on the Reuters-21578 and 20 Newsgroups...

  11. In silico analysis of conformational changes induced by mutation of aromatic binding residues: consequences for drug binding in the hERG K+ channel.

    Directory of Open Access Journals (Sweden)

    Kirsten Knape

    Full Text Available Pharmacological inhibition of cardiac hERG K(+ channels is associated with increased risk of lethal arrhythmias. Many drugs reduce hERG current by directly binding to the channel, thereby blocking ion conduction. Mutation of two aromatic residues (F656 and Y652 substantially decreases the potency of numerous structurally diverse compounds. Nevertheless, some drugs are only weakly affected by mutation Y652A. In this study we utilize molecular dynamics simulations and docking studies to analyze the different effects of mutation Y652A on a selected number of hERG blockers. MD simulations reveal conformational changes in the binding site induced by mutation Y652A. Loss of π-π-stacking between the two aromatic residues induces a conformational change of the F656 side chain from a cavity facing to cavity lining orientation. Docking studies and MD simulations qualitatively reproduce the diverse experimentally observed modulatory effects of mutation Y652A and provide a new structural interpretation for the sensitivity differences.

  12. Latent class models for classification

    NARCIS (Netherlands)

    Vermunt, J.K.; Magidson, J.

    2003-01-01

    An overview is provided of recent developments in the use of latent class (LC) and other types of finite mixture models for classification purposes. Several extensions of existing models are presented. Two basic types of LC models for classification are defined: supervised and unsupervised

  13. Kuifjes katholieke jeugd. De katholieke achtergrond van Hergé. Deel 1 van 2

    OpenAIRE

    de Groot, C.N.

    2013-01-01

    Following the launch of Steven Spielberg’s ‘Tintin and the Secret of the Unicorn’, l’Osservore Romano hailed Tintin as a ‘catholic hero’. This article demonstrates that the comic originates, more specifically, in conservative and reactionary milieus in Belgian Catholicism. Hergé (ps. for Georges Rémi) designed Tintin for the children’s weekly of a newspaper that, in this period, shared its main themes with the Catholic fascist movement Rex: anti-communism, anti-capitalism, anti-semitism and t...

  14. Interactions between charged residues in the transmembrane segments of the voltage-sensing domain in the hERG channel.

    Science.gov (United States)

    Zhang, M; Liu, J; Jiang, M; Wu, D-M; Sonawane, K; Guy, H R; Tseng, G-N

    2005-10-01

    Studies on voltage-gated K channels such as Shaker have shown that positive charges in the voltage-sensor (S4) can form salt bridges with negative charges in the surrounding transmembrane segments in a state-dependent manner, and different charge pairings can stabilize the channels in closed or open states. The goal of this study is to identify such charge interactions in the hERG channel. This knowledge can provide constraints on the spatial relationship among transmembrane segments in the channel's voltage-sensing domain, which are necessary for modeling its structure. We first study the effects of reversing S4's positive charges on channel activation. Reversing positive charges at the outer (K525D) and inner (K538D) ends of S4 markedly accelerates hERG activation, whereas reversing the 4 positive charges in between either has no effect or slows activation. We then use the 'mutant cycle analysis' to test whether D456 (outer end of S2) and D411 (inner end of S1) can pair with K525 and K538, respectively. Other positive charges predicted to be able, or unable, to interact with D456 or D411 are also included in the analysis. The results are consistent with predictions based on the distribution of these charged residues, and confirm that there is functional coupling between D456 and K525 and between D411 and K538.

  15. The Importance of Classification to Business Model Research

    OpenAIRE

    Susan Lambert

    2015-01-01

    Purpose: To bring to the fore the scientific significance of classification and its role in business model theory building. To propose a method by which existing classifications of business models can be analyzed and new ones developed. Design/Methodology/Approach: A review of the scholarly literature relevant to classifications of business models is presented along with a brief overview of classification theory applicable to business model research. Existing business model classification...

  16. Emotion models for textual emotion classification

    Science.gov (United States)

    Bruna, O.; Avetisyan, H.; Holub, J.

    2016-11-01

    This paper deals with textual emotion classification which gained attention in recent years. Emotion classification is used in user experience, product evaluation, national security, and tutoring applications. It attempts to detect the emotional content in the input text and based on different approaches establish what kind of emotional content is present, if any. Textual emotion classification is the most difficult to handle, since it relies mainly on linguistic resources and it introduces many challenges to assignment of text to emotion represented by a proper model. A crucial part of each emotion detector is emotion model. Focus of this paper is to introduce emotion models used for classification. Categorical and dimensional models of emotion are explained and some more advanced approaches are mentioned.

  17. Fuzzy One-Class Classification Model Using Contamination Neighborhoods

    Directory of Open Access Journals (Sweden)

    Lev V. Utkin

    2012-01-01

    Full Text Available A fuzzy classification model is studied in the paper. It is based on the contaminated (robust model which produces fuzzy expected risk measures characterizing classification errors. Optimal classification parameters of the models are derived by minimizing the fuzzy expected risk. It is shown that an algorithm for computing the classification parameters is reduced to a set of standard support vector machine tasks with weighted data points. Experimental results with synthetic data illustrate the proposed fuzzy model.

  18. Churn classification model for local telecommunication company ...

    African Journals Online (AJOL)

    ... model based on the Rough Set Theory to classify customer churn. The results of the study show that the proposed Rough Set classification model outperforms the existing models and contributes to significant accuracy improvement. Keywords: customer churn; classification model; telecommunication industry; data mining;

  19. The N-terminal tail of hERG contains an amphipathic α-helix that regulates channel deactivation.

    Directory of Open Access Journals (Sweden)

    Chai Ann Ng

    Full Text Available The cytoplasmic N-terminal domain of the human ether-a-go-go related gene (hERG K+ channel is critical for the slow deactivation kinetics of the channel. However, the mechanism(s by which the N-terminal domain regulates deactivation remains to be determined. Here we show that the solution NMR structure of the N-terminal 135 residues of hERG contains a previously described Per-Arnt-Sim (PAS domain (residues 26-135 as well as an amphipathic α-helix (residues 13-23 and an initial unstructured segment (residues 2-9. Deletion of residues 2-25, only the unstructured segment (residues 2-9 or replacement of the α-helix with a flexible linker all result in enhanced rates of deactivation. Thus, both the initial flexible segment and the α-helix are required but neither is sufficient to confer slow deactivation kinetics. Alanine scanning mutagenesis identified R5 and G6 in the initial flexible segment as critical for slow deactivation. Alanine mutants in the helical region had less dramatic phenotypes. We propose that the PAS domain is bound close to the central core of the channel and that the N-terminal α-helix ensures that the flexible tail is correctly orientated for interaction with the activation gating machinery to stabilize the open state of the channel.

  20. Learning classification models with soft-label information.

    Science.gov (United States)

    Nguyen, Quang; Valizadegan, Hamed; Hauskrecht, Milos

    2014-01-01

    Learning of classification models in medicine often relies on data labeled by a human expert. Since labeling of clinical data may be time-consuming, finding ways of alleviating the labeling costs is critical for our ability to automatically learn such models. In this paper we propose a new machine learning approach that is able to learn improved binary classification models more efficiently by refining the binary class information in the training phase with soft labels that reflect how strongly the human expert feels about the original class labels. Two types of methods that can learn improved binary classification models from soft labels are proposed. The first relies on probabilistic/numeric labels, the other on ordinal categorical labels. We study and demonstrate the benefits of these methods for learning an alerting model for heparin induced thrombocytopenia. The experiments are conducted on the data of 377 patient instances labeled by three different human experts. The methods are compared using the area under the receiver operating characteristic curve (AUC) score. Our AUC results show that the new approach is capable of learning classification models more efficiently compared to traditional learning methods. The improvement in AUC is most remarkable when the number of examples we learn from is small. A new classification learning framework that lets us learn from auxiliary soft-label information provided by a human expert is a promising new direction for learning classification models from expert labels, reducing the time and cost needed to label data.

  1. Dynamic Latent Classification Model

    DEFF Research Database (Denmark)

    Zhong, Shengtong; Martínez, Ana M.; Nielsen, Thomas Dyhre

    as possible. Motivated by this problem setting, we propose a generative model for dynamic classification in continuous domains. At each time point the model can be seen as combining a naive Bayes model with a mixture of factor analyzers (FA). The latent variables of the FA are used to capture the dynamics...

  2. Classification hierarchies for product data modelling

    NARCIS (Netherlands)

    Pels, H.J.

    2006-01-01

    Abstraction is an essential element in data modelling that appears mainly in one of the following forms: generalisation, classification or aggregation. In the design of complex products classification hierarchies can be found product families that are viewed as classes of product types, while

  3. Compensatory neurofuzzy model for discrete data classification in biomedical

    Science.gov (United States)

    Ceylan, Rahime

    2015-03-01

    Biomedical data is separated to two main sections: signals and discrete data. So, studies in this area are about biomedical signal classification or biomedical discrete data classification. There are artificial intelligence models which are relevant to classification of ECG, EMG or EEG signals. In same way, in literature, many models exist for classification of discrete data taken as value of samples which can be results of blood analysis or biopsy in medical process. Each algorithm could not achieve high accuracy rate on classification of signal and discrete data. In this study, compensatory neurofuzzy network model is presented for classification of discrete data in biomedical pattern recognition area. The compensatory neurofuzzy network has a hybrid and binary classifier. In this system, the parameters of fuzzy systems are updated by backpropagation algorithm. The realized classifier model is conducted to two benchmark datasets (Wisconsin Breast Cancer dataset and Pima Indian Diabetes dataset). Experimental studies show that compensatory neurofuzzy network model achieved 96.11% accuracy rate in classification of breast cancer dataset and 69.08% accuracy rate was obtained in experiments made on diabetes dataset with only 10 iterations.

  4. Novel Two-Step Classifier for Torsades de Pointes Risk Stratification from Direct Features

    Directory of Open Access Journals (Sweden)

    Jaimit Parikh

    2017-11-01

    Full Text Available While pre-clinical Torsades de Pointes (TdP risk classifiers had initially been based on drug-induced block of hERG potassium channels, it is now well established that improved risk prediction can be achieved by considering block of non-hERG ion channels. The current multi-channel TdP classifiers can be categorized into two classes. First, the classifiers that take as input the values of drug-induced block of ion channels (direct features. Second, the classifiers that are built on features extracted from output of the drug-induced multi-channel blockage simulations in the in-silico models (derived features. The classifiers built on derived features have thus far not consistently provided increased prediction accuracies, and hence casts doubt on the value of such approaches given the cost of including biophysical detail. Here, we propose a new two-step method for TdP risk classification, referred to as Multi-Channel Blockage at Early After Depolarization (MCB@EAD. In the first step, we classified the compound that produced insufficient hERG block as non-torsadogenic. In the second step, the role of non-hERG channels to modulate TdP risk are considered by constructing classifiers based on direct or derived features at critical hERG block concentrations that generates EADs in the computational cardiac cell models. MCB@EAD provides comparable or superior TdP risk classification of the drugs from the direct features in tests against published methods. TdP risk for the drugs highly correlated to the propensity to generate EADs in the model. However, the derived features of the biophysical models did not improve the predictive capability for TdP risk assessment.

  5. Co-occurrence Models in Music Genre Classification

    DEFF Research Database (Denmark)

    Ahrendt, Peter; Goutte, Cyril; Larsen, Jan

    2005-01-01

    Music genre classification has been investigated using many different methods, but most of them build on probabilistic models of feature vectors x\\_r which only represent the short time segment with index r of the song. Here, three different co-occurrence models are proposed which instead consider...... genre data set with a variety of modern music. The basis was a so-called AR feature representation of the music. Besides the benefit of having proper probabilistic models of the whole song, the lowest classification test errors were found using one of the proposed models....

  6. Formalization of the classification pattern: survey of classification modeling in information systems engineering.

    Science.gov (United States)

    Partridge, Chris; de Cesare, Sergio; Mitchell, Andrew; Odell, James

    2018-01-01

    Formalization is becoming more common in all stages of the development of information systems, as a better understanding of its benefits emerges. Classification systems are ubiquitous, no more so than in domain modeling. The classification pattern that underlies these systems provides a good case study of the move toward formalization in part because it illustrates some of the barriers to formalization, including the formal complexity of the pattern and the ontological issues surrounding the "one and the many." Powersets are a way of characterizing the (complex) formal structure of the classification pattern, and their formalization has been extensively studied in mathematics since Cantor's work in the late nineteenth century. One can use this formalization to develop a useful benchmark. There are various communities within information systems engineering (ISE) that are gradually working toward a formalization of the classification pattern. However, for most of these communities, this work is incomplete, in that they have not yet arrived at a solution with the expressiveness of the powerset benchmark. This contrasts with the early smooth adoption of powerset by other information systems communities to, for example, formalize relations. One way of understanding the varying rates of adoption is recognizing that the different communities have different historical baggage. Many conceptual modeling communities emerged from work done on database design, and this creates hurdles to the adoption of the high level of expressiveness of powersets. Another relevant factor is that these communities also often feel, particularly in the case of domain modeling, a responsibility to explain the semantics of whatever formal structures they adopt. This paper aims to make sense of the formalization of the classification pattern in ISE and surveys its history through the literature, starting from the relevant theoretical works of the mathematical literature and gradually shifting focus

  7. Acute leukemia classification by ensemble particle swarm model selection.

    Science.gov (United States)

    Escalante, Hugo Jair; Montes-y-Gómez, Manuel; González, Jesús A; Gómez-Gil, Pilar; Altamirano, Leopoldo; Reyes, Carlos A; Reta, Carolina; Rosales, Alejandro

    2012-07-01

    Acute leukemia is a malignant disease that affects a large proportion of the world population. Different types and subtypes of acute leukemia require different treatments. In order to assign the correct treatment, a physician must identify the leukemia type or subtype. Advanced and precise methods are available for identifying leukemia types, but they are very expensive and not available in most hospitals in developing countries. Thus, alternative methods have been proposed. An option explored in this paper is based on the morphological properties of bone marrow images, where features are extracted from medical images and standard machine learning techniques are used to build leukemia type classifiers. This paper studies the use of ensemble particle swarm model selection (EPSMS), which is an automated tool for the selection of classification models, in the context of acute leukemia classification. EPSMS is the application of particle swarm optimization to the exploration of the search space of ensembles that can be formed by heterogeneous classification models in a machine learning toolbox. EPSMS does not require prior domain knowledge and it is able to select highly accurate classification models without user intervention. Furthermore, specific models can be used for different classification tasks. We report experimental results for acute leukemia classification with real data and show that EPSMS outperformed the best results obtained using manually designed classifiers with the same data. The highest performance using EPSMS was of 97.68% for two-type classification problems and of 94.21% for more than two types problems. To the best of our knowledge, these are the best results reported for this data set. Compared with previous studies, these improvements were consistent among different type/subtype classification tasks, different features extracted from images, and different feature extraction regions. The performance improvements were statistically significant

  8. A Soft Intelligent Risk Evaluation Model for Credit Scoring Classification

    Directory of Open Access Journals (Sweden)

    Mehdi Khashei

    2015-09-01

    Full Text Available Risk management is one of the most important branches of business and finance. Classification models are the most popular and widely used analytical group of data mining approaches that can greatly help financial decision makers and managers to tackle credit risk problems. However, the literature clearly indicates that, despite proposing numerous classification models, credit scoring is often a difficult task. On the other hand, there is no universal credit-scoring model in the literature that can be accurately and explanatorily used in all circumstances. Therefore, the research for improving the efficiency of credit-scoring models has never stopped. In this paper, a hybrid soft intelligent classification model is proposed for credit-scoring problems. In the proposed model, the unique advantages of the soft computing techniques are used in order to modify the performance of the traditional artificial neural networks in credit scoring. Empirical results of Australian credit card data classifications indicate that the proposed hybrid model outperforms its components, and also other classification models presented for credit scoring. Therefore, the proposed model can be considered as an appropriate alternative tool for binary decision making in business and finance, especially in high uncertainty conditions.

  9. BmTx3, a scorpion toxin with two putative functional faces separately active on A-type K+ and HERG currents.

    OpenAIRE

    Huys, Isabelle; Xu, Chen-Qi; Wang, Cheng-Zhong; Vacher, Hélène; Martin-Eauclaire, Marie-France; Chi, Cheng-Wu; Tytgat, Jan

    2004-01-01

    A novel HERG channel blocker was isolated from the venom of the scorpion Buthus martensi Karsch, sequenced and characterized at the pharmacological level after chemical synthesis. According to the determined amino acid sequence, the cDNA and genomic genes were then cloned. The genomic gene consists of two exons interrupted by an intron of 65 bp at position -6 upstream from the mature toxin. The protein sequence of this toxin was completely identical with that of a known A-type K+ current bloc...

  10. Global Optimization Ensemble Model for Classification Methods

    Science.gov (United States)

    Anwar, Hina; Qamar, Usman; Muzaffar Qureshi, Abdul Wahab

    2014-01-01

    Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC) that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm complexity. PMID:24883382

  11. Global Optimization Ensemble Model for Classification Methods

    Directory of Open Access Journals (Sweden)

    Hina Anwar

    2014-01-01

    Full Text Available Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm complexity.

  12. Estudio electrofisiológico de canales Herg en células de cáncer de colón

    OpenAIRE

    Granja del Río, Alejandra

    2013-01-01

    En el presente estudio, mostramos que en las células de colon normales (NCM460) y en las tumorales (HT29) las principales corrientes activadas por voltaje son corrientes de K+. Sin embargo, el hallazgo más interesante fue mostrar que, en contraste con las células normales de colon, las células tumorales, al parecer, expresan corrientes de K+ activadas por voltaje que podrían ser mediadas por canales herg. Departamento de Bioquímica y Biología Molecular y Fisiología Máster en Investigaci...

  13. New binding site on common molecular scaffold provides HERG channel specificity of scorpion toxin BeKm-1

    DEFF Research Database (Denmark)

    Korolkova, Yuliya V; Bocharov, Eduard V; Angelo, Kamilla

    2002-01-01

    The scorpion toxin BeKm-1 is unique among a variety of known short scorpion toxins affecting potassium channels in its selective action on ether-a-go-go-related gene (ERG)-type channels. BeKm-1 shares the common molecular scaffold with other short scorpion toxins. The toxin spatial structure...... resolved by NMR consists of a short alpha-helix and a triple-stranded antiparallel beta-sheet. By toxin mutagenesis study we identified the residues that are important for the binding of BeKm-1 to the human ERG K+ (HERG) channel. The most critical residues (Tyr-11, Lys-18, Arg-20, Lys-23) are located...

  14. Nonlinear Inertia Classification Model and Application

    Directory of Open Access Journals (Sweden)

    Mei Wang

    2014-01-01

    Full Text Available Classification model of support vector machine (SVM overcomes the problem of a big number of samples. But the kernel parameter and the punishment factor have great influence on the quality of SVM model. Particle swarm optimization (PSO is an evolutionary search algorithm based on the swarm intelligence, which is suitable for parameter optimization. Accordingly, a nonlinear inertia convergence classification model (NICCM is proposed after the nonlinear inertia convergence (NICPSO is developed in this paper. The velocity of NICPSO is firstly defined as the weighted velocity of the inertia PSO, and the inertia factor is selected to be a nonlinear function. NICPSO is used to optimize the kernel parameter and a punishment factor of SVM. Then, NICCM classifier is trained by using the optical punishment factor and the optical kernel parameter that comes from the optimal particle. Finally, NICCM is applied to the classification of the normal state and fault states of online power cable. It is experimentally proved that the iteration number for the proposed NICPSO to reach the optimal position decreases from 15 to 5 compared with PSO; the training duration is decreased by 0.0052 s and the recognition precision is increased by 4.12% compared with SVM.

  15. "A Nightmare Land, a Place of Death": An Exploration of the Moon as a Motif in Herge's "Destination Moon" (1953) and "Explorers on the Moon" (1954)

    Science.gov (United States)

    Beauvais, Clementine

    2010-01-01

    This article analyses the symbolic meaning of the Moon in two "bande dessinee" books from the Tintin series, Herge's "Destination Moon" ("Objectif Lune," 1953) and its sequel "Explorers on the Moon" ("On a Marche sur la Lune," 1954). It argues that these two volumes stand out in the series for their graphic, narrative and philosophical emphasis on…

  16. General regression and representation model for classification.

    Directory of Open Access Journals (Sweden)

    Jianjun Qian

    Full Text Available Recently, the regularized coding-based classification methods (e.g. SRC and CRC show a great potential for pattern classification. However, most existing coding methods assume that the representation residuals are uncorrelated. In real-world applications, this assumption does not hold. In this paper, we take account of the correlations of the representation residuals and develop a general regression and representation model (GRR for classification. GRR not only has advantages of CRC, but also takes full use of the prior information (e.g. the correlations between representation residuals and representation coefficients and the specific information (weight matrix of image pixels to enhance the classification performance. GRR uses the generalized Tikhonov regularization and K Nearest Neighbors to learn the prior information from the training data. Meanwhile, the specific information is obtained by using an iterative algorithm to update the feature (or image pixel weights of the test sample. With the proposed model as a platform, we design two classifiers: basic general regression and representation classifier (B-GRR and robust general regression and representation classifier (R-GRR. The experimental results demonstrate the performance advantages of proposed methods over state-of-the-art algorithms.

  17. Inter Genre Similarity Modelling For Automatic Music Genre Classification

    OpenAIRE

    Bagci, Ulas; Erzin, Engin

    2009-01-01

    Music genre classification is an essential tool for music information retrieval systems and it has been finding critical applications in various media platforms. Two important problems of the automatic music genre classification are feature extraction and classifier design. This paper investigates inter-genre similarity modelling (IGS) to improve the performance of automatic music genre classification. Inter-genre similarity information is extracted over the mis-classified feature population....

  18. Signal classification using global dynamical models, Part I: Theory

    International Nuclear Information System (INIS)

    Kadtke, J.; Kremliovsky, M.

    1996-01-01

    Detection and classification of signals is one of the principal areas of signal processing, and the utilization of nonlinear information has long been considered as a way of improving performance beyond standard linear (e.g. spectral) techniques. Here, we develop a method for using global models of chaotic dynamical systems theory to define a signal classification processing chain, which is sensitive to nonlinear correlations in the data. We use it to demonstrate classification in high noise regimes (negative SNR), and argue that classification probabilities can be directly computed from ensemble statistics in the model coefficient space. We also develop a modification for non-stationary signals (i.e. transients) using non-autonomous ODEs. In Part II of this paper, we demonstrate the analysis on actual open ocean acoustic data from marine biologics. copyright 1996 American Institute of Physics

  19. Vertebrae classification models - Validating classification models that use morphometrics to identify ancient salmonid (Oncorhynchus spp.) vertebrae to species

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Using morphometric characteristics of modern salmonid (Oncorhynchus spp.) vertebrae, we have developed classification models to identify salmonid vertebrae to the...

  20. AN ADABOOST OPTIMIZED CCFIS BASED CLASSIFICATION MODEL FOR BREAST CANCER DETECTION

    Directory of Open Access Journals (Sweden)

    CHANDRASEKAR RAVI

    2017-06-01

    Full Text Available Classification is a Data Mining technique used for building a prototype of the data behaviour, using which an unseen data can be classified into one of the defined classes. Several researchers have proposed classification techniques but most of them did not emphasis much on the misclassified instances and storage space. In this paper, a classification model is proposed that takes into account the misclassified instances and storage space. The classification model is efficiently developed using a tree structure for reducing the storage complexity and uses single scan of the dataset. During the training phase, Class-based Closed Frequent ItemSets (CCFIS were mined from the training dataset in the form of a tree structure. The classification model has been developed using the CCFIS and a similarity measure based on Longest Common Subsequence (LCS. Further, the Particle Swarm Optimization algorithm is applied on the generated CCFIS, which assigns weights to the itemsets and their associated classes. Most of the classifiers are correctly classifying the common instances but they misclassify the rare instances. In view of that, AdaBoost algorithm has been used to boost the weights of the misclassified instances in the previous round so as to include them in the training phase to classify the rare instances. This improves the accuracy of the classification model. During the testing phase, the classification model is used to classify the instances of the test dataset. Breast Cancer dataset from UCI repository is used for experiment. Experimental analysis shows that the accuracy of the proposed classification model outperforms the PSOAdaBoost-Sequence classifier by 7% superior to other approaches like Naïve Bayes Classifier, Support Vector Machine Classifier, Instance Based Classifier, ID3 Classifier, J48 Classifier, etc.

  1. Polarimetric SAR image classification based on discriminative dictionary learning model

    Science.gov (United States)

    Sang, Cheng Wei; Sun, Hong

    2018-03-01

    Polarimetric SAR (PolSAR) image classification is one of the important applications of PolSAR remote sensing. It is a difficult high-dimension nonlinear mapping problem, the sparse representations based on learning overcomplete dictionary have shown great potential to solve such problem. The overcomplete dictionary plays an important role in PolSAR image classification, however for PolSAR image complex scenes, features shared by different classes will weaken the discrimination of learned dictionary, so as to degrade classification performance. In this paper, we propose a novel overcomplete dictionary learning model to enhance the discrimination of dictionary. The learned overcomplete dictionary by the proposed model is more discriminative and very suitable for PolSAR classification.

  2. Lean waste classification model to support the sustainable operational practice

    Science.gov (United States)

    Sutrisno, A.; Vanany, I.; Gunawan, I.; Asjad, M.

    2018-04-01

    Driven by growing pressure for a more sustainable operational practice, improvement on the classification of non-value added (waste) is one of the prerequisites to realize sustainability of a firm. While the use of the 7 (seven) types of the Ohno model now becoming a versatile tool to reveal the lean waste occurrence. In many recent investigations, the use of the Seven Waste model of Ohno is insufficient to cope with the types of waste occurred in industrial practices at various application levels. Intended to a narrowing down this limitation, this paper presented an improved waste classification model based on survey to recent studies discussing on waste at various operational stages. Implications on the waste classification model to the body of knowledge and industrial practices are provided.

  3. Classification using Hierarchical Naive Bayes models

    DEFF Research Database (Denmark)

    Langseth, Helge; Dyhre Nielsen, Thomas

    2006-01-01

    Classification problems have a long history in the machine learning literature. One of the simplest, and yet most consistently well-performing set of classifiers is the Naïve Bayes models. However, an inherent problem with these classifiers is the assumption that all attributes used to describe......, termed Hierarchical Naïve Bayes models. Hierarchical Naïve Bayes models extend the modeling flexibility of Naïve Bayes models by introducing latent variables to relax some of the independence statements in these models. We propose a simple algorithm for learning Hierarchical Naïve Bayes models...

  4. Active Learning of Classification Models with Likert-Scale Feedback.

    Science.gov (United States)

    Xue, Yanbing; Hauskrecht, Milos

    2017-01-01

    Annotation of classification data by humans can be a time-consuming and tedious process. Finding ways of reducing the annotation effort is critical for building the classification models in practice and for applying them to a variety of classification tasks. In this paper, we develop a new active learning framework that combines two strategies to reduce the annotation effort. First, it relies on label uncertainty information obtained from the human in terms of the Likert-scale feedback. Second, it uses active learning to annotate examples with the greatest expected change. We propose a Bayesian approach to calculate the expectation and an incremental SVM solver to reduce the time complexity of the solvers. We show the combination of our active learning strategy and the Likert-scale feedback can learn classification models more rapidly and with a smaller number of labeled instances than methods that rely on either Likert-scale labels or active learning alone.

  5. Design of a hybrid model for cardiac arrhythmia classification based on Daubechies wavelet transform.

    Science.gov (United States)

    Rajagopal, Rekha; Ranganathan, Vidhyapriya

    2018-06-05

    Automation in cardiac arrhythmia classification helps medical professionals make accurate decisions about the patient's health. The aim of this work was to design a hybrid classification model to classify cardiac arrhythmias. The design phase of the classification model comprises the following stages: preprocessing of the cardiac signal by eliminating detail coefficients that contain noise, feature extraction through Daubechies wavelet transform, and arrhythmia classification using a collaborative decision from the K nearest neighbor classifier (KNN) and a support vector machine (SVM). The proposed model is able to classify 5 arrhythmia classes as per the ANSI/AAMI EC57: 1998 classification standard. Level 1 of the proposed model involves classification using the KNN and the classifier is trained with examples from all classes. Level 2 involves classification using an SVM and is trained specifically to classify overlapped classes. The final classification of a test heartbeat pertaining to a particular class is done using the proposed KNN/SVM hybrid model. The experimental results demonstrated that the average sensitivity of the proposed model was 92.56%, the average specificity 99.35%, the average positive predictive value 98.13%, the average F-score 94.5%, and the average accuracy 99.78%. The results obtained using the proposed model were compared with the results of discriminant, tree, and KNN classifiers. The proposed model is able to achieve a high classification accuracy.

  6. Secondary structure classification of amino-acid sequences using state-space modeling

    OpenAIRE

    Brunnert, Marcus; Krahnke, Tillmann; Urfer, Wolfgang

    2001-01-01

    The secondary structure classification of amino acid sequences can be carried out by a statistical analysis of sequence and structure data using state-space models. Aiming at this classification, a modified filter algorithm programmed in S is applied to data of three proteins. The application leads to correct classifications of two proteins even when using relatively simple estimation methods for the parameters of the state-space models. Furthermore, it has been shown that the assumed initial...

  7. A Multi-Dimensional Classification Model for Scientific Workflow Characteristics

    Energy Technology Data Exchange (ETDEWEB)

    Ramakrishnan, Lavanya; Plale, Beth

    2010-04-05

    Workflows have been used to model repeatable tasks or operations in manufacturing, business process, and software. In recent years, workflows are increasingly used for orchestration of science discovery tasks that use distributed resources and web services environments through resource models such as grid and cloud computing. Workflows have disparate re uirements and constraints that affects how they might be managed in distributed environments. In this paper, we present a multi-dimensional classification model illustrated by workflow examples obtained through a survey of scientists from different domains including bioinformatics and biomedical, weather and ocean modeling, astronomy detailing their data and computational requirements. The survey results and classification model contribute to the high level understandingof scientific workflows.

  8. Functional characterization of a novel hERG variant in a family with recurrent sudden infant death syndrome: Retracting a genetic diagnosis.

    Science.gov (United States)

    Sergeev, Valentine; Perry, Frances; Roston, Thomas M; Sanatani, Shubhayan; Tibbits, Glen F; Claydon, Thomas W

    2018-03-01

    Long QT syndrome (LQTS) is the most common cardiac ion channelopathy and has been found to be responsible for approximately 10% of sudden infant death syndrome (SIDS) cases. Despite increasing use of broad panels and now whole exome sequencing (WES) in the investigation of SIDS, the probability of identifying a pathogenic mutation in a SIDS victim is low. We report a family-based study who are afflicted by recurrent SIDS in which several members harbor a variant, p.Pro963Thr, in the C-terminal region of the human-ether-a-go-go (hERG) gene, published to be responsible for cases of LQTS type 2. Functional characterization was undertaken due to the variable phenotype in carriers, the discrepancy with published cases, and the importance of identifying a cause for recurrent deaths in a single family. Studies of the mutated ion channel in in vitro heterologous expression systems revealed that the mutation has no detectable impact on membrane surface expression, biophysical gating properties such as activation, deactivation and inactivation, or the amplitude of the protective current conducted by hERG channels during early repolarization. These observations suggest that the p.Pro963Thr mutation is not a monogenic disease-causing LQTS mutation despite evidence of co-segregation in two siblings affected by SIDS. Our findings demonstrate some of the potential pitfalls in post-mortem molecular testing and the importance of functional testing of gene variants in determining disease-causation, especially where the impacts of cascade screening can affect multiple generations. Copyright © 2017 Elsevier B.V. All rights reserved.

  9. Classification rates: non‐parametric verses parametric models using ...

    African Journals Online (AJOL)

    This research sought to establish if non parametric modeling achieves a higher correct classification ratio than a parametric model. The local likelihood technique was used to model fit the data sets. The same sets of data were modeled using parametric logit and the abilities of the two models to correctly predict the binary ...

  10. Modeling time-to-event (survival) data using classification tree analysis.

    Science.gov (United States)

    Linden, Ariel; Yarnold, Paul R

    2017-12-01

    Time to the occurrence of an event is often studied in health research. Survival analysis differs from other designs in that follow-up times for individuals who do not experience the event by the end of the study (called censored) are accounted for in the analysis. Cox regression is the standard method for analysing censored data, but the assumptions required of these models are easily violated. In this paper, we introduce classification tree analysis (CTA) as a flexible alternative for modelling censored data. Classification tree analysis is a "decision-tree"-like classification model that provides parsimonious, transparent (ie, easy to visually display and interpret) decision rules that maximize predictive accuracy, derives exact P values via permutation tests, and evaluates model cross-generalizability. Using empirical data, we identify all statistically valid, reproducible, longitudinally consistent, and cross-generalizable CTA survival models and then compare their predictive accuracy to estimates derived via Cox regression and an unadjusted naïve model. Model performance is assessed using integrated Brier scores and a comparison between estimated survival curves. The Cox regression model best predicts average incidence of the outcome over time, whereas CTA survival models best predict either relatively high, or low, incidence of the outcome over time. Classification tree analysis survival models offer many advantages over Cox regression, such as explicit maximization of predictive accuracy, parsimony, statistical robustness, and transparency. Therefore, researchers interested in accurate prognoses and clear decision rules should consider developing models using the CTA-survival framework. © 2017 John Wiley & Sons, Ltd.

  11. A Robust Geometric Model for Argument Classification

    Science.gov (United States)

    Giannone, Cristina; Croce, Danilo; Basili, Roberto; de Cao, Diego

    Argument classification is the task of assigning semantic roles to syntactic structures in natural language sentences. Supervised learning techniques for frame semantics have been recently shown to benefit from rich sets of syntactic features. However argument classification is also highly dependent on the semantics of the involved lexicals. Empirical studies have shown that domain dependence of lexical information causes large performance drops in outside domain tests. In this paper a distributional approach is proposed to improve the robustness of the learning model against out-of-domain lexical phenomena.

  12. A Pruning Neural Network Model in Credit Classification Analysis

    Directory of Open Access Journals (Sweden)

    Yajiao Tang

    2018-01-01

    Full Text Available Nowadays, credit classification models are widely applied because they can help financial decision-makers to handle credit classification issues. Among them, artificial neural networks (ANNs have been widely accepted as the convincing methods in the credit industry. In this paper, we propose a pruning neural network (PNN and apply it to solve credit classification problem by adopting the well-known Australian and Japanese credit datasets. The model is inspired by synaptic nonlinearity of a dendritic tree in a biological neural model. And it is trained by an error back-propagation algorithm. The model is capable of realizing a neuronal pruning function by removing the superfluous synapses and useless dendrites and forms a tidy dendritic morphology at the end of learning. Furthermore, we utilize logic circuits (LCs to simulate the dendritic structures successfully which makes PNN be implemented on the hardware effectively. The statistical results of our experiments have verified that PNN obtains superior performance in comparison with other classical algorithms in terms of accuracy and computational efficiency.

  13. Data Field Modeling and Spectral-Spatial Feature Fusion for Hyperspectral Data Classification.

    Science.gov (United States)

    Liu, Da; Li, Jianxun

    2016-12-16

    Classification is a significant subject in hyperspectral remote sensing image processing. This study proposes a spectral-spatial feature fusion algorithm for the classification of hyperspectral images (HSI). Unlike existing spectral-spatial classification methods, the influences and interactions of the surroundings on each measured pixel were taken into consideration in this paper. Data field theory was employed as the mathematical realization of the field theory concept in physics, and both the spectral and spatial domains of HSI were considered as data fields. Therefore, the inherent dependency of interacting pixels was modeled. Using data field modeling, spatial and spectral features were transformed into a unified radiation form and further fused into a new feature by using a linear model. In contrast to the current spectral-spatial classification methods, which usually simply stack spectral and spatial features together, the proposed method builds the inner connection between the spectral and spatial features, and explores the hidden information that contributed to classification. Therefore, new information is included for classification. The final classification result was obtained using a random forest (RF) classifier. The proposed method was tested with the University of Pavia and Indian Pines, two well-known standard hyperspectral datasets. The experimental results demonstrate that the proposed method has higher classification accuracies than those obtained by the traditional approaches.

  14. Models of Marine Fish Biodiversity: Assessing Predictors from Three Habitat Classification Schemes.

    Science.gov (United States)

    Yates, Katherine L; Mellin, Camille; Caley, M Julian; Radford, Ben T; Meeuwig, Jessica J

    2016-01-01

    Prioritising biodiversity conservation requires knowledge of where biodiversity occurs. Such knowledge, however, is often lacking. New technologies for collecting biological and physical data coupled with advances in modelling techniques could help address these gaps and facilitate improved management outcomes. Here we examined the utility of environmental data, obtained using different methods, for developing models of both uni- and multivariate biodiversity metrics. We tested which biodiversity metrics could be predicted best and evaluated the performance of predictor variables generated from three types of habitat data: acoustic multibeam sonar imagery, predicted habitat classification, and direct observer habitat classification. We used boosted regression trees (BRT) to model metrics of fish species richness, abundance and biomass, and multivariate regression trees (MRT) to model biomass and abundance of fish functional groups. We compared model performance using different sets of predictors and estimated the relative influence of individual predictors. Models of total species richness and total abundance performed best; those developed for endemic species performed worst. Abundance models performed substantially better than corresponding biomass models. In general, BRT and MRTs developed using predicted habitat classifications performed less well than those using multibeam data. The most influential individual predictor was the abiotic categorical variable from direct observer habitat classification and models that incorporated predictors from direct observer habitat classification consistently outperformed those that did not. Our results show that while remotely sensed data can offer considerable utility for predictive modelling, the addition of direct observer habitat classification data can substantially improve model performance. Thus it appears that there are aspects of marine habitats that are important for modelling metrics of fish biodiversity that are

  15. Model sparsity and brain pattern interpretation of classification models in neuroimaging

    DEFF Research Database (Denmark)

    Rasmussen, Peter Mondrup; Madsen, Kristoffer Hougaard; Churchill, Nathan W

    2012-01-01

    Interest is increasing in applying discriminative multivariate analysis techniques to the analysis of functional neuroimaging data. Model interpretation is of great importance in the neuroimaging context, and is conventionally based on a ‘brain map’ derived from the classification model. In this ...

  16. Calibration of a Plastic Classification System with the Ccw Model

    International Nuclear Information System (INIS)

    Barcala Riveira, J. M.; Fernandez Marron, J. L.; Alberdi Primicia, J.; Navarrete Marin, J. J.; Oller Gonzalez, J. C.

    2003-01-01

    This document describes the calibration of a plastic Classification system with the Ccw model (Classification by Quantum's built with Wavelet Coefficients). The method is applied to spectra of plastics usually present in domestic wastes. Obtained results are showed. (Author) 16 refs

  17. A NEW WASTE CLASSIFYING MODEL: HOW WASTE CLASSIFICATION CAN BECOME MORE OBJECTIVE?

    Directory of Open Access Journals (Sweden)

    Burcea Stefan Gabriel

    2015-07-01

    documents available in the virtual space, on the websites of certain international organizations involved in the wide and complex issue of waste management. The second part of the paper contains a proposal classification model with four main criteria in order to make waste classification a more objective process. The new classification model has the main role of transforming the traditional patterns of waste classification into an objective waste classification system and a second role of eliminating the strong contextuality of the actual waste classification models.

  18. Application of a niche-based model for forest cover classification

    Directory of Open Access Journals (Sweden)

    Amici V

    2012-05-01

    Full Text Available In recent years, a surge of interest in biodiversity conservation have led to the development of new approaches to facilitate ecologically-based conservation policies and management plans. In particular, image classification and predictive distribution modeling applied to forest habitats, constitute a crucial issue as forests constitute the most widespread vegetation type and play a key role for ecosystem functioning. Then, the general purpose of this study is to develop a framework that in the absence of large amounts of field data for large areas may allow to select the most appropriate classification. In some cases, a hard division of classes is required, especially as support to environmental policies; despite this it is necessary to take into account problems which derive from a crisp view of ecological entities being mapped, since habitats are expected to be structurally complex and continuously vary within a landscape. In this paper, a niche model (MaxEnt, generally used to estimate species/habitat distribution, has been applied to classify forest cover in a complex Mediterranean area and to estimate the probability distribution of four forest types, producing continuous maps of forest cover. The use of the obtained models as validation of model for crisp classifications, highlighted that crisp classification, which is being continuously used in landscape research and planning, is not free from drawbacks as it is showing a high degree of inner variability. The modeling approach followed by this study, taking into account the uncertainty proper of the natural ecosystems and the use of environmental variables in land cover classification, may represent an useful approach to making more efficient and effective field inventories and to developing effective forest conservation policies.

  19. Reducing Spatial Data Complexity for Classification Models

    International Nuclear Information System (INIS)

    Ruta, Dymitr; Gabrys, Bogdan

    2007-01-01

    Intelligent data analytics gradually becomes a day-to-day reality of today's businesses. However, despite rapidly increasing storage and computational power current state-of-the-art predictive models still can not handle massive and noisy corporate data warehouses. What is more adaptive and real-time operational environment requires multiple models to be frequently retrained which further hinders their use. Various data reduction techniques ranging from data sampling up to density retention models attempt to address this challenge by capturing a summarised data structure, yet they either do not account for labelled data or degrade the classification performance of the model trained on the condensed dataset. Our response is a proposition of a new general framework for reducing the complexity of labelled data by means of controlled spatial redistribution of class densities in the input space. On the example of Parzen Labelled Data Compressor (PLDC) we demonstrate a simulatory data condensation process directly inspired by the electrostatic field interaction where the data are moved and merged following the attracting and repelling interactions with the other labelled data. The process is controlled by the class density function built on the original data that acts as a class-sensitive potential field ensuring preservation of the original class density distributions, yet allowing data to rearrange and merge joining together their soft class partitions. As a result we achieved a model that reduces the labelled datasets much further than any competitive approaches yet with the maximum retention of the original class densities and hence the classification performance. PLDC leaves the reduced dataset with the soft accumulative class weights allowing for efficient online updates and as shown in a series of experiments if coupled with Parzen Density Classifier (PDC) significantly outperforms competitive data condensation methods in terms of classification performance at the

  20. Reducing Spatial Data Complexity for Classification Models

    Science.gov (United States)

    Ruta, Dymitr; Gabrys, Bogdan

    2007-11-01

    Intelligent data analytics gradually becomes a day-to-day reality of today's businesses. However, despite rapidly increasing storage and computational power current state-of-the-art predictive models still can not handle massive and noisy corporate data warehouses. What is more adaptive and real-time operational environment requires multiple models to be frequently retrained which further hinders their use. Various data reduction techniques ranging from data sampling up to density retention models attempt to address this challenge by capturing a summarised data structure, yet they either do not account for labelled data or degrade the classification performance of the model trained on the condensed dataset. Our response is a proposition of a new general framework for reducing the complexity of labelled data by means of controlled spatial redistribution of class densities in the input space. On the example of Parzen Labelled Data Compressor (PLDC) we demonstrate a simulatory data condensation process directly inspired by the electrostatic field interaction where the data are moved and merged following the attracting and repelling interactions with the other labelled data. The process is controlled by the class density function built on the original data that acts as a class-sensitive potential field ensuring preservation of the original class density distributions, yet allowing data to rearrange and merge joining together their soft class partitions. As a result we achieved a model that reduces the labelled datasets much further than any competitive approaches yet with the maximum retention of the original class densities and hence the classification performance. PLDC leaves the reduced dataset with the soft accumulative class weights allowing for efficient online updates and as shown in a series of experiments if coupled with Parzen Density Classifier (PDC) significantly outperforms competitive data condensation methods in terms of classification performance at the

  1. OmniGA: Optimized Omnivariate Decision Trees for Generalizable Classification Models

    KAUST Repository

    Magana-Mora, Arturo

    2017-06-14

    Classification problems from different domains vary in complexity, size, and imbalance of the number of samples from different classes. Although several classification models have been proposed, selecting the right model and parameters for a given classification task to achieve good performance is not trivial. Therefore, there is a constant interest in developing novel robust and efficient models suitable for a great variety of data. Here, we propose OmniGA, a framework for the optimization of omnivariate decision trees based on a parallel genetic algorithm, coupled with deep learning structure and ensemble learning methods. The performance of the OmniGA framework is evaluated on 12 different datasets taken mainly from biomedical problems and compared with the results obtained by several robust and commonly used machine-learning models with optimized parameters. The results show that OmniGA systematically outperformed these models for all the considered datasets, reducing the F score error in the range from 100% to 2.25%, compared to the best performing model. This demonstrates that OmniGA produces robust models with improved performance. OmniGA code and datasets are available at www.cbrc.kaust.edu.sa/omniga/.

  2. OmniGA: Optimized Omnivariate Decision Trees for Generalizable Classification Models

    KAUST Repository

    Magana-Mora, Arturo; Bajic, Vladimir B.

    2017-01-01

    Classification problems from different domains vary in complexity, size, and imbalance of the number of samples from different classes. Although several classification models have been proposed, selecting the right model and parameters for a given classification task to achieve good performance is not trivial. Therefore, there is a constant interest in developing novel robust and efficient models suitable for a great variety of data. Here, we propose OmniGA, a framework for the optimization of omnivariate decision trees based on a parallel genetic algorithm, coupled with deep learning structure and ensemble learning methods. The performance of the OmniGA framework is evaluated on 12 different datasets taken mainly from biomedical problems and compared with the results obtained by several robust and commonly used machine-learning models with optimized parameters. The results show that OmniGA systematically outperformed these models for all the considered datasets, reducing the F score error in the range from 100% to 2.25%, compared to the best performing model. This demonstrates that OmniGA produces robust models with improved performance. OmniGA code and datasets are available at www.cbrc.kaust.edu.sa/omniga/.

  3. Development of a definition, classification system, and model for cultural geology

    Science.gov (United States)

    Mitchell, Lloyd W., III

    The concept for this study is based upon a personal interest by the author, an American Indian, in promoting cultural perspectives in undergraduate college teaching and learning environments. Most academicians recognize that merged fields can enhance undergraduate curricula. However, conflict may occur when instructors attempt to merge social science fields such as history or philosophy with geoscience fields such as mining and geomorphology. For example, ideologies of Earth structures derived from scientific methodologies may conflict with historical and spiritual understandings of Earth structures held by American Indians. Specifically, this study addresses the problem of how to combine cultural studies with the geosciences into a new merged academic discipline called cultural geology. This study further attempts to develop the merged field of cultural geology using an approach consisting of three research foci: a definition, a classification system, and a model. Literature reviews were conducted for all three foci. Additionally, to better understand merged fields, a literature review was conducted specifically for academic fields that merged social and physical sciences. Methodologies concentrated on the three research foci: definition, classification system, and model. The definition was derived via a two-step process. The first step, developing keyword hierarchical ranking structures, was followed by creating and analyzing semantic word meaning lists. The classification system was developed by reviewing 102 classification systems and incorporating selected components into a system framework. The cultural geology model was created also utilizing a two-step process. A literature review of scientific models was conducted. Then, the definition and classification system were incorporated into a model felt to reflect the realm of cultural geology. A course syllabus was then developed that incorporated the resulting definition, classification system, and model. This

  4. Group-Based Active Learning of Classification Models.

    Science.gov (United States)

    Luo, Zhipeng; Hauskrecht, Milos

    2017-05-01

    Learning of classification models from real-world data often requires additional human expert effort to annotate the data. However, this process can be rather costly and finding ways of reducing the human annotation effort is critical for this task. The objective of this paper is to develop and study new ways of providing human feedback for efficient learning of classification models by labeling groups of examples. Briefly, unlike traditional active learning methods that seek feedback on individual examples, we develop a new group-based active learning framework that solicits label information on groups of multiple examples. In order to describe groups in a user-friendly way, conjunctive patterns are used to compactly represent groups. Our empirical study on 12 UCI data sets demonstrates the advantages and superiority of our approach over both classic instance-based active learning work, as well as existing group-based active-learning methods.

  5. Latent Partially Ordered Classification Models and Normal Mixtures

    Science.gov (United States)

    Tatsuoka, Curtis; Varadi, Ferenc; Jaeger, Judith

    2013-01-01

    Latent partially ordered sets (posets) can be employed in modeling cognitive functioning, such as in the analysis of neuropsychological (NP) and educational test data. Posets are cognitively diagnostic in the sense that classification states in these models are associated with detailed profiles of cognitive functioning. These profiles allow for…

  6. Various forms of indexing HDMR for modelling multivariate classification problems

    Energy Technology Data Exchange (ETDEWEB)

    Aksu, Çağrı [Bahçeşehir University, Information Technologies Master Program, Beşiktaş, 34349 İstanbul (Turkey); Tunga, M. Alper [Bahçeşehir University, Software Engineering Department, Beşiktaş, 34349 İstanbul (Turkey)

    2014-12-10

    The Indexing HDMR method was recently developed for modelling multivariate interpolation problems. The method uses the Plain HDMR philosophy in partitioning the given multivariate data set into less variate data sets and then constructing an analytical structure through these partitioned data sets to represent the given multidimensional problem. Indexing HDMR makes HDMR be applicable to classification problems having real world data. Mostly, we do not know all possible class values in the domain of the given problem, that is, we have a non-orthogonal data structure. However, Plain HDMR needs an orthogonal data structure in the given problem to be modelled. In this sense, the main idea of this work is to offer various forms of Indexing HDMR to successfully model these real life classification problems. To test these different forms, several well-known multivariate classification problems given in UCI Machine Learning Repository were used and it was observed that the accuracy results lie between 80% and 95% which are very satisfactory.

  7. Dynamic classification of fetal heart rates by hierarchical Dirichlet process mixture models.

    Directory of Open Access Journals (Sweden)

    Kezi Yu

    Full Text Available In this paper, we propose an application of non-parametric Bayesian (NPB models for classification of fetal heart rate (FHR recordings. More specifically, we propose models that are used to differentiate between FHR recordings that are from fetuses with or without adverse outcomes. In our work, we rely on models based on hierarchical Dirichlet processes (HDP and the Chinese restaurant process with finite capacity (CRFC. Two mixture models were inferred from real recordings, one that represents healthy and another, non-healthy fetuses. The models were then used to classify new recordings and provide the probability of the fetus being healthy. First, we compared the classification performance of the HDP models with that of support vector machines on real data and concluded that the HDP models achieved better performance. Then we demonstrated the use of mixture models based on CRFC for dynamic classification of the performance of (FHR recordings in a real-time setting.

  8. Music genre classification via likelihood fusion from multiple feature models

    Science.gov (United States)

    Shiu, Yu; Kuo, C.-C. J.

    2005-01-01

    Music genre provides an efficient way to index songs in a music database, and can be used as an effective means to retrieval music of a similar type, i.e. content-based music retrieval. A new two-stage scheme for music genre classification is proposed in this work. At the first stage, we examine a couple of different features, construct their corresponding parametric models (e.g. GMM and HMM) and compute their likelihood functions to yield soft classification results. In particular, the timbre, rhythm and temporal variation features are considered. Then, at the second stage, these soft classification results are integrated to result in a hard decision for final music genre classification. Experimental results are given to demonstrate the performance of the proposed scheme.

  9. Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification.

    Science.gov (United States)

    Wen, Zaidao; Hou, Biao; Jiao, Licheng

    2017-05-03

    Linear synthesis model based dictionary learning framework has achieved remarkable performances in image classification in the last decade. Behaved as a generative feature model, it however suffers from some intrinsic deficiencies. In this paper, we propose a novel parametric nonlinear analysis cosparse model (NACM) with which a unique feature vector will be much more efficiently extracted. Additionally, we derive a deep insight to demonstrate that NACM is capable of simultaneously learning the task adapted feature transformation and regularization to encode our preferences, domain prior knowledge and task oriented supervised information into the features. The proposed NACM is devoted to the classification task as a discriminative feature model and yield a novel discriminative nonlinear analysis operator learning framework (DNAOL). The theoretical analysis and experimental performances clearly demonstrate that DNAOL will not only achieve the better or at least competitive classification accuracies than the state-of-the-art algorithms but it can also dramatically reduce the time complexities in both training and testing phases.

  10. Wearable-Sensor-Based Classification Models of Faller Status in Older Adults.

    Directory of Open Access Journals (Sweden)

    Jennifer Howcroft

    Full Text Available Wearable sensors have potential for quantitative, gait-based, point-of-care fall risk assessment that can be easily and quickly implemented in clinical-care and older-adult living environments. This investigation generated models for wearable-sensor based fall-risk classification in older adults and identified the optimal sensor type, location, combination, and modelling method; for walking with and without a cognitive load task. A convenience sample of 100 older individuals (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence walked 7.62 m under single-task and dual-task conditions while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, and left and right shanks. Participants also completed the Activities-specific Balance Confidence scale, Community Health Activities Model Program for Seniors questionnaire, six minute walk test, and ranked their fear of falling. Fall risk classification models were assessed for all sensor combinations and three model types: multi-layer perceptron neural network, naïve Bayesian, and support vector machine. The best performing model was a multi-layer perceptron neural network with input parameters from pressure-sensing insoles and head, pelvis, and left shank accelerometers (accuracy = 84%, F1 score = 0.600, MCC score = 0.521. Head sensor-based models had the best performance of the single-sensor models for single-task gait assessment. Single-task gait assessment models outperformed models based on dual-task walking or clinical assessment data. Support vector machines and neural networks were the best modelling technique for fall risk classification. Fall risk classification models developed for point-of-care environments should be developed using support vector machines and neural networks, with a multi-sensor single-task gait assessment.

  11. Likelihood ratio model for classification of forensic evidence

    Energy Technology Data Exchange (ETDEWEB)

    Zadora, G., E-mail: gzadora@ies.krakow.pl [Institute of Forensic Research, Westerplatte 9, 31-033 Krakow (Poland); Neocleous, T., E-mail: tereza@stats.gla.ac.uk [University of Glasgow, Department of Statistics, 15 University Gardens, Glasgow G12 8QW (United Kingdom)

    2009-05-29

    One of the problems of analysis of forensic evidence such as glass fragments, is the determination of their use-type category, e.g. does a glass fragment originate from an unknown window or container? Very small glass fragments arise during various accidents and criminal offences, and could be carried on the clothes, shoes and hair of participants. It is therefore necessary to obtain information on their physicochemical composition in order to solve the classification problem. Scanning Electron Microscopy coupled with an Energy Dispersive X-ray Spectrometer and the Glass Refractive Index Measurement method are routinely used in many forensic institutes for the investigation of glass. A natural form of glass evidence evaluation for forensic purposes is the likelihood ratio-LR = p(E|H{sub 1})/p(E|H{sub 2}). The main aim of this paper was to study the performance of LR models for glass object classification which considered one or two sources of data variability, i.e. between-glass-object variability and(or) within-glass-object variability. Within the proposed model a multivariate kernel density approach was adopted for modelling the between-object distribution and a multivariate normal distribution was adopted for modelling within-object distributions. Moreover, a graphical method of estimating the dependence structure was employed to reduce the highly multivariate problem to several lower-dimensional problems. The performed analysis showed that the best likelihood model was the one which allows to include information about between and within-object variability, and with variables derived from elemental compositions measured by SEM-EDX, and refractive values determined before (RI{sub b}) and after (RI{sub a}) the annealing process, in the form of dRI = log{sub 10}|RI{sub a} - RI{sub b}|. This model gave better results than the model with only between-object variability considered. In addition, when dRI and variables derived from elemental compositions were used, this

  12. Likelihood ratio model for classification of forensic evidence

    International Nuclear Information System (INIS)

    Zadora, G.; Neocleous, T.

    2009-01-01

    One of the problems of analysis of forensic evidence such as glass fragments, is the determination of their use-type category, e.g. does a glass fragment originate from an unknown window or container? Very small glass fragments arise during various accidents and criminal offences, and could be carried on the clothes, shoes and hair of participants. It is therefore necessary to obtain information on their physicochemical composition in order to solve the classification problem. Scanning Electron Microscopy coupled with an Energy Dispersive X-ray Spectrometer and the Glass Refractive Index Measurement method are routinely used in many forensic institutes for the investigation of glass. A natural form of glass evidence evaluation for forensic purposes is the likelihood ratio-LR = p(E|H 1 )/p(E|H 2 ). The main aim of this paper was to study the performance of LR models for glass object classification which considered one or two sources of data variability, i.e. between-glass-object variability and(or) within-glass-object variability. Within the proposed model a multivariate kernel density approach was adopted for modelling the between-object distribution and a multivariate normal distribution was adopted for modelling within-object distributions. Moreover, a graphical method of estimating the dependence structure was employed to reduce the highly multivariate problem to several lower-dimensional problems. The performed analysis showed that the best likelihood model was the one which allows to include information about between and within-object variability, and with variables derived from elemental compositions measured by SEM-EDX, and refractive values determined before (RI b ) and after (RI a ) the annealing process, in the form of dRI = log 10 |RI a - RI b |. This model gave better results than the model with only between-object variability considered. In addition, when dRI and variables derived from elemental compositions were used, this model outperformed two other

  13. Applications of Diagnostic Classification Models: A Literature Review and Critical Commentary

    Science.gov (United States)

    Sessoms, John; Henson, Robert A.

    2018-01-01

    Diagnostic classification models (DCMs) classify examinees based on the skills they have mastered given their test performance. This classification enables targeted feedback that can inform remedial instruction. Unfortunately, applications of DCMs have been criticized (e.g., no validity support). Generally, these evaluations have been brief and…

  14. Objects Classification by Learning-Based Visual Saliency Model and Convolutional Neural Network.

    Science.gov (United States)

    Li, Na; Zhao, Xinbo; Yang, Yongjia; Zou, Xiaochun

    2016-01-01

    Humans can easily classify different kinds of objects whereas it is quite difficult for computers. As a hot and difficult problem, objects classification has been receiving extensive interests with broad prospects. Inspired by neuroscience, deep learning concept is proposed. Convolutional neural network (CNN) as one of the methods of deep learning can be used to solve classification problem. But most of deep learning methods, including CNN, all ignore the human visual information processing mechanism when a person is classifying objects. Therefore, in this paper, inspiring the completed processing that humans classify different kinds of objects, we bring forth a new classification method which combines visual attention model and CNN. Firstly, we use the visual attention model to simulate the processing of human visual selection mechanism. Secondly, we use CNN to simulate the processing of how humans select features and extract the local features of those selected areas. Finally, not only does our classification method depend on those local features, but also it adds the human semantic features to classify objects. Our classification method has apparently advantages in biology. Experimental results demonstrated that our method made the efficiency of classification improve significantly.

  15. Generative embedding for model-based classification of fMRI data.

    Directory of Open Access Journals (Sweden)

    Kay H Brodersen

    2011-06-01

    Full Text Available Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI. The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in 'hidden' physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and

  16. Validation of visualized transgenic zebrafish as a high throughput model to assay bradycardia related cardio toxicity risk candidates.

    Science.gov (United States)

    Wen, Dingsheng; Liu, Aiming; Chen, Feng; Yang, Julin; Dai, Renke

    2012-10-01

    Drug-induced QT prolongation usually leads to torsade de pointes (TdP), thus for drugs in the early phase of development this risk should be evaluated. In the present study, we demonstrated a visualized transgenic zebrafish as an in vivo high-throughput model to assay the risk of drug-induced QT prolongation. Zebrafish larvae 48 h post-fertilization expressing green fluorescent protein in myocardium were incubated with compounds reported to induce QT prolongation or block the human ether-a-go-go-related gene (hERG) K⁺ current. The compounds sotalol, indapaminde, erythromycin, ofoxacin, levofloxacin, sparfloxacin and roxithromycin were additionally administrated by microinjection into the larvae yolk sac. The ventricle heart rate was recorded using the automatic monitoring system after incubation or microinjection. As a result, 14 out of 16 compounds inducing dog QT prolongation caused bradycardia in zebrafish. A similar result was observed with 21 out of 26 compounds which block hERG current. Among the 30 compounds which induced human QT prolongation, 25 caused bradycardia in this model. Thus, the risk of compounds causing bradycardia in this transgenic zebrafish correlated with that causing QT prolongation and hERG K⁺ current blockage in established models. The tendency that high logP values lead to high risk of QT prolongation in this model was indicated, and non-sensitivity of this model to antibacterial agents was revealed. These data suggest application of this transgenic zebrafish as a high-throughput model to screen QT prolongation-related cardio toxicity of the drug candidates. Copyright © 2012 John Wiley & Sons, Ltd.

  17. Credit Risk Evaluation Using a C-Variable Least Squares Support Vector Classification Model

    Science.gov (United States)

    Yu, Lean; Wang, Shouyang; Lai, K. K.

    Credit risk evaluation is one of the most important issues in financial risk management. In this paper, a C-variable least squares support vector classification (C-VLSSVC) model is proposed for credit risk analysis. The main idea of this model is based on the prior knowledge that different classes may have different importance for modeling and more weights should be given to those classes with more importance. The C-VLSSVC model can be constructed by a simple modification of the regularization parameter in LSSVC, whereby more weights are given to the lease squares classification errors with important classes than the lease squares classification errors with unimportant classes while keeping the regularized terms in its original form. For illustration purpose, a real-world credit dataset is used to test the effectiveness of the C-VLSSVC model.

  18. A Dirichlet process mixture model for brain MRI tissue classification.

    Science.gov (United States)

    Ferreira da Silva, Adelino R

    2007-04-01

    Accurate classification of magnetic resonance images according to tissue type or region of interest has become a critical requirement in diagnosis, treatment planning, and cognitive neuroscience. Several authors have shown that finite mixture models give excellent results in the automated segmentation of MR images of the human normal brain. However, performance and robustness of finite mixture models deteriorate when the models have to deal with a variety of anatomical structures. In this paper, we propose a nonparametric Bayesian model for tissue classification of MR images of the brain. The model, known as Dirichlet process mixture model, uses Dirichlet process priors to overcome the limitations of current parametric finite mixture models. To validate the accuracy and robustness of our method we present the results of experiments carried out on simulated MR brain scans, as well as on real MR image data. The results are compared with similar results from other well-known MRI segmentation methods.

  19. A classification model of Hyperion image base on SAM combined decision tree

    Science.gov (United States)

    Wang, Zhenghai; Hu, Guangdao; Zhou, YongZhang; Liu, Xin

    2009-10-01

    Monitoring the Earth using imaging spectrometers has necessitated more accurate analyses and new applications to remote sensing. A very high dimensional input space requires an exponentially large amount of data to adequately and reliably represent the classes in that space. On the other hand, with increase in the input dimensionality the hypothesis space grows exponentially, which makes the classification performance highly unreliable. Traditional classification algorithms Classification of hyperspectral images is challenging. New algorithms have to be developed for hyperspectral data classification. The Spectral Angle Mapper (SAM) is a physically-based spectral classification that uses an ndimensional angle to match pixels to reference spectra. The algorithm determines the spectral similarity between two spectra by calculating the angle between the spectra, treating them as vectors in a space with dimensionality equal to the number of bands. The key and difficulty is that we should artificial defining the threshold of SAM. The classification precision depends on the rationality of the threshold of SAM. In order to resolve this problem, this paper proposes a new automatic classification model of remote sensing image using SAM combined with decision tree. It can automatic choose the appropriate threshold of SAM and improve the classify precision of SAM base on the analyze of field spectrum. The test area located in Heqing Yunnan was imaged by EO_1 Hyperion imaging spectrometer using 224 bands in visual and near infrared. The area included limestone areas, rock fields, soil and forests. The area was classified into four different vegetation and soil types. The results show that this method choose the appropriate threshold of SAM and eliminates the disturbance and influence of unwanted objects effectively, so as to improve the classification precision. Compared with the likelihood classification by field survey data, the classification precision of this model

  20. Classification of finite reparametrization symmetry groups in the three-Higgs-doublet model

    International Nuclear Information System (INIS)

    Ivanov, Igor P.; Vdovin, E.

    2013-01-01

    Symmetries play a crucial role in electroweak symmetry breaking models with non-minimal Higgs content. Within each class of these models, it is desirable to know which symmetry groups can be implemented via the scalar sector. In N-Higgs-doublet models, this classification problem was solved only for N=2 doublets. Very recently, we suggested a method to classify all realizable finite symmetry groups of Higgs-family transformations in the three-Higgs-doublet model (3HDM). Here, we present this classification in all detail together with an introduction to the theory of solvable groups, which play the key role in our derivation. We also consider generalized-CP symmetries, and discuss the interplay between Higgs-family symmetries and CP-conservation. In particular, we prove that presence of the Z 4 symmetry guarantees the explicit CP-conservation of the potential. This work completes classification of finite reparametrization symmetry groups in 3HDM. (orig.)

  1. Music Genre Classification using an Auditory Memory Model

    DEFF Research Database (Denmark)

    Jensen, Kristoffer

    2011-01-01

    Audio feature estimation is potentially improved by including higher- level models. One such model is the Auditory Short Term Memory (STM) model. A new paradigm of audio feature estimation is obtained by adding the influence of notes in the STM. These notes are identified when the perceptual...... results, and an initial experiment with sensory dissonance has been undertaken with good results. The parameters obtained form the auditory memory model, along with the dissonance measure, are shown here to be of interest in genre classification....

  2. Proposing a Hybrid Model Based on Robson's Classification for Better Impact on Trends of Cesarean Deliveries.

    Science.gov (United States)

    Hans, Punit; Rohatgi, Renu

    2017-06-01

    To construct a hybrid model classification for cesarean section (CS) deliveries based on the woman-characteristics (Robson's classification with additional layers of indications for CS, keeping in view low-resource settings available in India). This is a cross-sectional study conducted at Nalanda Medical College, Patna. All the women delivered from January 2016 to May 2016 in the labor ward were included. Results obtained were compared with the values obtained for India, from secondary analysis of WHO multi-country survey (2010-2011) by Joshua Vogel and colleagues' study published in "The Lancet Global Health." The three classifications (indication-based, Robson's and hybrid model) applied for categorization of the cesarean deliveries from the same sample of data and a semiqualitative evaluations done, considering the main characteristics, strengths and weaknesses of each classification system. The total number of women delivered during study period was 1462, out of which CS deliveries were 471. Overall, CS rate calculated for NMCH, hospital in this specified period, was 32.21% ( p  = 0.001). Hybrid model scored 23/23, and scores of Robson classification and indication-based classification were 21/23 and 10/23, respectively. Single-study centre and referral bias are the limitations of the study. Given the flexibility of the classifications, we constructed a hybrid model based on the woman-characteristics system with additional layers of other classification. Indication-based classification answers why, Robson classification answers on whom, while through our hybrid model we get to know why and on whom cesarean deliveries are being performed.

  3. Sparse Representation Based Binary Hypothesis Model for Hyperspectral Image Classification

    Directory of Open Access Journals (Sweden)

    Yidong Tang

    2016-01-01

    Full Text Available The sparse representation based classifier (SRC and its kernel version (KSRC have been employed for hyperspectral image (HSI classification. However, the state-of-the-art SRC often aims at extended surface objects with linear mixture in smooth scene and assumes that the number of classes is given. Considering the small target with complex background, a sparse representation based binary hypothesis (SRBBH model is established in this paper. In this model, a query pixel is represented in two ways, which are, respectively, by background dictionary and by union dictionary. The background dictionary is composed of samples selected from the local dual concentric window centered at the query pixel. Thus, for each pixel the classification issue becomes an adaptive multiclass classification problem, where only the number of desired classes is required. Furthermore, the kernel method is employed to improve the interclass separability. In kernel space, the coding vector is obtained by using kernel-based orthogonal matching pursuit (KOMP algorithm. Then the query pixel can be labeled by the characteristics of the coding vectors. Instead of directly using the reconstruction residuals, the different impacts the background dictionary and union dictionary have on reconstruction are used for validation and classification. It enhances the discrimination and hence improves the performance.

  4. Structural classification and a binary structure model for superconductors

    Institute of Scientific and Technical Information of China (English)

    Dong Cheng

    2006-01-01

    Based on structural and bonding features, a new classification scheme of superconductors is proposed to classify conductors can be partitioned into two parts, a superconducting active component and a supplementary component.Partially metallic covalent bonding is found to be a common feature in all superconducting active components, and the electron states of the atoms in the active components usually make a dominant contribution to the energy band near the Fermi surface. Possible directions to explore new superconductors are discussed based on the structural classification and the binary structure model.

  5. Cross-validation pitfalls when selecting and assessing regression and classification models.

    Science.gov (United States)

    Krstajic, Damjan; Buturovic, Ljubomir J; Leahy, David E; Thomas, Simon

    2014-03-29

    We address the problem of selecting and assessing classification and regression models using cross-validation. Current state-of-the-art methods can yield models with high variance, rendering them unsuitable for a number of practical applications including QSAR. In this paper we describe and evaluate best practices which improve reliability and increase confidence in selected models. A key operational component of the proposed methods is cloud computing which enables routine use of previously infeasible approaches. We describe in detail an algorithm for repeated grid-search V-fold cross-validation for parameter tuning in classification and regression, and we define a repeated nested cross-validation algorithm for model assessment. As regards variable selection and parameter tuning we define two algorithms (repeated grid-search cross-validation and double cross-validation), and provide arguments for using the repeated grid-search in the general case. We show results of our algorithms on seven QSAR datasets. The variation of the prediction performance, which is the result of choosing different splits of the dataset in V-fold cross-validation, needs to be taken into account when selecting and assessing classification and regression models. We demonstrate the importance of repeating cross-validation when selecting an optimal model, as well as the importance of repeating nested cross-validation when assessing a prediction error.

  6. Visualization of Nonlinear Classification Models in Neuroimaging - Signed Sensitivity Maps

    DEFF Research Database (Denmark)

    Rasmussen, Peter Mondrup; Schmah, Tanya; Madsen, Kristoffer Hougaard

    2012-01-01

    Classification models are becoming increasing popular tools in the analysis of neuroimaging data sets. Besides obtaining good prediction accuracy, a competing goal is to interpret how the classifier works. From a neuroscientific perspective, we are interested in the brain pattern reflecting...... the underlying neural encoding of an experiment defining multiple brain states. In this relation there is a great desire for the researcher to generate brain maps, that highlight brain locations of importance to the classifiers decisions. Based on sensitivity analysis, we develop further procedures for model...... direction the individual locations influence the classification. We illustrate the visualization procedure on a real data from a simple functional magnetic resonance imaging experiment....

  7. Text document classification based on mixture models

    Czech Academy of Sciences Publication Activity Database

    Novovičová, Jana; Malík, Antonín

    2004-01-01

    Roč. 40, č. 3 (2004), s. 293-304 ISSN 0023-5954 R&D Projects: GA AV ČR IAA2075302; GA ČR GA102/03/0049; GA AV ČR KSK1019101 Institutional research plan: CEZ:AV0Z1075907 Keywords : text classification * text categorization * multinomial mixture model Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.224, year: 2004

  8. ATLS Hypovolemic Shock Classification by Prediction of Blood Loss in Rats Using Regression Models.

    Science.gov (United States)

    Choi, Soo Beom; Choi, Joon Yul; Park, Jee Soo; Kim, Deok Won

    2016-07-01

    In our previous study, our input data set consisted of 78 rats, the blood loss in percent as a dependent variable, and 11 independent variables (heart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, pulse pressure, respiration rate, temperature, perfusion index, lactate concentration, shock index, and new index (lactate concentration/perfusion)). The machine learning methods for multicategory classification were applied to a rat model in acute hemorrhage to predict the four Advanced Trauma Life Support (ATLS) hypovolemic shock classes for triage in our previous study. However, multicategory classification is much more difficult and complicated than binary classification. We introduce a simple approach for classifying ATLS hypovolaemic shock class by predicting blood loss in percent using support vector regression and multivariate linear regression (MLR). We also compared the performance of the classification models using absolute and relative vital signs. The accuracies of support vector regression and MLR models with relative values by predicting blood loss in percent were 88.5% and 84.6%, respectively. These were better than the best accuracy of 80.8% of the direct multicategory classification using the support vector machine one-versus-one model in our previous study for the same validation data set. Moreover, the simple MLR models with both absolute and relative values could provide possibility of the future clinical decision support system for ATLS classification. The perfusion index and new index were more appropriate with relative changes than absolute values.

  9. Comparing the performance of flat and hierarchical Habitat/Land-Cover classification models in a NATURA 2000 site

    Science.gov (United States)

    Gavish, Yoni; O'Connell, Jerome; Marsh, Charles J.; Tarantino, Cristina; Blonda, Palma; Tomaselli, Valeria; Kunin, William E.

    2018-02-01

    The increasing need for high quality Habitat/Land-Cover (H/LC) maps has triggered considerable research into novel machine-learning based classification models. In many cases, H/LC classes follow pre-defined hierarchical classification schemes (e.g., CORINE), in which fine H/LC categories are thematically nested within more general categories. However, none of the existing machine-learning algorithms account for this pre-defined hierarchical structure. Here we introduce a novel Random Forest (RF) based application of hierarchical classification, which fits a separate local classification model in every branching point of the thematic tree, and then integrates all the different local models to a single global prediction. We applied the hierarchal RF approach in a NATURA 2000 site in Italy, using two land-cover (CORINE, FAO-LCCS) and one habitat classification scheme (EUNIS) that differ from one another in the shape of the class hierarchy. For all 3 classification schemes, both the hierarchical model and a flat model alternative provided accurate predictions, with kappa values mostly above 0.9 (despite using only 2.2-3.2% of the study area as training cells). The flat approach slightly outperformed the hierarchical models when the hierarchy was relatively simple, while the hierarchical model worked better under more complex thematic hierarchies. Most misclassifications came from habitat pairs that are thematically distant yet spectrally similar. In 2 out of 3 classification schemes, the additional constraints of the hierarchical model resulted with fewer such serious misclassifications relative to the flat model. The hierarchical model also provided valuable information on variable importance which can shed light into "black-box" based machine learning algorithms like RF. We suggest various ways by which hierarchical classification models can increase the accuracy and interpretability of H/LC classification maps.

  10. Zone-specific logistic regression models improve classification of prostate cancer on multi-parametric MRI

    Energy Technology Data Exchange (ETDEWEB)

    Dikaios, Nikolaos; Halligan, Steve; Taylor, Stuart; Atkinson, David; Punwani, Shonit [University College London, Centre for Medical Imaging, London (United Kingdom); University College London Hospital, Departments of Radiology, London (United Kingdom); Alkalbani, Jokha; Sidhu, Harbir Singh [University College London, Centre for Medical Imaging, London (United Kingdom); Abd-Alazeez, Mohamed; Ahmed, Hashim U.; Emberton, Mark [University College London, Research Department of Urology, Division of Surgery and Interventional Science, London (United Kingdom); Kirkham, Alex [University College London Hospital, Departments of Radiology, London (United Kingdom); Freeman, Alex [University College London Hospital, Department of Histopathology, London (United Kingdom)

    2015-09-15

    To assess the interchangeability of zone-specific (peripheral-zone (PZ) and transition-zone (TZ)) multiparametric-MRI (mp-MRI) logistic-regression (LR) models for classification of prostate cancer. Two hundred and thirty-one patients (70 TZ training-cohort; 76 PZ training-cohort; 85 TZ temporal validation-cohort) underwent mp-MRI and transperineal-template-prostate-mapping biopsy. PZ and TZ uni/multi-variate mp-MRI LR-models for classification of significant cancer (any cancer-core-length (CCL) with Gleason > 3 + 3 or any grade with CCL ≥ 4 mm) were derived from the respective cohorts and validated within the same zone by leave-one-out analysis. Inter-zonal performance was tested by applying TZ models to the PZ training-cohort and vice-versa. Classification performance of TZ models for TZ cancer was further assessed in the TZ validation-cohort. ROC area-under-curve (ROC-AUC) analysis was used to compare models. The univariate parameters with the best classification performance were the normalised T2 signal (T2nSI) within the TZ (ROC-AUC = 0.77) and normalized early contrast-enhanced T1 signal (DCE-nSI) within the PZ (ROC-AUC = 0.79). Performance was not significantly improved by bi-variate/tri-variate modelling. PZ models that contained DCE-nSI performed poorly in classification of TZ cancer. The TZ model based solely on maximum-enhancement poorly classified PZ cancer. LR-models dependent on DCE-MRI parameters alone are not interchangeable between prostatic zones; however, models based exclusively on T2 and/or ADC are more robust for inter-zonal application. (orig.)

  11. ISBDD Model for Classification of Hyperspectral Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Na Li

    2018-03-01

    Full Text Available The diverse density (DD algorithm was proposed to handle the problem of low classification accuracy when training samples contain interference such as mixed pixels. The DD algorithm can learn a feature vector from training bags, which comprise instances (pixels. However, the feature vector learned by the DD algorithm cannot always effectively represent one type of ground cover. To handle this problem, an instance space-based diverse density (ISBDD model that employs a novel training strategy is proposed in this paper. In the ISBDD model, DD values of each pixel are computed instead of learning a feature vector, and as a result, the pixel can be classified according to its DD values. Airborne hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS sensor and the Push-broom Hyperspectral Imager (PHI are applied to evaluate the performance of the proposed model. Results show that the overall classification accuracy of ISBDD model on the AVIRIS and PHI images is up to 97.65% and 89.02%, respectively, while the kappa coefficient is up to 0.97 and 0.88, respectively.

  12. Acute alteration of cardiac ECG, action potential, I{sub Kr} and the human ether-a-go-go-related gene (hERG) K{sup +} channel by PCB 126 and PCB 77

    Energy Technology Data Exchange (ETDEWEB)

    Park, Mi-Hyeong; Park, Won Sun; Jo, Su-Hyun, E-mail: suhyunjo@kangwon.ac.kr

    2012-07-01

    Polychlorinated biphenyls (PCBs) have been known as serious persistent organic pollutants (POPs), causing developmental delays and motor dysfunction. We have investigated the effects of two PCB congeners, 3,3′,4,4′-tetrachlorobiphenyl (PCB 77) and 3,3′,4,4′,5-pentachlorobiphenyl (PCB 126) on ECG, action potential, and the rapidly activating delayed rectifier K{sup +} current (I{sub Kr}) of guinea pigs' hearts, and hERG K{sup +} current expressed in Xenopus oocytes. PCB 126 shortened the corrected QT interval (QTc) of ECG and decreased the action potential duration at 90% (APD{sub 90}), and 50% of repolarization (APD{sub 50}) (P < 0.05) without changing the action potential duration at 20% (APD{sub 20}). PCB 77 decreased APD{sub 20} (P < 0.05) without affecting QTc, APD{sub 90}, and APD{sub 50}. The PCB 126 increased the I{sub Kr} in guinea-pig ventricular myocytes held at 36 °C and hERG K{sup +} current amplitude at the end of the voltage steps in voltage-dependent mode (P < 0.05); however, PCB 77 did not change the hERG K{sup +} current amplitude. The PCB 77 increased the diastolic Ca{sup 2+} and decreased Ca{sup 2+} transient amplitude (P < 0.05), however PCB 126 did not change. The results suggest that PCB 126 shortened the QTc and decreased the APD{sub 90} possibly by increasing I{sub Kr}, while PCB 77 decreased the APD{sub 20} possibly by other modulation related with intracellular Ca{sup 2+}. The present data indicate that the environmental toxicants, PCBs, can acutely affect cardiac electrophysiology including ECG, action potential, intracellular Ca{sup 2+}, and channel activity, resulting in toxic effects on the cardiac function in view of the possible accumulation of the PCBs in human body. -- Highlights: ► PCBs are known as serious environmental pollutants and developmental disruptors. ► PCB 126 shortened QT interval of ECG and action potential duration. ► PCB 126 increased human ether-a-go-go-related K{sup +} current and I{sub Kr}.

  13. SEMIPARAMETRIC VERSUS PARAMETRIC CLASSIFICATION MODELS - AN APPLICATION TO DIRECT MARKETING

    NARCIS (Netherlands)

    BULT, [No Value

    In this paper we are concerned with estimation of a classification model using semiparametric and parametric methods. Benefits and limitations of semiparametric models in general, and of Manski's maximum score method in particular, are discussed. The maximum score method yields consistent estimates

  14. Social Media Text Classification by Enhancing Well-Formed Text Trained Model

    Directory of Open Access Journals (Sweden)

    Phat Jotikabukkana

    2016-09-01

    Full Text Available Social media are a powerful communication tool in our era of digital information. The large amount of user-generated data is a useful novel source of data, even though it is not easy to extract the treasures from this vast and noisy trove. Since classification is an important part of text mining, many techniques have been proposed to classify this kind of information. We developed an effective technique of social media text classification by semi-supervised learning utilizing an online news source consisting of well-formed text. The computer first automatically extracts news categories, well-categorized by publishers, as classes for topic classification. A bag of words taken from news articles provides the initial keywords related to their category in the form of word vectors. The principal task is to retrieve a set of new productive keywords. Term Frequency-Inverse Document Frequency weighting (TF-IDF and Word Article Matrix (WAM are used as main methods. A modification of WAM is recomputed until it becomes the most effective model for social media text classification. The key success factor was enhancing our model with effective keywords from social media. A promising result of 99.50% accuracy was achieved, with more than 98.5% of Precision, Recall, and F-measure after updating the model three times.

  15. Model-based object classification using unification grammars and abstract representations

    Science.gov (United States)

    Liburdy, Kathleen A.; Schalkoff, Robert J.

    1993-04-01

    The design and implementation of a high level computer vision system which performs object classification is described. General object labelling and functional analysis require models of classes which display a wide range of geometric variations. A large representational gap exists between abstract criteria such as `graspable' and current geometric image descriptions. The vision system developed and described in this work addresses this problem and implements solutions based on a fusion of semantics, unification, and formal language theory. Object models are represented using unification grammars, which provide a framework for the integration of structure and semantics. A methodology for the derivation of symbolic image descriptions capable of interacting with the grammar-based models is described and implemented. A unification-based parser developed for this system achieves object classification by determining if the symbolic image description can be unified with the abstract criteria of an object model. Future research directions are indicated.

  16. A bayesian hierarchical model for classification with selection of functional predictors.

    Science.gov (United States)

    Zhu, Hongxiao; Vannucci, Marina; Cox, Dennis D

    2010-06-01

    In functional data classification, functional observations are often contaminated by various systematic effects, such as random batch effects caused by device artifacts, or fixed effects caused by sample-related factors. These effects may lead to classification bias and thus should not be neglected. Another issue of concern is the selection of functions when predictors consist of multiple functions, some of which may be redundant. The above issues arise in a real data application where we use fluorescence spectroscopy to detect cervical precancer. In this article, we propose a Bayesian hierarchical model that takes into account random batch effects and selects effective functions among multiple functional predictors. Fixed effects or predictors in nonfunctional form are also included in the model. The dimension of the functional data is reduced through orthonormal basis expansion or functional principal components. For posterior sampling, we use a hybrid Metropolis-Hastings/Gibbs sampler, which suffers slow mixing. An evolutionary Monte Carlo algorithm is applied to improve the mixing. Simulation and real data application show that the proposed model provides accurate selection of functional predictors as well as good classification.

  17. 2D Modeling and Classification of Extended Objects in a Network of HRR Radars

    NARCIS (Netherlands)

    Fasoula, A.

    2011-01-01

    In this thesis, the modeling of extended objects with low-dimensional representations of their 2D geometry is addressed. The ultimate objective is the classification of the objects using libraries of such compact 2D object models that are much smaller than in the state-of-the-art classification

  18. Quantitative Comparison of Effects of Dofetilide, Sotalol, Quinidine, and Verapamil between Human Ex vivo Trabeculae and In silico Ventricular Models Incorporating Inter-Individual Action Potential Variability

    Directory of Open Access Journals (Sweden)

    Oliver J. Britton

    2017-08-01

    Full Text Available Background:In silico modeling could soon become a mainstream method of pro-arrhythmic risk assessment in drug development. However, a lack of human-specific data and appropriate modeling techniques has previously prevented quantitative comparison of drug effects between in silico models and recordings from human cardiac preparations. Here, we directly compare changes in repolarization biomarkers caused by dofetilide, dl-sotalol, quinidine, and verapamil, between in silico populations of human ventricular cell models and ex vivo human ventricular trabeculae.Methods and Results:Ex vivo recordings from human ventricular trabeculae in control conditions were used to develop populations of in silico human ventricular cell models that integrated intra- and inter-individual variability in action potential (AP biomarker values. Models were based on the O'Hara-Rudy ventricular cardiomyocyte model, but integrated experimental AP variability through variation in underlying ionic conductances. Changes to AP duration, triangulation and early after-depolarization occurrence from application of the four drugs at multiple concentrations and pacing frequencies were compared between simulations and experiments. To assess the impact of variability in IC50 measurements, and the effects of including state-dependent drug binding dynamics, each drug simulation was repeated with two different IC50 datasets, and with both the original O'Hara-Rudy hERG model and a recently published state-dependent model of hERG and hERG block. For the selective hERG blockers dofetilide and sotalol, simulation predictions of AP prolongation and repolarization abnormality occurrence showed overall good agreement with experiments. However, for multichannel blockers quinidine and verapamil, simulations were not in agreement with experiments across all IC50 datasets and IKr block models tested. Quinidine simulations resulted in overprolonged APs and high incidence of repolarization

  19. MODEL-BASED CLUSTERING FOR CLASSIFICATION OF AQUATIC SYSTEMS AND DIAGNOSIS OF ECOLOGICAL STRESS

    Science.gov (United States)

    Clustering approaches were developed using the classification likelihood, the mixture likelihood, and also using a randomization approach with a model index. Using a clustering approach based on the mixture and classification likelihoods, we have developed an algorithm that...

  20. A strategy learning model for autonomous agents based on classification

    Directory of Open Access Journals (Sweden)

    Śnieżyński Bartłomiej

    2015-09-01

    Full Text Available In this paper we propose a strategy learning model for autonomous agents based on classification. In the literature, the most commonly used learning method in agent-based systems is reinforcement learning. In our opinion, classification can be considered a good alternative. This type of supervised learning can be used to generate a classifier that allows the agent to choose an appropriate action for execution. Experimental results show that this model can be successfully applied for strategy generation even if rewards are delayed. We compare the efficiency of the proposed model and reinforcement learning using the farmer-pest domain and configurations of various complexity. In complex environments, supervised learning can improve the performance of agents much faster that reinforcement learning. If an appropriate knowledge representation is used, the learned knowledge may be analyzed by humans, which allows tracking the learning process

  1. A tool for urban soundscape evaluation applying Support Vector Machines for developing a soundscape classification model.

    Science.gov (United States)

    Torija, Antonio J; Ruiz, Diego P; Ramos-Ridao, Angel F

    2014-06-01

    To ensure appropriate soundscape management in urban environments, the urban-planning authorities need a range of tools that enable such a task to be performed. An essential step during the management of urban areas from a sound standpoint should be the evaluation of the soundscape in such an area. In this sense, it has been widely acknowledged that a subjective and acoustical categorization of a soundscape is the first step to evaluate it, providing a basis for designing or adapting it to match people's expectations as well. In this sense, this work proposes a model for automatic classification of urban soundscapes. This model is intended for the automatic classification of urban soundscapes based on underlying acoustical and perceptual criteria. Thus, this classification model is proposed to be used as a tool for a comprehensive urban soundscape evaluation. Because of the great complexity associated with the problem, two machine learning techniques, Support Vector Machines (SVM) and Support Vector Machines trained with Sequential Minimal Optimization (SMO), are implemented in developing model classification. The results indicate that the SMO model outperforms the SVM model in the specific task of soundscape classification. With the implementation of the SMO algorithm, the classification model achieves an outstanding performance (91.3% of instances correctly classified). © 2013 Elsevier B.V. All rights reserved.

  2. AN APPLICATION OF FUNCTIONAL MULTIVARIATE REGRESSION MODEL TO MULTICLASS CLASSIFICATION

    OpenAIRE

    Krzyśko, Mirosław; Smaga, Łukasz

    2017-01-01

    In this paper, the scale response functional multivariate regression model is considered. By using the basis functions representation of functional predictors and regression coefficients, this model is rewritten as a multivariate regression model. This representation of the functional multivariate regression model is used for multiclass classification for multivariate functional data. Computational experiments performed on real labelled data sets demonstrate the effectiveness of the proposed ...

  3. Introduction of a methodology for visualization and graphical interpretation of Bayesian classification models.

    Science.gov (United States)

    Balfer, Jenny; Bajorath, Jürgen

    2014-09-22

    Supervised machine learning models are widely used in chemoinformatics, especially for the prediction of new active compounds or targets of known actives. Bayesian classification methods are among the most popular machine learning approaches for the prediction of activity from chemical structure. Much work has focused on predicting structure-activity relationships (SARs) on the basis of experimental training data. By contrast, only a few efforts have thus far been made to rationalize the performance of Bayesian or other supervised machine learning models and better understand why they might succeed or fail. In this study, we introduce an intuitive approach for the visualization and graphical interpretation of naïve Bayesian classification models. Parameters derived during supervised learning are visualized and interactively analyzed to gain insights into model performance and identify features that determine predictions. The methodology is introduced in detail and applied to assess Bayesian modeling efforts and predictions on compound data sets of varying structural complexity. Different classification models and features determining their performance are characterized in detail. A prototypic implementation of the approach is provided.

  4. Probabilistic topic modeling for the analysis and classification of genomic sequences

    Science.gov (United States)

    2015-01-01

    Background Studies on genomic sequences for classification and taxonomic identification have a leading role in the biomedical field and in the analysis of biodiversity. These studies are focusing on the so-called barcode genes, representing a well defined region of the whole genome. Recently, alignment-free techniques are gaining more importance because they are able to overcome the drawbacks of sequence alignment techniques. In this paper a new alignment-free method for DNA sequences clustering and classification is proposed. The method is based on k-mers representation and text mining techniques. Methods The presented method is based on Probabilistic Topic Modeling, a statistical technique originally proposed for text documents. Probabilistic topic models are able to find in a document corpus the topics (recurrent themes) characterizing classes of documents. This technique, applied on DNA sequences representing the documents, exploits the frequency of fixed-length k-mers and builds a generative model for a training group of sequences. This generative model, obtained through the Latent Dirichlet Allocation (LDA) algorithm, is then used to classify a large set of genomic sequences. Results and conclusions We performed classification of over 7000 16S DNA barcode sequences taken from Ribosomal Database Project (RDP) repository, training probabilistic topic models. The proposed method is compared to the RDP tool and Support Vector Machine (SVM) classification algorithm in a extensive set of trials using both complete sequences and short sequence snippets (from 400 bp to 25 bp). Our method reaches very similar results to RDP classifier and SVM for complete sequences. The most interesting results are obtained when short sequence snippets are considered. In these conditions the proposed method outperforms RDP and SVM with ultra short sequences and it exhibits a smooth decrease of performance, at every taxonomic level, when the sequence length is decreased. PMID:25916734

  5. A Novel Computer Virus Propagation Model under Security Classification

    Directory of Open Access Journals (Sweden)

    Qingyi Zhu

    2017-01-01

    Full Text Available In reality, some computers have specific security classification. For the sake of safety and cost, the security level of computers will be upgraded with increasing of threats in networks. Here we assume that there exists a threshold value which determines when countermeasures should be taken to level up the security of a fraction of computers with low security level. And in some specific realistic environments the propagation network can be regarded as fully interconnected. Inspired by these facts, this paper presents a novel computer virus dynamics model considering the impact brought by security classification in full interconnection network. By using the theory of dynamic stability, the existence of equilibria and stability conditions is analysed and proved. And the above optimal threshold value is given analytically. Then, some numerical experiments are made to justify the model. Besides, some discussions and antivirus measures are given.

  6. Model of high-tech businesses management under the trends of explicit and implicit knowledge markets: classification and business model

    OpenAIRE

    Guzel Isayevna Gumerova; Elmira Shamilevna Shaimieva

    2015-01-01

    Objective to define the notion of ldquohightech businessrdquo to elaborate classification of hightech businesses to elaborate the business model for hightech business management. Methods general scientific methods of theoretical and empirical cognition. Results the research presents a business model of hightech businesses management basing on the trends of explicit and explicit knowledge market with the dominating implicit knowledge market classification of hightech business...

  7. From the harmonic oscillator to the A-D-E classification of conformal models

    International Nuclear Information System (INIS)

    Itzykson, C.

    1988-01-01

    Arithmetical aspects of the solution of systems involving dimensional statistical models and conformal field theory. From this perspective, the analysis of the harmonic oscillator, the free particle in a box, the rational billards is effectuated. Moreover, the description of the classification of minimal conformal models and Weiss-Lumino-Witten models, based on the simplest affine algebra is also given. Attempts to interpret and justify the appearance of A-D-E classification of algebra in W-Z-W model are made. Extensions of W-Z-W model, based on SU(N) level one, and the ways to deal with rank two Lie groups, using the arithmetics of quadratic intergers, are described

  8. Do pre-trained deep learning models improve computer-aided classification of digital mammograms?

    Science.gov (United States)

    Aboutalib, Sarah S.; Mohamed, Aly A.; Zuley, Margarita L.; Berg, Wendie A.; Luo, Yahong; Wu, Shandong

    2018-02-01

    Digital mammography screening is an important exam for the early detection of breast cancer and reduction in mortality. False positives leading to high recall rates, however, results in unnecessary negative consequences to patients and health care systems. In order to better aid radiologists, computer-aided tools can be utilized to improve distinction between image classifications and thus potentially reduce false recalls. The emergence of deep learning has shown promising results in the area of biomedical imaging data analysis. This study aimed to investigate deep learning and transfer learning methods that can improve digital mammography classification performance. In particular, we evaluated the effect of pre-training deep learning models with other imaging datasets in order to boost classification performance on a digital mammography dataset. Two types of datasets were used for pre-training: (1) a digitized film mammography dataset, and (2) a very large non-medical imaging dataset. By using either of these datasets to pre-train the network initially, and then fine-tuning with the digital mammography dataset, we found an increase in overall classification performance in comparison to a model without pre-training, with the very large non-medical dataset performing the best in improving the classification accuracy.

  9. Modeling and evaluating repeatability and reproducibility of ordinal classifications

    NARCIS (Netherlands)

    de Mast, J.; van Wieringen, W.N.

    2010-01-01

    This paper argues that currently available methods for the assessment of the repeatability and reproducibility of ordinal classifications are not satisfactory. The paper aims to study whether we can modify a class of models from Item Response Theory, well established for the study of the reliability

  10. Finite mixture models for sub-pixel coastal land cover classification

    CSIR Research Space (South Africa)

    Ritchie, Michaela C

    2017-05-01

    Full Text Available Models for Sub- pixel Coastal Land Cover Classification M. Ritchie Dr. M. Lück-Vogel Dr. P. Debba Dr. V. Goodall ISRSE - 37 Tshwane, South Africa 10 May 2017 2Study Area Africa South Africa FALSE BAY 3Strand Gordon’s Bay Study Area WorldView-2 Image.../Urban 1 10 10 Herbaceous Vegetation 1 5 5 Shadow 1 8 8 Sparse Vegetation 1 3 3 Water 1 10 10 Woody Vegetation 1 5 5 11 Maximum Likelihood Classification (MLC) 12 Gaussian Mixture Discriminant Analysis (GMDA) 13 A B C t-distribution Mixture Discriminant...

  11. Predictive mapping of soil organic carbon in wet cultivated lands using classification-tree based models

    DEFF Research Database (Denmark)

    Kheir, Rania Bou; Greve, Mogens Humlekrog; Bøcher, Peder Klith

    2010-01-01

    the geographic distribution of SOC across Denmark using remote sensing (RS), geographic information systems (GISs) and decision-tree modeling (un-pruned and pruned classification trees). Seventeen parameters, i.e. parent material, soil type, landscape type, elevation, slope gradient, slope aspect, mean curvature...... field measurements in the area of interest (Denmark). A large number of tree-based classification models (588) were developed using (i) all of the parameters, (ii) all Digital Elevation Model (DEM) parameters only, (iii) the primary DEM parameters only, (iv), the remote sensing (RS) indices only, (v......) selected pairs of parameters, (vi) soil type, parent material and landscape type only, and (vii) the parameters having a high impact on SOC distribution in built pruned trees. The best constructed classification tree models (in the number of three) with the lowest misclassification error (ME...

  12. Pitch Based Sound Classification

    DEFF Research Database (Denmark)

    Nielsen, Andreas Brinch; Hansen, Lars Kai; Kjems, U

    2006-01-01

    A sound classification model is presented that can classify signals into music, noise and speech. The model extracts the pitch of the signal using the harmonic product spectrum. Based on the pitch estimate and a pitch error measure, features are created and used in a probabilistic model with soft......-max output function. Both linear and quadratic inputs are used. The model is trained on 2 hours of sound and tested on publicly available data. A test classification error below 0.05 with 1 s classification windows is achieved. Further more it is shown that linear input performs as well as a quadratic......, and that even though classification gets marginally better, not much is achieved by increasing the window size beyond 1 s....

  13. Bone Turnover Status: Classification Model and Clinical Implications

    Science.gov (United States)

    Fisher, Alexander; Fisher, Leon; Srikusalanukul, Wichat; Smith, Paul N

    2018-01-01

    Aim: To develop a practical model for classification bone turnover status and evaluate its clinical usefulness. Methods: Our classification of bone turnover status is based on internationally recommended biomarkers of both bone formation (N-terminal propeptide of type1 procollagen, P1NP) and bone resorption (beta C-terminal cross-linked telopeptide of type I collagen, bCTX), using the cutoffs proposed as therapeutic targets. The relationships between turnover subtypes and clinical characteristic were assessed in1223 hospitalised orthogeriatric patients (846 women, 377 men; mean age 78.1±9.50 years): 451(36.9%) subjects with hip fracture (HF), 396(32.4%) with other non-vertebral (non-HF) fractures (HF) and 376 (30.7%) patients without fractures. Resalts: Six subtypes of bone turnover status were identified: 1 - normal turnover (P1NP>32 μg/L, bCTX≤0.250 μg/L and P1NP/bCTX>100.0[(median value]); 2- low bone formation (P1NP ≤32 μg/L), normal bone resorption (bCTX≤0.250 μg/L) and P1NP/bCTX>100.0 (subtype2A) or P1NP/bCTX0.250 μg/L) and P1NP/bCTXturnover (both markers elevated ) and P1NP/bCTX>100.0 (subtype 4A) or P1NP/bCTX75 years and hyperparathyroidism. Hypoalbuminaemia and not using osteoporotic therapy were two independent indicators common for subtypes 3, 4A and 4B; these three subtypes were associated with in-hospital mortality. Subtype 3 was associated with fractures (OR 1.7, for HF OR 2.4), age>75 years, chronic heart failure (CHF), anaemia, and history of malignancy, and predicted post-operative myocardial injury, high inflammatory response and length of hospital stay (LOS) above10 days. Subtype 4A was associated with chronic kidney disease (CKD), anaemia, history of malignancy and walking aids use and predicted LOS>20 days, but was not discriminative for fractures. Subtype 4B was associated with fractures (OR 2.1, for HF OR 2.5), age>75 years, CKD and indicated risks of myocardial injury, high inflammatory response and LOS>10 days. Conclusions: We

  14. Methodological notes on model comparisons and strategy classification: A falsificationist proposition

    OpenAIRE

    Morten Moshagen; Benjamin E. Hilbig

    2011-01-01

    Taking a falsificationist perspective, the present paper identifies two major shortcomings of existing approaches to comparative model evaluations in general and strategy classifications in particular. These are (1) failure to consider systematic error and (2) neglect of global model fit. Using adherence measures to evaluate competing models implicitly makes the unrealistic assumption that the error associated with the model predictions is entirely random. By means of simple schematic example...

  15. Model for Detection and Classification of DDoS Traffic Based on Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    D. Peraković

    2017-06-01

    Full Text Available Detection of DDoS (Distributed Denial of Service traffic is of great importance for the availability protection of services and other information and communication resources. The research presented in this paper shows the application of artificial neural networks in the development of detection and classification model for three types of DDoS attacks and legitimate network traffic. Simulation results of developed model showed accuracy of 95.6% in classification of pre-defined classes of traffic.

  16. Diagnostics of enterprise bankruptcy occurrence probability in an anti-crisis management: modern approaches and classification of models

    Directory of Open Access Journals (Sweden)

    I.V. Zhalinska

    2015-09-01

    Full Text Available Diagnostics of enterprise bankruptcy occurrence probability is defined as an important tool ensuring the viability of an organization under conditions of unpredictable dynamic environment. The paper aims to define the basic features of diagnostics of bankruptcy occurrence probability models and their classification. The article grounds the objective increasing of crisis probability in modern enterprises where such increasing leads to the need to improve the efficiency of anti-crisis enterprise activities. The system of anti-crisis management is based on the subsystem of diagnostics of bankruptcy occurrence probability. Such a subsystem is the main one for further measures to prevent and overcome the crisis. The classification of existing models of enterprise bankruptcy occurrence probability has been suggested. The classification is based on methodical and methodological principles of models. The following main groups of models are determined: the models using financial ratios, aggregates and scores, the models of discriminated analysis, the methods of strategic analysis, informal models, artificial intelligence systems and the combination of the models. The classification made it possible to identify the analytical capabilities of each of the groups of models suggested.

  17. Using ELM-based weighted probabilistic model in the classification of synchronous EEG BCI.

    Science.gov (United States)

    Tan, Ping; Tan, Guan-Zheng; Cai, Zi-Xing; Sa, Wei-Ping; Zou, Yi-Qun

    2017-01-01

    Extreme learning machine (ELM) is an effective machine learning technique with simple theory and fast implementation, which has gained increasing interest from various research fields recently. A new method that combines ELM with probabilistic model method is proposed in this paper to classify the electroencephalography (EEG) signals in synchronous brain-computer interface (BCI) system. In the proposed method, the softmax function is used to convert the ELM output to classification probability. The Chernoff error bound, deduced from the Bayesian probabilistic model in the training process, is adopted as the weight to take the discriminant process. Since the proposed method makes use of the knowledge from all preceding training datasets, its discriminating performance improves accumulatively. In the test experiments based on the datasets from BCI competitions, the proposed method is compared with other classification methods, including the linear discriminant analysis, support vector machine, ELM and weighted probabilistic model methods. For comparison, the mutual information, classification accuracy and information transfer rate are considered as the evaluation indicators for these classifiers. The results demonstrate that our method shows competitive performance against other methods.

  18. A Hierarchical Feature Extraction Model for Multi-Label Mechanical Patent Classification

    Directory of Open Access Journals (Sweden)

    Jie Hu

    2018-01-01

    Full Text Available Various studies have focused on feature extraction methods for automatic patent classification in recent years. However, most of these approaches are based on the knowledge from experts in related domains. Here we propose a hierarchical feature extraction model (HFEM for multi-label mechanical patent classification, which is able to capture both local features of phrases as well as global and temporal semantics. First, a n-gram feature extractor based on convolutional neural networks (CNNs is designed to extract salient local lexical-level features. Next, a long dependency feature extraction model based on the bidirectional long–short-term memory (BiLSTM neural network model is proposed to capture sequential correlations from higher-level sequence representations. Then the HFEM algorithm and its hierarchical feature extraction architecture are detailed. We establish the training, validation and test datasets, containing 72,532, 18,133, and 2679 mechanical patent documents, respectively, and then check the performance of HFEMs. Finally, we compared the results of the proposed HFEM and three other single neural network models, namely CNN, long–short-term memory (LSTM, and BiLSTM. The experimental results indicate that our proposed HFEM outperforms the other compared models in both precision and recall.

  19. Assessing the Accuracy and Consistency of Language Proficiency Classification under Competing Measurement Models

    Science.gov (United States)

    Zhang, Bo

    2010-01-01

    This article investigates how measurement models and statistical procedures can be applied to estimate the accuracy of proficiency classification in language testing. The paper starts with a concise introduction of four measurement models: the classical test theory (CTT) model, the dichotomous item response theory (IRT) model, the testlet response…

  20. A Classification Methodology and Retrieval Model to Support Software Reuse

    Science.gov (United States)

    1988-01-01

    Dewey Decimal Classification ( DDC 18), an enumerative scheme, occupies 40 pages [Buchanan 19791. Langridge [19731 states that the facets listed in the...sense of historical importance or wide spread use. The schemes are: Dewey Decimal Classification ( DDC ), Universal Decimal Classification (UDC...Classification Systems ..... ..... 2.3.3 Library Classification__- .52 23.3.1 Dewey Decimal Classification -53 2.33.2 Universal Decimal Classification 55 2333

  1. A model to facilitate implementation of the International Classification of Functioning, Disability and Health into prosthetics and orthotics.

    Science.gov (United States)

    Jarl, Gustav; Ramstrand, Nerrolyn

    2017-09-01

    The International Classification of Functioning, Disability and Health is a classification of human functioning and disability and is based on a biopsychosocial model of health. As such, International Classification of Functioning, Disability and Health seems suitable as a basis for constructing models defining the clinical P&O process. The aim was to use International Classification of Functioning, Disability and Health to facilitate development of such a model. Proposed model: A model, the Prosthetic and Orthotic Process (POP) model, is proposed. The Prosthetic and Orthotic Process model is based on the concepts of the International Classification of Functioning, Disability and Health and comprises four steps in a cycle: (1) Assessment, including the medical history and physical examination of the patient. (2) Goals, specified on four levels including those related to participation, activity, body functions and structures and technical requirements of the device. (3) Intervention, in which the appropriate course of action is determined based on the specified goal and evidence-based practice. (4) Evaluation of outcomes, where the outcomes are assessed and compared to the corresponding goals. After the evaluation of goal fulfilment, the first cycle in the process is complete, and a broad evaluation is now made including overriding questions about the patient's satisfaction with the outcomes and the process. This evaluation will determine if the process should be ended or if another cycle in the process should be initiated. The Prosthetic and Orthotic Process model can provide a common understanding of the P&O process. Concepts of International Classification of Functioning, Disability and Health have been incorporated into the model to facilitate communication with other rehabilitation professionals and encourage a holistic and patient-centred approach in clinical practice. Clinical relevance The Prosthetic and Orthotic Process model can support the implementation

  2. Models of parallel computation :a survey and classification

    Institute of Scientific and Technical Information of China (English)

    ZHANG Yunquan; CHEN Guoliang; SUN Guangzhong; MIAO Qiankun

    2007-01-01

    In this paper,the state-of-the-art parallel computational model research is reviewed.We will introduce various models that were developed during the past decades.According to their targeting architecture features,especially memory organization,we classify these parallel computational models into three generations.These models and their characteristics are discussed based on three generations classification.We believe that with the ever increasing speed gap between the CPU and memory systems,incorporating non-uniform memory hierarchy into computational models will become unavoidable.With the emergence of multi-core CPUs,the parallelism hierarchy of current computing platforms becomes more and more complicated.Describing this complicated parallelism hierarchy in future computational models becomes more and more important.A semi-automatic toolkit that can extract model parameters and their values on real computers can reduce the model analysis complexity,thus allowing more complicated models with more parameters to be adopted.Hierarchical memory and hierarchical parallelism will be two very important features that should be considered in future model design and research.

  3. Real-time classification of humans versus animals using profiling sensors and hidden Markov tree model

    Science.gov (United States)

    Hossen, Jakir; Jacobs, Eddie L.; Chari, Srikant

    2015-07-01

    Linear pyroelectric array sensors have enabled useful classifications of objects such as humans and animals to be performed with relatively low-cost hardware in border and perimeter security applications. Ongoing research has sought to improve the performance of these sensors through signal processing algorithms. In the research presented here, we introduce the use of hidden Markov tree (HMT) models for object recognition in images generated by linear pyroelectric sensors. HMTs are trained to statistically model the wavelet features of individual objects through an expectation-maximization learning process. Human versus animal classification for a test object is made by evaluating its wavelet features against the trained HMTs using the maximum-likelihood criterion. The classification performance of this approach is compared to two other techniques; a texture, shape, and spectral component features (TSSF) based classifier and a speeded-up robust feature (SURF) classifier. The evaluation indicates that among the three techniques, the wavelet-based HMT model works well, is robust, and has improved classification performance compared to a SURF-based algorithm in equivalent computation time. When compared to the TSSF-based classifier, the HMT model has a slightly degraded performance but almost an order of magnitude improvement in computation time enabling real-time implementation.

  4. Effects of sample survey design on the accuracy of classification tree models in species distribution models

    Science.gov (United States)

    Thomas C. Edwards; D. Richard Cutler; Niklaus E. Zimmermann; Linda Geiser; Gretchen G. Moisen

    2006-01-01

    We evaluated the effects of probabilistic (hereafter DESIGN) and non-probabilistic (PURPOSIVE) sample surveys on resultant classification tree models for predicting the presence of four lichen species in the Pacific Northwest, USA. Models derived from both survey forms were assessed using an independent data set (EVALUATION). Measures of accuracy as gauged by...

  5. A classification model for non-alcoholic steatohepatitis (NASH) using confocal Raman micro-spectroscopy

    Science.gov (United States)

    Yan, Jie; Yu, Yang; Kang, Jeon Woong; Tam, Zhi Yang; Xu, Shuoyu; Fong, Eliza Li Shan; Singh, Surya Pratap; Song, Ziwei; Tucker Kellogg, Lisa; So, Peter; Yu, Hanry

    2017-07-01

    We combined Raman micro-spectroscopy and machine learning techniques to develop a classification model based on a well-established non-alcoholic steatohepatitis (NASH) mouse model, using spectrum pre-processing, biochemical component analysis (BCA) and logistic regression.

  6. [Establishment of Schatzker classification digital models of tibial plateau fractures and its application on virtual surgery].

    Science.gov (United States)

    Liu, Yong-gang; Zuo, Li-xin; Pei, Guo-xian; Dai, Ke; Sang, Jing-wei

    2013-08-20

    To explore the establishment of Schatzker classification digital model of tibial plateau fractures and its application in virtual surgery. Proximal tibial of one healthy male volunteer was examined with 64-slice spiral computed tomography (CT). The data were processed by software Mimics 10.01 and a model of proximal tibia was reconstructed. According to the Schatzker classification criteria of tibial plateau fractures, each type of fracture model was simulated.Screen-captures of fracture model were saved from different directions.Each type of fracture model was exported as video mode.Fracture model was imported into FreeForm modeling system.With a force feedback device, a surgeon could conduct virtual fracture operation simulation.Utilizing the GHOST of FreeForm modeling system, the software of virtual cutting, fracture reduction and fixation was developed.With a force feedback device PHANTOM, a surgeon could manipulate virtual surgical instruments and fracture classification model and simulate surgical actions such as assembly of surgical instruments, drilling, implantation of screw, reduction of fracture, bone grafting and fracture fixation, etc. The digital fracture model was intuitive, three-dimensional and realistic and it had excellent visual effect.Fracture could be observed and charted from optional direction and angle.Fracture model could rotate 360 ° in the corresponding video mode. The virtual surgical environment had a strong sense of reality, immersion and telepresence as well as good interaction and force feedback function in the FreeForm modeling system. The user could make the corresponding decisions about surgical method and choice of internal fixation according to the specific type of tibial plateau fracture as well as repeated operational practice in virtual surgery system. The digital fracture model of Schatzker classification is intuitive, three-dimensional, realistic and dynamic. The virtual surgery systems of Schatzker classifications make

  7. Habitat classification modelling with incomplete data: Pushing the habitat envelope

    Science.gov (United States)

    Phoebe L. Zarnetske; Thomas C. Edwards; Gretchen G. Moisen

    2007-01-01

    Habitat classification models (HCMs) are invaluable tools for species conservation, land-use planning, reserve design, and metapopulation assessments, particularly at broad spatial scales. However, species occurrence data are often lacking and typically limited to presence points at broad scales. This lack of absence data precludes the use of many statistical...

  8. Modelling the results of health promotion activities in Switzerland: development of the Swiss Model for Outcome Classification in Health Promotion and Prevention.

    Science.gov (United States)

    Spencer, Brenda; Broesskamp-Stone, Ursel; Ruckstuhl, Brigitte; Ackermann, Günter; Spoerri, Adrian; Cloetta, Bernhard

    2008-03-01

    This paper describes the Model for Outcome Classification in Health Promotion and Prevention adopted by Health Promotion Switzerland (SMOC, Swiss Model for Outcome Classification) and the process of its development. The context and method of model development, and the aim and objectives of the model are outlined. Preliminary experience with application of the model in evaluation planning and situation analysis is reported. On the basis of an extensive literature search, the model is situated within the wider international context of similar efforts to meet the challenge of developing tools to assess systematically the activities of health promotion and prevention.

  9. Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application

    Directory of Open Access Journals (Sweden)

    Junbao Zheng

    2012-03-01

    Full Text Available Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor as well as its parallel channels (inner factor. The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6~8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3~5 pattern classes considering the trade-off between time consumption and classification rate.

  10. Classification of customer lifetime value models using Markov chain

    Science.gov (United States)

    Permana, Dony; Pasaribu, Udjianna S.; Indratno, Sapto W.; Suprayogi

    2017-10-01

    A firm’s potential reward in future time from a customer can be determined by customer lifetime value (CLV). There are some mathematic methods to calculate it. One method is using Markov chain stochastic model. Here, a customer is assumed through some states. Transition inter the states follow Markovian properties. If we are given some states for a customer and the relationships inter states, then we can make some Markov models to describe the properties of the customer. As Markov models, CLV is defined as a vector contains CLV for a customer in the first state. In this paper we make a classification of Markov Models to calculate CLV. Start from two states of customer model, we make develop in many states models. The development a model is based on weaknesses in previous model. Some last models can be expected to describe how real characters of customers in a firm.

  11. Computerized Classification Testing with the Rasch Model

    Science.gov (United States)

    Eggen, Theo J. H. M.

    2011-01-01

    If classification in a limited number of categories is the purpose of testing, computerized adaptive tests (CATs) with algorithms based on sequential statistical testing perform better than estimation-based CATs (e.g., Eggen & Straetmans, 2000). In these computerized classification tests (CCTs), the Sequential Probability Ratio Test (SPRT) (Wald,…

  12. BCDForest: a boosting cascade deep forest model towards the classification of cancer subtypes based on gene expression data.

    Science.gov (United States)

    Guo, Yang; Liu, Shuhui; Li, Zhanhuai; Shang, Xuequn

    2018-04-11

    The classification of cancer subtypes is of great importance to cancer disease diagnosis and therapy. Many supervised learning approaches have been applied to cancer subtype classification in the past few years, especially of deep learning based approaches. Recently, the deep forest model has been proposed as an alternative of deep neural networks to learn hyper-representations by using cascade ensemble decision trees. It has been proved that the deep forest model has competitive or even better performance than deep neural networks in some extent. However, the standard deep forest model may face overfitting and ensemble diversity challenges when dealing with small sample size and high-dimensional biology data. In this paper, we propose a deep learning model, so-called BCDForest, to address cancer subtype classification on small-scale biology datasets, which can be viewed as a modification of the standard deep forest model. The BCDForest distinguishes from the standard deep forest model with the following two main contributions: First, a named multi-class-grained scanning method is proposed to train multiple binary classifiers to encourage diversity of ensemble. Meanwhile, the fitting quality of each classifier is considered in representation learning. Second, we propose a boosting strategy to emphasize more important features in cascade forests, thus to propagate the benefits of discriminative features among cascade layers to improve the classification performance. Systematic comparison experiments on both microarray and RNA-Seq gene expression datasets demonstrate that our method consistently outperforms the state-of-the-art methods in application of cancer subtype classification. The multi-class-grained scanning and boosting strategy in our model provide an effective solution to ease the overfitting challenge and improve the robustness of deep forest model working on small-scale data. Our model provides a useful approach to the classification of cancer subtypes

  13. Comparison analysis for classification algorithm in data mining and the study of model use

    Science.gov (United States)

    Chen, Junde; Zhang, Defu

    2018-04-01

    As a key technique in data mining, classification algorithm was received extensive attention. Through an experiment of classification algorithm in UCI data set, we gave a comparison analysis method for the different algorithms and the statistical test was used here. Than that, an adaptive diagnosis model for preventive electricity stealing and leakage was given as a specific case in the paper.

  14. Median Filter Noise Reduction of Image and Backpropagation Neural Network Model for Cervical Cancer Classification

    Science.gov (United States)

    Wutsqa, D. U.; Marwah, M.

    2017-06-01

    In this paper, we consider spatial operation median filter to reduce the noise in the cervical images yielded by colposcopy tool. The backpropagation neural network (BPNN) model is applied to the colposcopy images to classify cervical cancer. The classification process requires an image extraction by using a gray level co-occurrence matrix (GLCM) method to obtain image features that are used as inputs of BPNN model. The advantage of noise reduction is evaluated by comparing the performances of BPNN models with and without spatial operation median filter. The experimental result shows that the spatial operation median filter can improve the accuracy of the BPNN model for cervical cancer classification.

  15. Best Practices in Academic Management. Study Programs Classification Model

    Directory of Open Access Journals (Sweden)

    Ofelia Ema Aleca

    2016-05-01

    Full Text Available This article proposes and tests a set of performance indicators for the assessment of Bachelor and Master studies, from two perspectives: the study programs and the disciplines. The academic performance at the level of a study program shall be calculated based on success and efficiency rates, and at discipline level, on the basis of rates of efficiency, success and absenteeism. This research proposes a model of classification of the study programs within a Bachelor and Master cycle based on the education performance and efficiency. What recommends this model as a best practice model in academic management is the possibility of grouping a study program or a discipline in a particular category of efficiency

  16. SVM classification model in depression recognition based on mutation PSO parameter optimization

    Directory of Open Access Journals (Sweden)

    Zhang Ming

    2017-01-01

    Full Text Available At present, the clinical diagnosis of depression is mainly through structured interviews by psychiatrists, which is lack of objective diagnostic methods, so it causes the higher rate of misdiagnosis. In this paper, a method of depression recognition based on SVM and particle swarm optimization algorithm mutation is proposed. To address on the problem that particle swarm optimization (PSO algorithm easily trap in local optima, we propose a feedback mutation PSO algorithm (FBPSO to balance the local search and global exploration ability, so that the parameters of the classification model is optimal. We compared different PSO mutation algorithms about classification accuracy for depression, and found the classification accuracy of support vector machine (SVM classifier based on feedback mutation PSO algorithm is the highest. Our study promotes important reference value for establishing auxiliary diagnostic used in depression recognition of clinical diagnosis.

  17. Protein Structure Classification and Loop Modeling Using Multiple Ramachandran Distributions

    KAUST Repository

    Najibi, Seyed Morteza; Maadooliat, Mehdi; Zhou, Lan; Huang, Jianhua Z.; Gao, Xin

    2017-01-01

    Recently, the study of protein structures using angular representations has attracted much attention among structural biologists. The main challenge is how to efficiently model the continuous conformational space of the protein structures based on the differences and similarities between different Ramachandran plots. Despite the presence of statistical methods for modeling angular data of proteins, there is still a substantial need for more sophisticated and faster statistical tools to model the large-scale circular datasets. To address this need, we have developed a nonparametric method for collective estimation of multiple bivariate density functions for a collection of populations of protein backbone angles. The proposed method takes into account the circular nature of the angular data using trigonometric spline which is more efficient compared to existing methods. This collective density estimation approach is widely applicable when there is a need to estimate multiple density functions from different populations with common features. Moreover, the coefficients of adaptive basis expansion for the fitted densities provide a low-dimensional representation that is useful for visualization, clustering, and classification of the densities. The proposed method provides a novel and unique perspective to two important and challenging problems in protein structure research: structure-based protein classification and angular-sampling-based protein loop structure prediction.

  18. Protein Structure Classification and Loop Modeling Using Multiple Ramachandran Distributions

    KAUST Repository

    Najibi, Seyed Morteza

    2017-02-08

    Recently, the study of protein structures using angular representations has attracted much attention among structural biologists. The main challenge is how to efficiently model the continuous conformational space of the protein structures based on the differences and similarities between different Ramachandran plots. Despite the presence of statistical methods for modeling angular data of proteins, there is still a substantial need for more sophisticated and faster statistical tools to model the large-scale circular datasets. To address this need, we have developed a nonparametric method for collective estimation of multiple bivariate density functions for a collection of populations of protein backbone angles. The proposed method takes into account the circular nature of the angular data using trigonometric spline which is more efficient compared to existing methods. This collective density estimation approach is widely applicable when there is a need to estimate multiple density functions from different populations with common features. Moreover, the coefficients of adaptive basis expansion for the fitted densities provide a low-dimensional representation that is useful for visualization, clustering, and classification of the densities. The proposed method provides a novel and unique perspective to two important and challenging problems in protein structure research: structure-based protein classification and angular-sampling-based protein loop structure prediction.

  19. Classification of human cancers based on DNA copy number amplification modeling

    Directory of Open Access Journals (Sweden)

    Knuutila Sakari

    2008-05-01

    Full Text Available Abstract Background DNA amplifications alter gene dosage in cancer genomes by multiplying the gene copy number. Amplifications are quintessential in a considerable number of advanced cancers of various anatomical locations. The aims of this study were to classify human cancers based on their amplification patterns, explore the biological and clinical fundamentals behind their amplification-pattern based classification, and understand the characteristics in human genomic architecture that associate with amplification mechanisms. Methods We applied a machine learning approach to model DNA copy number amplifications using a data set of binary amplification records at chromosome sub-band resolution from 4400 cases that represent 82 cancer types. Amplification data was fused with background data: clinical, histological and biological classifications, and cytogenetic annotations. Statistical hypothesis testing was used to mine associations between the data sets. Results Probabilistic clustering of each chromosome identified 111 amplification models and divided the cancer cases into clusters. The distribution of classification terms in the amplification-model based clustering of cancer cases revealed cancer classes that were associated with specific DNA copy number amplification models. Amplification patterns – finite or bounded descriptions of the ranges of the amplifications in the chromosome – were extracted from the clustered data and expressed according to the original cytogenetic nomenclature. This was achieved by maximal frequent itemset mining using the cluster-specific data sets. The boundaries of amplification patterns were shown to be enriched with fragile sites, telomeres, centromeres, and light chromosome bands. Conclusions Our results demonstrate that amplifications are non-random chromosomal changes and specifically selected in tumor tissue microenvironment. Furthermore, statistical evidence showed that specific chromosomal features

  20. Effects of the small molecule HERG activator NS1643 on Kv11.3 channels.

    Directory of Open Access Journals (Sweden)

    Arne Bilet

    Full Text Available NS1643 is one of the small molecule HERG (Kv11.1 channel activators and has also been found to increase erg2 (Kv11.2 currents. We now investigated whether NS1643 is also able to act as an activator of Kv11.3 (erg3 channels expressed in CHO cells. Activation of rat Kv11.3 current occurred in a dose-dependent manner and maximal current increasing effects were obtained with 10 µM NS1643. At this concentration, steady-state outward current increased by about 80% and the current increase was associated with a significant shift in the voltage dependence of activation to more negative potentials by about 15 mV. In addition, activation kinetics were accelerated, whereas deactivation was slowed. There was no significant effect on the kinetics of inactivation and recovery from inactivation. The strong current-activating agonistic effect of NS1643 did not result from a shift in the voltage dependence of Kv11.3 channel inactivation and was independent from external Na(+ or Ca(2+. At the higher concentration of 20 µM, NS1643 induced clearly less current increase. The left shift in the voltage dependence of activation reversed and the voltage sensitivity of activation dramatically decreased along with a slowing of Kv11.3 channel activation. These data show that, in comparison to other Kv11 family members, NS1643 exerts distinct effects on Kv11.3 channels with especially pronounced partial antagonistic effects at higher concentration.

  1. A physiologically-inspired model of numerical classification based on graded stimulus coding

    Directory of Open Access Journals (Sweden)

    John Pearson

    2010-01-01

    Full Text Available In most natural decision contexts, the process of selecting among competing actions takes place in the presence of informative, but potentially ambiguous, stimuli. Decisions about magnitudes—quantities like time, length, and brightness that are linearly ordered—constitute an important subclass of such decisions. It has long been known that perceptual judgments about such quantities obey Weber’s Law, wherein the just-noticeable difference in a magnitude is proportional to the magnitude itself. Current physiologically inspired models of numerical classification assume discriminations are made via a labeled line code of neurons selectively tuned for numerosity, a pattern observed in the firing rates of neurons in the ventral intraparietal area (VIP of the macaque. By contrast, neurons in the contiguous lateral intraparietal area (LIP signal numerosity in a graded fashion, suggesting the possibility that numerical classification could be achieved in the absence of neurons tuned for number. Here, we consider the performance of a decision model based on this analog coding scheme in a paradigmatic discrimination task—numerosity bisection. We demonstrate that a basic two-neuron classifier model, derived from experimentally measured monotonic responses of LIP neurons, is sufficient to reproduce the numerosity bisection behavior of monkeys, and that the threshold of the classifier can be set by reward maximization via a simple learning rule. In addition, our model predicts deviations from Weber Law scaling of choice behavior at high numerosity. Together, these results suggest both a generic neuronal framework for magnitude-based decisions and a role for reward contingency in the classification of such stimuli.

  2. CCM: A Text Classification Method by Clustering

    DEFF Research Database (Denmark)

    Nizamani, Sarwat; Memon, Nasrullah; Wiil, Uffe Kock

    2011-01-01

    In this paper, a new Cluster based Classification Model (CCM) for suspicious email detection and other text classification tasks, is presented. Comparative experiments of the proposed model against traditional classification models and the boosting algorithm are also discussed. Experimental results...... show that the CCM outperforms traditional classification models as well as the boosting algorithm for the task of suspicious email detection on terrorism domain email dataset and topic categorization on the Reuters-21578 and 20 Newsgroups datasets. The overall finding is that applying a cluster based...

  3. An Investigation of Feature Models for Music Genre Classification using the Support Vector Classifier

    DEFF Research Database (Denmark)

    Meng, Anders; Shawe-Taylor, John

    2005-01-01

    In music genre classification the decision time is typically of the order of several seconds however most automatic music genre classification systems focus on short time features derived from 10-50ms. This work investigates two models, the multivariate gaussian model and the multivariate...... probability kernel. In order to examine the different methods an 11 genre music setup was utilized. In this setup the Mel Frequency Cepstral Coefficients (MFCC) were used as short time features. The accuracy of the best performing model on this data set was 44% as compared to a human performance of 52...... autoregressive model for modelling short time features. Furthermore, it was investigated how these models can be integrated over a segment of short time features into a kernel such that a support vector machine can be applied. Two kernels with this property were considered, the convolution kernel and product...

  4. Incremental Validity of Multidimensional Proficiency Scores from Diagnostic Classification Models: An Illustration for Elementary School Mathematics

    Science.gov (United States)

    Kunina-Habenicht, Olga; Rupp, André A.; Wilhelm, Oliver

    2017-01-01

    Diagnostic classification models (DCMs) hold great potential for applications in summative and formative assessment by providing discrete multivariate proficiency scores that yield statistically driven classifications of students. Using data from a newly developed diagnostic arithmetic assessment that was administered to 2032 fourth-grade students…

  5. Evaluation of soft segment modeling on a context independent phoneme classification system

    International Nuclear Information System (INIS)

    Razzazi, F.; Sayadiyan, A.

    2007-01-01

    The geometric distribution of states duration is one of the main performance limiting assumptions of hidden Markov modeling of speech signals. Stochastic segment models, generally, and segmental HMM, specifically overcome this deficiency partly at the cost of more complexity in both training and recognition phases. In addition to this assumption, the gradual temporal changes of speech statistics has not been modeled in HMM. In this paper, a new duration modeling approach is presented. The main idea of the model is to consider the effect of adjacent segments on the probability density function estimation and evaluation of each acoustic segment. This idea not only makes the model robust against segmentation errors, but also it models gradual change from one segment to the next one with a minimum set of parameters. The proposed idea is analytically formulated and tested on a TIMIT based context independent phenomena classification system. During the test procedure, the phoneme classification of different phoneme classes was performed by applying various proposed recognition algorithms. The system was optimized and the results have been compared with a continuous density hidden Markov model (CDHMM) with similar computational complexity. The results show 8-10% improvement in phoneme recognition rate in comparison with standard continuous density hidden Markov model. This indicates improved compatibility of the proposed model with the speech nature. (author)

  6. Video event classification and image segmentation based on noncausal multidimensional hidden Markov models.

    Science.gov (United States)

    Ma, Xiang; Schonfeld, Dan; Khokhar, Ashfaq A

    2009-06-01

    In this paper, we propose a novel solution to an arbitrary noncausal, multidimensional hidden Markov model (HMM) for image and video classification. First, we show that the noncausal model can be solved by splitting it into multiple causal HMMs and simultaneously solving each causal HMM using a fully synchronous distributed computing framework, therefore referred to as distributed HMMs. Next we present an approximate solution to the multiple causal HMMs that is based on an alternating updating scheme and assumes a realistic sequential computing framework. The parameters of the distributed causal HMMs are estimated by extending the classical 1-D training and classification algorithms to multiple dimensions. The proposed extension to arbitrary causal, multidimensional HMMs allows state transitions that are dependent on all causal neighbors. We, thus, extend three fundamental algorithms to multidimensional causal systems, i.e., 1) expectation-maximization (EM), 2) general forward-backward (GFB), and 3) Viterbi algorithms. In the simulations, we choose to limit ourselves to a noncausal 2-D model whose noncausality is along a single dimension, in order to significantly reduce the computational complexity. Simulation results demonstrate the superior performance, higher accuracy rate, and applicability of the proposed noncausal HMM framework to image and video classification.

  7. Model-based classification of CPT data and automated lithostratigraphic mapping for high-resolution characterization of a heterogeneous sedimentary aquifer.

    Science.gov (United States)

    Rogiers, Bart; Mallants, Dirk; Batelaan, Okke; Gedeon, Matej; Huysmans, Marijke; Dassargues, Alain

    2017-01-01

    Cone penetration testing (CPT) is one of the most efficient and versatile methods currently available for geotechnical, lithostratigraphic and hydrogeological site characterization. Currently available methods for soil behaviour type classification (SBT) of CPT data however have severe limitations, often restricting their application to a local scale. For parameterization of regional groundwater flow or geotechnical models, and delineation of regional hydro- or lithostratigraphy, regional SBT classification would be very useful. This paper investigates the use of model-based clustering for SBT classification, and the influence of different clustering approaches on the properties and spatial distribution of the obtained soil classes. We additionally propose a methodology for automated lithostratigraphic mapping of regionally occurring sedimentary units using SBT classification. The methodology is applied to a large CPT dataset, covering a groundwater basin of ~60 km2 with predominantly unconsolidated sandy sediments in northern Belgium. Results show that the model-based approach is superior in detecting the true lithological classes when compared to more frequently applied unsupervised classification approaches or literature classification diagrams. We demonstrate that automated mapping of lithostratigraphic units using advanced SBT classification techniques can provide a large gain in efficiency, compared to more time-consuming manual approaches and yields at least equally accurate results.

  8. Model-based classification of CPT data and automated lithostratigraphic mapping for high-resolution characterization of a heterogeneous sedimentary aquifer.

    Directory of Open Access Journals (Sweden)

    Bart Rogiers

    Full Text Available Cone penetration testing (CPT is one of the most efficient and versatile methods currently available for geotechnical, lithostratigraphic and hydrogeological site characterization. Currently available methods for soil behaviour type classification (SBT of CPT data however have severe limitations, often restricting their application to a local scale. For parameterization of regional groundwater flow or geotechnical models, and delineation of regional hydro- or lithostratigraphy, regional SBT classification would be very useful. This paper investigates the use of model-based clustering for SBT classification, and the influence of different clustering approaches on the properties and spatial distribution of the obtained soil classes. We additionally propose a methodology for automated lithostratigraphic mapping of regionally occurring sedimentary units using SBT classification. The methodology is applied to a large CPT dataset, covering a groundwater basin of ~60 km2 with predominantly unconsolidated sandy sediments in northern Belgium. Results show that the model-based approach is superior in detecting the true lithological classes when compared to more frequently applied unsupervised classification approaches or literature classification diagrams. We demonstrate that automated mapping of lithostratigraphic units using advanced SBT classification techniques can provide a large gain in efficiency, compared to more time-consuming manual approaches and yields at least equally accurate results.

  9. Chromatographic profiles of Phyllanthus aqueous extracts samples: a proposition of classification using chemometric models.

    Science.gov (United States)

    Martins, Lucia Regina Rocha; Pereira-Filho, Edenir Rodrigues; Cass, Quezia Bezerra

    2011-04-01

    Taking in consideration the global analysis of complex samples, proposed by the metabolomic approach, the chromatographic fingerprint encompasses an attractive chemical characterization of herbal medicines. Thus, it can be used as a tool in quality control analysis of phytomedicines. The generated multivariate data are better evaluated by chemometric analyses, and they can be modeled by classification methods. "Stone breaker" is a popular Brazilian plant of Phyllanthus genus, used worldwide to treat renal calculus, hepatitis, and many other diseases. In this study, gradient elution at reversed-phase conditions with detection at ultraviolet region were used to obtain chemical profiles (fingerprints) of botanically identified samples of six Phyllanthus species. The obtained chromatograms, at 275 nm, were organized in data matrices, and the time shifts of peaks were adjusted using the Correlation Optimized Warping algorithm. Principal Component Analyses were performed to evaluate similarities among cultivated and uncultivated samples and the discrimination among the species and, after that, the samples were used to compose three classification models using Soft Independent Modeling of Class analogy, K-Nearest Neighbor, and Partial Least Squares for Discriminant Analysis. The ability of classification models were discussed after their successful application for authenticity evaluation of 25 commercial samples of "stone breaker."

  10. Model of Numerical Spatial Classification for Sustainable Agriculture in Badung Regency and Denpasar City, Indonesia

    Science.gov (United States)

    Trigunasih, N. M.; Lanya, I.; Subadiyasa, N. N.; Hutauruk, J.

    2018-02-01

    Increasing number and activity of the population to meet the needs of their lives greatly affect the utilization of land resources. Land needs for activities of the population continue to grow, while the availability of land is limited. Therefore, there will be changes in land use. As a result, the problems faced by land degradation and conversion of agricultural land become non-agricultural. The objectives of this research are: (1) to determine parameter of spatial numerical classification of sustainable food agriculture in Badung Regency and Denpasar City (2) to know the projection of food balance in Badung Regency and Denpasar City in 2020, 2030, 2040, and 2050 (3) to specify of function of spatial numerical classification in the making of zonation model of sustainable agricultural land area in Badung regency and Denpasar city (4) to determine the appropriate model of the area to protect sustainable agricultural land in spatial and time scale in Badung and Denpasar regencies. The method used in this research was quantitative method include: survey, soil analysis, spatial data development, geoprocessing analysis (spatial analysis of overlay and proximity analysis), interpolation of raster digital elevation model data, and visualization (cartography). Qualitative methods consisted of literature studies, and interviews. The parameters observed for a total of 11 parameters Badung regency and Denpasar as much as 9 parameters. Numerical classification parameter analysis results used the standard deviation and the mean of the population data and projections relationship rice field in the food balance sheet by modelling. The result of the research showed that, the number of different numerical classification parameters in rural areas (Badung) and urban areas (Denpasar), in urban areas the number of parameters is less than the rural areas. The based on numerical classification weighting and scores generate population distribution parameter analysis results of a standard

  11. Research on evaluating water resource resilience based on projection pursuit classification model

    Science.gov (United States)

    Liu, Dong; Zhao, Dan; Liang, Xu; Wu, Qiuchen

    2016-03-01

    Water is a fundamental natural resource while agriculture water guarantees the grain output, which shows that the utilization and management of water resource have a significant practical meaning. Regional agricultural water resource system features with unpredictable, self-organization, and non-linear which lays a certain difficulty on the evaluation of regional agriculture water resource resilience. The current research on water resource resilience remains to focus on qualitative analysis and the quantitative analysis is still in the primary stage, thus, according to the above issues, projection pursuit classification model is brought forward. With the help of artificial fish-swarm algorithm (AFSA), it optimizes the projection index function, seeks for the optimal projection direction, and improves AFSA with the application of self-adaptive artificial fish step and crowding factor. Taking Hongxinglong Administration of Heilongjiang as the research base and on the basis of improving AFSA, it established the evaluation of projection pursuit classification model to agriculture water resource system resilience besides the proceeding analysis of projection pursuit classification model on accelerating genetic algorithm. The research shows that the water resource resilience of Hongxinglong is the best than Raohe Farm, and the last 597 Farm. And the further analysis shows that the key driving factors influencing agricultural water resource resilience are precipitation and agriculture water consumption. The research result reveals the restoring situation of the local water resource system, providing foundation for agriculture water resource management.

  12. Calpain activation by ROS mediates human ether-a-go-go-related gene protein degradation by intermittent hypoxia.

    Science.gov (United States)

    Wang, N; Kang, H S; Ahmmed, G; Khan, S A; Makarenko, V V; Prabhakar, N R; Nanduri, J

    2016-03-01

    Human ether-a-go-go-related gene (hERG) channels conduct delayed rectifier K(+) current. However, little information is available on physiological situations affecting hERG channel protein and function. In the present study we examined the effects of intermittent hypoxia (IH), which is a hallmark manifestation of sleep apnea, on hERG channel protein and function. Experiments were performed on SH-SY5Y neuroblastoma cells, which express hERG protein. Cells were exposed to IH consisting of alternating cycles of 30 s of hypoxia (1.5% O2) and 5 min of 20% O2. IH decreased hERG protein expression in a stimulus-dependent manner. A similar reduction in hERG protein was also seen in adrenal medullary chromaffin cells from IH-exposed neonatal rats. The decreased hERG protein was associated with attenuated hERG K(+) current. IH-evoked hERG protein degradation was not due to reduced transcription or increased proteosome/lysomal degradation. Rather it was mediated by calcium-activated calpain proteases. Both COOH- and NH2-terminal sequences of the hERG protein were the targets of calpain-dependent degradation. IH increased reactive oxygen species (ROS) levels, intracellular Ca(2+) concentration ([Ca(2+)]i), calpain enzyme activity, and hERG protein degradation, and all these effects were prevented by manganese-(111)-tetrakis-(1-methyl-4-pyridyl)-porphyrin pentachloride, a membrane-permeable ROS scavenger. These results demonstrate that activation of calpains by ROS-dependent elevation of [Ca(2+)]i mediates hERG protein degradation by IH. Copyright © 2016 the American Physiological Society.

  13. Acute and Chronic Toxicity, Cytochrome P450 Enzyme Inhibition, and hERG Channel Blockade Studies with a Polyherbal, Ayurvedic Formulation for Inflammation

    Directory of Open Access Journals (Sweden)

    Debendranath Dey

    2015-01-01

    Full Text Available Ayurvedic plants are known for thousands of years to have anti-inflammatory and antiarthritic effect. We have recently shown that BV-9238, a proprietary formulation of Withania somnifera, Boswellia serrata, Zingiber officinale, and Curcuma longa, inhibits LPS-induced TNF-alpha and nitric oxide production from mouse macrophage and reduces inflammation in different animal models. To evaluate the safety parameters of BV-9238, we conducted a cytotoxicity study in RAW 264.7 cells (0.005–1 mg/mL by MTT/formazan method, an acute single dose (2–10 g/kg bodyweight toxicity study and a 180-day chronic study with 1 g and 2 g/kg bodyweight in Sprague Dawley rats. Some sedation, ptosis, and ataxia were observed for first 15–20 min in very high acute doses and hence not used for further chronic studies. At the end of 180 days, gross and histopathology, blood cell counts, liver and renal functions were all at normal levels. Further, a modest attempt was made to assess the effects of BV-9238 (0.5 µg/mL on six major human cytochrome P450 enzymes and 3H radioligand binding assay with human hERG receptors. BV-9238 did not show any significant inhibition of these enzymes at the tested dose. All these suggest that BV-9238 has potential as a safe and well tolerated anti-inflammatory formulation for future use.

  14. Classification Model for Forest Fire Hotspot Occurrences Prediction Using ANFIS Algorithm

    Science.gov (United States)

    Wijayanto, A. K.; Sani, O.; Kartika, N. D.; Herdiyeni, Y.

    2017-01-01

    This study proposed the application of data mining technique namely Adaptive Neuro-Fuzzy inference system (ANFIS) on forest fires hotspot data to develop classification models for hotspots occurrence in Central Kalimantan. Hotspot is a point that is indicated as the location of fires. In this study, hotspot distribution is categorized as true alarm and false alarm. ANFIS is a soft computing method in which a given inputoutput data set is expressed in a fuzzy inference system (FIS). The FIS implements a nonlinear mapping from its input space to the output space. The method of this study classified hotspots as target objects by correlating spatial attributes data using three folds in ANFIS algorithm to obtain the best model. The best result obtained from the 3rd fold provided low error for training (error = 0.0093676) and also low error testing result (error = 0.0093676). Attribute of distance to road is the most determining factor that influences the probability of true and false alarm where the level of human activities in this attribute is higher. This classification model can be used to develop early warning system of forest fire.

  15. Assimilation of a knowledge base and physical models to reduce errors in passive-microwave classifications of sea ice

    Science.gov (United States)

    Maslanik, J. A.; Key, J.

    1992-01-01

    An expert system framework has been developed to classify sea ice types using satellite passive microwave data, an operational classification algorithm, spatial and temporal information, ice types estimated from a dynamic-thermodynamic model, output from a neural network that detects the onset of melt, and knowledge about season and region. The rule base imposes boundary conditions upon the ice classification, modifies parameters in the ice algorithm, determines a `confidence' measure for the classified data, and under certain conditions, replaces the algorithm output with model output. Results demonstrate the potential power of such a system for minimizing overall error in the classification and for providing non-expert data users with a means of assessing the usefulness of the classification results for their applications.

  16. Sensitivity analysis of the GEMS soil organic carbon model to land cover land use classification uncertainties under different climate scenarios in Senegal

    Science.gov (United States)

    Dieye, A.M.; Roy, David P.; Hanan, N.P.; Liu, S.; Hansen, M.; Toure, A.

    2012-01-01

    Spatially explicit land cover land use (LCLU) change information is needed to drive biogeochemical models that simulate soil organic carbon (SOC) dynamics. Such information is increasingly being mapped using remotely sensed satellite data with classification schemes and uncertainties constrained by the sensing system, classification algorithms and land cover schemes. In this study, automated LCLU classification of multi-temporal Landsat satellite data were used to assess the sensitivity of SOC modeled by the Global Ensemble Biogeochemical Modeling System (GEMS). The GEMS was run for an area of 1560 km2 in Senegal under three climate change scenarios with LCLU maps generated using different Landsat classification approaches. This research provides a method to estimate the variability of SOC, specifically the SOC uncertainty due to satellite classification errors, which we show is dependent not only on the LCLU classification errors but also on where the LCLU classes occur relative to the other GEMS model inputs.

  17. Fuzzy Continuous Review Inventory Model using ABC Multi-Criteria Classification Approach: A Single Case Study

    Directory of Open Access Journals (Sweden)

    Meriastuti - Ginting

    2015-07-01

    Full Text Available Abstract. Inventory is considered as the most expensive, yet important,to any companies. It representsapproximately 50% of the total investment. Inventory cost has become one of the majorcontributorsto inefficiency, therefore it should be managed effectively. This study aims to propose an alternative inventory model,  by using ABC multi-criteria classification approach to minimize total cost. By combining FANP (Fuzzy Analytical Network Process and TOPSIS (Technique of Order Preferences by Similarity to the Ideal Solution, the ABC multi-criteria classification approach identified 12 items of 69 inventory items as “outstanding important class” that contributed to 80% total inventory cost. This finding  is then used as the basis to determine the proposed continuous review inventory model.This study found that by using fuzzy trapezoidal cost, the inventory  turnover ratio can be increased, and inventory cost can be decreased by 78% for each item in “class A” inventory.Keywords:ABC multi-criteria classification, FANP-TOPSIS, continuous review inventory model lead-time demand distribution, trapezoidal fuzzy number 

  18. Butterfly Classification by HSI and RGB Color Models Using Neural Networks

    Directory of Open Access Journals (Sweden)

    Jorge E. Grajales-Múnera

    2013-11-01

    Full Text Available This study aims the classification of Butterfly species through the implementation of Neural Networks and Image Processing. A total of 9 species of Morpho genre which has blue as a characteristic color are processed. For Butterfly segmentation we used image processing tools such as: Binarization, edge processing and mathematical morphology. For data processing RGB values are obtained for every image which are converted to HSI color model to identify blue pixels and obtain the data to the proposed Neural Networks: Back-Propagation and Perceptron. For analysis and verification of results confusion matrix are built and analyzed with the results of neural networks with the lowest error levels. We obtain error levels close to 1% in classification of some Butterfly species.

  19. Educational Objectives and the Learning Domains: A New Formulation [And] Summary: Pierce-Gray Classification Model for the Cognitive, Affective and Psychomotor Domains.

    Science.gov (United States)

    Gray, Charles E.; Pierce, Walter D.

    This paper examines and summarizes the "Pierce-Gray Classification Model for the Cognitive, Affective, and Psychomotor Domains," a model developed for the classification of educational objectives. The classification system was developed to provide a framework that teachers could use as a guide when developing specific instructional objectives for…

  20. Binary classification of dyslipidemia from the waist-to-hip ratio and body mass index: a comparison of linear, logistic, and CART models

    Directory of Open Access Journals (Sweden)

    Paccaud Fred

    2004-04-01

    Full Text Available Abstract Background We sought to improve upon previously published statistical modeling strategies for binary classification of dyslipidemia for general population screening purposes based on the waist-to-hip circumference ratio and body mass index anthropometric measurements. Methods Study subjects were participants in WHO-MONICA population-based surveys conducted in two Swiss regions. Outcome variables were based on the total serum cholesterol to high density lipoprotein cholesterol ratio. The other potential predictor variables were gender, age, current cigarette smoking, and hypertension. The models investigated were: (i linear regression; (ii logistic classification; (iii regression trees; (iv classification trees (iii and iv are collectively known as "CART". Binary classification performance of the region-specific models was externally validated by classifying the subjects from the other region. Results Waist-to-hip circumference ratio and body mass index remained modest predictors of dyslipidemia. Correct classification rates for all models were 60–80%, with marked gender differences. Gender-specific models provided only small gains in classification. The external validations provided assurance about the stability of the models. Conclusions There were no striking differences between either the algebraic (i, ii vs. non-algebraic (iii, iv, or the regression (i, iii vs. classification (ii, iv modeling approaches. Anticipated advantages of the CART vs. simple additive linear and logistic models were less than expected in this particular application with a relatively small set of predictor variables. CART models may be more useful when considering main effects and interactions between larger sets of predictor variables.

  1. Model of high-tech businesses management under the trends of explicit and implicit knowledge markets: classification and business model

    Directory of Open Access Journals (Sweden)

    Guzel Isayevna Gumerova

    2015-03-01

    Full Text Available Objective to define the notion of ldquohightech businessrdquo to elaborate classification of hightech businesses to elaborate the business model for hightech business management. Methods general scientific methods of theoretical and empirical cognition. Results the research presents a business model of hightech businesses management basing on the trends of explicit and explicit knowledge market with the dominating implicit knowledge market classification of hightech businesses taking into consideration the three types of economic activity possibilities to manage hightech business basing on its market cost technological innovations costs and business indicators. Scientific novelty the interpretation of the notion of ldquohightech businessrdquo has been renewed the classification of hightech businesses has been elaborated for the first time allocating three groups of enterprises. Practical value theoretical significance ndash development of notional apparatus of hightech business management practical significancenbsp ndash grounding of the necessity to manage enterprises under development of explicit and explicit knowledge markets in Russia as a complex of capital and noncapital assets with dominating indicators of ldquomarket valuerdquo and ldquolife span of a companyrdquo. nbsp

  2. The Pediatric Home Care/Expenditure Classification Model (P/ECM): A Home Care Case-Mix Model for Children Facing Special Health Care Challenges

    Science.gov (United States)

    Phillips, Charles D.

    2015-01-01

    Case-mix classification and payment systems help assure that persons with similar needs receive similar amounts of care resources, which is a major equity concern for consumers, providers, and programs. Although health service programs for adults regularly use case-mix payment systems, programs providing health services to children and youth rarely use such models. This research utilized Medicaid home care expenditures and assessment data on 2,578 children receiving home care in one large state in the USA. Using classification and regression tree analyses, a case-mix model for long-term pediatric home care was developed. The Pediatric Home Care/Expenditure Classification Model (P/ECM) grouped children and youth in the study sample into 24 groups, explaining 41% of the variance in annual home care expenditures. The P/ECM creates the possibility of a more equitable, and potentially more effective, allocation of home care resources among children and youth facing serious health care challenges. PMID:26740744

  3. The Pediatric Home Care/Expenditure Classification Model (P/ECM): A Home Care Case-Mix Model for Children Facing Special Health Care Challenges.

    Science.gov (United States)

    Phillips, Charles D

    2015-01-01

    Case-mix classification and payment systems help assure that persons with similar needs receive similar amounts of care resources, which is a major equity concern for consumers, providers, and programs. Although health service programs for adults regularly use case-mix payment systems, programs providing health services to children and youth rarely use such models. This research utilized Medicaid home care expenditures and assessment data on 2,578 children receiving home care in one large state in the USA. Using classification and regression tree analyses, a case-mix model for long-term pediatric home care was developed. The Pediatric Home Care/Expenditure Classification Model (P/ECM) grouped children and youth in the study sample into 24 groups, explaining 41% of the variance in annual home care expenditures. The P/ECM creates the possibility of a more equitable, and potentially more effective, allocation of home care resources among children and youth facing serious health care challenges.

  4. The Pediatric Home Care/Expenditure Classification Model (P/ECM: A Home Care Case-Mix Model for Children Facing Special Health Care Challenges

    Directory of Open Access Journals (Sweden)

    Charles D. Phillips

    2015-01-01

    Full Text Available Case-mix classification and payment systems help assure that persons with similar needs receive similar amounts of care resources, which is a major equity concern for consumers, providers, and programs. Although health service programs for adults regularly use case-mix payment systems, programs providing health services to children and youth rarely use such models. This research utilized Medicaid home care expenditures and assessment data on 2,578 children receiving home care in one large state in the USA. Using classification and regression tree analyses, a case-mix model for long-term pediatric home care was developed. The Pediatric Home Care/Expenditure Classification Model (P/ECM grouped children and youth in the study sample into 24 groups, explaining 41% of the variance in annual home care expenditures. The P/ECM creates the possibility of a more equitable, and potentially more effective, allocation of home care resources among children and youth facing serious health care challenges.

  5. Classification of proteins: available structural space for molecular modeling.

    Science.gov (United States)

    Andreeva, Antonina

    2012-01-01

    The wealth of available protein structural data provides unprecedented opportunity to study and better understand the underlying principles of protein folding and protein structure evolution. A key to achieving this lies in the ability to analyse these data and to organize them in a coherent classification scheme. Over the past years several protein classifications have been developed that aim to group proteins based on their structural relationships. Some of these classification schemes explore the concept of structural neighbourhood (structural continuum), whereas other utilize the notion of protein evolution and thus provide a discrete rather than continuum view of protein structure space. This chapter presents a strategy for classification of proteins with known three-dimensional structure. Steps in the classification process along with basic definitions are introduced. Examples illustrating some fundamental concepts of protein folding and evolution with a special focus on the exceptions to them are presented.

  6. A Categorical Framework for Model Classification in the Geosciences

    Science.gov (United States)

    Hauhs, Michael; Trancón y Widemann, Baltasar; Lange, Holger

    2016-04-01

    Models have a mixed record of success in the geosciences. In meteorology, model development and implementation has been among the first and most successful examples of triggering computer technology in science. On the other hand, notorious problems such as the 'equifinality issue' in hydrology lead to a rather mixed reputation of models in other areas. The most successful models in geosciences are applications of dynamic systems theory to non-living systems or phenomena. Thus, we start from the hypothesis that the success of model applications relates to the influence of life on the phenomenon under study. We thus focus on the (formal) representation of life in models. The aim is to investigate whether disappointment in model performance is due to system properties such as heterogeneity and historicity of ecosystems, or rather reflects an abstraction and formalisation problem at a fundamental level. As a formal framework for this investigation, we use category theory as applied in computer science to specify behaviour at an interface. Its methods have been developed for translating and comparing formal structures among different application areas and seems highly suited for a classification of the current "model zoo" in the geosciences. The approach is rather abstract, with a high degree of generality but a low level of expressibility. Here, category theory will be employed to check the consistency of assumptions about life in different models. It will be shown that it is sufficient to distinguish just four logical cases to check for consistency of model content. All four cases can be formalised as variants of coalgebra-algebra homomorphisms. It can be demonstrated that transitions between the four variants affect the relevant observations (time series or spatial maps), the formalisms used (equations, decision trees) and the test criteria of success (prediction, classification) of the resulting model types. We will present examples from hydrology and ecology in

  7. Use of circulation types classifications to evaluate AR4 climate models over the Euro-Atlantic region

    Energy Technology Data Exchange (ETDEWEB)

    Pastor, M.A.; Casado, M.J. [Agencia Estatal de Meteorologia (AEMET), Madrid (Spain)

    2012-10-15

    This paper presents an evaluation of the multi-model simulations for the 4th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) in terms of their ability to simulate the ERA40 circulation types over the Euro-Atlantic region in winter season. Two classification schemes, k-means and SANDRA, have been considered to test the sensitivity of the evaluation results to the classification procedure. The assessment allows establishing different rankings attending spatial and temporal features of the circulation types. Regarding temporal characteristics, in general, all AR4 models tend to underestimate the frequency of occurrence. The best model simulating spatial characteristics is the UKMO-HadGEM1 whereas CCSM3, UKMO-HadGEM1 and CGCM3.1(T63) are the best simulating the temporal features, for both classification schemes. This result agrees with the AR4 models ranking obtained when having analysed the ability of the same AR4 models to simulate Euro-Atlantic variability modes. This study has proved the utility of applying such a synoptic climatology approach as a diagnostic tool for models' assessment. The ability of the models to properly reproduce the position of ridges and troughs and the frequency of synoptic patterns, will therefore improve our confidence in the response of models to future climate changes. (orig.)

  8. Optimal Non-Invasive Fault Classification Model for Packaged Ceramic Tile Quality Monitoring Using MMW Imaging

    Science.gov (United States)

    Agarwal, Smriti; Singh, Dharmendra

    2016-04-01

    Millimeter wave (MMW) frequency has emerged as an efficient tool for different stand-off imaging applications. In this paper, we have dealt with a novel MMW imaging application, i.e., non-invasive packaged goods quality estimation for industrial quality monitoring applications. An active MMW imaging radar operating at 60 GHz has been ingeniously designed for concealed fault estimation. Ceramic tiles covered with commonly used packaging cardboard were used as concealed targets for undercover fault classification. A comparison of computer vision-based state-of-the-art feature extraction techniques, viz, discrete Fourier transform (DFT), wavelet transform (WT), principal component analysis (PCA), gray level co-occurrence texture (GLCM), and histogram of oriented gradient (HOG) has been done with respect to their efficient and differentiable feature vector generation capability for undercover target fault classification. An extensive number of experiments were performed with different ceramic tile fault configurations, viz., vertical crack, horizontal crack, random crack, diagonal crack along with the non-faulty tiles. Further, an independent algorithm validation was done demonstrating classification accuracy: 80, 86.67, 73.33, and 93.33 % for DFT, WT, PCA, GLCM, and HOG feature-based artificial neural network (ANN) classifier models, respectively. Classification results show good capability for HOG feature extraction technique towards non-destructive quality inspection with appreciably low false alarm as compared to other techniques. Thereby, a robust and optimal image feature-based neural network classification model has been proposed for non-invasive, automatic fault monitoring for a financially and commercially competent industrial growth.

  9. Rapid Identification of Asteraceae Plants with Improved RBF-ANN Classification Models Based on MOS Sensor E-Nose

    Directory of Open Access Journals (Sweden)

    Hui-Qin Zou

    2014-01-01

    Full Text Available Plants from Asteraceae family are widely used as herbal medicines and food ingredients, especially in Asian area. Therefore, authentication and quality control of these different Asteraceae plants are important for ensuring consumers’ safety and efficacy. In recent decades, electronic nose (E-nose has been studied as an alternative approach. In this paper, we aim to develop a novel discriminative model by improving radial basis function artificial neural network (RBF-ANN classification model. Feature selection algorithms, including principal component analysis (PCA and BestFirst + CfsSubsetEval (BC, were applied in the improvement of RBF-ANN models. Results illustrate that in the improved RBF-ANN models with lower dimension data classification accuracies (100% remained the same as in the original model with higher-dimension data. It is the first time to introduce feature selection methods to get valuable information on how to attribute more relevant MOS sensors; namely, in this case, S1, S3, S4, S6, and S7 show better capability to distinguish these Asteraceae plants. This paper also gives insights to further research in this area, for instance, sensor array optimization and performance improvement of classification model.

  10. Classification of integrable two-dimensional models of relativistic field theory by means of computer

    International Nuclear Information System (INIS)

    Getmanov, B.S.

    1988-01-01

    The results of classification of two-dimensional relativistic field models (1) spinor; (2) essentially-nonlinear scalar) possessing higher conservation laws using the system of symbolic computer calculations are presented shortly

  11. Tissue Classification

    DEFF Research Database (Denmark)

    Van Leemput, Koen; Puonti, Oula

    2015-01-01

    Computational methods for automatically segmenting magnetic resonance images of the brain have seen tremendous advances in recent years. So-called tissue classification techniques, aimed at extracting the three main brain tissue classes (white matter, gray matter, and cerebrospinal fluid), are now...... well established. In their simplest form, these methods classify voxels independently based on their intensity alone, although much more sophisticated models are typically used in practice. This article aims to give an overview of often-used computational techniques for brain tissue classification...

  12. Sentiment classification technology based on Markov logic networks

    Science.gov (United States)

    He, Hui; Li, Zhigang; Yao, Chongchong; Zhang, Weizhe

    2016-07-01

    With diverse online media emerging, there is a growing concern of sentiment classification problem. At present, text sentiment classification mainly utilizes supervised machine learning methods, which feature certain domain dependency. On the basis of Markov logic networks (MLNs), this study proposed a cross-domain multi-task text sentiment classification method rooted in transfer learning. Through many-to-one knowledge transfer, labeled text sentiment classification, knowledge was successfully transferred into other domains, and the precision of the sentiment classification analysis in the text tendency domain was improved. The experimental results revealed the following: (1) the model based on a MLN demonstrated higher precision than the single individual learning plan model. (2) Multi-task transfer learning based on Markov logical networks could acquire more knowledge than self-domain learning. The cross-domain text sentiment classification model could significantly improve the precision and efficiency of text sentiment classification.

  13. Development of classification models to detect Salmonella Enteritidis and Salmonella Typhimurium found in poultry carcass rinses by visible-near infrared hyperspectral imaging

    Science.gov (United States)

    Seo, Young Wook; Yoon, Seung Chul; Park, Bosoon; Hinton, Arthur; Windham, William R.; Lawrence, Kurt C.

    2013-05-01

    Salmonella is a major cause of foodborne disease outbreaks resulting from the consumption of contaminated food products in the United States. This paper reports the development of a hyperspectral imaging technique for detecting and differentiating two of the most common Salmonella serotypes, Salmonella Enteritidis (SE) and Salmonella Typhimurium (ST), from background microflora that are often found in poultry carcass rinse. Presumptive positive screening of colonies with a traditional direct plating method is a labor intensive and time consuming task. Thus, this paper is concerned with the detection of differences in spectral characteristics among the pure SE, ST, and background microflora grown on brilliant green sulfa (BGS) and xylose lysine tergitol 4 (XLT4) agar media with a spread plating technique. Visible near-infrared hyperspectral imaging, providing the spectral and spatial information unique to each microorganism, was utilized to differentiate SE and ST from the background microflora. A total of 10 classification models, including five machine learning algorithms, each without and with principal component analysis (PCA), were validated and compared to find the best model in classification accuracy. The five machine learning (classification) algorithms used in this study were Mahalanobis distance (MD), k-nearest neighbor (kNN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM). The average classification accuracy of all 10 models on a calibration (or training) set of the pure cultures on BGS agar plates was 98% (Kappa coefficient = 0.95) in determining the presence of SE and/or ST although it was difficult to differentiate between SE and ST. The average classification accuracy of all 10 models on a training set for ST detection on XLT4 agar was over 99% (Kappa coefficient = 0.99) although SE colonies on XLT4 agar were difficult to differentiate from background microflora. The average classification

  14. Objective classification of latent behavioral states in bio-logging data using multivariate-normal hidden Markov models.

    Science.gov (United States)

    Phillips, Joe Scutt; Patterson, Toby A; Leroy, Bruno; Pilling, Graham M; Nicol, Simon J

    2015-07-01

    Analysis of complex time-series data from ecological system study requires quantitative tools for objective description and classification. These tools must take into account largely ignored problems of bias in manual classification, autocorrelation, and noise. Here we describe a method using existing estimation techniques for multivariate-normal hidden Markov models (HMMs) to develop such a classification. We use high-resolution behavioral data from bio-loggers attached to free-roaming pelagic tuna as an example. Observed patterns are assumed to be generated by an unseen Markov process that switches between several multivariate-normal distributions. Our approach is assessed in two parts. The first uses simulation experiments, from which the ability of the HMM to estimate known parameter values is examined using artificial time series of data consistent with hypotheses about pelagic predator foraging ecology. The second is the application to time series of continuous vertical movement data from yellowfin and bigeye tuna taken from tuna tagging experiments. These data were compressed into summary metrics capturing the variation of patterns in diving behavior and formed into a multivariate time series used to estimate a HMM. Each observation was associated with covariate information incorporating the effect of day and night on behavioral switching. Known parameter values were well recovered by the HMMs in our simulation experiments, resulting in mean correct classification rates of 90-97%, although some variance-covariance parameters were estimated less accurately. HMMs with two distinct behavioral states were selected for every time series of real tuna data, predicting a shallow warm state, which was similar across all individuals, and a deep colder state, which was more variable. Marked diurnal behavioral switching was predicted, consistent with many previous empirical studies on tuna. HMMs provide easily interpretable models for the objective classification of

  15. Classification of Building Object Types

    DEFF Research Database (Denmark)

    Jørgensen, Kaj Asbjørn

    2011-01-01

    made. This is certainly the case in the Danish development. Based on the theories about these abstraction mechanisms, the basic principles for classification systems are presented and the observed misconceptions are analyses and explained. Furthermore, it is argued that the purpose of classification...... systems has changed and that new opportunities should be explored. Some proposals for new applications are presented and carefully aligned with IT opportunities. Especially, the use of building modelling will give new benefits and many of the traditional uses of classification systems will instead...... be managed by software applications and on the basis of building models. Classification systems with taxonomies of building object types have many application opportunities but can still be beneficial in data exchange between building construction partners. However, this will be performed by new methods...

  16. Classification of NLO operators for composite Higgs models

    Science.gov (United States)

    Alanne, Tommi; Bizot, Nicolas; Cacciapaglia, Giacomo; Sannino, Francesco

    2018-04-01

    We provide a general classification of template operators, up to next-to-leading order, that appear in chiral perturbation theories based on the two flavor patterns of spontaneous symmetry breaking SU (NF)/Sp (NF) and SU (NF)/SO (NF). All possible explicit-breaking sources parametrized by spurions transforming in the fundamental and in the two-index representations of the flavor symmetry are included. While our general framework can be applied to any model of strong dynamics, we specialize to composite-Higgs models, where the main explicit breaking sources are a current mass, the gauging of flavor symmetries, and the Yukawa couplings (for the top). For the top, we consider both bilinear couplings and linear ones à la partial compositeness. Our templates provide a basis for lattice calculations in specific models. As a special example, we consider the SU (4 )/Sp (4 )≅SO (6 )/SO (5 ) pattern which corresponds to the minimal fundamental composite-Higgs model. We further revisit issues related to the misalignment of the vacuum. In particular, we shed light on the physical properties of the singlet η , showing that it cannot develop a vacuum expectation value without explicit C P violation in the underlying theory.

  17. In Vivo Mouse Intervertebral Disc Degeneration Model Based on a New Histological Classification.

    Directory of Open Access Journals (Sweden)

    Takashi Ohnishi

    Full Text Available Although human intervertebral disc degeneration can lead to several spinal diseases, its pathogenesis remains unclear. This study aimed to create a new histological classification applicable to an in vivo mouse intervertebral disc degeneration model induced by needle puncture. One hundred six mice were operated and the L4/5 intervertebral disc was punctured with a 35- or 33-gauge needle. Micro-computed tomography scanning was performed, and the punctured region was confirmed. Evaluation was performed by using magnetic resonance imaging and histology by employing our classification scoring system. Our histological classification scores correlated well with the findings of magnetic resonance imaging and could detect degenerative progression, irrespective of the punctured region. However, the magnetic resonance imaging analysis revealed that there was no significant degenerative intervertebral disc change between the ventrally punctured and non-punctured control groups. To induce significant degeneration in the lumbar intervertebral discs, the central or dorsal region should be punctured instead of the ventral region.

  18. Enhancing Classification Performance of Functional Near-Infrared Spectroscopy- Brain–Computer Interface Using Adaptive Estimation of General Linear Model Coefficients

    Directory of Open Access Journals (Sweden)

    Nauman Khalid Qureshi

    2017-07-01

    Full Text Available In this paper, a novel methodology for enhanced classification of functional near-infrared spectroscopy (fNIRS signals utilizable in a two-class [motor imagery (MI and rest; mental rotation (MR and rest] brain–computer interface (BCI is presented. First, fNIRS signals corresponding to MI and MR are acquired from the motor and prefrontal cortex, respectively, afterward, filtered to remove physiological noises. Then, the signals are modeled using the general linear model, the coefficients of which are adaptively estimated using the least squares technique. Subsequently, multiple feature combinations of estimated coefficients were used for classification. The best classification accuracies achieved for five subjects, for MI versus rest are 79.5, 83.7, 82.6, 81.4, and 84.1% whereas those for MR versus rest are 85.5, 85.2, 87.8, 83.7, and 84.8%, respectively, using support vector machine. These results are compared with the best classification accuracies obtained using the conventional hemodynamic response. By means of the proposed methodology, the average classification accuracy obtained was significantly higher (p < 0.05. These results serve to demonstrate the feasibility of developing a high-classification-performance fNIRS-BCI.

  19. First steps towards a state classification in the random-field Ising model

    International Nuclear Information System (INIS)

    Basso, Vittorio; Magni, Alessandro; Bertotti, Giorgio

    2006-01-01

    The properties of locally stable states of the random-field Ising model are studied. A map is defined for the dynamics driven by the field starting from a locally stable state. The fixed points of the map are connected with the limit hysteresis loops that appear in the classification of the states

  20. Learning Supervised Topic Models for Classification and Regression from Crowds.

    Science.gov (United States)

    Rodrigues, Filipe; Lourenco, Mariana; Ribeiro, Bernardete; Pereira, Francisco C

    2017-12-01

    The growing need to analyze large collections of documents has led to great developments in topic modeling. Since documents are frequently associated with other related variables, such as labels or ratings, much interest has been placed on supervised topic models. However, the nature of most annotation tasks, prone to ambiguity and noise, often with high volumes of documents, deem learning under a single-annotator assumption unrealistic or unpractical for most real-world applications. In this article, we propose two supervised topic models, one for classification and another for regression problems, which account for the heterogeneity and biases among different annotators that are encountered in practice when learning from crowds. We develop an efficient stochastic variational inference algorithm that is able to scale to very large datasets, and we empirically demonstrate the advantages of the proposed model over state-of-the-art approaches.

  1. Estimating Classification Errors under Edit Restrictions in Composite Survey-Register Data Using Multiple Imputation Latent Class Modelling (MILC)

    NARCIS (Netherlands)

    Boeschoten, Laura; Oberski, Daniel; De Waal, Ton

    2017-01-01

    Both registers and surveys can contain classification errors. These errors can be estimated by making use of a composite data set. We propose a new method based on latent class modelling to estimate the number of classification errors across several sources while taking into account impossible

  2. Setting a generalized functional linear model (GFLM for the classification of different types of cancer

    Directory of Open Access Journals (Sweden)

    Miguel Flores

    2016-11-01

    Full Text Available This work aims to classify the DNA sequences of healthy and malignant cancer respectively. For this, supervised and unsupervised classification methods from a functional context are used; i.e. each strand of DNA is an observation. The observations are discretized, for that reason different ways to represent these observations with functions are evaluated. In addition, an exploratory study is done: estimating the mean and variance of each functional type of cancer. For the unsupervised classification method, hierarchical clustering with different measures of functional distance is used. On the other hand, for the supervised classification method, a functional generalized linear model is used. For this model the first and second derivatives are used which are included as discriminating variables. It has been verified that one of the advantages of working in the functional context is to obtain a model to correctly classify cancers by 100%. For the implementation of the methods it has been used the fda.usc R package that includes all the techniques of functional data analysis used in this work. In addition, some that have been developed in recent decades. For more details of these techniques can be consulted Ramsay, J. O. and Silverman (2005 and Ferraty et al. (2006.

  3. Comparison of models of automatic classification of textural patterns of mineral presents in Colombian coals

    International Nuclear Information System (INIS)

    Lopez Carvajal, Jaime; Branch Bedoya, John Willian

    2005-01-01

    The automatic classification of objects is a very interesting approach under several problem domains. This paper outlines some results obtained under different classification models to categorize textural patterns of minerals using real digital images. The data set used was characterized by a small size and noise presence. The implemented models were the Bayesian classifier, Neural Network (2-5-1), support vector machine, decision tree and 3-nearest neighbors. The results after applying crossed validation show that the Bayesian model (84%) proved better predictive capacity than the others, mainly due to its noise robustness behavior. The neuronal network (68%) and the SVM (67%) gave promising results, because they could be improved increasing the data amount used, while the decision tree (55%) and K-NN (54%) did not seem to be adequate for this problem, because of their sensibility to noise

  4. Fuzzy classification of phantom parent groups in an animal model

    Directory of Open Access Journals (Sweden)

    Fikse Freddy

    2009-09-01

    Full Text Available Abstract Background Genetic evaluation models often include genetic groups to account for unequal genetic level of animals with unknown parentage. The definition of phantom parent groups usually includes a time component (e.g. years. Combining several time periods to ensure sufficiently large groups may create problems since all phantom parents in a group are considered contemporaries. Methods To avoid the downside of such distinct classification, a fuzzy logic approach is suggested. A phantom parent can be assigned to several genetic groups, with proportions between zero and one that sum to one. Rules were presented for assigning coefficients to the inverse of the relationship matrix for fuzzy-classified genetic groups. This approach was illustrated with simulated data from ten generations of mass selection. Observations and pedigree records were randomly deleted. Phantom parent groups were defined on the basis of gender and generation number. In one scenario, uncertainty about generation of birth was simulated for some animals with unknown parents. In the distinct classification, one of the two possible generations of birth was randomly chosen to assign phantom parents to genetic groups for animals with simulated uncertainty, whereas the phantom parents were assigned to both possible genetic groups in the fuzzy classification. Results The empirical prediction error variance (PEV was somewhat lower for fuzzy-classified genetic groups. The ranking of animals with unknown parents was more correct and less variable across replicates in comparison with distinct genetic groups. In another scenario, each phantom parent was assigned to three groups, one pertaining to its gender, and two pertaining to the first and last generation, with proportion depending on the (true generation of birth. Due to the lower number of groups, the empirical PEV of breeding values was smaller when genetic groups were fuzzy-classified. Conclusion Fuzzy-classification

  5. Maximum mutual information regularized classification

    KAUST Repository

    Wang, Jim Jing-Yan

    2014-09-07

    In this paper, a novel pattern classification approach is proposed by regularizing the classifier learning to maximize mutual information between the classification response and the true class label. We argue that, with the learned classifier, the uncertainty of the true class label of a data sample should be reduced by knowing its classification response as much as possible. The reduced uncertainty is measured by the mutual information between the classification response and the true class label. To this end, when learning a linear classifier, we propose to maximize the mutual information between classification responses and true class labels of training samples, besides minimizing the classification error and reducing the classifier complexity. An objective function is constructed by modeling mutual information with entropy estimation, and it is optimized by a gradient descend method in an iterative algorithm. Experiments on two real world pattern classification problems show the significant improvements achieved by maximum mutual information regularization.

  6. Maximum mutual information regularized classification

    KAUST Repository

    Wang, Jim Jing-Yan; Wang, Yi; Zhao, Shiguang; Gao, Xin

    2014-01-01

    In this paper, a novel pattern classification approach is proposed by regularizing the classifier learning to maximize mutual information between the classification response and the true class label. We argue that, with the learned classifier, the uncertainty of the true class label of a data sample should be reduced by knowing its classification response as much as possible. The reduced uncertainty is measured by the mutual information between the classification response and the true class label. To this end, when learning a linear classifier, we propose to maximize the mutual information between classification responses and true class labels of training samples, besides minimizing the classification error and reducing the classifier complexity. An objective function is constructed by modeling mutual information with entropy estimation, and it is optimized by a gradient descend method in an iterative algorithm. Experiments on two real world pattern classification problems show the significant improvements achieved by maximum mutual information regularization.

  7. Learning Supervised Topic Models for Classification and Regression from Crowds

    DEFF Research Database (Denmark)

    Rodrigues, Filipe; Lourenco, Mariana; Ribeiro, Bernardete

    2017-01-01

    problems, which account for the heterogeneity and biases among different annotators that are encountered in practice when learning from crowds. We develop an efficient stochastic variational inference algorithm that is able to scale to very large datasets, and we empirically demonstrate the advantages...... annotation tasks, prone to ambiguity and noise, often with high volumes of documents, deem learning under a single-annotator assumption unrealistic or unpractical for most real-world applications. In this article, we propose two supervised topic models, one for classification and another for regression...

  8. TENSOR MODELING BASED FOR AIRBORNE LiDAR DATA CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    N. Li

    2016-06-01

    Full Text Available Feature selection and description is a key factor in classification of Earth observation data. In this paper a classification method based on tensor decomposition is proposed. First, multiple features are extracted from raw LiDAR point cloud, and raster LiDAR images are derived by accumulating features or the “raw” data attributes. Then, the feature rasters of LiDAR data are stored as a tensor, and tensor decomposition is used to select component features. This tensor representation could keep the initial spatial structure and insure the consideration of the neighborhood. Based on a small number of component features a k nearest neighborhood classification is applied.

  9. A classification of marked hijaiyah letters' pronunciation using hidden Markov model

    Science.gov (United States)

    Wisesty, Untari N.; Mubarok, M. Syahrul; Adiwijaya

    2017-08-01

    Hijaiyah letters are the letters that arrange the words in Al Qur'an consisting of 28 letters. They symbolize the consonant sounds. On the other hand, the vowel sounds are symbolized by harokat/marks. Speech recognition system is a system used to process the sound signal to be data so that it can be recognized by computer. To build the system, some stages are needed i.e characteristics/feature extraction and classification. In this research, LPC and MFCC extraction method, K-Means Quantization vector and Hidden Markov Model classification are used. The data used are the 28 letters and 6 harakat with the total class of 168. After several are testing done, it can be concluded that the system can recognize the pronunciation pattern of marked hijaiyah letter very well in the training data with its highest accuracy of 96.1% using the feature of LPC extraction and 94% using the MFCC. Meanwhile, when testing system is used, the accuracy decreases up to 41%.

  10. Featureless classification of light curves

    Science.gov (United States)

    Kügler, S. D.; Gianniotis, N.; Polsterer, K. L.

    2015-08-01

    In the era of rapidly increasing amounts of time series data, classification of variable objects has become the main objective of time-domain astronomy. Classification of irregularly sampled time series is particularly difficult because the data cannot be represented naturally as a vector which can be directly fed into a classifier. In the literature, various statistical features serve as vector representations. In this work, we represent time series by a density model. The density model captures all the information available, including measurement errors. Hence, we view this model as a generalization to the static features which directly can be derived, e.g. as moments from the density. Similarity between each pair of time series is quantified by the distance between their respective models. Classification is performed on the obtained distance matrix. In the numerical experiments, we use data from the OGLE (Optical Gravitational Lensing Experiment) and ASAS (All Sky Automated Survey) surveys and demonstrate that the proposed representation performs up to par with the best currently used feature-based approaches. The density representation preserves all static information present in the observational data, in contrast to a less-complete description by features. The density representation is an upper boundary in terms of information made available to the classifier. Consequently, the predictive power of the proposed classification depends on the choice of similarity measure and classifier, only. Due to its principled nature, we advocate that this new approach of representing time series has potential in tasks beyond classification, e.g. unsupervised learning.

  11. AdOn HDP-HMM: An Adaptive Online Model for Segmentation and Classification of Sequential Data.

    Science.gov (United States)

    Bargi, Ava; Xu, Richard Yi Da; Piccardi, Massimo

    2017-09-21

    Recent years have witnessed an increasing need for the automated classification of sequential data, such as activities of daily living, social media interactions, financial series, and others. With the continuous flow of new data, it is critical to classify the observations on-the-fly and without being limited by a predetermined number of classes. In addition, a model should be able to update its parameters in response to a possible evolution in the distributions of the classes. This compelling problem, however, does not seem to have been adequately addressed in the literature, since most studies focus on offline classification over predefined class sets. In this paper, we present a principled solution for this problem based on an adaptive online system leveraging Markov switching models and hierarchical Dirichlet process priors. This adaptive online approach is capable of classifying the sequential data over an unlimited number of classes while meeting the memory and delay constraints typical of streaming contexts. In this paper, we introduce an adaptive ''learning rate'' that is responsible for balancing the extent to which the model retains its previous parameters or adapts to new observations. Experimental results on stationary and evolving synthetic data and two video data sets, TUM Assistive Kitchen and collated Weizmann, show a remarkable performance in terms of segmentation and classification, particularly for sequences from evolutionary distributions and/or those containing previously unseen classes.

  12. PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

    Energy Technology Data Exchange (ETDEWEB)

    Lochner, Michelle; Peiris, Hiranya V.; Lahav, Ofer; Winter, Max K. [Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT (United Kingdom); McEwen, Jason D., E-mail: dr.michelle.lochner@gmail.com [Mullard Space Science Laboratory, University College London, Surrey RH5 6NT (United Kingdom)

    2016-08-01

    Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k -nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.

  13. PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

    International Nuclear Information System (INIS)

    Lochner, Michelle; Peiris, Hiranya V.; Lahav, Ofer; Winter, Max K.; McEwen, Jason D.

    2016-01-01

    Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k -nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.

  14. Generalized outcome-based strategy classification: comparing deterministic and probabilistic choice models.

    Science.gov (United States)

    Hilbig, Benjamin E; Moshagen, Morten

    2014-12-01

    Model comparisons are a vital tool for disentangling which of several strategies a decision maker may have used--that is, which cognitive processes may have governed observable choice behavior. However, previous methodological approaches have been limited to models (i.e., decision strategies) with deterministic choice rules. As such, psychologically plausible choice models--such as evidence-accumulation and connectionist models--that entail probabilistic choice predictions could not be considered appropriately. To overcome this limitation, we propose a generalization of Bröder and Schiffer's (Journal of Behavioral Decision Making, 19, 361-380, 2003) choice-based classification method, relying on (1) parametric order constraints in the multinomial processing tree framework to implement probabilistic models and (2) minimum description length for model comparison. The advantages of the generalized approach are demonstrated through recovery simulations and an experiment. In explaining previous methods and our generalization, we maintain a nontechnical focus--so as to provide a practical guide for comparing both deterministic and probabilistic choice models.

  15. Statistical Fractal Models Based on GND-PCA and Its Application on Classification of Liver Diseases

    Directory of Open Access Journals (Sweden)

    Huiyan Jiang

    2013-01-01

    Full Text Available A new method is proposed to establish the statistical fractal model for liver diseases classification. Firstly, the fractal theory is used to construct the high-order tensor, and then Generalized -dimensional Principal Component Analysis (GND-PCA is used to establish the statistical fractal model and select the feature from the region of liver; at the same time different features have different weights, and finally, Support Vector Machine Optimized Ant Colony (ACO-SVM algorithm is used to establish the classifier for the recognition of liver disease. In order to verify the effectiveness of the proposed method, PCA eigenface method and normal SVM method are chosen as the contrast methods. The experimental results show that the proposed method can reconstruct liver volume better and improve the classification accuracy of liver diseases.

  16. A comparative study of deep learning models for medical image classification

    Science.gov (United States)

    Dutta, Suvajit; Manideep, B. C. S.; Rai, Shalva; Vijayarajan, V.

    2017-11-01

    Deep Learning(DL) techniques are conquering over the prevailing traditional approaches of neural network, when it comes to the huge amount of dataset, applications requiring complex functions demanding increase accuracy with lower time complexities. Neurosciences has already exploited DL techniques, thus portrayed itself as an inspirational source for researchers exploring the domain of Machine learning. DL enthusiasts cover the areas of vision, speech recognition, motion planning and NLP as well, moving back and forth among fields. This concerns with building models that can successfully solve variety of tasks requiring intelligence and distributed representation. The accessibility to faster CPUs, introduction of GPUs-performing complex vector and matrix computations, supported agile connectivity to network. Enhanced software infrastructures for distributed computing worked in strengthening the thought that made researchers suffice DL methodologies. The paper emphases on the following DL procedures to traditional approaches which are performed manually for classifying medical images. The medical images are used for the study Diabetic Retinopathy(DR) and computed tomography (CT) emphysema data. Both DR and CT data diagnosis is difficult task for normal image classification methods. The initial work was carried out with basic image processing along with K-means clustering for identification of image severity levels. After determining image severity levels ANN has been applied on the data to get the basic classification result, then it is compared with the result of DNNs (Deep Neural Networks), which performed efficiently because of its multiple hidden layer features basically which increases accuracy factors, but the problem of vanishing gradient in DNNs made to consider Convolution Neural Networks (CNNs) as well for better results. The CNNs are found to be providing better outcomes when compared to other learning models aimed at classification of images. CNNs are

  17. Pharmacokinetic–pharmacodynamic modelling of drug‐induced QTc interval prolongation in man: prediction from in vitro human ether‐à‐go‐go‐related gene binding and functional inhibition assays and conscious dog studies

    Science.gov (United States)

    Dubois, V F S; Casarotto, E; Danhof, M

    2016-01-01

    Background and Purpose Functional measures of human ether‐à‐go‐go‐related gene (hERG; Kv11.1) channel inhibition have been prioritized as an in vitro screening tool for candidate molecules. However, it is unclear how these results can be translated to humans. Here, we explore how data on drug binding and functional inhibition in vitro relate to QT prolongation in vivo. Using cisapride, sotalol and moxifloxacin as paradigm compounds, we assessed the relationship between drug concentrations, binding, functional measures and in vivo effects in preclinical species and humans. Experimental Approach Pharmacokinetic–pharmacodynamic modelling was used to characterize the drug effects in hERG functional patch clamp, hERG radio‐labelled dofetilide displacement experiments and QT interval in conscious dogs. Data were analysed in parallel to identify potential correlations between pharmacological activity in vitro and in vivo. Key Results An Emax model could not be used due to large variability in the functional patch clamp assay. Dofetilide displacement revealed that binding curves are unrelated to the in vivo potency estimates for QTc prolongation in dogs and humans. Mean in vitro potency estimates ranged from 99.9 nM for cisapride to 1030 μM for moxifloxacin. Conclusions and Implications The lack of standardized protocols for in vitro assays leads to significant differences in experimental conditions, making the assessment of in vitro–in vivo correlations unreliable. Identification of an accurate safety window during the screening of candidate molecules requires a quantitative framework that disentangles system‐ from drug‐specific properties under physiological conditions, enabling translation of the results to humans. Similar considerations will be relevant for the comprehensive in vitro pro‐arrhythmia assay initiative. PMID:27427789

  18. Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification

    Directory of Open Access Journals (Sweden)

    C. Fernandez-Lozano

    2013-01-01

    Full Text Available Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM. Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA, the most representative variables for a specific classification problem can be selected.

  19. Approach for Text Classification Based on the Similarity Measurement between Normal Cloud Models

    Directory of Open Access Journals (Sweden)

    Jin Dai

    2014-01-01

    Full Text Available The similarity between objects is the core research area of data mining. In order to reduce the interference of the uncertainty of nature language, a similarity measurement between normal cloud models is adopted to text classification research. On this basis, a novel text classifier based on cloud concept jumping up (CCJU-TC is proposed. It can efficiently accomplish conversion between qualitative concept and quantitative data. Through the conversion from text set to text information table based on VSM model, the text qualitative concept, which is extraction from the same category, is jumping up as a whole category concept. According to the cloud similarity between the test text and each category concept, the test text is assigned to the most similar category. By the comparison among different text classifiers in different feature selection set, it fully proves that not only does CCJU-TC have a strong ability to adapt to the different text features, but also the classification performance is also better than the traditional classifiers.

  20. SVM Based Descriptor Selection and Classification of Neurodegenerative Disease Drugs for Pharmacological Modeling.

    Science.gov (United States)

    Shahid, Mohammad; Shahzad Cheema, Muhammad; Klenner, Alexander; Younesi, Erfan; Hofmann-Apitius, Martin

    2013-03-01

    Systems pharmacological modeling of drug mode of action for the next generation of multitarget drugs may open new routes for drug design and discovery. Computational methods are widely used in this context amongst which support vector machines (SVM) have proven successful in addressing the challenge of classifying drugs with similar features. We have applied a variety of such SVM-based approaches, namely SVM-based recursive feature elimination (SVM-RFE). We use the approach to predict the pharmacological properties of drugs widely used against complex neurodegenerative disorders (NDD) and to build an in-silico computational model for the binary classification of NDD drugs from other drugs. Application of an SVM-RFE model to a set of drugs successfully classified NDD drugs from non-NDD drugs and resulted in overall accuracy of ∼80 % with 10 fold cross validation using 40 top ranked molecular descriptors selected out of total 314 descriptors. Moreover, SVM-RFE method outperformed linear discriminant analysis (LDA) based feature selection and classification. The model reduced the multidimensional descriptors space of drugs dramatically and predicted NDD drugs with high accuracy, while avoiding over fitting. Based on these results, NDD-specific focused libraries of drug-like compounds can be designed and existing NDD-specific drugs can be characterized by a well-characterized set of molecular descriptors. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  1. Chinese Sentence Classification Based on Convolutional Neural Network

    Science.gov (United States)

    Gu, Chengwei; Wu, Ming; Zhang, Chuang

    2017-10-01

    Sentence classification is one of the significant issues in Natural Language Processing (NLP). Feature extraction is often regarded as the key point for natural language processing. Traditional ways based on machine learning can not take high level features into consideration, such as Naive Bayesian Model. The neural network for sentence classification can make use of contextual information to achieve greater results in sentence classification tasks. In this paper, we focus on classifying Chinese sentences. And the most important is that we post a novel architecture of Convolutional Neural Network (CNN) to apply on Chinese sentence classification. In particular, most of the previous methods often use softmax classifier for prediction, we embed a linear support vector machine to substitute softmax in the deep neural network model, minimizing a margin-based loss to get a better result. And we use tanh as an activation function, instead of ReLU. The CNN model improve the result of Chinese sentence classification tasks. Experimental results on the Chinese news title database validate the effectiveness of our model.

  2. Predicting student satisfaction with courses based on log data from a virtual learning environment – a neural network and classification tree model

    Directory of Open Access Journals (Sweden)

    Ivana Đurđević Babić

    2015-03-01

    Full Text Available Student satisfaction with courses in academic institutions is an important issue and is recognized as a form of support in ensuring effective and quality education, as well as enhancing student course experience. This paper investigates whether there is a connection between student satisfaction with courses and log data on student courses in a virtual learning environment. Furthermore, it explores whether a successful classification model for predicting student satisfaction with course can be developed based on course log data and compares the results obtained from implemented methods. The research was conducted at the Faculty of Education in Osijek and included analysis of log data and course satisfaction on a sample of third and fourth year students. Multilayer Perceptron (MLP with different activation functions and Radial Basis Function (RBF neural networks as well as classification tree models were developed, trained and tested in order to classify students into one of two categories of course satisfaction. Type I and type II errors, and input variable importance were used for model comparison and classification accuracy. The results indicate that a successful classification model using tested methods can be created. The MLP model provides the highest average classification accuracy and the lowest preference in misclassification of students with a low level of course satisfaction, although a t-test for the difference in proportions showed that the difference in performance between the compared models is not statistically significant. Student involvement in forum discussions is recognized as a valuable predictor of student satisfaction with courses in all observed models.

  3. Neutral face classification using personalized appearance models for fast and robust emotion detection.

    Science.gov (United States)

    Chiranjeevi, Pojala; Gopalakrishnan, Viswanath; Moogi, Pratibha

    2015-09-01

    Facial expression recognition is one of the open problems in computer vision. Robust neutral face recognition in real time is a major challenge for various supervised learning-based facial expression recognition methods. This is due to the fact that supervised methods cannot accommodate all appearance variability across the faces with respect to race, pose, lighting, facial biases, and so on, in the limited amount of training data. Moreover, processing each and every frame to classify emotions is not required, as user stays neutral for majority of the time in usual applications like video chat or photo album/web browsing. Detecting neutral state at an early stage, thereby bypassing those frames from emotion classification would save the computational power. In this paper, we propose a light-weight neutral versus emotion classification engine, which acts as a pre-processer to the traditional supervised emotion classification approaches. It dynamically learns neutral appearance at key emotion (KE) points using a statistical texture model, constructed by a set of reference neutral frames for each user. The proposed method is made robust to various types of user head motions by accounting for affine distortions based on a statistical texture model. Robustness to dynamic shift of KE points is achieved by evaluating the similarities on a subset of neighborhood patches around each KE point using the prior information regarding the directionality of specific facial action units acting on the respective KE point. The proposed method, as a result, improves emotion recognition (ER) accuracy and simultaneously reduces computational complexity of the ER system, as validated on multiple databases.

  4. Classification of perovskites with supervised self-organizing maps

    International Nuclear Information System (INIS)

    Kuzmanovski, Igor; Dimitrovska-Lazova, Sandra; Aleksovska, Slobotka

    2007-01-01

    In this work supervised self-organizing maps were used for structural classification of perovskites. For this purpose, structural data for total number of 286 perovskites, belonging to ABO 3 and/or A 2 BB'O 6 types, were collected from literature: 130 of these are cubic, 85 orthorhombic and 71 monoclinic. For classification purposes, the effective ionic radii of the cations, electronegativities of the cations in B-position, as well as, the oxidation states of these cations, were used as input variables. The parameters of the developed models, as well as, the most suitable variables for classification purposes were selected using genetic algorithms. Two-third of all the compounds were used in the training phase. During the optimization process the performances of the models were checked using cross-validation leave-1/10-out. The performances of obtained solutions were checked using the test set composed of the remaining one-third of the compounds. The obtained models for classification of these three classes of perovskite compounds show very good results. Namely, the classification of the compounds in the test set resulted in small number of discrepancies (4.2-6.4%) between the actual crystallographic class and the one predicted by the models. All these results are strong arguments for the validity of supervised self-organizing maps for performing such types of classification. Therefore, the proposed procedure could be successfully used for crystallographic classification of perovskites in one of these three classes

  5. Indexing Density Models for Incremental Learning and Anytime Classification on Data Streams

    DEFF Research Database (Denmark)

    Seidl, Thomas; Assent, Ira; Kranen, Philipp

    2009-01-01

    Classification of streaming data faces three basic challenges: it has to deal with huge amounts of data, the varying time between two stream data items must be used best possible (anytime classification) and additional training data must be incrementally learned (anytime learning) for applying...... to the individual object to be classified) a hierarchy of mixture densities that represent kernel density estimators at successively coarser levels. Our probability density queries together with novel classification improvement strategies provide the necessary information for very effective classification at any...... point of interruption. Moreover, we propose a novel evaluation method for anytime classification using Poisson streams and demonstrate the anytime learning performance of the Bayes tree....

  6. Classification of research reactors and discussion of thinking of safety regulation based on the classification

    International Nuclear Information System (INIS)

    Song Chenxiu; Zhu Lixin

    2013-01-01

    Research reactors have different characteristics in the fields of reactor type, use, power level, design principle, operation model and safety performance, etc, and also have significant discrepancy in the aspect of nuclear safety regulation. This paper introduces classification of research reactors and discusses thinking of safety regulation based on the classification of research reactors. (authors)

  7. Effective Exchange Rate Classifications and Growth

    OpenAIRE

    Justin M. Dubas; Byung-Joo Lee; Nelson C. Mark

    2005-01-01

    We propose an econometric procedure for obtaining de facto exchange rate regime classifications which we apply to study the relationship between exchange rate regimes and economic growth. Our classification method models the de jure regimes as outcomes of a multinomial logit choice problem conditional on the volatility of a country's effective exchange rate, a bilateral exchange rate and international reserves. An `effective' de facto exchange rate regime classification is then obtained by as...

  8. A new ambulatory classification and funding model for radiation oncology: non-admitted patients in Victorian hospitals.

    Science.gov (United States)

    Antioch, K M; Walsh, M K; Anderson, D; Wilson, R; Chambers, C; Willmer, P

    1998-01-01

    The Victorian Department of Human Services has developed a classification and funding model for non-admitted radiation oncology patients. Agencies were previously funded on an historical cost input basis. For 1996-97, payments were made according to the new Non-admitted Radiation Oncology Classification System and include four key components. Fixed grants are based on Weighted Radiation Therapy Services targets for megavoltage courses, planning procedures (dosimetry and simulation) and consultations. The additional throughput pool covers additional Weighted Radiation Therapy Services once targets are reached, with access conditional on the utilisation of a minimum number of megavoltage fields by each hospital. Block grants cover specialised treatments, such as brachytherapy, allied health payments and other support services. Compensation grants were available to bring payments up to the level of the previous year. There is potential to provide incentives to promote best practice in Australia through linking appropriate practice to funding models. Key Australian and international developments should be monitored, including economic evaluation studies, classification and funding models, and the deliberations of the American College of Radiology, the American Society for Therapeutic Radiology and Oncology, the Trans-Tasman Radiation Oncology Group and the Council of Oncology Societies of Australia. National impact on clinical practice guidelines in Australia can be achieved through the Quality of Care and Health Outcomes Committee of the National Health and Medical Research Council.

  9. A unified model for context-based behavioural modelling and classification.

    CSIR Research Space (South Africa)

    Dabrowski, JJ

    2015-11-01

    Full Text Available the continuous dynamics of a entity and incorporating various contextual elements that influence behaviour. The entity is classified according to its behaviour. Classification is expressed as a conditional probability of the entity class given its tracked...

  10. Transport of cohesive sediments : Classification and requirements for turbulence modelling

    NARCIS (Netherlands)

    Bruens, A.W.

    1999-01-01

    This report describes a classification of sediment-laden flows, which gives an overview of the different transport forms of fine sediment and the interactions of the different processes as acting in an estuary. At the outs et of the proposed classification a distinction in physical states of

  11. Comparison of Enzymes / Non-Enzymes Proteins Classification Models Based on 3D, Composition, Sequences and Topological Indices

    OpenAIRE

    Munteanu, Cristian Robert

    2014-01-01

    Comparison of Enzymes / Non-Enzymes Proteins Classification Models Based on 3D, Composition, Sequences and Topological Indices, German Conference on Bioinformatics (GCB), Potsdam, Germany (September, 2007)

  12. Deep learning-based fine-grained car make/model classification for visual surveillance

    Science.gov (United States)

    Gundogdu, Erhan; Parıldı, Enes Sinan; Solmaz, Berkan; Yücesoy, Veysel; Koç, Aykut

    2017-10-01

    Fine-grained object recognition is a potential computer vision problem that has been recently addressed by utilizing deep Convolutional Neural Networks (CNNs). Nevertheless, the main disadvantage of classification methods relying on deep CNN models is the need for considerably large amount of data. In addition, there exists relatively less amount of annotated data for a real world application, such as the recognition of car models in a traffic surveillance system. To this end, we mainly concentrate on the classification of fine-grained car make and/or models for visual scenarios by the help of two different domains. First, a large-scale dataset including approximately 900K images is constructed from a website which includes fine-grained car models. According to their labels, a state-of-the-art CNN model is trained on the constructed dataset. The second domain that is dealt with is the set of images collected from a camera integrated to a traffic surveillance system. These images, which are over 260K, are gathered by a special license plate detection method on top of a motion detection algorithm. An appropriately selected size of the image is cropped from the region of interest provided by the detected license plate location. These sets of images and their provided labels for more than 30 classes are employed to fine-tune the CNN model which is already trained on the large scale dataset described above. To fine-tune the network, the last two fully-connected layers are randomly initialized and the remaining layers are fine-tuned in the second dataset. In this work, the transfer of a learned model on a large dataset to a smaller one has been successfully performed by utilizing both the limited annotated data of the traffic field and a large scale dataset with available annotations. Our experimental results both in the validation dataset and the real field show that the proposed methodology performs favorably against the training of the CNN model from scratch.

  13. Classifying Classifications

    DEFF Research Database (Denmark)

    Debus, Michael S.

    2017-01-01

    This paper critically analyzes seventeen game classifications. The classifications were chosen on the basis of diversity, ranging from pre-digital classification (e.g. Murray 1952), over game studies classifications (e.g. Elverdam & Aarseth 2007) to classifications of drinking games (e.g. LaBrie et...... al. 2013). The analysis aims at three goals: The classifications’ internal consistency, the abstraction of classification criteria and the identification of differences in classification across fields and/or time. Especially the abstraction of classification criteria can be used in future endeavors...... into the topic of game classifications....

  14. A New Value Classification and Values to Be Acquired by Students Related to This Classification

    Science.gov (United States)

    Acat, M. Bahaddin; Aslan, Mecit

    2012-01-01

    The aim of this study is to access a new value classification and analyse the views of teacher and parents related to this classification. The general survey model was employed in this study. The population of this study is composed of school teachers working in primary schools and parents of their students in Eskisehir. The present study adopted…

  15. Extension classification method for low-carbon product cases

    Directory of Open Access Journals (Sweden)

    Yanwei Zhao

    2016-05-01

    Full Text Available In product low-carbon design, intelligent decision systems integrated with certain classification algorithms recommend the existing design cases to designers. However, these systems mostly dependent on prior experience, and product designers not only expect to get a satisfactory case from an intelligent system but also hope to achieve assistance in modifying unsatisfactory cases. In this article, we proposed a new categorization method composed of static and dynamic classification based on extension theory. This classification method can be integrated into case-based reasoning system to get accurate classification results and to inform designers of detailed information about unsatisfactory cases. First, we establish the static classification model for cases by dependent function in a hierarchical structure. Then for dynamic classification, we make transformation for cases based on case model, attributes, attribute values, and dependent function, thus cases can take qualitative changes. Finally, the applicability of proposed method is demonstrated through a case study of screw air compressor cases.

  16. Proposed Core Competencies and Empirical Validation Procedure in Competency Modeling: Confirmation and Classification.

    Science.gov (United States)

    Baczyńska, Anna K; Rowiński, Tomasz; Cybis, Natalia

    2016-01-01

    Competency models provide insight into key skills which are common to many positions in an organization. Moreover, there is a range of competencies that is used by many companies. Researchers have developed core competency terminology to underline their cross-organizational value. The article presents a theoretical model of core competencies consisting of two main higher-order competencies called performance and entrepreneurship. Each of them consists of three elements: the performance competency includes cooperation, organization of work and goal orientation, while entrepreneurship includes innovativeness, calculated risk-taking and pro-activeness. However, there is lack of empirical validation of competency concepts in organizations and this would seem crucial for obtaining reliable results from organizational research. We propose a two-step empirical validation procedure: (1) confirmation factor analysis, and (2) classification of employees. The sample consisted of 636 respondents (M = 44.5; SD = 15.1). Participants were administered a questionnaire developed for the study purpose. The reliability, measured by Cronbach's alpha, ranged from 0.60 to 0.83 for six scales. Next, we tested the model using a confirmatory factor analysis. The two separate, single models of performance and entrepreneurial orientations fit quite well to the data, while a complex model based on the two single concepts needs further research. In the classification of employees based on the two higher order competencies we obtained four main groups of employees. Their profiles relate to those found in the literature, including so-called niche finders and top performers. Some proposal for organizations is discussed.

  17. Organizational information assets classification model and security architecture methodology

    Directory of Open Access Journals (Sweden)

    Mostafa Tamtaji

    2015-12-01

    Full Text Available Today's, Organizations are exposed with huge and diversity of information and information assets that are produced in different systems shuch as KMS, financial and accounting systems, official and industrial automation sysytems and so on and protection of these information is necessary. Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released.several benefits of this model cuses that organization has a great trend to implementing Cloud computing. Maintaining and management of information security is the main challenges in developing and accepting of this model. In this paper, at first, according to "design science research methodology" and compatible with "design process at information systems research", a complete categorization of organizational assets, including 355 different types of information assets in 7 groups and 3 level, is presented to managers be able to plan corresponding security controls according to importance of each groups. Then, for directing of organization to architect it’s information security in cloud computing environment, appropriate methodology is presented. Presented cloud computing security architecture , resulted proposed methodology, and presented classification model according to Delphi method and expers comments discussed and verified.

  18. Functional characterization of Kv11.1 (hERG) potassium channels split in the voltage-sensing domain.

    Science.gov (United States)

    de la Peña, Pilar; Domínguez, Pedro; Barros, Francisco

    2018-03-23

    Voltage-dependent KCNH family potassium channel functionality can be reconstructed using non-covalently linked voltage-sensing domain (VSD) and pore modules (split channels). However, the necessity of a covalent continuity for channel function has not been evaluated at other points within the two functionally independent channel modules. We find here that by cutting Kv11.1 (hERG, KCNH2) channels at the different loops linking the transmembrane spans of the channel core, not only channels split at the S4-S5 linker level, but also those split at the intracellular S2-S3 and the extracellular S3-S4 loops, yield fully functional channel proteins. Our data indicate that albeit less markedly, channels split after residue 482 in the S2-S3 linker resemble the uncoupled gating phenotype of those split at the C-terminal end of the VSD S4 transmembrane segment. Channels split after residues 514 and 518 in the S3-S4 linker show gating characteristics similar to those of the continuous wild-type channel. However, breaking the covalent link at this level strongly accelerates the voltage-dependent accessibility of a membrane impermeable methanethiosulfonate reagent to an engineered cysteine at the N-terminal region of the S4 transmembrane helix. Thus, besides that of the S4-S5 linker, structural integrity of the intracellular S2-S3 linker seems to constitute an important factor for proper transduction of VSD rearrangements to opening and closing the cytoplasmic gate. Furthermore, our data suggest that the short and probably rigid characteristics of the extracellular S3-S4 linker are not an essential component of the Kv11.1 voltage sensing machinery.

  19. The research on business rules classification and specification methods

    OpenAIRE

    Baltrušaitis, Egidijus

    2005-01-01

    The work is based on the research of business rules classification and specification methods. The basics of business rules approach are discussed. The most common business rules classification and modeling methods are analyzed. Business rules modeling techniques and tools for supporting them in the information systems are presented. Basing on the analysis results business rules classification method is proposed. Templates for every business rule type are presented. Business rules structuring ...

  20. Logical-Rule Models of Classification Response Times: A Synthesis of Mental-Architecture, Random-Walk, and Decision-Bound Approaches

    Science.gov (United States)

    Fific, Mario; Little, Daniel R.; Nosofsky, Robert M.

    2010-01-01

    We formalize and provide tests of a set of logical-rule models for predicting perceptual classification response times (RTs) and choice probabilities. The models are developed by synthesizing mental-architecture, random-walk, and decision-bound approaches. According to the models, people make independent decisions about the locations of stimuli…

  1. A Hidden Markov Models Approach for Crop Classification: Linking Crop Phenology to Time Series of Multi-Sensor Remote Sensing Data

    Directory of Open Access Journals (Sweden)

    Sofia Siachalou

    2015-03-01

    Full Text Available Vegetation monitoring and mapping based on multi-temporal imagery has recently received much attention due to the plethora of medium-high spatial resolution satellites and the improved classification accuracies attained compared to uni-temporal approaches. Efficient image processing strategies are needed to exploit the phenological information present in temporal image sequences and to limit data redundancy and computational complexity. Within this framework, we implement the theory of Hidden Markov Models in crop classification, based on the time-series analysis of phenological states, inferred by a sequence of remote sensing observations. More specifically, we model the dynamics of vegetation over an agricultural area of Greece, characterized by spatio-temporal heterogeneity and small-sized fields, using RapidEye and Landsat ETM+ imagery. In addition, the classification performance of image sequences with variable spatial and temporal characteristics is evaluated and compared. The classification model considering one RapidEye and four pan-sharpened Landsat ETM+ images was found superior, resulting in a conditional kappa from 0.77 to 0.94 per class and an overall accuracy of 89.7%. The results highlight the potential of the method for operational crop mapping in Euro-Mediterranean areas and provide some hints for optimal image acquisition windows regarding major crop types in Greece.

  2. QSAR classification models for the prediction of endocrine disrupting activity of brominated flame retardants.

    Science.gov (United States)

    Kovarich, Simona; Papa, Ester; Gramatica, Paola

    2011-06-15

    The identification of potential endocrine disrupting (ED) chemicals is an important task for the scientific community due to their diffusion in the environment; the production and use of such compounds will be strictly regulated through the authorization process of the REACH regulation. To overcome the problem of insufficient experimental data, the quantitative structure-activity relationship (QSAR) approach is applied to predict the ED activity of new chemicals. In the present study QSAR classification models are developed, according to the OECD principles, to predict the ED potency for a class of emerging ubiquitary pollutants, viz. brominated flame retardants (BFRs). Different endpoints related to ED activity (i.e. aryl hydrocarbon receptor agonism and antagonism, estrogen receptor agonism and antagonism, androgen and progesterone receptor antagonism, T4-TTR competition, E2SULT inhibition) are modeled using the k-NN classification method. The best models are selected by maximizing the sensitivity and external predictive ability. We propose simple QSARs (based on few descriptors) characterized by internal stability, good predictive power and with a verified applicability domain. These models are simple tools that are applicable to screen BFRs in relation to their ED activity, and also to design safer alternatives, in agreement with the requirements of REACH regulation at the authorization step. Copyright © 2011 Elsevier B.V. All rights reserved.

  3. Calibration of a Plastic Classification System with the CCW Model; Calibracion de un Sistema de Clasification de Plasticos segun el Modelo CCW

    Energy Technology Data Exchange (ETDEWEB)

    Barcala Riveira, J M; Fernandez Marron, J L; Alberdi Primicia, J; Navarrete Marin, J J; Oller Gonzalez, J C

    2003-07-01

    This document describes the calibration of a plastic Classification system with the CCW model (Classification by Quaternions built Wavelet Coefficients). The method is applied to spectra of plastics usually present in domestic wastes. Obtained results are showed. (Author) 16 refs.

  4. A Classification Model and an Open E-Learning System Based on Intuitionistic Fuzzy Sets for Instructional Design Concepts

    Science.gov (United States)

    Güyer, Tolga; Aydogdu, Seyhmus

    2016-01-01

    This study suggests a classification model and an e-learning system based on this model for all instructional theories, approaches, models, strategies, methods, and technics being used in the process of instructional design that constitutes a direct or indirect resource for educational technology based on the theory of intuitionistic fuzzy sets…

  5. Estimating Classification Errors Under Edit Restrictions in Composite Survey-Register Data Using Multiple Imputation Latent Class Modelling (MILC

    Directory of Open Access Journals (Sweden)

    Boeschoten Laura

    2017-12-01

    Full Text Available Both registers and surveys can contain classification errors. These errors can be estimated by making use of a composite data set. We propose a new method based on latent class modelling to estimate the number of classification errors across several sources while taking into account impossible combinations with scores on other variables. Furthermore, the latent class model, by multiply imputing a new variable, enhances the quality of statistics based on the composite data set. The performance of this method is investigated by a simulation study, which shows that whether or not the method can be applied depends on the entropy R2 of the latent class model and the type of analysis a researcher is planning to do. Finally, the method is applied to public data from Statistics Netherlands.

  6. Nonlinear programming for classification problems in machine learning

    Science.gov (United States)

    Astorino, Annabella; Fuduli, Antonio; Gaudioso, Manlio

    2016-10-01

    We survey some nonlinear models for classification problems arising in machine learning. In the last years this field has become more and more relevant due to a lot of practical applications, such as text and web classification, object recognition in machine vision, gene expression profile analysis, DNA and protein analysis, medical diagnosis, customer profiling etc. Classification deals with separation of sets by means of appropriate separation surfaces, which is generally obtained by solving a numerical optimization model. While linear separability is the basis of the most popular approach to classification, the Support Vector Machine (SVM), in the recent years using nonlinear separating surfaces has received some attention. The objective of this work is to recall some of such proposals, mainly in terms of the numerical optimization models. In particular we tackle the polyhedral, ellipsoidal, spherical and conical separation approaches and, for some of them, we also consider the semisupervised versions.

  7. SAR Imagery Simulation of Ship Based on Electromagnetic Calculations and Sea Clutter Modelling for Classification Applications

    International Nuclear Information System (INIS)

    Ji, K F; Zhao, Z; Xing, X W; Zou, H X; Zhou, S L

    2014-01-01

    Ship detection and classification with space-borne SAR has many potential applications within the maritime surveillance, fishery activity management, monitoring ship traffic, and military security. While ship detection techniques with SAR imagery are well established, ship classification is still an open issue. One of the main reasons may be ascribed to the difficulties on acquiring the required quantities of real data of vessels under different observation and environmental conditions with precise ground truth. Therefore, simulation of SAR images with high scenario flexibility and reasonable computation costs is compulsory for ship classification algorithms development. However, the simulation of SAR imagery of ship over sea surface is challenging. Though great efforts have been devoted to tackle this difficult problem, it is far from being conquered. This paper proposes a novel scheme for SAR imagery simulation of ship over sea surface. The simulation is implemented based on high frequency electromagnetic calculations methods of PO, MEC, PTD and GO. SAR imagery of sea clutter is modelled by the representative K-distribution clutter model. Then, the simulated SAR imagery of ship can be produced by inserting the simulated SAR imagery chips of ship into the SAR imagery of sea clutter. The proposed scheme has been validated with canonical and complex ship targets over a typical sea scene

  8. Voice based gender classification using machine learning

    Science.gov (United States)

    Raahul, A.; Sapthagiri, R.; Pankaj, K.; Vijayarajan, V.

    2017-11-01

    Gender identification is one of the major problem speech analysis today. Tracing the gender from acoustic data i.e., pitch, median, frequency etc. Machine learning gives promising results for classification problem in all the research domains. There are several performance metrics to evaluate algorithms of an area. Our Comparative model algorithm for evaluating 5 different machine learning algorithms based on eight different metrics in gender classification from acoustic data. Agenda is to identify gender, with five different algorithms: Linear Discriminant Analysis (LDA), K-Nearest Neighbour (KNN), Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) on basis of eight different metrics. The main parameter in evaluating any algorithms is its performance. Misclassification rate must be less in classification problems, which says that the accuracy rate must be high. Location and gender of the person have become very crucial in economic markets in the form of AdSense. Here with this comparative model algorithm, we are trying to assess the different ML algorithms and find the best fit for gender classification of acoustic data.

  9. A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.

    Science.gov (United States)

    Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

    2017-06-19

    A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.

  10. Integration of fuzzy theory into Kano model for classification of service quality elements: A case study in a machinery industry of China

    Directory of Open Access Journals (Sweden)

    Qingliang Meng

    2015-11-01

    Full Text Available Purpose: The purpose of study is to meet customer requirements and improve customer satisfaction that aims to classify customer requirements more effectively. And the classification is focused on the customer psychology. Design/methodology/approach: In this study, considering the advantages of Kano model in taking into account both customer’s consuming psychology and motivation, and combining with fuzzy theory which is effective to cope with uncertainty and ambiguity, a Kano model based on fuzzy theory is proposed. In view of the strong subjectivity of traditional Kano questionnaires, a fuzzy Kano questionnaire to classify the service quality elements more objectively is proposed. Furthermore, this study will also develop a mathematical calculation performance according to the quality classification of fuzzy Kano model. It’s more objective than traditional Kano model to realize the service quality elements classification. With this method, the accurate mentality can be fully reasonable reflected in some unknown circumstances. Finally, an empirical study in Xuzhou Construction Machinery Group Co., Ltd, the largest manufacturing industry in China, is showed to testify its feasibility and validity. Findings: The calculation results indicate that the proposed model has good performance in classifying customer requirements. With this method, the accurate mentality can be fully reasonable reflected in unknown circumstances and it is more objective than traditional Kano model to classify the service quality elements. Originality/value: This study provides a method to integrate fuzzy theory and Kano model, and develops a mathematical calculation performance according to the quality classification of fuzzy Kano model.

  11. Progressive Classification Using Support Vector Machines

    Science.gov (United States)

    Wagstaff, Kiri; Kocurek, Michael

    2009-01-01

    An algorithm for progressive classification of data, analogous to progressive rendering of images, makes it possible to compromise between speed and accuracy. This algorithm uses support vector machines (SVMs) to classify data. An SVM is a machine learning algorithm that builds a mathematical model of the desired classification concept by identifying the critical data points, called support vectors. Coarse approximations to the concept require only a few support vectors, while precise, highly accurate models require far more support vectors. Once the model has been constructed, the SVM can be applied to new observations. The cost of classifying a new observation is proportional to the number of support vectors in the model. When computational resources are limited, an SVM of the appropriate complexity can be produced. However, if the constraints are not known when the model is constructed, or if they can change over time, a method for adaptively responding to the current resource constraints is required. This capability is particularly relevant for spacecraft (or any other real-time systems) that perform onboard data analysis. The new algorithm enables the fast, interactive application of an SVM classifier to a new set of data. The classification process achieved by this algorithm is characterized as progressive because a coarse approximation to the true classification is generated rapidly and thereafter iteratively refined. The algorithm uses two SVMs: (1) a fast, approximate one and (2) slow, highly accurate one. New data are initially classified by the fast SVM, producing a baseline approximate classification. For each classified data point, the algorithm calculates a confidence index that indicates the likelihood that it was classified correctly in the first pass. Next, the data points are sorted by their confidence indices and progressively reclassified by the slower, more accurate SVM, starting with the items most likely to be incorrectly classified. The user

  12. Audio-visual Classification and Fusion of Spontaneous Affect Data in Likelihood Space

    NARCIS (Netherlands)

    Nicolaou, Mihalis A.; Gunes, Hatice; Pantic, Maja

    2010-01-01

    This paper focuses on audio-visual (using facial expression, shoulder and audio cues) classification of spontaneous affect, utilising generative models for classification (i) in terms of Maximum Likelihood Classification with the assumption that the generative model structure in the classifier is

  13. Selecting statistical models and variable combinations for optimal classification using otolith microchemistry.

    Science.gov (United States)

    Mercier, Lény; Darnaude, Audrey M; Bruguier, Olivier; Vasconcelos, Rita P; Cabral, Henrique N; Costa, Maria J; Lara, Monica; Jones, David L; Mouillot, David

    2011-06-01

    Reliable assessment of fish origin is of critical importance for exploited species, since nursery areas must be identified and protected to maintain recruitment to the adult stock. During the last two decades, otolith chemical signatures (or "fingerprints") have been increasingly used as tools to discriminate between coastal habitats. However, correct assessment of fish origin from otolith fingerprints depends on various environmental and methodological parameters, including the choice of the statistical method used to assign fish to unknown origin. Among the available methods of classification, Linear Discriminant Analysis (LDA) is the most frequently used, although it assumes data are multivariate normal with homogeneous within-group dispersions, conditions that are not always met by otolith chemical data, even after transformation. Other less constrained classification methods are available, but there is a current lack of comparative analysis in applications to otolith microchemistry. Here, we assessed stock identification accuracy for four classification methods (LDA, Quadratic Discriminant Analysis [QDA], Random Forests [RF], and Artificial Neural Networks [ANN]), through the use of three distinct data sets. In each case, all possible combinations of chemical elements were examined to identify the elements to be used for optimal accuracy in fish assignment to their actual origin. Our study shows that accuracy differs according to the model and the number of elements considered. Best combinations did not include all the elements measured, and it was not possible to define an ad hoc multielement combination for accurate site discrimination. Among all the models tested, RF and ANN performed best, especially for complex data sets (e.g., with numerous fish species and/or chemical elements involved). However, for these data, RF was less time-consuming and more interpretable than ANN, and far more efficient and less demanding in terms of assumptions than LDA or QDA

  14. Simultaneous data pre-processing and SVM classification model selection based on a parallel genetic algorithm applied to spectroscopic data of olive oils.

    Science.gov (United States)

    Devos, Olivier; Downey, Gerard; Duponchel, Ludovic

    2014-04-01

    Classification is an important task in chemometrics. For several years now, support vector machines (SVMs) have proven to be powerful for infrared spectral data classification. However such methods require optimisation of parameters in order to control the risk of overfitting and the complexity of the boundary. Furthermore, it is established that the prediction ability of classification models can be improved using pre-processing in order to remove unwanted variance in the spectra. In this paper we propose a new methodology based on genetic algorithm (GA) for the simultaneous optimisation of SVM parameters and pre-processing (GENOPT-SVM). The method has been tested for the discrimination of the geographical origin of Italian olive oil (Ligurian and non-Ligurian) on the basis of near infrared (NIR) or mid infrared (FTIR) spectra. Different classification models (PLS-DA, SVM with mean centre data, GENOPT-SVM) have been tested and statistically compared using McNemar's statistical test. For the two datasets, SVM with optimised pre-processing give models with higher accuracy than the one obtained with PLS-DA on pre-processed data. In the case of the NIR dataset, most of this accuracy improvement (86.3% compared with 82.8% for PLS-DA) occurred using only a single pre-processing step. For the FTIR dataset, three optimised pre-processing steps are required to obtain SVM model with significant accuracy improvement (82.2%) compared to the one obtained with PLS-DA (78.6%). Furthermore, this study demonstrates that even SVM models have to be developed on the basis of well-corrected spectral data in order to obtain higher classification rates. Copyright © 2013 Elsevier Ltd. All rights reserved.

  15. Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds

    Science.gov (United States)

    Suffredini, Tony; Wessells, Stephen M.; Bloom, Peter H.; Lanzone, Michael; Blackshire, Sheldon; Sridhar, Srisarguru; Katzner, Todd

    2017-01-01

    Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle (Aquila chrysaetos) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its accuracy of classifying basic behavior classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data. PMID:28403159

  16. Tweet-based Target Market Classification Using Ensemble Method

    Directory of Open Access Journals (Sweden)

    Muhammad Adi Khairul Anshary

    2016-09-01

    Full Text Available Target market classification is aimed at focusing marketing activities on the right targets. Classification of target markets can be done through data mining and by utilizing data from social media, e.g. Twitter. The end result of data mining are learning models that can classify new data. Ensemble methods can improve the accuracy of the models and therefore provide better results. In this study, classification of target markets was conducted on a dataset of 3000 tweets in order to extract features. Classification models were constructed to manipulate the training data using two ensemble methods (bagging and boosting. To investigate the effectiveness of the ensemble methods, this study used the CART (classification and regression tree algorithm for comparison. Three categories of consumer goods (computers, mobile phones and cameras and three categories of sentiments (positive, negative and neutral were classified towards three target-market categories. Machine learning was performed using Weka 3.6.9. The results of the test data showed that the bagging method improved the accuracy of CART with 1.9% (to 85.20%. On the other hand, for sentiment classification, the ensemble methods were not successful in increasing the accuracy of CART. The results of this study may be taken into consideration by companies who approach their customers through social media, especially Twitter.

  17. Mitragynine and its potential blocking effects on specific cardiac potassium channels

    Energy Technology Data Exchange (ETDEWEB)

    Tay, Yea Lu; Teah, Yi Fan; Chong, Yoong Min [Malaysian Institute of Pharmaceuticals & Nutraceuticals, NIBM, Ministry of Science, Technology & Innovation (MOSTI), Pulau Pinang (Malaysia); Jamil, Mohd Fadzly Amar [Clinical Research Center, Hospital Seberang Jaya, Kementerian Kesihatan Malaysia, Pulau Pinang (Malaysia); Kollert, Sina [Institute of Physiology, University of Wurzburg, Wurzburg (Germany); Adenan, Mohd Ilham [Atta-ur-Rahman Institute for Natural Product Discovery, Universiti Teknologi MARA (UiTM), Selangor Darul Ehsan (Malaysia); Wahab, Habibah Abdul [Pharmaceutical Design & Simulation (PhDS) Laboratory, School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau Pinang (Malaysia); Döring, Frank; Wischmeyer, Erhard [Institute of Physiology, University of Wurzburg, Wurzburg (Germany); Tan, Mei Lan, E-mail: tanml@usm.my [Malaysian Institute of Pharmaceuticals & Nutraceuticals, NIBM, Ministry of Science, Technology & Innovation (MOSTI), Pulau Pinang (Malaysia); Advanced Medical and Dental Institute, Universiti Sains Malaysia, Pulau Pinang (Malaysia)

    2016-08-15

    Mitragyna speciosa Korth is known for its euphoric properties and is frequently used for recreational purposes. Several poisoning and fatal cases involving mitragynine have been reported but the underlying causes remain unclear. Human ether-a-go-go-related gene (hERG) encodes the cardiac I{sub Kr} current which is a determinant of the duration of ventricular action potentials and QT interval. On the other hand, I{sub K1}, a Kir current mediated by Kir2.1 channel and I{sub KACh}, a receptor-activated Kir current mediated by GIRK channel are also known to be important in maintaining the cardiac function. This study investigated the effects of mitragynine on the current, mRNA and protein expression of hERG channel in hERG-transfected HEK293 cells and Xenopus oocytes. The effects on Kir2.1 and GIRK channels currents were also determined in the oocytes. The hERG tail currents following depolarization pulses were inhibited by mitragynine with an IC{sub 50} value of 1.62 μM and 1.15 μM in the transfected cell line and Xenopus oocytes, respectively. The S6 point mutations of Y652A and F656A attenuated the inhibitor effects of mitragynine, indicating that mitragynine interacts with these high affinity drug-binding sites in the hERG channel pore cavity which was consistent with the molecular docking simulation. Interestingly, mitragynine does not affect the hERG expression at the transcriptional level but inhibits the protein expression. Mitragynine is also found to inhibit I{sub KACh} current with an IC{sub 50} value of 3.32 μM but has no significant effects on I{sub K1}. Blocking of both hERG and GIRK channels may cause additive cardiotoxicity risks. - Highlights: • The potential cardiac potassium channel blocking properties of mitragynine were investigated. • Mitragynine blocks hERG channel and I{sub Kr} in hERG-transfected HEK293 cells and hERG cRNA-injected Xenopus oocytes. • Mitragynine inhibits the hERG protein but not the mRNA expression. • Mitragynine

  18. Mitragynine and its potential blocking effects on specific cardiac potassium channels

    International Nuclear Information System (INIS)

    Tay, Yea Lu; Teah, Yi Fan; Chong, Yoong Min; Jamil, Mohd Fadzly Amar; Kollert, Sina; Adenan, Mohd Ilham; Wahab, Habibah Abdul; Döring, Frank; Wischmeyer, Erhard; Tan, Mei Lan

    2016-01-01

    Mitragyna speciosa Korth is known for its euphoric properties and is frequently used for recreational purposes. Several poisoning and fatal cases involving mitragynine have been reported but the underlying causes remain unclear. Human ether-a-go-go-related gene (hERG) encodes the cardiac I Kr current which is a determinant of the duration of ventricular action potentials and QT interval. On the other hand, I K1 , a Kir current mediated by Kir2.1 channel and I KACh , a receptor-activated Kir current mediated by GIRK channel are also known to be important in maintaining the cardiac function. This study investigated the effects of mitragynine on the current, mRNA and protein expression of hERG channel in hERG-transfected HEK293 cells and Xenopus oocytes. The effects on Kir2.1 and GIRK channels currents were also determined in the oocytes. The hERG tail currents following depolarization pulses were inhibited by mitragynine with an IC 50 value of 1.62 μM and 1.15 μM in the transfected cell line and Xenopus oocytes, respectively. The S6 point mutations of Y652A and F656A attenuated the inhibitor effects of mitragynine, indicating that mitragynine interacts with these high affinity drug-binding sites in the hERG channel pore cavity which was consistent with the molecular docking simulation. Interestingly, mitragynine does not affect the hERG expression at the transcriptional level but inhibits the protein expression. Mitragynine is also found to inhibit I KACh current with an IC 50 value of 3.32 μM but has no significant effects on I K1 . Blocking of both hERG and GIRK channels may cause additive cardiotoxicity risks. - Highlights: • The potential cardiac potassium channel blocking properties of mitragynine were investigated. • Mitragynine blocks hERG channel and I Kr in hERG-transfected HEK293 cells and hERG cRNA-injected Xenopus oocytes. • Mitragynine inhibits the hERG protein but not the mRNA expression. • Mitragynine inhibits GIRK channel. • Simultaneous

  19. [Landscape classification: research progress and development trend].

    Science.gov (United States)

    Liang, Fa-Chao; Liu, Li-Ming

    2011-06-01

    Landscape classification is the basis of the researches on landscape structure, process, and function, and also, the prerequisite for landscape evaluation, planning, protection, and management, directly affecting the precision and practicability of landscape research. This paper reviewed the research progress on the landscape classification system, theory, and methodology, and summarized the key problems and deficiencies of current researches. Some major landscape classification systems, e. g. , LANMAP and MUFIC, were introduced and discussed. It was suggested that a qualitative and quantitative comprehensive classification based on the ideology of functional structure shape and on the integral consideration of landscape classification utility, landscape function, landscape structure, physiogeographical factors, and human disturbance intensity should be the major research directions in the future. The integration of mapping, 3S technology, quantitative mathematics modeling, computer artificial intelligence, and professional knowledge to enhance the precision of landscape classification would be the key issues and the development trend in the researches of landscape classification.

  20. Prototype-based Models for the Supervised Learning of Classification Schemes

    Science.gov (United States)

    Biehl, Michael; Hammer, Barbara; Villmann, Thomas

    2017-06-01

    An introduction is given to the use of prototype-based models in supervised machine learning. The main concept of the framework is to represent previously observed data in terms of so-called prototypes, which reflect typical properties of the data. Together with a suitable, discriminative distance or dissimilarity measure, prototypes can be used for the classification of complex, possibly high-dimensional data. We illustrate the framework in terms of the popular Learning Vector Quantization (LVQ). Most frequently, standard Euclidean distance is employed as a distance measure. We discuss how LVQ can be equipped with more general dissimilarites. Moreover, we introduce relevance learning as a tool for the data-driven optimization of parameterized distances.

  1. Classification of Multiple Seizure-Like States in Three Different Rodent Models of Epileptogenesis.

    Science.gov (United States)

    Guirgis, Mirna; Serletis, Demitre; Zhang, Jane; Florez, Carlos; Dian, Joshua A; Carlen, Peter L; Bardakjian, Berj L

    2014-01-01

    Epilepsy is a dynamical disease and its effects are evident in over fifty million people worldwide. This study focused on objective classification of the multiple states involved in the brain's epileptiform activity. Four datasets from three different rodent hippocampal preparations were explored, wherein seizure-like-events (SLE) were induced by the perfusion of a low - Mg(2+) /high-K(+) solution or 4-Aminopyridine. Local field potentials were recorded from CA3 pyramidal neurons and interneurons and modeled as Markov processes. Specifically, hidden Markov models (HMM) were used to determine the nature of the states present. Properties of the Hilbert transform were used to construct the feature spaces for HMM training. By sequentially applying the HMM training algorithm, multiple states were identified both in episodes of SLE and nonSLE activity. Specifically, preSLE and postSLE states were differentiated and multiple inner SLE states were identified. This was accomplished using features extracted from the lower frequencies (1-4 Hz, 4-8 Hz) alongside those of both the low- (40-100 Hz) and high-gamma (100-200 Hz) of the recorded electrical activity. The learning paradigm of this HMM-based system eliminates the inherent bias associated with other learning algorithms that depend on predetermined state segmentation and renders it an appropriate candidate for SLE classification.

  2. Lossless Compression of Classification-Map Data

    Science.gov (United States)

    Hua, Xie; Klimesh, Matthew

    2009-01-01

    A lossless image-data-compression algorithm intended specifically for application to classification-map data is based on prediction, context modeling, and entropy coding. The algorithm was formulated, in consideration of the differences between classification maps and ordinary images of natural scenes, so as to be capable of compressing classification- map data more effectively than do general-purpose image-data-compression algorithms. Classification maps are typically generated from remote-sensing images acquired by instruments aboard aircraft (see figure) and spacecraft. A classification map is a synthetic image that summarizes information derived from one or more original remote-sensing image(s) of a scene. The value assigned to each pixel in such a map is the index of a class that represents some type of content deduced from the original image data for example, a type of vegetation, a mineral, or a body of water at the corresponding location in the scene. When classification maps are generated onboard the aircraft or spacecraft, it is desirable to compress the classification-map data in order to reduce the volume of data that must be transmitted to a ground station.

  3. Hot complaint intelligent classification based on text mining

    Directory of Open Access Journals (Sweden)

    XIA Haifeng

    2013-10-01

    Full Text Available The complaint recognizer system plays an important role in making sure the correct classification of the hot complaint,improving the service quantity of telecommunications industry.The customers’ complaint in telecommunications industry has its special particularity which should be done in limited time,which cause the error in classification of hot complaint.The paper presents a model of complaint hot intelligent classification based on text mining,which can classify the hot complaint in the correct level of the complaint navigation.The examples show that the model can be efficient to classify the text of the complaint.

  4. Classification of Birds and Bats Using Flight Tracks

    Energy Technology Data Exchange (ETDEWEB)

    Cullinan, Valerie I.; Matzner, Shari; Duberstein, Corey A.

    2015-05-01

    Classification of birds and bats that use areas targeted for offshore wind farm development and the inference of their behavior is essential to evaluating the potential effects of development. The current approach to assessing the number and distribution of birds at sea involves transect surveys using trained individuals in boats or airplanes or using high-resolution imagery. These approaches are costly and have safety concerns. Based on a limited annotated library extracted from a single-camera thermal video, we provide a framework for building models that classify birds and bats and their associated behaviors. As an example, we developed a discriminant model for theoretical flight paths and applied it to data (N = 64 tracks) extracted from 5-min video clips. The agreement between model- and observer-classified path types was initially only 41%, but it increased to 73% when small-scale jitter was censored and path types were combined. Classification of 46 tracks of bats, swallows, gulls, and terns on average was 82% accurate, based on a jackknife cross-validation. Model classification of bats and terns (N = 4 and 2, respectively) was 94% and 91% correct, respectively; however, the variance associated with the tracks from these targets is poorly estimated. Model classification of gulls and swallows (N ≥ 18) was on average 73% and 85% correct, respectively. The models developed here should be considered preliminary because they are based on a small data set both in terms of the numbers of species and the identified flight tracks. Future classification models would be greatly improved by including a measure of distance between the camera and the target.

  5. Motor Oil Classification using Color Histograms and Pattern Recognition Techniques.

    Science.gov (United States)

    Ahmadi, Shiva; Mani-Varnosfaderani, Ahmad; Habibi, Biuck

    2018-04-20

    Motor oil classification is important for quality control and the identification of oil adulteration. In thiswork, we propose a simple, rapid, inexpensive and nondestructive approach based on image analysis and pattern recognition techniques for the classification of nine different types of motor oils according to their corresponding color histograms. For this, we applied color histogram in different color spaces such as red green blue (RGB), grayscale, and hue saturation intensity (HSI) in order to extract features that can help with the classification procedure. These color histograms and their combinations were used as input for model development and then were statistically evaluated by using linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM) techniques. Here, two common solutions for solving a multiclass classification problem were applied: (1) transformation to binary classification problem using a one-against-all (OAA) approach and (2) extension from binary classifiers to a single globally optimized multilabel classification model. In the OAA strategy, LDA, QDA, and SVM reached up to 97% in terms of accuracy, sensitivity, and specificity for both the training and test sets. In extension from binary case, despite good performances by the SVM classification model, QDA and LDA provided better results up to 92% for RGB-grayscale-HSI color histograms and up to 93% for the HSI color map, respectively. In order to reduce the numbers of independent variables for modeling, a principle component analysis algorithm was used. Our results suggest that the proposed method is promising for the identification and classification of different types of motor oils.

  6. Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

    Science.gov (United States)

    Huo, Guanying

    2017-01-01

    As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases. PMID:28316614

  7. Multi-Agent Information Classification Using Dynamic Acquaintance Lists.

    Science.gov (United States)

    Mukhopadhyay, Snehasis; Peng, Shengquan; Raje, Rajeev; Palakal, Mathew; Mostafa, Javed

    2003-01-01

    Discussion of automated information services focuses on information classification and collaborative agents, i.e. intelligent computer programs. Highlights include multi-agent systems; distributed artificial intelligence; thesauri; document representation and classification; agent modeling; acquaintances, or remote agents discovered through…

  8. Classification of multiple sclerosis lesions using adaptive dictionary learning.

    Science.gov (United States)

    Deshpande, Hrishikesh; Maurel, Pierre; Barillot, Christian

    2015-12-01

    This paper presents a sparse representation and an adaptive dictionary learning based method for automated classification of multiple sclerosis (MS) lesions in magnetic resonance (MR) images. Manual delineation of MS lesions is a time-consuming task, requiring neuroradiology experts to analyze huge volume of MR data. This, in addition to the high intra- and inter-observer variability necessitates the requirement of automated MS lesion classification methods. Among many image representation models and classification methods that can be used for such purpose, we investigate the use of sparse modeling. In the recent years, sparse representation has evolved as a tool in modeling data using a few basis elements of an over-complete dictionary and has found applications in many image processing tasks including classification. We propose a supervised classification approach by learning dictionaries specific to the lesions and individual healthy brain tissues, which include white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The size of the dictionaries learned for each class plays a major role in data representation but it is an even more crucial element in the case of competitive classification. Our approach adapts the size of the dictionary for each class, depending on the complexity of the underlying data. The algorithm is validated using 52 multi-sequence MR images acquired from 13 MS patients. The results demonstrate the effectiveness of our approach in MS lesion classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

  9. Tree Species Abundance Predictions in a Tropical Agricultural Landscape with a Supervised Classification Model and Imbalanced Data

    Directory of Open Access Journals (Sweden)

    Sarah J. Graves

    2016-02-01

    Full Text Available Mapping species through classification of imaging spectroscopy data is facilitating research to understand tree species distributions at increasingly greater spatial scales. Classification requires a dataset of field observations matched to the image, which will often reflect natural species distributions, resulting in an imbalanced dataset with many samples for common species and few samples for less common species. Despite the high prevalence of imbalanced datasets in multiclass species predictions, the effect on species prediction accuracy and landscape species abundance has not yet been quantified. First, we trained and assessed the accuracy of a support vector machine (SVM model with a highly imbalanced dataset of 20 tropical species and one mixed-species class of 24 species identified in a hyperspectral image mosaic (350–2500 nm of Panamanian farmland and secondary forest fragments. The model, with an overall accuracy of 62% ± 2.3% and F-score of 59% ± 2.7%, was applied to the full image mosaic (23,000 ha at a 2-m resolution to produce a species prediction map, which suggested that this tropical agricultural landscape is more diverse than what has been presented in field-based studies. Second, we quantified the effect of class imbalance on model accuracy. Model assessment showed a trend where species with more samples were consistently over predicted while species with fewer samples were under predicted. Standardizing sample size reduced model accuracy, but also reduced the level of species over- and under-prediction. This study advances operational species mapping of diverse tropical landscapes by detailing the effect of imbalanced data on classification accuracy and providing estimates of tree species abundance in an agricultural landscape. Species maps using data and methods presented here can be used in landscape analyses of species distributions to understand human or environmental effects, in addition to focusing conservation

  10. A New Classification Approach Based on Multiple Classification Rules

    OpenAIRE

    Zhongmei Zhou

    2014-01-01

    A good classifier can correctly predict new data for which the class label is unknown, so it is important to construct a high accuracy classifier. Hence, classification techniques are much useful in ubiquitous computing. Associative classification achieves higher classification accuracy than some traditional rule-based classification approaches. However, the approach also has two major deficiencies. First, it generates a very large number of association classification rules, especially when t...

  11. Automatic earthquake detection and classification with continuous hidden Markov models: a possible tool for monitoring Las Canadas caldera in Tenerife

    Energy Technology Data Exchange (ETDEWEB)

    Beyreuther, Moritz; Wassermann, Joachim [Department of Earth and Environmental Sciences (Geophys. Observatory), Ludwig Maximilians Universitaet Muenchen, D-80333 (Germany); Carniel, Roberto [Dipartimento di Georisorse e Territorio Universitat Degli Studi di Udine, I-33100 (Italy)], E-mail: roberto.carniel@uniud.it

    2008-10-01

    A possible interaction of (volcano-) tectonic earthquakes with the continuous seismic noise recorded in the volcanic island of Tenerife was recently suggested, but existing catalogues seem to be far from being self consistent, calling for the development of automatic detection and classification algorithms. In this work we propose the adoption of a methodology based on Hidden Markov Models (HMMs), widely used already in other fields, such as speech classification.

  12. Looking at the ICF and human communication through the lens of classification theory.

    Science.gov (United States)

    Walsh, Regina

    2011-08-01

    This paper explores the insights that classification theory can provide about the application of the International Classification of Functioning, Disability and Health (ICF) to communication. It first considers the relationship between conceptual models and classification systems, highlighting that classification systems in speech-language pathology (SLP) have not historically been based on conceptual models of human communication. It then overviews the key concepts and criteria of classification theory. Applying classification theory to the ICF and communication raises a number of issues, some previously highlighted through clinical application. Six focus questions from classification theory are used to explore these issues, and to propose the creation of an ICF-related conceptual model of communicating for the field of communication disability, which would address some of the issues raised. Developing a conceptual model of communication for SLP purposes closely articulated with the ICF would foster productive intra-professional discourse, while at the same time allow the profession to continue to use the ICF for purposes in inter-disciplinary discourse. The paper concludes by suggesting the insights of classification theory can assist professionals to apply the ICF to communication with the necessary rigour, and to work further in developing a conceptual model of human communication.

  13. DEEP LEARNING MODEL FOR BILINGUAL SENTIMENT CLASSIFICATION OF SHORT TEXTS

    Directory of Open Access Journals (Sweden)

    Y. B. Abdullin

    2017-01-01

    Full Text Available Sentiment analysis of short texts such as Twitter messages and comments in news portals is challenging due to the lack of contextual information. We propose a deep neural network model that uses bilingual word embeddings to effectively solve sentiment classification problem for a given pair of languages. We apply our approach to two corpora of two different language pairs: English-Russian and Russian-Kazakh. We show how to train a classifier in one language and predict in another. Our approach achieves 73% accuracy for English and 74% accuracy for Russian. For Kazakh sentiment analysis, we propose a baseline method, that achieves 60% accuracy; and a method to learn bilingual embeddings from a large unlabeled corpus using a bilingual word pairs.

  14. SQL based cardiovascular ultrasound image classification.

    Science.gov (United States)

    Nandagopalan, S; Suryanarayana, Adiga B; Sudarshan, T S B; Chandrashekar, Dhanalakshmi; Manjunath, C N

    2013-01-01

    This paper proposes a novel method to analyze and classify the cardiovascular ultrasound echocardiographic images using Naïve-Bayesian model via database OLAP-SQL. Efficient data mining algorithms based on tightly-coupled model is used to extract features. Three algorithms are proposed for classification namely Naïve-Bayesian Classifier for Discrete variables (NBCD) with SQL, NBCD with OLAP-SQL, and Naïve-Bayesian Classifier for Continuous variables (NBCC) using OLAP-SQL. The proposed model is trained with 207 patient images containing normal and abnormal categories. Out of the three proposed algorithms, a high classification accuracy of 96.59% was achieved from NBCC which is better than the earlier methods.

  15. 78 FR 68983 - Cotton Futures Classification: Optional Classification Procedure

    Science.gov (United States)

    2013-11-18

    ...-AD33 Cotton Futures Classification: Optional Classification Procedure AGENCY: Agricultural Marketing... regulations to allow for the addition of an optional cotton futures classification procedure--identified and... response to requests from the U.S. cotton industry and ICE, AMS will offer a futures classification option...

  16. EEG Signal Classification With Super-Dirichlet Mixture Model

    DEFF Research Database (Denmark)

    Ma, Zhanyu; Tan, Zheng-Hua; Prasad, Swati

    2012-01-01

    Classification of the Electroencephalogram (EEG) signal is a challengeable task in the brain-computer interface systems. The marginalized discrete wavelet transform (mDWT) coefficients extracted from the EEG signals have been frequently used in researches since they reveal features related...

  17. Modeling Wood Fibre Length in Black Spruce (Picea mariana (Mill. BSP Based on Ecological Land Classification

    Directory of Open Access Journals (Sweden)

    Elisha Townshend

    2015-09-01

    Full Text Available Effective planning to optimize the forest value chain requires accurate and detailed information about the resource; however, estimates of the distribution of fibre properties on the landscape are largely unavailable prior to harvest. Our objective was to fit a model of the tree-level average fibre length related to ecosite classification and other forest inventory variables depicted at the landscape scale. A series of black spruce increment cores were collected at breast height from trees in nine different ecosite groups within the boreal forest of northeastern Ontario, and processed using standard techniques for maceration and fibre length measurement. Regression tree analysis and random forests were used to fit hierarchical classification models and find the most important predictor variables for the response variable area-weighted mean stem-level fibre length. Ecosite group was the best predictor in the regression tree. Longer mean fibre-length was associated with more productive ecosites that supported faster growth. The explanatory power of the model of fitted data was good; however, random forests simulations indicated poor generalizability. These results suggest the potential to develop localized models linking wood fibre length in black spruce to landscape-level attributes, and improve the sustainability of forest management by identifying ideal locations to harvest wood that has desirable fibre characteristics.

  18. Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

    Directory of Open Access Journals (Sweden)

    Fei Wang

    2017-12-01

    Full Text Available Accurate solar photovoltaic (PV power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase the accuracy of day-ahead short-term (DAST solar PV power forecasting because PV output power is strongly dependent on the specific weather conditions in a given time period. However, the accuracy of daily weather classification relies on both the applied classifiers and the training data. This paper aims to reveal how these two factors impact the classification performance and to delineate the relation between classification accuracy and sample dataset scale. Two commonly used classification methods, K-nearest neighbors (KNN and support vector machines (SVM are applied to classify the daily local weather types for DAST solar PV power forecasting using the operation data from a grid-connected PV plant in Hohhot, Inner Mongolia, China. We assessed the performance of SVM and KNN approaches, and then investigated the influences of sample scale, the number of categories, and the data distribution in different categories on the daily weather classification results. The simulation results illustrate that SVM performs well with small sample scale, while KNN is more sensitive to the length of the training dataset and can achieve higher accuracy than SVM with sufficient samples.

  19. Spectral classification of emission-line galaxies

    International Nuclear Information System (INIS)

    Veilleux, S.; Osterbrock, D.E.

    1987-01-01

    A revised method of classification of narrow-line active galaxies and H II region-like galaxies is proposed. It involves the line ratios which take full advantage of the physical distinction between the two types of objects and minimize the effects of reddening correction and errors in the flux calibration. Large sets of internally consistent data are used, including new, previously unpublished measurements. Predictions of recent photoionization models by power-law spectra and by hot stars are compared with the observations. The classification is based on the observational data interpreted on the basis of these models. 63 references

  20. A novel transferable individual tree crown delineation model based on Fishing Net Dragging and boundary classification

    Science.gov (United States)

    Liu, Tao; Im, Jungho; Quackenbush, Lindi J.

    2015-12-01

    This study provides a novel approach to individual tree crown delineation (ITCD) using airborne Light Detection and Ranging (LiDAR) data in dense natural forests using two main steps: crown boundary refinement based on a proposed Fishing Net Dragging (FiND) method, and segment merging based on boundary classification. FiND starts with approximate tree crown boundaries derived using a traditional watershed method with Gaussian filtering and refines these boundaries using an algorithm that mimics how a fisherman drags a fishing net. Random forest machine learning is then used to classify boundary segments into two classes: boundaries between trees and boundaries between branches that belong to a single tree. Three groups of LiDAR-derived features-two from the pseudo waveform generated along with crown boundaries and one from a canopy height model (CHM)-were used in the classification. The proposed ITCD approach was tested using LiDAR data collected over a mountainous region in the Adirondack Park, NY, USA. Overall accuracy of boundary classification was 82.4%. Features derived from the CHM were generally more important in the classification than the features extracted from the pseudo waveform. A comprehensive accuracy assessment scheme for ITCD was also introduced by considering both area of crown overlap and crown centroids. Accuracy assessment using this new scheme shows the proposed ITCD achieved 74% and 78% as overall accuracy, respectively, for deciduous and mixed forest.

  1. More than a name: Heterogeneity in characteristics of models of maternity care reported from the Australian Maternity Care Classification System validation study.

    Science.gov (United States)

    Donnolley, Natasha R; Chambers, Georgina M; Butler-Henderson, Kerryn A; Chapman, Michael G; Sullivan, Elizabeth A

    2017-08-01

    Without a standard terminology to classify models of maternity care, it is problematic to compare and evaluate clinical outcomes across different models. The Maternity Care Classification System is a novel system developed in Australia to classify models of maternity care based on their characteristics and an overarching broad model descriptor (Major Model Category). This study aimed to assess the extent of variability in the defining characteristics of models of care grouped to the same Major Model Category, using the Maternity Care Classification System. All public hospital maternity services in New South Wales, Australia, were invited to complete a web-based survey classifying two local models of care using the Maternity Care Classification System. A descriptive analysis of the variation in 15 attributes of models of care was conducted to evaluate the level of heterogeneity within and across Major Model Categories. Sixty-nine out of seventy hospitals responded, classifying 129 models of care. There was wide variation in a number of important attributes of models classified to the same Major Model Category. The category of 'Public hospital maternity care' contained the most variation across all characteristics. This study demonstrated that although models of care can be grouped into a distinct set of Major Model Categories, there are significant variations in models of the same type. This could result in seemingly 'like' models of care being incorrectly compared if grouped only by the Major Model Category. Copyright © 2017 Australian College of Midwives. Published by Elsevier Ltd. All rights reserved.

  2. Comparative analysis of tree classification models for detecting fusarium oxysporum f. sp cubense (TR4) based on multi soil sensor parameters

    Science.gov (United States)

    Estuar, Maria Regina Justina; Victorino, John Noel; Coronel, Andrei; Co, Jerelyn; Tiausas, Francis; Señires, Chiara Veronica

    2017-09-01

    Use of wireless sensor networks and smartphone integration design to monitor environmental parameters surrounding plantations is made possible because of readily available and affordable sensors. Providing low cost monitoring devices would be beneficial, especially to small farm owners, in a developing country like the Philippines, where agriculture covers a significant amount of the labor market. This study discusses the integration of wireless soil sensor devices and smartphones to create an application that will use multidimensional analysis to detect the presence or absence of plant disease. Specifically, soil sensors are designed to collect soil quality parameters in a sink node from which the smartphone collects data from via Bluetooth. Given these, there is a need to develop a classification model on the mobile phone that will report infection status of a soil. Though tree classification is the most appropriate approach for continuous parameter-based datasets, there is a need to determine whether tree models will result to coherent results or not. Soil sensor data that resides on the phone is modeled using several variations of decision tree, namely: decision tree (DT), best-fit (BF) decision tree, functional tree (FT), Naive Bayes (NB) decision tree, J48, J48graft and LAD tree, where decision tree approaches the problem by considering all sensor nodes as one. Results show that there are significant differences among soil sensor parameters indicating that there are variances in scores between the infected and uninfected sites. Furthermore, analysis of variance in accuracy, recall, precision and F1 measure scores from tree classification models homogeneity among NBTree, J48graft and J48 tree classification models.

  3. The Implementation of ABC Classification and (Q, R with Economic Order Quantity (EOQ Model on the Travel Agency

    Directory of Open Access Journals (Sweden)

    Anggi Oktaviani

    2017-03-01

    Full Text Available To support customer loyalty programs, the travel agencies gave a souvenir to their customers. In one of the travel agencies in Jakarta, the demand for travel agency services could not be ensured. This had an impact on inventory items that were surplus to requirements. Inventory management was done by combining classifications ABC and (Q, R with Economic Order Quantity (EOQ model, which was usually used for uncertain demand. With “A” classification of the goods, two model (Q, R scenarios were made and then simulated with software Arena. From these two scenarios, the results show that both have a tendency to decline, or the stockouts occur. However, the second scenario is more optimistic because a dummy variable is added the second scenario. Thus, the tendency is stable and does not decline.

  4. Rough set classification based on quantum logic

    Science.gov (United States)

    Hassan, Yasser F.

    2017-11-01

    By combining the advantages of quantum computing and soft computing, the paper shows that rough sets can be used with quantum logic for classification and recognition systems. We suggest the new definition of rough set theory as quantum logic theory. Rough approximations are essential elements in rough set theory, the quantum rough set model for set-valued data directly construct set approximation based on a kind of quantum similarity relation which is presented here. Theoretical analyses demonstrate that the new model for quantum rough sets has new type of decision rule with less redundancy which can be used to give accurate classification using principles of quantum superposition and non-linear quantum relations. To our knowledge, this is the first attempt aiming to define rough sets in representation of a quantum rather than logic or sets. The experiments on data-sets have demonstrated that the proposed model is more accuracy than the traditional rough sets in terms of finding optimal classifications.

  5. Novelty detection for breast cancer image classification

    Science.gov (United States)

    Cichosz, Pawel; Jagodziński, Dariusz; Matysiewicz, Mateusz; Neumann, Łukasz; Nowak, Robert M.; Okuniewski, Rafał; Oleszkiewicz, Witold

    2016-09-01

    Using classification learning algorithms for medical applications may require not only refined model creation techniques and careful unbiased model evaluation, but also detecting the risk of misclassification at the time of model application. This is addressed by novelty detection, which identifies instances for which the training set is not sufficiently representative and for which it may be safer to restrain from classification and request a human expert diagnosis. The paper investigates two techniques for isolated instance identification, based on clustering and one-class support vector machines, which represent two different approaches to multidimensional outlier detection. The prediction quality for isolated instances in breast cancer image data is evaluated using the random forest algorithm and found to be substantially inferior to the prediction quality for non-isolated instances. Each of the two techniques is then used to create a novelty detection model which can be combined with a classification model and used at the time of prediction to detect instances for which the latter cannot be reliably applied. Novelty detection is demonstrated to improve random forest prediction quality and argued to deserve further investigation in medical applications.

  6. Classification using diffraction patterns for single-particle analysis

    International Nuclear Information System (INIS)

    Hu, Hongli; Zhang, Kaiming; Meng, Xing

    2016-01-01

    An alternative method has been assessed; diffraction patterns derived from the single particle data set were used to perform the first round of classification in creating the initial averages for proteins data with symmetrical morphology. The test protein set was a collection of Caenorhabditis elegans small heat shock protein 17 obtained by Cryo EM, which has a tetrahedral (12-fold) symmetry. It is demonstrated that the initial classification on diffraction patterns is workable as well as the real-space classification that is based on the phase contrast. The test results show that the information from diffraction patterns has the enough details to make the initial model faithful. The potential advantage using the alternative method is twofold, the ability to handle the sets with poor signal/noise or/and that break the symmetry properties. - Highlights: • New classification method. • Create the accurate initial model. • Better in handling noisy data.

  7. Classification using diffraction patterns for single-particle analysis

    Energy Technology Data Exchange (ETDEWEB)

    Hu, Hongli; Zhang, Kaiming [Department of Biophysics, the Health Science Centre, Peking University, Beijing 100191 (China); Meng, Xing, E-mail: xmeng101@gmail.com [Wadsworth Centre, New York State Department of Health, Albany, New York 12201 (United States)

    2016-05-15

    An alternative method has been assessed; diffraction patterns derived from the single particle data set were used to perform the first round of classification in creating the initial averages for proteins data with symmetrical morphology. The test protein set was a collection of Caenorhabditis elegans small heat shock protein 17 obtained by Cryo EM, which has a tetrahedral (12-fold) symmetry. It is demonstrated that the initial classification on diffraction patterns is workable as well as the real-space classification that is based on the phase contrast. The test results show that the information from diffraction patterns has the enough details to make the initial model faithful. The potential advantage using the alternative method is twofold, the ability to handle the sets with poor signal/noise or/and that break the symmetry properties. - Highlights: • New classification method. • Create the accurate initial model. • Better in handling noisy data.

  8. Identification and classification of similar looking food grains

    Science.gov (United States)

    Anami, B. S.; Biradar, Sunanda D.; Savakar, D. G.; Kulkarni, P. V.

    2013-01-01

    This paper describes the comparative study of Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers by taking a case study of identification and classification of four pairs of similar looking food grains namely, Finger Millet, Mustard, Soyabean, Pigeon Pea, Aniseed, Cumin-seeds, Split Greengram and Split Blackgram. Algorithms are developed to acquire and process color images of these grains samples. The developed algorithms are used to extract 18 colors-Hue Saturation Value (HSV), and 42 wavelet based texture features. Back Propagation Neural Network (BPNN)-based classifier is designed using three feature sets namely color - HSV, wavelet-texture and their combined model. SVM model for color- HSV model is designed for the same set of samples. The classification accuracies ranging from 93% to 96% for color-HSV, ranging from 78% to 94% for wavelet texture model and from 92% to 97% for combined model are obtained for ANN based models. The classification accuracy ranging from 80% to 90% is obtained for color-HSV based SVM model. Training time required for the SVM based model is substantially lesser than ANN for the same set of images.

  9. Classification of non-performing loans portfolio using Multilayer Perceptron artificial neural networks

    Directory of Open Access Journals (Sweden)

    Flávio Clésio Silva de Souza

    2014-06-01

    Full Text Available The purpose of the present research is to apply a Multilayer Perceptron (MLP neural network technique to create classification models from a portfolio of Non-Performing Loans (NPLs to classify this type of credit derivative. These credit derivatives are characterized as the amount of loans that were not paid and are already overdue more than 90 days. Since these titles are, because of legislative motives, moved by losses, Credit Rights Investment Funds (FDIC performs the purchase of these debts and the recovery of the credits. Using the Multilayer Perceptron (MLP architecture of Artificial Neural Network (ANN, classification models regarding the posterior recovery of these debts were created. To evaluate the performance of the models, evaluation metrics of classification relating to the neural networks with different architectures were presented. The results of the classifications were satisfactory, given the classification models were successful in the presented economics costs structure.

  10. Typecasting catchments: Classification, directionality, and the pursuit of universality

    Science.gov (United States)

    Smith, Tyler; Marshall, Lucy; McGlynn, Brian

    2018-02-01

    Catchment classification poses a significant challenge to hydrology and hydrologic modeling, restricting widespread transfer of knowledge from well-studied sites. The identification of important physical, climatological, or hydrologic attributes (to varying degrees depending on application/data availability) has traditionally been the focus for catchment classification. Classification approaches are regularly assessed with regard to their ability to provide suitable hydrologic predictions - commonly by transferring fitted hydrologic parameters at a data-rich catchment to a data-poor catchment deemed similar by the classification. While such approaches to hydrology's grand challenges are intuitive, they often ignore the most uncertain aspect of the process - the model itself. We explore catchment classification and parameter transferability and the concept of universal donor/acceptor catchments. We identify the implications of the assumption that the transfer of parameters between "similar" catchments is reciprocal (i.e., non-directional). These concepts are considered through three case studies situated across multiple gradients that include model complexity, process description, and site characteristics. Case study results highlight that some catchments are more successfully used as donor catchments and others are better suited as acceptor catchments. These results were observed for both black-box and process consistent hydrologic models, as well as for differing levels of catchment similarity. Therefore, we suggest that similarity does not adequately satisfy the underlying assumptions being made in parameter regionalization approaches regardless of model appropriateness. Furthermore, we suggest that the directionality of parameter transfer is an important factor in determining the success of parameter regionalization approaches.

  11. An automated cirrus classification

    Science.gov (United States)

    Gryspeerdt, Edward; Quaas, Johannes; Goren, Tom; Klocke, Daniel; Brueck, Matthias

    2018-05-01

    Cirrus clouds play an important role in determining the radiation budget of the earth, but many of their properties remain uncertain, particularly their response to aerosol variations and to warming. Part of the reason for this uncertainty is the dependence of cirrus cloud properties on the cloud formation mechanism, which itself is strongly dependent on the local meteorological conditions. In this work, a classification system (Identification and Classification of Cirrus or IC-CIR) is introduced to identify cirrus clouds by the cloud formation mechanism. Using reanalysis and satellite data, cirrus clouds are separated into four main types: orographic, frontal, convective and synoptic. Through a comparison to convection-permitting model simulations and back-trajectory-based analysis, it is shown that these observation-based regimes can provide extra information on the cloud-scale updraughts and the frequency of occurrence of liquid-origin ice, with the convective regime having higher updraughts and a greater occurrence of liquid-origin ice compared to the synoptic regimes. Despite having different cloud formation mechanisms, the radiative properties of the regimes are not distinct, indicating that retrieved cloud properties alone are insufficient to completely describe them. This classification is designed to be easily implemented in GCMs, helping improve future model-observation comparisons and leading to improved parametrisations of cirrus cloud processes.

  12. LDA boost classification: boosting by topics

    Science.gov (United States)

    Lei, La; Qiao, Guo; Qimin, Cao; Qitao, Li

    2012-12-01

    AdaBoost is an efficacious classification algorithm especially in text categorization (TC) tasks. The methodology of setting up a classifier committee and voting on the documents for classification can achieve high categorization precision. However, traditional Vector Space Model can easily lead to the curse of dimensionality and feature sparsity problems; so it affects classification performance seriously. This article proposed a novel classification algorithm called LDABoost based on boosting ideology which uses Latent Dirichlet Allocation (LDA) to modeling the feature space. Instead of using words or phrase, LDABoost use latent topics as the features. In this way, the feature dimension is significantly reduced. Improved Naïve Bayes (NB) is designed as the weaker classifier which keeps the efficiency advantage of classic NB algorithm and has higher precision. Moreover, a two-stage iterative weighted method called Cute Integration in this article is proposed for improving the accuracy by integrating weak classifiers into strong classifier in a more rational way. Mutual Information is used as metrics of weights allocation. The voting information and the categorization decision made by basis classifiers are fully utilized for generating the strong classifier. Experimental results reveals LDABoost making categorization in a low-dimensional space, it has higher accuracy than traditional AdaBoost algorithms and many other classic classification algorithms. Moreover, its runtime consumption is lower than different versions of AdaBoost, TC algorithms based on support vector machine and Neural Networks.

  13. Modeling and Classification of Kinetic Patterns of Dynamic Metabolic Biomarkers in Physical Activity.

    Directory of Open Access Journals (Sweden)

    Marc Breit

    2015-08-01

    Full Text Available The objectives of this work were the classification of dynamic metabolic biomarker candidates and the modeling and characterization of kinetic regulatory mechanisms in human metabolism with response to external perturbations by physical activity. Longitudinal metabolic concentration data of 47 individuals from 4 different groups were examined, obtained from a cycle ergometry cohort study. In total, 110 metabolites (within the classes of acylcarnitines, amino acids, and sugars were measured through a targeted metabolomics approach, combining tandem mass spectrometry (MS/MS with the concept of stable isotope dilution (SID for metabolite quantitation. Biomarker candidates were selected by combined analysis of maximum fold changes (MFCs in concentrations and P-values resulting from statistical hypothesis testing. Characteristic kinetic signatures were identified through a mathematical modeling approach utilizing polynomial fitting. Modeled kinetic signatures were analyzed for groups with similar behavior by applying hierarchical cluster analysis. Kinetic shape templates were characterized, defining different forms of basic kinetic response patterns, such as sustained, early, late, and other forms, that can be used for metabolite classification. Acetylcarnitine (C2, showing a late response pattern and having the highest values in MFC and statistical significance, was classified as late marker and ranked as strong predictor (MFC = 1.97, P < 0.001. In the class of amino acids, highest values were shown for alanine (MFC = 1.42, P < 0.001, classified as late marker and strong predictor. Glucose yields a delayed response pattern, similar to a hockey stick function, being classified as delayed marker and ranked as moderate predictor (MFC = 1.32, P < 0.001. These findings coincide with existing knowledge on central metabolic pathways affected in exercise physiology, such as β-oxidation of fatty acids, glycolysis, and glycogenolysis. The presented modeling

  14. Persistent pulmonary subsolid nodules: model-based iterative reconstruction for nodule classification and measurement variability on low-dose CT

    International Nuclear Information System (INIS)

    Kim, Hyungjin; Kim, Seong Ho; Lee, Sang Min; Lee, Kyung Hee; Park, Chang Min; Park, Sang Joon; Goo, Jin Mo

    2014-01-01

    To compare the pulmonary subsolid nodule (SSN) classification agreement and measurement variability between filtered back projection (FBP) and model-based iterative reconstruction (MBIR). Low-dose CTs were reconstructed using FBP and MBIR for 47 patients with 47 SSNs. Two readers independently classified SSNs into pure or part-solid ground-glass nodules, and measured the size of the whole nodule and solid portion twice on both reconstruction algorithms. Nodule classification agreement was analyzed using Cohen's kappa and compared between reconstruction algorithms using McNemar's test. Measurement variability was investigated using Bland-Altman analysis and compared with the paired t-test. Cohen's kappa for inter-reader SSN classification agreement was 0.541-0.662 on FBP and 0.778-0.866 on MBIR. Between the two readers, nodule classification was consistent in 79.8 % (75/94) with FBP and 91.5 % (86/94) with MBIR (p = 0.027). Inter-reader measurement variability range was -5.0-2.1 mm on FBP and -3.3-1.8 mm on MBIR for whole nodule size, and was -6.5-0.9 mm on FBP and -5.5-1.5 mm on MBIR for solid portion size. Inter-reader measurement differences were significantly smaller on MBIR (p = 0.027, whole nodule; p = 0.011, solid portion). MBIR significantly improved SSN classification agreement and reduced measurement variability of both whole nodules and solid portions between readers. (orig.)

  15. Is overall similarity classification less effortful than single-dimension classification?

    Science.gov (United States)

    Wills, Andy J; Milton, Fraser; Longmore, Christopher A; Hester, Sarah; Robinson, Jo

    2013-01-01

    It is sometimes argued that the implementation of an overall similarity classification is less effortful than the implementation of a single-dimension classification. In the current article, we argue that the evidence securely in support of this view is limited, and report additional evidence in support of the opposite proposition--overall similarity classification is more effortful than single-dimension classification. Using a match-to-standards procedure, Experiments 1A, 1B and 2 demonstrate that concurrent load reduces the prevalence of overall similarity classification, and that this effect is robust to changes in the concurrent load task employed, the level of time pressure experienced, and the short-term memory requirements of the classification task. Experiment 3 demonstrates that participants who produced overall similarity classifications from the outset have larger working memory capacities than those who produced single-dimension classifications initially, and Experiment 4 demonstrates that instructions to respond meticulously increase the prevalence of overall similarity classification.

  16. Modelling the Happiness Classification of Addicted, Addiction Risk, Threshold and Non-Addicted Groups on Internet Usage

    Science.gov (United States)

    Sapmaz, Fatma; Totan, Tarik

    2018-01-01

    The aim of this study is to model the happiness classification of university students--grouped as addicted, addiction risk, threshold and non-addicted to internet usage--with compatibility analysis on a map as happiness, average and unhappiness. The participants in this study were 400 university students from Turkey. According to the results of…

  17. Emotions Classification for Arabic Tweets

    African Journals Online (AJOL)

    pc

    2018-03-05

    Mar 5, 2018 ... learning methods for referring to all areas of detecting, analyzing, and classifying ... In this paper, an adaptive model is proposed for emotions classification of ... WEKA data mining tool is used to implement this model and evaluate the ... defined using vector representation, storing a numerical. "importance" ...

  18. Vulnerable land ecosystems classification using spatial context and spectral indices

    Science.gov (United States)

    Ibarrola-Ulzurrun, Edurne; Gonzalo-Martín, Consuelo; Marcello, Javier

    2017-10-01

    Natural habitats are exposed to growing pressure due to intensification of land use and tourism development. Thus, obtaining information on the vegetation is necessary for conservation and management projects. In this context, remote sensing is an important tool for monitoring and managing habitats, being classification a crucial stage. The majority of image classifications techniques are based upon the pixel-based approach. An alternative is the object-based (OBIA) approach, in which a previous segmentation step merges image pixels to create objects that are then classified. Besides, improved results may be gained by incorporating additional spatial information and specific spectral indices into the classification process. The main goal of this work was to implement and assess object-based classification techniques on very-high resolution imagery incorporating spectral indices and contextual spatial information in the classification models. The study area was Teide National Park in Canary Islands (Spain) using Worldview-2 orthoready imagery. In the classification model, two common indices were selected Normalized Difference Vegetation Index (NDVI) and Optimized Soil Adjusted Vegetation Index (OSAVI), as well as two specific Worldview-2 sensor indices, Worldview Vegetation Index and Worldview Soil Index. To include the contextual information, Grey Level Co-occurrence Matrices (GLCM) were used. The classification was performed training a Support Vector Machine with sufficient and representative number of vegetation samples (Spartocytisus supranubius, Pterocephalus lasiospermus, Descurainia bourgaeana and Pinus canariensis) as well as urban, road and bare soil classes. Confusion Matrices were computed to evaluate the results from each classification model obtaining the highest overall accuracy (90.07%) combining both Worldview indices with the GLCM-dissimilarity.

  19. Object-oriented classification of drumlins from digital elevation models

    Science.gov (United States)

    Saha, Kakoli

    Drumlins are common elements of glaciated landscapes which are easily identified by their distinct morphometric characteristics including shape, length/width ratio, elongation ratio, and uniform direction. To date, most researchers have mapped drumlins by tracing contours on maps, or through on-screen digitization directly on top of hillshaded digital elevation models (DEMs). This paper seeks to utilize the unique morphometric characteristics of drumlins and investigates automated extraction of the landforms as objects from DEMs by Definiens Developer software (V.7), using the 30 m United States Geological Survey National Elevation Dataset DEM as input. The Chautauqua drumlin field in Pennsylvania and upstate New York, USA was chosen as a study area. As the study area is huge (approximately covers 2500 sq.km. of area), small test areas were selected for initial testing of the method. Individual polygons representing the drumlins were extracted from the elevation data set by automated recognition, using Definiens' Multiresolution Segmentation tool, followed by rule-based classification. Subsequently parameters such as length, width and length-width ratio, perimeter and area were measured automatically. To test the accuracy of the method, a second base map was produced by manual on-screen digitization of drumlins from topographic maps and the same morphometric parameters were extracted from the mapped landforms using Definiens Developer. Statistical comparison showed a high agreement between the two methods confirming that object-oriented classification for extraction of drumlins can be used for mapping these landforms. The proposed method represents an attempt to solve the problem by providing a generalized rule-set for mass extraction of drumlins. To check that the automated extraction process was next applied to a larger area. Results showed that the proposed method is as successful for the bigger area as it was for the smaller test areas.

  20. A comparative study of machine learning models for ethnicity classification

    Science.gov (United States)

    Trivedi, Advait; Bessie Amali, D. Geraldine

    2017-11-01

    This paper endeavours to adopt a machine learning approach to solve the problem of ethnicity recognition. Ethnicity identification is an important vision problem with its use cases being extended to various domains. Despite the multitude of complexity involved, ethnicity identification comes naturally to humans. This meta information can be leveraged to make several decisions, be it in target marketing or security. With the recent development of intelligent systems a sub module to efficiently capture ethnicity would be useful in several use cases. Several attempts to identify an ideal learning model to represent a multi-ethnic dataset have been recorded. A comparative study of classifiers such as support vector machines, logistic regression has been documented. Experimental results indicate that the logical classifier provides a much accurate classification than the support vector machine.

  1. [Severity classification of chronic obstructive pulmonary disease based on deep learning].

    Science.gov (United States)

    Ying, Jun; Yang, Ceyuan; Li, Quanzheng; Xue, Wanguo; Li, Tanshi; Cao, Wenzhe

    2017-12-01

    In this paper, a deep learning method has been raised to build an automatic classification algorithm of severity of chronic obstructive pulmonary disease. Large sample clinical data as input feature were analyzed for their weights in classification. Through feature selection, model training, parameter optimization and model testing, a classification prediction model based on deep belief network was built to predict severity classification criteria raised by the Global Initiative for Chronic Obstructive Lung Disease (GOLD). We get accuracy over 90% in prediction for two different standardized versions of severity criteria raised in 2007 and 2011 respectively. Moreover, we also got the contribution ranking of different input features through analyzing the model coefficient matrix and confirmed that there was a certain degree of agreement between the more contributive input features and the clinical diagnostic knowledge. The validity of the deep belief network model was proved by this result. This study provides an effective solution for the application of deep learning method in automatic diagnostic decision making.

  2. Binary classification posed as a quadratically constrained quadratic ...

    Indian Academy of Sciences (India)

    Binary classification is posed as a quadratically constrained quadratic problem and solved using the proposed method. Each class in the binary classification problem is modeled as a multidimensional ellipsoid to forma quadratic constraint in the problem. Particle swarms help in determining the optimal hyperplane or ...

  3. Ligand and structure-based classification models for Prediction of P-glycoprotein inhibitors

    DEFF Research Database (Denmark)

    Klepsch, Freya; Poongavanam, Vasanthanathan; Ecker, Gerhard Franz

    2014-01-01

    an algorithm based on Euclidean distance. Results show that random forest and SVM performed best for classification of P-gp inhibitors and non-inhibitors, correctly predicting 73/75 % of the external test set compounds. Classification based on the docking experiments using the scoring function Chem...

  4. A Procedure for Classification of Cup-Anemometers

    DEFF Research Database (Denmark)

    Friis Pedersen, Troels; Paulsen, Uwe Schmidt

    1997-01-01

    The paper proposes a classification procedure for cup-anemometers based on similar principles as for power converters. A range of operational parameters are established within which the response of the cup-anemometer is evaluated. The characteristics of real cup-anemometers are fitted...... to a realistic 3D cup-anemometer model. Afterwards, the model is used to calculate the response under the range of operational conditions which are set up for the classification. Responses are compared to the normal linear calibration relationship, derived from Wind tunnel calibrations. Results of the 3D cup...

  5. Prospective identification of adolescent suicide ideation using classification tree analysis: Models for community-based screening.

    Science.gov (United States)

    Hill, Ryan M; Oosterhoff, Benjamin; Kaplow, Julie B

    2017-07-01

    Although a large number of risk markers for suicide ideation have been identified, little guidance has been provided to prospectively identify adolescents at risk for suicide ideation within community settings. The current study addressed this gap in the literature by utilizing classification tree analysis (CTA) to provide a decision-making model for screening adolescents at risk for suicide ideation. Participants were N = 4,799 youth (Mage = 16.15 years, SD = 1.63) who completed both Waves 1 and 2 of the National Longitudinal Study of Adolescent to Adult Health. CTA was used to generate a series of decision rules for identifying adolescents at risk for reporting suicide ideation at Wave 2. Findings revealed 3 distinct solutions with varying sensitivity and specificity for identifying adolescents who reported suicide ideation. Sensitivity of the classification trees ranged from 44.6% to 77.6%. The tree with greatest specificity and lowest sensitivity was based on a history of suicide ideation. The tree with moderate sensitivity and high specificity was based on depressive symptoms, suicide attempts or suicide among family and friends, and social support. The most sensitive but least specific tree utilized these factors and gender, ethnicity, hours of sleep, school-related factors, and future orientation. These classification trees offer community organizations options for instituting large-scale screenings for suicide ideation risk depending on the available resources and modality of services to be provided. This study provides a theoretically and empirically driven model for prospectively identifying adolescents at risk for suicide ideation and has implications for preventive interventions among at-risk youth. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  6. Utility of BRDF Models for Estimating Optimal View Angles in Classification of Remotely Sensed Images

    Science.gov (United States)

    Valdez, P. F.; Donohoe, G. W.

    1997-01-01

    Statistical classification of remotely sensed images attempts to discriminate between surface cover types on the basis of the spectral response recorded by a sensor. It is well known that surfaces reflect incident radiation as a function of wavelength producing a spectral signature specific to the material under investigation. Multispectral and hyperspectral sensors sample the spectral response over tens and even hundreds of wavelength bands to capture the variation of spectral response with wavelength. Classification algorithms then exploit these differences in spectral response to distinguish between materials of interest. Sensors of this type, however, collect detailed spectral information from one direction (usually nadir); consequently, do not consider the directional nature of reflectance potentially detectable at different sensor view angles. Improvements in sensor technology have resulted in remote sensing platforms capable of detecting reflected energy across wavelengths (spectral signatures) and from multiple view angles (angular signatures) in the fore and aft directions. Sensors of this type include: the moderate resolution imaging spectroradiometer (MODIS), the multiangle imaging spectroradiometer (MISR), and the airborne solid-state array spectroradiometer (ASAS). A goal of this paper, then, is to explore the utility of Bidirectional Reflectance Distribution Function (BRDF) models in the selection of optimal view angles for the classification of remotely sensed images by employing a strategy of searching for the maximum difference between surface BRDFs. After a brief discussion of directional reflect ante in Section 2, attention is directed to the Beard-Maxwell BRDF model and its use in predicting the bidirectional reflectance of a surface. The selection of optimal viewing angles is addressed in Section 3, followed by conclusions and future work in Section 4.

  7. Protein classification using modified n-grams and skip-grams.

    Science.gov (United States)

    Islam, S M Ashiqul; Heil, Benjamin J; Kearney, Christopher Michel; Baker, Erich J

    2018-05-01

    Classification by supervised machine learning greatly facilitates the annotation of protein characteristics from their primary sequence. However, the feature generation step in this process requires detailed knowledge of attributes used to classify the proteins. Lack of this knowledge risks the selection of irrelevant features, resulting in a faulty model. In this study, we introduce a supervised protein classification method with a novel means of automating the work-intensive feature generation step via a Natural Language Processing (NLP)-dependent model, using a modified combination of n-grams and skip-grams (m-NGSG). A meta-comparison of cross-validation accuracy with twelve training datasets from nine different published studies demonstrates a consistent increase in accuracy of m-NGSG when compared to contemporary classification and feature generation models. We expect this model to accelerate the classification of proteins from primary sequence data and increase the accessibility of protein characteristic prediction to a broader range of scientists. m-NGSG is freely available at Bitbucket: https://bitbucket.org/sm_islam/mngsg/src. A web server is available at watson.ecs.baylor.edu/ngsg. erich_baker@baylor.edu. Supplementary data are available at Bioinformatics online.

  8. A theory of fine structure image models with an application to detection and classification of dementia.

    Science.gov (United States)

    O'Neill, William; Penn, Richard; Werner, Michael; Thomas, Justin

    2015-06-01

    Estimation of stochastic process models from data is a common application of time series analysis methods. Such system identification processes are often cast as hypothesis testing exercises whose intent is to estimate model parameters and test them for statistical significance. Ordinary least squares (OLS) regression and the Levenberg-Marquardt algorithm (LMA) have proven invaluable computational tools for models being described by non-homogeneous, linear, stationary, ordinary differential equations. In this paper we extend stochastic model identification to linear, stationary, partial differential equations in two independent variables (2D) and show that OLS and LMA apply equally well to these systems. The method employs an original nonparametric statistic as a test for the significance of estimated parameters. We show gray scale and color images are special cases of 2D systems satisfying a particular autoregressive partial difference equation which estimates an analogous partial differential equation. Several applications to medical image modeling and classification illustrate the method by correctly classifying demented and normal OLS models of axial magnetic resonance brain scans according to subject Mini Mental State Exam (MMSE) scores. Comparison with 13 image classifiers from the literature indicates our classifier is at least 14 times faster than any of them and has a classification accuracy better than all but one. Our modeling method applies to any linear, stationary, partial differential equation and the method is readily extended to 3D whole-organ systems. Further, in addition to being a robust image classifier, estimated image models offer insights into which parameters carry the most diagnostic image information and thereby suggest finer divisions could be made within a class. Image models can be estimated in milliseconds which translate to whole-organ models in seconds; such runtimes could make real-time medicine and surgery modeling possible.

  9. SAW Classification Algorithm for Chinese Text Classification

    OpenAIRE

    Xiaoli Guo; Huiyu Sun; Tiehua Zhou; Ling Wang; Zhaoyang Qu; Jiannan Zang

    2015-01-01

    Considering the explosive growth of data, the increased amount of text data’s effect on the performance of text categorization forward the need for higher requirements, such that the existing classification method cannot be satisfied. Based on the study of existing text classification technology and semantics, this paper puts forward a kind of Chinese text classification oriented SAW (Structural Auxiliary Word) algorithm. The algorithm uses the special space effect of Chinese text where words...

  10. Deep learning for tumor classification in imaging mass spectrometry.

    Science.gov (United States)

    Behrmann, Jens; Etmann, Christian; Boskamp, Tobias; Casadonte, Rita; Kriegsmann, Jörg; Maaß, Peter

    2018-04-01

    Tumor classification using imaging mass spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, automated feature extraction and classification steps are required to fully process the data. Since mass spectra exhibit certain structural similarities to image data, deep learning may offer a promising strategy for classification of IMS data as it has been successfully applied to image classification. Methodologically, we propose an adapted architecture based on deep convolutional networks to handle the characteristics of mass spectrometry data, as well as a strategy to interpret the learned model in the spectral domain based on a sensitivity analysis. The proposed methods are evaluated on two algorithmically challenging tumor classification tasks and compared to a baseline approach. Competitiveness of the proposed methods is shown on both tasks by studying the performance via cross-validation. Moreover, the learned models are analyzed by the proposed sensitivity analysis revealing biologically plausible effects as well as confounding factors of the considered tasks. Thus, this study may serve as a starting point for further development of deep learning approaches in IMS classification tasks. https://gitlab.informatik.uni-bremen.de/digipath/Deep_Learning_for_Tumor_Classification_in_IMS. jbehrmann@uni-bremen.de or christianetmann@uni-bremen.de. Supplementary data are available at Bioinformatics online.

  11. Persistent pulmonary subsolid nodules: model-based iterative reconstruction for nodule classification and measurement variability on low-dose CT.

    Science.gov (United States)

    Kim, Hyungjin; Park, Chang Min; Kim, Seong Ho; Lee, Sang Min; Park, Sang Joon; Lee, Kyung Hee; Goo, Jin Mo

    2014-11-01

    To compare the pulmonary subsolid nodule (SSN) classification agreement and measurement variability between filtered back projection (FBP) and model-based iterative reconstruction (MBIR). Low-dose CTs were reconstructed using FBP and MBIR for 47 patients with 47 SSNs. Two readers independently classified SSNs into pure or part-solid ground-glass nodules, and measured the size of the whole nodule and solid portion twice on both reconstruction algorithms. Nodule classification agreement was analyzed using Cohen's kappa and compared between reconstruction algorithms using McNemar's test. Measurement variability was investigated using Bland-Altman analysis and compared with the paired t-test. Cohen's kappa for inter-reader SSN classification agreement was 0.541-0.662 on FBP and 0.778-0.866 on MBIR. Between the two readers, nodule classification was consistent in 79.8 % (75/94) with FBP and 91.5 % (86/94) with MBIR (p = 0.027). Inter-reader measurement variability range was -5.0-2.1 mm on FBP and -3.3-1.8 mm on MBIR for whole nodule size, and was -6.5-0.9 mm on FBP and -5.5-1.5 mm on MBIR for solid portion size. Inter-reader measurement differences were significantly smaller on MBIR (p = 0.027, whole nodule; p = 0.011, solid portion). MBIR significantly improved SSN classification agreement and reduced measurement variability of both whole nodules and solid portions between readers. • Low-dose CT using MBIR algorithm improves reproducibility in the classification of SSNs. • MBIR would enable more confident clinical planning according to the SSN type. • Reduced measurement variability on MBIR allows earlier detection of potentially malignant nodules.

  12. Classification in context

    DEFF Research Database (Denmark)

    Mai, Jens Erik

    2004-01-01

    This paper surveys classification research literature, discusses various classification theories, and shows that the focus has traditionally been on establishing a scientific foundation for classification research. This paper argues that a shift has taken place, and suggests that contemporary...... classification research focus on contextual information as the guide for the design and construction of classification schemes....

  13. Statistical methods for segmentation and classification of images

    DEFF Research Database (Denmark)

    Rosholm, Anders

    1997-01-01

    The central matter of the present thesis is Bayesian statistical inference applied to classification of images. An initial review of Markov Random Fields relates to the modeling aspect of the indicated main subject. In that connection, emphasis is put on the relatively unknown sub-class of Pickard...... with a Pickard Random Field modeling of a considered (categorical) image phenomemon. An extension of the fast PRF based classification technique is presented. The modification introduces auto-correlation into the model of an involved noise process, which previously has been assumed independent. The suitability...... of the extended model is documented by tests on controlled image data containing auto-correlated noise....

  14. Classification

    DEFF Research Database (Denmark)

    Hjørland, Birger

    2017-01-01

    This article presents and discusses definitions of the term “classification” and the related concepts “Concept/conceptualization,”“categorization,” “ordering,” “taxonomy” and “typology.” It further presents and discusses theories of classification including the influences of Aristotle...... and Wittgenstein. It presents different views on forming classes, including logical division, numerical taxonomy, historical classification, hermeneutical and pragmatic/critical views. Finally, issues related to artificial versus natural classification and taxonomic monism versus taxonomic pluralism are briefly...

  15. A Method for Application of Classification Tree Models to Map Aquatic Vegetation Using Remotely Sensed Images from Different Sensors and Dates

    Directory of Open Access Journals (Sweden)

    Ying Cai

    2012-09-01

    Full Text Available In previous attempts to identify aquatic vegetation from remotely-sensed images using classification trees (CT, the images used to apply CT models to different times or locations necessarily originated from the same satellite sensor as that from which the original images used in model development came, greatly limiting the application of CT. We have developed an effective normalization method to improve the robustness of CT models when applied to images originating from different sensors and dates. A total of 965 ground-truth samples of aquatic vegetation types were obtained in 2009 and 2010 in Taihu Lake, China. Using relevant spectral indices (SI as classifiers, we manually developed a stable CT model structure and then applied a standard CT algorithm to obtain quantitative (optimal thresholds from 2009 ground-truth data and images from Landsat7-ETM+, HJ-1B-CCD, Landsat5-TM and ALOS-AVNIR-2 sensors. Optimal CT thresholds produced average classification accuracies of 78.1%, 84.7% and 74.0% for emergent vegetation, floating-leaf vegetation and submerged vegetation, respectively. However, the optimal CT thresholds for different sensor images differed from each other, with an average relative variation (RV of 6.40%. We developed and evaluated three new approaches to normalizing the images. The best-performing method (Method of 0.1% index scaling normalized the SI images using tailored percentages of extreme pixel values. Using the images normalized by Method of 0.1% index scaling, CT models for a particular sensor in which thresholds were replaced by those from the models developed for images originating from other sensors provided average classification accuracies of 76.0%, 82.8% and 68.9% for emergent vegetation, floating-leaf vegetation and submerged vegetation, respectively. Applying the CT models developed for normalized 2009 images to 2010 images resulted in high classification (78.0%–93.3% and overall (92.0%–93.1% accuracies. Our

  16. A stylistic classification of Russian-language texts based on the random walk model

    Science.gov (United States)

    Kramarenko, A. A.; Nekrasov, K. A.; Filimonov, V. V.; Zhivoderov, A. A.; Amieva, A. A.

    2017-09-01

    A formal approach to text analysis is suggested that is based on the random walk model. The frequencies and reciprocal positions of the vowel letters are matched up by a process of quasi-particle migration. Statistically significant difference in the migration parameters for the texts of different functional styles is found. Thus, a possibility of classification of texts using the suggested method is demonstrated. Five groups of the texts are singled out that can be distinguished from one another by the parameters of the quasi-particle migration process.

  17. Weakly supervised classification in high energy physics

    Energy Technology Data Exchange (ETDEWEB)

    Dery, Lucio Mwinmaarong [Physics Department, Stanford University,Stanford, CA, 94305 (United States); Nachman, Benjamin [Physics Division, Lawrence Berkeley National Laboratory,1 Cyclotron Rd, Berkeley, CA, 94720 (United States); Rubbo, Francesco; Schwartzman, Ariel [SLAC National Accelerator Laboratory, Stanford University,2575 Sand Hill Rd, Menlo Park, CA, 94025 (United States)

    2017-05-29

    As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations. This paper presents a new approach called weakly supervised classification in which class proportions are the only input into the machine learning algorithm. Using one of the most challenging binary classification tasks in high energy physics — quark versus gluon tagging — we show that weakly supervised classification can match the performance of fully supervised algorithms. Furthermore, by design, the new algorithm is insensitive to any mis-modeling of discriminating features in the data by the simulation. Weakly supervised classification is a general procedure that can be applied to a wide variety of learning problems to boost performance and robustness when detailed simulations are not reliable or not available.

  18. Weakly supervised classification in high energy physics

    International Nuclear Information System (INIS)

    Dery, Lucio Mwinmaarong; Nachman, Benjamin; Rubbo, Francesco; Schwartzman, Ariel

    2017-01-01

    As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations. This paper presents a new approach called weakly supervised classification in which class proportions are the only input into the machine learning algorithm. Using one of the most challenging binary classification tasks in high energy physics — quark versus gluon tagging — we show that weakly supervised classification can match the performance of fully supervised algorithms. Furthermore, by design, the new algorithm is insensitive to any mis-modeling of discriminating features in the data by the simulation. Weakly supervised classification is a general procedure that can be applied to a wide variety of learning problems to boost performance and robustness when detailed simulations are not reliable or not available.

  19. ACCUWIND - Methods for classification of cup anemometers

    DEFF Research Database (Denmark)

    Dahlberg, J.-Å.; Friis Pedersen, Troels; Busche, P.

    2006-01-01

    the errors associated with the use of cup anemometers, and to develop a classification system for quantification of systematic errors of cup anemometers. This classification system has now been implementedin the IEC 61400-12-1 standard on power performance measurements in annex I and J. The classification...... of cup anemometers requires general external climatic operational ranges to be applied for the analysis of systematic errors. A Class A categoryclassification is connected to reasonably flat sites, and another Class B category is connected to complex terrain, General classification indices are the result...... developed in the CLASSCUP projectand earlier. A number of approaches including the use of two cup anemometer models, two methods of torque coefficient measurement, two angular response measurements, and inclusion and exclusion of influence of friction have been implemented in theclassification process...

  20. Classification of quantum phases and topology of logical operators in an exactly solved model of quantum codes

    International Nuclear Information System (INIS)

    Yoshida, Beni

    2011-01-01

    Searches for possible new quantum phases and classifications of quantum phases have been central problems in physics. Yet, they are indeed challenging problems due to the computational difficulties in analyzing quantum many-body systems and the lack of a general framework for classifications. While frustration-free Hamiltonians, which appear as fixed point Hamiltonians of renormalization group transformations, may serve as representatives of quantum phases, it is still difficult to analyze and classify quantum phases of arbitrary frustration-free Hamiltonians exhaustively. Here, we address these problems by sharpening our considerations to a certain subclass of frustration-free Hamiltonians, called stabilizer Hamiltonians, which have been actively studied in quantum information science. We propose a model of frustration-free Hamiltonians which covers a large class of physically realistic stabilizer Hamiltonians, constrained to only three physical conditions; the locality of interaction terms, translation symmetries and scale symmetries, meaning that the number of ground states does not grow with the system size. We show that quantum phases arising in two-dimensional models can be classified exactly through certain quantum coding theoretical operators, called logical operators, by proving that two models with topologically distinct shapes of logical operators are always separated by quantum phase transitions.

  1. A comparative study of PCA, SIMCA and Cole model for classification of bioimpedance spectroscopy measurements.

    Science.gov (United States)

    Nejadgholi, Isar; Bolic, Miodrag

    2015-08-01

    Due to safety and low cost of bioimpedance spectroscopy (BIS), classification of BIS can be potentially a preferred way of detecting changes in living tissues. However, for longitudinal datasets linear classifiers fail to classify conventional Cole parameters extracted from BIS measurements because of their high variability. In some applications, linear classification based on Principal Component Analysis (PCA) has shown more accurate results. Yet, these methods have not been established for BIS classification, since PCA features have neither been investigated in combination with other classifiers nor have been compared to conventional Cole features in benchmark classification tasks. In this work, PCA and Cole features are compared in three synthesized benchmark classification tasks which are expected to be detected by BIS. These three tasks are classification of before and after geometry change, relative composition change and blood perfusion in a cylindrical organ. Our results show that in all tasks the features extracted by PCA are more discriminant than Cole parameters. Moreover, a pilot study was done on a longitudinal arm BIS dataset including eight subjects and three arm positions. The goal of the study was to compare different methods in arm position classification which includes all three synthesized changes mentioned above. Our comparative study on various classification methods shows that the best classification accuracy is obtained when PCA features are classified by a K-Nearest Neighbors (KNN) classifier. The results of this work suggest that PCA+KNN is a promising method to be considered for classification of BIS datasets that deal with subject and time variability. Copyright © 2015 Elsevier Ltd. All rights reserved.

  2. Model-based Clustering of Categorical Time Series with Multinomial Logit Classification

    Science.gov (United States)

    Frühwirth-Schnatter, Sylvia; Pamminger, Christoph; Winter-Ebmer, Rudolf; Weber, Andrea

    2010-09-01

    A common problem in many areas of applied statistics is to identify groups of similar time series in a panel of time series. However, distance-based clustering methods cannot easily be extended to time series data, where an appropriate distance-measure is rather difficult to define, particularly for discrete-valued time series. Markov chain clustering, proposed by Pamminger and Frühwirth-Schnatter [6], is an approach for clustering discrete-valued time series obtained by observing a categorical variable with several states. This model-based clustering method is based on finite mixtures of first-order time-homogeneous Markov chain models. In order to further explain group membership we present an extension to the approach of Pamminger and Frühwirth-Schnatter [6] by formulating a probabilistic model for the latent group indicators within the Bayesian classification rule by using a multinomial logit model. The parameters are estimated for a fixed number of clusters within a Bayesian framework using an Markov chain Monte Carlo (MCMC) sampling scheme representing a (full) Gibbs-type sampler which involves only draws from standard distributions. Finally, an application to a panel of Austrian wage mobility data is presented which leads to an interesting segmentation of the Austrian labour market.

  3. 78 FR 54970 - Cotton Futures Classification: Optional Classification Procedure

    Science.gov (United States)

    2013-09-09

    ... Service 7 CFR Part 27 [AMS-CN-13-0043] RIN 0581-AD33 Cotton Futures Classification: Optional Classification Procedure AGENCY: Agricultural Marketing Service, USDA. ACTION: Proposed rule. SUMMARY: The... optional cotton futures classification procedure--identified and known as ``registration'' by the U.S...

  4. Spatial and Spectral Hybrid Image Classification for Rice Lodging Assessment through UAV Imagery

    Directory of Open Access Journals (Sweden)

    Ming-Der Yang

    2017-06-01

    Full Text Available Rice lodging identification relies on manual in situ assessment and often leads to a compensation dispute in agricultural disaster assessment. Therefore, this study proposes a comprehensive and efficient classification technique for agricultural lands that entails using unmanned aerial vehicle (UAV imagery. In addition to spectral information, digital surface model (DSM and texture information of the images was obtained through image-based modeling and texture analysis. Moreover, single feature probability (SFP values were computed to evaluate the contribution of spectral and spatial hybrid image information to classification accuracy. The SFP results revealed that texture information was beneficial for the classification of rice and water, DSM information was valuable for lodging and tree classification, and the combination of texture and DSM information was helpful in distinguishing between artificial surface and bare land. Furthermore, a decision tree classification model incorporating SFP values yielded optimal results, with an accuracy of 96.17% and a Kappa value of 0.941, compared with that of a maximum likelihood classification model (90.76%. The rice lodging ratio in paddies at the study site was successfully identified, with three paddies being eligible for disaster relief. The study demonstrated that the proposed spatial and spectral hybrid image classification technology is a promising tool for rice lodging assessment.

  5. A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem

    Directory of Open Access Journals (Sweden)

    Zekić-Sušac Marijana

    2014-09-01

    Full Text Available Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the high-dimensional classification problem of recognizing entrepreneurial intentions of students by machine learning methods. Methods/Approach: Four methods were tested: artificial neural networks, CART classification trees, support vector machines, and k-nearest neighbour on the same dataset in order to compare their efficiency in the sense of classification accuracy. The performance of each method was compared on ten subsamples in a 10-fold cross-validation procedure in order to assess computing sensitivity and specificity of each model. Results: The artificial neural network model based on multilayer perceptron yielded a higher classification rate than the models produced by other methods. The pairwise t-test showed a statistical significance between the artificial neural network and the k-nearest neighbour model, while the difference among other methods was not statistically significant. Conclusions: Tested machine learning methods are able to learn fast and achieve high classification accuracy. However, further advancement can be assured by testing a few additional methodological refinements in machine learning methods.

  6. Classification of forensic autopsy reports through conceptual graph-based document representation model.

    Science.gov (United States)

    Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa; Al-Garadi, Mohammed Ali

    2018-06-01

    Text categorization has been used extensively in recent years to classify plain-text clinical reports. This study employs text categorization techniques for the classification of open narrative forensic autopsy reports. One of the key steps in text classification is document representation. In document representation, a clinical report is transformed into a format that is suitable for classification. The traditional document representation technique for text categorization is the bag-of-words (BoW) technique. In this study, the traditional BoW technique is ineffective in classifying forensic autopsy reports because it merely extracts frequent but discriminative features from clinical reports. Moreover, this technique fails to capture word inversion, as well as word-level synonymy and polysemy, when classifying autopsy reports. Hence, the BoW technique suffers from low accuracy and low robustness unless it is improved with contextual and application-specific information. To overcome the aforementioned limitations of the BoW technique, this research aims to develop an effective conceptual graph-based document representation (CGDR) technique to classify 1500 forensic autopsy reports from four (4) manners of death (MoD) and sixteen (16) causes of death (CoD). Term-based and Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) based conceptual features were extracted and represented through graphs. These features were then used to train a two-level text classifier. The first level classifier was responsible for predicting MoD. In addition, the second level classifier was responsible for predicting CoD using the proposed conceptual graph-based document representation technique. To demonstrate the significance of the proposed technique, its results were compared with those of six (6) state-of-the-art document representation techniques. Lastly, this study compared the effects of one-level classification and two-level classification on the experimental results

  7. Site effect classification based on microtremor data analysis using concentration-area fractal model

    Science.gov (United States)

    Adib, A.; Afzal, P.; Heydarzadeh, K.

    2014-07-01

    The aim of this study is to classify the site effect using concentration-area (C-A) fractal model in Meybod city, Central Iran, based on microtremor data analysis. Log-log plots of the frequency, amplification and vulnerability index (k-g) indicate a multifractal nature for the parameters in the area. The results obtained from the C-A fractal modeling reveal that proper soil types are located around the central city. The results derived via the fractal modeling were utilized to improve the Nogoshi's classification results in the Meybod city. The resulted categories are: (1) hard soil and weak rock with frequency of 6.2 to 8 Hz, (2) stiff soil with frequency of about 4.9 to 6.2 Hz, (3) moderately soft soil with the frequency of 2.4 to 4.9 Hz, and (4) soft soil with the frequency lower than 2.4 Hz.

  8. Classification of refrigerants; Classification des fluides frigorigenes

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2001-07-01

    This document was made from the US standard ANSI/ASHRAE 34 published in 2001 and entitled 'designation and safety classification of refrigerants'. This classification allows to clearly organize in an international way the overall refrigerants used in the world thanks to a codification of the refrigerants in correspondence with their chemical composition. This note explains this codification: prefix, suffixes (hydrocarbons and derived fluids, azeotropic and non-azeotropic mixtures, various organic compounds, non-organic compounds), safety classification (toxicity, flammability, case of mixtures). (J.S.)

  9. The Zipf Law revisited: An evolutionary model of emerging classification

    Energy Technology Data Exchange (ETDEWEB)

    Levitin, L.B. [Boston Univ., MA (United States); Schapiro, B. [TINA, Brandenburg (Germany); Perlovsky, L. [NRC, Wakefield, MA (United States)

    1996-12-31

    Zipf`s Law is a remarkable rank-frequency relationship observed in linguistics (the frequencies of the use of words are approximately inversely proportional to their ranks in the decreasing frequency order) as well as in the behavior of many complex systems of surprisingly different nature. We suggest an evolutionary model of emerging classification of objects into classes corresponding to concepts and denoted by words. The evolution of the system is derived from two basic assumptions: first, the probability to recognize an object as belonging to a known class is proportional to the number of objects in this class already recognized, and, second, there exists a small probability to observe an object that requires creation of a new class ({open_quotes}mutation{close_quotes} that gives birth to a new {open_quotes}species{close_quotes}). It is shown that the populations of classes in such a system obey the Zipf Law provided that the rate of emergence of new classes is small. The model leads also to the emergence of a second-tier structure of {open_quotes}super-classes{close_quotes} - groups of classes with almost equal populations.

  10. Data Clustering and Evolving Fuzzy Decision Tree for Data Base Classification Problems

    Science.gov (United States)

    Chang, Pei-Chann; Fan, Chin-Yuan; Wang, Yen-Wen

    Data base classification suffers from two well known difficulties, i.e., the high dimensionality and non-stationary variations within the large historic data. This paper presents a hybrid classification model by integrating a case based reasoning technique, a Fuzzy Decision Tree (FDT), and Genetic Algorithms (GA) to construct a decision-making system for data classification in various data base applications. The model is major based on the idea that the historic data base can be transformed into a smaller case-base together with a group of fuzzy decision rules. As a result, the model can be more accurately respond to the current data under classifying from the inductions by these smaller cases based fuzzy decision trees. Hit rate is applied as a performance measure and the effectiveness of our proposed model is demonstrated by experimentally compared with other approaches on different data base classification applications. The average hit rate of our proposed model is the highest among others.

  11. Classification of structurally related commercial contrast media by near infrared spectroscopy.

    Science.gov (United States)

    Yip, Wai Lam; Soosainather, Tom Collin; Dyrstad, Knut; Sande, Sverre Arne

    2014-03-01

    Near infrared spectroscopy (NIRS) is a non-destructive measurement technique with broad application in pharmaceutical industry. Correct identification of pharmaceutical ingredients is an important task for quality control. Failure in this step can result in several adverse consequences, varied from economic loss to negative impact on patient safety. We have compared different methods in classification of a set of commercially available structurally related contrast media, Iodixanol (Visipaque(®)), Iohexol (Omnipaque(®)), Caldiamide Sodium and Gadodiamide (Omniscan(®)), by using NIR spectroscopy. The performance of classification models developed by soft independent modelling of class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA) and Main and Interactions of Individual Principal Components Regression (MIPCR) were compared. Different variable selection methods were applied to optimize the classification models. Models developed by backward variable elimination partial least squares regression (BVE-PLS) and MIPCR were found to be most effective for classification of the set of contrast media. Below 1.5% of samples from the independent test set were not recognized by the BVE-PLS and MIPCR models, compared to up to 15% when models developed by other techniques were applied. Copyright © 2013 Elsevier B.V. All rights reserved.

  12. A Parallel Adaboost-Backpropagation Neural Network for Massive Image Dataset Classification

    Science.gov (United States)

    Cao, Jianfang; Chen, Lichao; Wang, Min; Shi, Hao; Tian, Yun

    2016-01-01

    Image classification uses computers to simulate human understanding and cognition of images by automatically categorizing images. This study proposes a faster image classification approach that parallelizes the traditional Adaboost-Backpropagation (BP) neural network using the MapReduce parallel programming model. First, we construct a strong classifier by assembling the outputs of 15 BP neural networks (which are individually regarded as weak classifiers) based on the Adaboost algorithm. Second, we design Map and Reduce tasks for both the parallel Adaboost-BP neural network and the feature extraction algorithm. Finally, we establish an automated classification model by building a Hadoop cluster. We use the Pascal VOC2007 and Caltech256 datasets to train and test the classification model. The results are superior to those obtained using traditional Adaboost-BP neural network or parallel BP neural network approaches. Our approach increased the average classification accuracy rate by approximately 14.5% and 26.0% compared to the traditional Adaboost-BP neural network and parallel BP neural network, respectively. Furthermore, the proposed approach requires less computation time and scales very well as evaluated by speedup, sizeup and scaleup. The proposed approach may provide a foundation for automated large-scale image classification and demonstrates practical value. PMID:27905520

  13. Optimal Subset Selection of Time-Series MODIS Images and Sample Data Transfer with Random Forests for Supervised Classification Modelling.

    Science.gov (United States)

    Zhou, Fuqun; Zhang, Aining

    2016-10-25

    Nowadays, various time-series Earth Observation data with multiple bands are freely available, such as Moderate Resolution Imaging Spectroradiometer (MODIS) datasets including 8-day composites from NASA, and 10-day composites from the Canada Centre for Remote Sensing (CCRS). It is challenging to efficiently use these time-series MODIS datasets for long-term environmental monitoring due to their vast volume and information redundancy. This challenge will be greater when Sentinel 2-3 data become available. Another challenge that researchers face is the lack of in-situ data for supervised modelling, especially for time-series data analysis. In this study, we attempt to tackle the two important issues with a case study of land cover mapping using CCRS 10-day MODIS composites with the help of Random Forests' features: variable importance, outlier identification. The variable importance feature is used to analyze and select optimal subsets of time-series MODIS imagery for efficient land cover mapping, and the outlier identification feature is utilized for transferring sample data available from one year to an adjacent year for supervised classification modelling. The results of the case study of agricultural land cover classification at a regional scale show that using only about a half of the variables we can achieve land cover classification accuracy close to that generated using the full dataset. The proposed simple but effective solution of sample transferring could make supervised modelling possible for applications lacking sample data.

  14. Molecular determinants of interactions between the N-terminal domain and the transmembrane core that modulate hERG K+ channel gating.

    Directory of Open Access Journals (Sweden)

    Jorge Fernández-Trillo

    Full Text Available A conserved eag domain in the cytoplasmic amino terminus of the human ether-a-go-go-related gene (hERG potassium channel is critical for its slow deactivation gating. Introduction of gene fragments encoding the eag domain are able to restore normal deactivation properties of channels from which most of the amino terminus has been deleted, and also those lacking exclusively the eag domain or carrying a single point mutation in the initial residues of the N-terminus. Deactivation slowing in the presence of the recombinant domain is not observed with channels carrying a specific Y542C point mutation in the S4-S5 linker. On the other hand, mutations in some initial positions of the recombinant fragment also impair its ability to restore normal deactivation. Fluorescence resonance energy transfer (FRET analysis of fluorophore-tagged proteins under total internal reflection fluorescence (TIRF conditions revealed a substantial level of FRET between the introduced N-terminal eag fragments and the eag domain-deleted channels expressed at the membrane, but not between the recombinant eag domain and full-length channels with an intact amino terminus. The FRET signals were also minimized when the recombinant eag fragments carried single point mutations in the initial portion of their amino end, and when Y542C mutated channels were used. These data suggest that the restoration of normal deactivation gating by the N-terminal recombinant eag fragment is an intrinsic effect of this domain directed by the interaction of its N-terminal segment with the gating machinery, likely at the level of the S4-S5 linker.

  15. Gating mechanism of Kv11.1 (hERG) K+ channels without covalent connection between voltage sensor and pore domains.

    Science.gov (United States)

    de la Peña, Pilar; Domínguez, Pedro; Barros, Francisco

    2018-03-01

    Kv11.1 (hERG, KCNH2) is a voltage-gated potassium channel crucial in setting the cardiac rhythm and the electrical behaviour of several non-cardiac cell types. Voltage-dependent gating of Kv11.1 can be reconstructed from non-covalently linked voltage sensing and pore modules (split channels), challenging classical views of voltage-dependent channel activation based on a S4-S5 linker acting as a rigid mechanical lever to open the gate. Progressive displacement of the split position from the end to the beginning of the S4-S5 linker induces an increasing negative shift in activation voltage dependence, a reduced z g value and a more negative ΔG 0 for current activation, an almost complete abolition of the activation time course sigmoid shape and a slowing of the voltage-dependent deactivation. Channels disconnected at the S4-S5 linker near the S4 helix show a destabilization of the closed state(s). Furthermore, the isochronal ion current mode shift magnitude is clearly reduced in the different splits. Interestingly, the progressive modifications of voltage dependence activation gating by changing the split position are accompanied by a shift in the voltage-dependent availability to a methanethiosulfonate reagent of a Cys introduced at the upper S4 helix. Our data demonstrate for the first time that alterations in the covalent connection between the voltage sensor and the pore domains impact on the structural reorganizations of the voltage sensor domain. Also, they support the hypothesis that the S4-S5 linker integrates signals coming from other cytoplasmic domains that constitute either an important component or a crucial regulator of the gating machinery in Kv11.1 and other KCNH channels.

  16. Odor Classification using Agent Technology

    Directory of Open Access Journals (Sweden)

    Sigeru OMATU

    2014-03-01

    Full Text Available In order to measure and classify odors, Quartz Crystal Microbalance (QCM can be used. In the present study, seven QCM sensors and three different odors are used. The system has been developed as a virtual organization of agents using an agent platform called PANGEA (Platform for Automatic coNstruction of orGanizations of intElligent Agents. This is a platform for developing open multi-agent systems, specifically those including organizational aspects. The main reason for the use of agents is the scalability of the platform, i.e. the way in which it models the services. The system models functionalities as services inside the agents, or as Service Oriented Approach (SOA architecture compliant services using Web Services. This way the adaptation of the odor classification systems with new algorithms, tools and classification techniques is allowed.

  17. Towards low carbon business park energy systems: Classification of techno-economic energy models

    International Nuclear Information System (INIS)

    Timmerman, Jonas; Vandevelde, Lieven; Van Eetvelde, Greet

    2014-01-01

    To mitigate climate destabilisation, human-induced greenhouse gas emissions urgently need to be curbed. A major share of these emissions originates from the industry and energy sectors. Hence, a low carbon shift in industrial and business park energy systems is called for. Low carbon business parks minimise energy-related carbon dioxide emissions by maximal exploitation of local renewable energy production, enhanced energy efficiency, and inter-firm heat exchange, combined in a collective energy system. The holistic approach of techno-economic energy models facilitates the design of such systems, while yielding an optimal trade-off between energetic, economic and environmental performances. However, no models custom-tailored for industrial park energy systems are detected in literature. In this paper, existing energy model classifications are scanned for adequate model characteristics and accordingly, a confined number of models are selected and described. Subsequently, a practical typology is proposed, existing of energy system evolution, optimisation, simulation, accounting and integration models, and key model features are compared. Finally, important features for a business park energy model are identified. - Highlights: • A holistic perspective on (low carbon) business park energy systems is introduced. • A new categorisation of techno-economic energy models is proposed. • Model characteristics are described per model category. • Essential model features for business park energy system modelling are identified. • A strategy towards a techno-economic energy model for business parks is proposed

  18. Rehabilitating drug-induced long-QT promoters: in-silico design of hERG-neutral cisapride analogues with retained pharmacological activity.

    Science.gov (United States)

    Durdagi, Serdar; Randall, Trevor; Duff, Henry J; Chamberlin, Adam; Noskov, Sergei Y

    2014-03-08

    The human ether-a-go-go related gene 1 (hERG1), which codes for a potassium ion channel, is a key element in the cardiac delayed rectified potassium current, IKr, and plays an important role in the normal repolarization of the heart's action potential. Many approved drugs have been withdrawn from the market due to their prolongation of the QT interval. Most of these drugs have high potencies for their principal targets and are often irreplaceable, thus "rehabilitation" studies for decreasing their high hERG1 blocking affinities, while keeping them active at the binding sites of their targets, have been proposed to enable these drugs to re-enter the market. In this proof-of-principle study, we focus on cisapride, a gastroprokinetic agent withdrawn from the market due to its high hERG1 blocking affinity. Here we tested an a priori strategy to predict a compound's cardiotoxicity using de novo drug design with molecular docking and Molecular Dynamics (MD) simulations to generate a strategy for the rehabilitation of cisapride. We focused on two key receptors, a target interaction with the (adenosine) receptor and an off-target interaction with hERG1 channels. An analysis of the fragment interactions of cisapride at human A2A adenosine receptors and hERG1 central cavities helped us to identify the key chemical groups responsible for the drug activity and hERG1 blockade. A set of cisapride derivatives with reduced cardiotoxicity was then proposed using an in-silico two-tier approach. This set was compared against a large dataset of commercially available cisapride analogs and derivatives. An interaction decomposition of cisapride and cisapride derivatives allowed for the identification of key active scaffolds and functional groups that may be responsible for the unwanted blockade of hERG1.

  19. Automatic sleep classification using a data-driven topic model reveals latent sleep states

    DEFF Research Database (Denmark)

    Koch, Henriette; Christensen, Julie Anja Engelhard; Frandsen, Rune

    2014-01-01

    Latent Dirichlet Allocation. Model application was tested on control subjects and patients with periodic leg movements (PLM) representing a non-neurodegenerative group, and patients with idiopathic REM sleep behavior disorder (iRBD) and Parkinson's Disease (PD) representing a neurodegenerative group......Background: The golden standard for sleep classification uses manual scoring of polysomnography despite points of criticism such as oversimplification, low inter-rater reliability and the standard being designed on young and healthy subjects. New method: To meet the criticism and reveal the latent...... sleep states, this study developed a general and automatic sleep classifier using a data-driven approach. Spectral EEG and EOG measures and eye correlation in 1 s windows were calculated and each sleep epoch was expressed as a mixture of probabilities of latent sleep states by using the topic model...

  20. Quality-Oriented Classification of Aircraft Material Based on SVM

    Directory of Open Access Journals (Sweden)

    Hongxia Cai

    2014-01-01

    Full Text Available The existing material classification is proposed to improve the inventory management. However, different materials have the different quality-related attributes, especially in the aircraft industry. In order to reduce the cost without sacrificing the quality, we propose a quality-oriented material classification system considering the material quality character, Quality cost, and Quality influence. Analytic Hierarchy Process helps to make feature selection and classification decision. We use the improved Kraljic Portfolio Matrix to establish the three-dimensional classification model. The aircraft materials can be divided into eight types, including general type, key type, risk type, and leveraged type. Aiming to improve the classification accuracy of various materials, the algorithm of Support Vector Machine is introduced. Finally, we compare the SVM and BP neural network in the application. The results prove that the SVM algorithm is more efficient and accurate and the quality-oriented material classification is valuable.

  1. Automated Decision Tree Classification of Corneal Shape

    Science.gov (United States)

    Twa, Michael D.; Parthasarathy, Srinivasan; Roberts, Cynthia; Mahmoud, Ashraf M.; Raasch, Thomas W.; Bullimore, Mark A.

    2011-01-01

    Purpose The volume and complexity of data produced during videokeratography examinations present a challenge of interpretation. As a consequence, results are often analyzed qualitatively by subjective pattern recognition or reduced to comparisons of summary indices. We describe the application of decision tree induction, an automated machine learning classification method, to discriminate between normal and keratoconic corneal shapes in an objective and quantitative way. We then compared this method with other known classification methods. Methods The corneal surface was modeled with a seventh-order Zernike polynomial for 132 normal eyes of 92 subjects and 112 eyes of 71 subjects diagnosed with keratoconus. A decision tree classifier was induced using the C4.5 algorithm, and its classification performance was compared with the modified Rabinowitz–McDonnell index, Schwiegerling’s Z3 index (Z3), Keratoconus Prediction Index (KPI), KISA%, and Cone Location and Magnitude Index using recommended classification thresholds for each method. We also evaluated the area under the receiver operator characteristic (ROC) curve for each classification method. Results Our decision tree classifier performed equal to or better than the other classifiers tested: accuracy was 92% and the area under the ROC curve was 0.97. Our decision tree classifier reduced the information needed to distinguish between normal and keratoconus eyes using four of 36 Zernike polynomial coefficients. The four surface features selected as classification attributes by the decision tree method were inferior elevation, greater sagittal depth, oblique toricity, and trefoil. Conclusions Automated decision tree classification of corneal shape through Zernike polynomials is an accurate quantitative method of classification that is interpretable and can be generated from any instrument platform capable of raw elevation data output. This method of pattern classification is extendable to other classification

  2. Classification of technogenic impacts on the geological medium

    International Nuclear Information System (INIS)

    Trofimov, V.T.; Korolev, V.A.; Gerasimova, A.S.

    1995-01-01

    The available systems of classification of technology-induced impacts on the geological environment are analyzed and a classification which is elaborated by the authors and allows to break the integrated impact into individual components for their subsequent analysis, evaluation and reflection in cartographic models. This classification assumes the division of technology-induced impacts into classes and subclasses. The first class-impacts of physical nature-includes a subclass of radioactive impacts where, in its turn, two types of impacts are distinguished: radioactive contamination and radiation decontamination of the components of the geological environment. The proposed classification can serve the basis for developing standards and regulations of typification and evaluation of technology-induced impacts o the geological environment. 27 refs., 1 tab

  3. Twitter classification model: the ABC of two million fitness tweets.

    Science.gov (United States)

    Vickey, Theodore A; Ginis, Kathleen Martin; Dabrowski, Maciej

    2013-09-01

    The purpose of this project was to design and test data collection and management tools that can be used to study the use of mobile fitness applications and social networking within the context of physical activity. This project was conducted over a 6-month period and involved collecting publically shared Twitter data from five mobile fitness apps (Nike+, RunKeeper, MyFitnessPal, Endomondo, and dailymile). During that time, over 2.8 million tweets were collected, processed, and categorized using an online tweet collection application and a customized JavaScript. Using the grounded theory, a classification model was developed to categorize and understand the types of information being shared by application users. Our data show that by tracking mobile fitness app hashtags, a wealth of information can be gathered to include but not limited to daily use patterns, exercise frequency, location-based workouts, and overall workout sentiment.

  4. Solving and interpreting binary classification problems in marketing with SVMs

    NARCIS (Netherlands)

    J.C. Bioch (Cor); P.J.F. Groenen (Patrick); G.I. Nalbantov (Georgi)

    2005-01-01

    textabstractMarketing problems often involve inary classification of customers into ``buyers'' versus ``non-buyers'' or ``prefers brand A'' versus ``prefers brand B''. These cases require binary classification models such as logistic regression, linear, and quadratic discriminant analysis. A

  5. Predicting Battle Outcomes with Classification Trees

    National Research Council Canada - National Science Library

    Coban, Muzaffer

    2001-01-01

    ... from the actual battlefield, The models built by using classification trees reveal that the objective variables alone cannot explain the outcome of battles, Relative factors, such as leadership, have deep...

  6. Evolving regulatory paradigm for proarrhythmic risk assessment for new drugs.

    Science.gov (United States)

    Vicente, Jose; Stockbridge, Norman; Strauss, David G

    Fourteen drugs were removed from the market worldwide because their potential to cause torsade de pointes (torsade), a potentially fatal ventricular arrhythmia. The observation that most drugs that cause torsade block the potassium channel encoded by the human ether-à-go-go related gene (hERG) and prolong the heart rate corrected QT interval (QTc) on the ECG, led to a focus on screening new drugs for their potential to block the hERG potassium channel and prolong QTc. This has been a successful strategy keeping torsadogenic drugs off the market, but has resulted in drugs being dropped from development, sometimes inappropriately. This is because not all drugs that block the hERG potassium channel and prolong QTc cause torsade, sometimes because they block other channels. The regulatory paradigm is evolving to improve proarrhythmic risk prediction. ECG studies can now use exposure-response modeling for assessing the effect of a drug on the QTc in small sample size first-in-human studies. Furthermore, the Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative is developing and validating a new in vitro paradigm for cardiac safety evaluation of new drugs that provides a more accurate and comprehensive mechanistic-based assessment of proarrhythmic potential. Under CiPA, the prediction of proarrhythmic potential will come from in vitro ion channel assessments coupled with an in silico model of the human ventricular myocyte. The preclinical assessment will be checked with an assessment of human phase 1 ECG data to determine if there are unexpected ion channel effects in humans compared to preclinical ion channel data. While there is ongoing validation work, the heart rate corrected J-T peak interval is likely to be assessed under CiPA to detect inward current block in presence of hERG potassium channel block. Copyright © 2016 Elsevier Inc. All rights reserved.

  7. Stream classification of the Apalachicola-Chattahoochee-Flint River System to support modeling of aquatic habitat response to climate change

    Science.gov (United States)

    Elliott, Caroline M.; Jacobson, Robert B.; Freeman, Mary C.

    2014-01-01

    A stream classification and associated datasets were developed for the Apalachicola-Chattahoochee-Flint River Basin to support biological modeling of species response to climate change in the southeastern United States. The U.S. Geological Survey and the Department of the Interior’s National Climate Change and Wildlife Science Center established the Southeast Regional Assessment Project (SERAP) which used downscaled general circulation models to develop landscape-scale assessments of climate change and subsequent effects on land cover, ecosystems, and priority species in the southeastern United States. The SERAP aquatic and hydrologic dynamics modeling efforts involve multiscale watershed hydrology, stream-temperature, and fish-occupancy models, which all are based on the same stream network. Models were developed for the Apalachicola-Chattahoochee-Flint River Basin and subbasins in Alabama, Florida, and Georgia, and for the Upper Roanoke River Basin in Virginia. The stream network was used as the spatial scheme through which information was shared across the various models within SERAP. Because these models operate at different scales, coordinated pair versions of the network were delineated, characterized, and parameterized for coarse- and fine-scale hydrologic and biologic modeling. The stream network used for the SERAP aquatic models was extracted from a 30-meter (m) scale digital elevation model (DEM) using standard topographic analysis of flow accumulation. At the finer scale, reaches were delineated to represent lengths of stream channel with fairly homogenous physical characteristics (mean reach length = 350 m). Every reach in the network is designated with geomorphic attributes including upstream drainage basin area, channel gradient, channel width, valley width, Strahler and Shreve stream order, stream power, and measures of stream confinement. The reach network was aggregated from tributary junction to tributary junction to define segments for the

  8. A New Method for Solving Supervised Data Classification Problems

    Directory of Open Access Journals (Sweden)

    Parvaneh Shabanzadeh

    2014-01-01

    Full Text Available Supervised data classification is one of the techniques used to extract nontrivial information from data. Classification is a widely used technique in various fields, including data mining, industry, medicine, science, and law. This paper considers a new algorithm for supervised data classification problems associated with the cluster analysis. The mathematical formulations for this algorithm are based on nonsmooth, nonconvex optimization. A new algorithm for solving this optimization problem is utilized. The new algorithm uses a derivative-free technique, with robustness and efficiency. To improve classification performance and efficiency in generating classification model, a new feature selection algorithm based on techniques of convex programming is suggested. Proposed methods are tested on real-world datasets. Results of numerical experiments have been presented which demonstrate the effectiveness of the proposed algorithms.

  9. Classification of hydrocephalus: critical analysis of classification categories and advantages of "Multi-categorical Hydrocephalus Classification" (Mc HC).

    Science.gov (United States)

    Oi, Shizuo

    2011-10-01

    Hydrocephalus is a complex pathophysiology with disturbed cerebrospinal fluid (CSF) circulation. There are numerous numbers of classification trials published focusing on various criteria, such as associated anomalies/underlying lesions, CSF circulation/intracranial pressure patterns, clinical features, and other categories. However, no definitive classification exists comprehensively to cover the variety of these aspects. The new classification of hydrocephalus, "Multi-categorical Hydrocephalus Classification" (Mc HC), was invented and developed to cover the entire aspects of hydrocephalus with all considerable classification items and categories. Ten categories include "Mc HC" category I: onset (age, phase), II: cause, III: underlying lesion, IV: symptomatology, V: pathophysiology 1-CSF circulation, VI: pathophysiology 2-ICP dynamics, VII: chronology, VII: post-shunt, VIII: post-endoscopic third ventriculostomy, and X: others. From a 100-year search of publication related to the classification of hydrocephalus, 14 representative publications were reviewed and divided into the 10 categories. The Baumkuchen classification graph made from the round o'clock classification demonstrated the historical tendency of deviation to the categories in pathophysiology, either CSF or ICP dynamics. In the preliminary clinical application, it was concluded that "Mc HC" is extremely effective in expressing the individual state with various categories in the past and present condition or among the compatible cases of hydrocephalus along with the possible chronological change in the future.

  10. Classification

    Science.gov (United States)

    Clary, Renee; Wandersee, James

    2013-01-01

    In this article, Renee Clary and James Wandersee describe the beginnings of "Classification," which lies at the very heart of science and depends upon pattern recognition. Clary and Wandersee approach patterns by first telling the story of the "Linnaean classification system," introduced by Carl Linnacus (1707-1778), who is…

  11. Random forests for classification in ecology

    Science.gov (United States)

    Cutler, D.R.; Edwards, T.C.; Beard, K.H.; Cutler, A.; Hess, K.T.; Gibson, J.; Lawler, J.J.

    2007-01-01

    Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature. ?? 2007 by the Ecological Society of America.

  12. Pildiseiklusi maal ja merel / Neeme Korv

    Index Scriptorium Estoniae

    Korv, Neeme, 1974-

    2010-01-01

    Arvustus: Hergé, pseud. Kuldsõrgadega krabi / [prantsuse keelest tõlkinud Katrin Kaarep. Tallinn] : Tänapäev, 2010. (Tintini seiklused) ; Hergé, pseud. Must last / [prantsuse keelest tõlkinud Katrin Kaarep. Tallinn] : Tänapäev, 2010. (Tintini seiklused)

  13. Improving the Classification Accuracy for Near-Infrared Spectroscopy of Chinese Salvia miltiorrhiza Using Local Variable Selection

    Directory of Open Access Journals (Sweden)

    Lianqing Zhu

    2018-01-01

    Full Text Available In order to improve the classification accuracy of Chinese Salvia miltiorrhiza using near-infrared spectroscopy, a novel local variable selection strategy is thus proposed. Combining the strengths of the local algorithm and interval partial least squares, the spectra data have firstly been divided into several pairs of classes in sample direction and equidistant subintervals in variable direction. Then, a local classification model has been built, and the most proper spectral region has been selected based on the new evaluation criterion considering both classification error rate and best predictive ability under the leave-one-out cross validation scheme for each pair of classes. Finally, each observation can be assigned to belong to the class according to the statistical analysis of classification results of the local classification model built on selected variables. The performance of the proposed method was demonstrated through near-infrared spectra of cultivated or wild Salvia miltiorrhiza, which are collected from 8 geographical origins in 5 provinces of China. For comparison, soft independent modelling of class analogy and partial least squares discriminant analysis methods are, respectively, employed as the classification model. Experimental results showed that classification performance of the classification model with local variable selection was obvious better than that without variable selection.

  14. Introduction of the gross motor function classification system in Venezuela--a model for knowledge dissemination.

    Science.gov (United States)

    Löwing, Kristina; Arredondo, Ynes C; Tedroff, Marika; Tedroff, Kristina

    2015-09-04

    A current worldwide common goal is to optimize the health and well-being of children with cerebral palsy (CP). In order to reach that goal, for this heterogeneous group, a common language and classification systems are required to predict development and offer evidence based interventions. In most countries in Africa, South America, Asia and Eastern Europe the classification systems for CP are unfamiliar and rarely used. Education and implementation are required. The specific aims of this study were to examine a model in order to introduce the Gross Motor Function Classification System (GMFCS-E&R) in Venezuela, and to examine the validity and the reliability. Children with CP, registered at a National child rehabilitation centre in Venezuela, were invited to participate. The Spanish version of GMFCS-E&R was used. The Wilson mobility scale was translated and used to examine the concurrent validity. A structured questionnaire, comprising aspects of mobility and gross motor function, was constructed. In addition, each child was filmed. A paediatrician in Venezuela received supervised self-education in GMFCS-E&R and the Wilson mobility scale. A Swedish student was educated in GMFCS-E&R and the Wilson mobility scale prior to visiting Venezuela. In Venezuela, all children were classified and scored by the paediatrician and student independently. An experienced paediatric physiotherapist (PT) in Sweden made independent GMFCS-E&R classifications and Wilson mobility scale scorings, accomplished through merging data from the structured questionnaire with observations of the films. Descriptive statistics were used and reliability was presented with weighted Kappa (Kw). Spearman's correlation coefficient was calculated to explore the concurrent validity between GMFCS-E&R and Wilson mobility scale. Eighty-eight children (56 boys), mean age 10 years (3-18), with CP participated. The inter-rater reliability of GMFCS-E&R between; the paediatrician and the PT was Kw = 0.85 (95% CI

  15. Iris Image Classification Based on Hierarchical Visual Codebook.

    Science.gov (United States)

    Zhenan Sun; Hui Zhang; Tieniu Tan; Jianyu Wang

    2014-06-01

    Iris recognition as a reliable method for personal identification has been well-studied with the objective to assign the class label of each iris image to a unique subject. In contrast, iris image classification aims to classify an iris image to an application specific category, e.g., iris liveness detection (classification of genuine and fake iris images), race classification (e.g., classification of iris images of Asian and non-Asian subjects), coarse-to-fine iris identification (classification of all iris images in the central database into multiple categories). This paper proposes a general framework for iris image classification based on texture analysis. A novel texture pattern representation method called Hierarchical Visual Codebook (HVC) is proposed to encode the texture primitives of iris images. The proposed HVC method is an integration of two existing Bag-of-Words models, namely Vocabulary Tree (VT), and Locality-constrained Linear Coding (LLC). The HVC adopts a coarse-to-fine visual coding strategy and takes advantages of both VT and LLC for accurate and sparse representation of iris texture. Extensive experimental results demonstrate that the proposed iris image classification method achieves state-of-the-art performance for iris liveness detection, race classification, and coarse-to-fine iris identification. A comprehensive fake iris image database simulating four types of iris spoof attacks is developed as the benchmark for research of iris liveness detection.

  16. Classification of Specialized Farms Applying Multivariate Statistical Methods

    Directory of Open Access Journals (Sweden)

    Zuzana Hloušková

    2017-01-01

    Full Text Available Classification of specialized farms applying multivariate statistical methods The paper is aimed at application of advanced multivariate statistical methods when classifying cattle breeding farming enterprises by their economic size. Advantage of the model is its ability to use a few selected indicators compared to the complex methodology of current classification model that requires knowledge of detailed structure of the herd turnover and structure of cultivated crops. Output of the paper is intended to be applied within farm structure research focused on future development of Czech agriculture. As data source, the farming enterprises database for 2014 has been used, from the FADN CZ system. The predictive model proposed exploits knowledge of actual size classes of the farms tested. Outcomes of the linear discriminatory analysis multifactor classification method have supported the chance of filing farming enterprises in the group of Small farms (98 % filed correctly, and the Large and Very Large enterprises (100 % filed correctly. The Medium Size farms have been correctly filed at 58.11 % only. Partial shortages of the process presented have been found when discriminating Medium and Small farms.

  17. Echo-waveform classification using model and model free techniques: Experimental study results from central western continental shelf of India

    Digital Repository Service at National Institute of Oceanography (India)

    Chakraborty, B.; Navelkar, G.S.; Desai, R.G.P.; Janakiraman, G.; Mahale, V.; Fernandes, W.A.; Rao, N.

    seafloor of India, but unable to provide a suitable means for seafloor classification. This paper also suggests a hybrid artificial neural network (ANN) architecture i.e. Learning Vector Quantisation (LVQ) for seafloor classification. An analysis...

  18. A Classification System for Hospital-Based Infection Outbreaks

    Directory of Open Access Journals (Sweden)

    Paul S. Ganney

    2010-01-01

    Full Text Available Outbreaks of infection within semi-closed environments such as hospitals, whether inherent in the environment (such as Clostridium difficile (C.Diff or Methicillinresistant Staphylococcus aureus (MRSA or imported from the wider community (such as Norwalk-like viruses (NLVs, are difficult to manage. As part of our work on modelling such outbreaks, we have developed a classification system to describe the impact of a particular outbreak upon an organization. This classification system may then be used in comparing appropriate computer models to real outbreaks, as well as in comparing different real outbreaks in, for example, the comparison of differing management and containment techniques and strategies. Data from NLV outbreaks in the Hull and East Yorkshire Hospitals NHS Trust (the Trust over several previous years are analysed and classified, both for infection within staff (where the end of infection date may not be known and within patients (where it generally is known. A classification system consisting of seven elements is described, along with a goodness-of-fit method for comparing a new classification to previously known ones, for use in evaluating a simulation against history and thereby determining how ‘realistic’ (or otherwise it is.

  19. A classification system for hospital-based infection outbreaks.

    Science.gov (United States)

    Ganney, Paul S; Madeo, Maurice; Phillips, Roger

    2010-12-01

    Outbreaks of infection within semi-closed environments such as hospitals, whether inherent in the environment (such as Clostridium difficile (C.Diff) or Methicillin-resistant Staphylococcus aureus (MRSA) or imported from the wider community (such as Norwalk-like viruses (NLVs)), are difficult to manage. As part of our work on modelling such outbreaks, we have developed a classification system to describe the impact of a particular outbreak upon an organization. This classification system may then be used in comparing appropriate computer models to real outbreaks, as well as in comparing different real outbreaks in, for example, the comparison of differing management and containment techniques and strategies. Data from NLV outbreaks in the Hull and East Yorkshire Hospitals NHS Trust (the Trust) over several previous years are analysed and classified, both for infection within staff (where the end of infection date may not be known) and within patients (where it generally is known). A classification system consisting of seven elements is described, along with a goodness-of-fit method for comparing a new classification to previously known ones, for use in evaluating a simulation against history and thereby determining how 'realistic' (or otherwise) it is.

  20. A Novel Imbalanced Data Classification Approach Based on Logistic Regression and Fisher Discriminant

    Directory of Open Access Journals (Sweden)

    Baofeng Shi

    2015-01-01

    Full Text Available We introduce an imbalanced data classification approach based on logistic regression significant discriminant and Fisher discriminant. First of all, a key indicators extraction model based on logistic regression significant discriminant and correlation analysis is derived to extract features for customer classification. Secondly, on the basis of the linear weighted utilizing Fisher discriminant, a customer scoring model is established. And then, a customer rating model where the customer number of all ratings follows normal distribution is constructed. The performance of the proposed model and the classical SVM classification method are evaluated in terms of their ability to correctly classify consumers as default customer or nondefault customer. Empirical results using the data of 2157 customers in financial engineering suggest that the proposed approach better performance than the SVM model in dealing with imbalanced data classification. Moreover, our approach contributes to locating the qualified customers for the banks and the bond investors.

  1. Overfitting Reduction of Text Classification Based on AdaBELM

    Directory of Open Access Journals (Sweden)

    Xiaoyue Feng

    2017-07-01

    Full Text Available Overfitting is an important problem in machine learning. Several algorithms, such as the extreme learning machine (ELM, suffer from this issue when facing high-dimensional sparse data, e.g., in text classification. One common issue is that the extent of overfitting is not well quantified. In this paper, we propose a quantitative measure of overfitting referred to as the rate of overfitting (RO and a novel model, named AdaBELM, to reduce the overfitting. With RO, the overfitting problem can be quantitatively measured and identified. The newly proposed model can achieve high performance on multi-class text classification. To evaluate the generalizability of the new model, we designed experiments based on three datasets, i.e., the 20 Newsgroups, Reuters-21578, and BioMed corpora, which represent balanced, unbalanced, and real application data, respectively. Experiment results demonstrate that AdaBELM can reduce overfitting and outperform classical ELM, decision tree, random forests, and AdaBoost on all three text-classification datasets; for example, it can achieve 62.2% higher accuracy than ELM. Therefore, the proposed model has a good generalizability.

  2. High-frequency autonomic modulation: a new model for analysis of autonomic cardiac control.

    Science.gov (United States)

    Champéroux, Pascal; Fesler, Pierre; Judé, Sebastien; Richard, Serge; Le Guennec, Jean-Yves; Thireau, Jérôme

    2018-05-03

    Increase in high-frequency beat-to-beat heart rate oscillations by torsadogenic hERG blockers appears to be associated with signs of parasympathetic and sympathetic co-activation which cannot be assessed directly using classic methods of heart rate variability analysis. The present work aimed to find a translational model that would allow this particular state of the autonomic control of heart rate to be assessed. High-frequency heart rate and heart period oscillations were analysed within discrete 10 s intervals in a cohort of 200 healthy human subjects. Results were compared to data collected in non-human primates and beagle dogs during pharmacological challenges and torsadogenic hERG blockers exposure, in 127 genotyped LQT1 patients on/off β-blocker treatment and in subgroups of smoking and non-smoking subjects. Three states of autonomic modulation, S1 (parasympathetic predominance) to S3 (reciprocal parasympathetic withdrawal/sympathetic activation), were differentiated to build a new model of heart rate variability referred to as high-frequency autonomic modulation. The S2 state corresponded to a specific state during which both parasympathetic and sympathetic systems were coexisting or co-activated. S2 oscillations were proportionally increased by torsadogenic hERG-blocking drugs, whereas smoking caused an increase in S3 oscillations. The combined analysis of the magnitude of high-frequency heart rate and high-frequency heart period oscillations allows a refined assessment of heart rate autonomic modulation applicable to long-term ECG recordings and offers new approaches to assessment of the risk of sudden death both in terms of underlying mechanisms and sensitivity. © 2018 The Authors. British Journal of Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.

  3. A note on multi-criteria inventory classification using weighted linear optimization

    Directory of Open Access Journals (Sweden)

    Rezaei Jafar

    2010-01-01

    Full Text Available Recently, Ramanathan (R., Ramanathan, ABC inventory classification with multiple-criteria using weighted linear optimization, Computer and Operations Research, 33(3 (2006 695-700 introduced a simple DEA-like model to classify inventory items on the basis of multiple criteria. However, the classification results produced by Ramanathan are not consistent with the domination concept encouraged some researchers to extend his model. In this paper, we produce the correct results and compare them to the original results and those of the extended models. We also improve this model to rank items with an optimal score of 1 using a cross-efficiency technique. The classification results are considerably different from the original results. Despite the fact that the correct results are obtained in this paper, there is no significant difference between the original model and its extensions, while the original model is more simple and suitable for the situations in which decision-maker cannot assign specific weights to individual criteria.

  4. Integrated tracking, classification, and sensor management theory and applications

    CERN Document Server

    Krishnamurthy, Vikram; Vo, Ba-Ngu

    2012-01-01

    A unique guide to the state of the art of tracking, classification, and sensor management. This book addresses the tremendous progress made over the last few decades in algorithm development and mathematical analysis for filtering, multi-target multi-sensor tracking, sensor management and control, and target classification. It provides for the first time an integrated treatment of these advanced topics, complete with careful mathematical formulation, clear description of the theory, and real-world applications. Written by experts in the field, Integrated Tracking, Classification, and Sensor Management provides readers with easy access to key Bayesian modeling and filtering methods, multi-target tracking approaches, target classification procedures, and large scale sensor management problem-solving techniques.

  5. Hand eczema classification

    DEFF Research Database (Denmark)

    Diepgen, T L; Andersen, Klaus Ejner; Brandao, F M

    2008-01-01

    of the disease is rarely evidence based, and a classification system for different subdiagnoses of hand eczema is not agreed upon. Randomized controlled trials investigating the treatment of hand eczema are called for. For this, as well as for clinical purposes, a generally accepted classification system...... A classification system for hand eczema is proposed. Conclusions It is suggested that this classification be used in clinical work and in clinical trials....

  6. Sentiment classification of Roman-Urdu opinions using Naïve Bayesian, Decision Tree and KNN classification techniques

    Directory of Open Access Journals (Sweden)

    Muhammad Bilal

    2016-07-01

    Full Text Available Sentiment mining is a field of text mining to determine the attitude of people about a particular product, topic, politician in newsgroup posts, review sites, comments on facebook posts twitter, etc. There are many issues involved in opinion mining. One important issue is that opinions could be in different languages (English, Urdu, Arabic, etc.. To tackle each language according to its orientation is a challenging task. Most of the research work in sentiment mining has been done in English language. Currently, limited research is being carried out on sentiment classification of other languages like Arabic, Italian, Urdu and Hindi. In this paper, three classification models are used for text classification using Waikato Environment for Knowledge Analysis (WEKA. Opinions written in Roman-Urdu and English are extracted from a blog. These extracted opinions are documented in text files to prepare a training dataset containing 150 positive and 150 negative opinions, as labeled examples. Testing data set is supplied to three different models and the results in each case are analyzed. The results show that Naïve Bayesian outperformed Decision Tree and KNN in terms of more accuracy, precision, recall and F-measure.

  7. CIN classification and prediction using machine learning methods

    Science.gov (United States)

    Chirkina, Anastasia; Medvedeva, Marina; Komotskiy, Evgeny

    2017-06-01

    The aim of this paper is a comparison of the existing classification algorithms with different parameters, and selection those ones, which allows solving the problem of primary diagnosis of cervical intraepithelial neoplasia (CIN), as it characterizes the condition of the body in the precancerous stage. The paper describes a feature selection process, as well as selection of the best models for a multiclass classification.

  8. Multi-Temporal Land Cover Classification with Long Short-Term Memory Neural Networks

    Science.gov (United States)

    Rußwurm, M.; Körner, M.

    2017-05-01

    Land cover classification (LCC) is a central and wide field of research in earth observation and has already put forth a variety of classification techniques. Many approaches are based on classification techniques considering observation at certain points in time. However, some land cover classes, such as crops, change their spectral characteristics due to environmental influences and can thus not be monitored effectively with classical mono-temporal approaches. Nevertheless, these temporal observations should be utilized to benefit the classification process. After extensive research has been conducted on modeling temporal dynamics by spectro-temporal profiles using vegetation indices, we propose a deep learning approach to utilize these temporal characteristics for classification tasks. In this work, we show how long short-term memory (LSTM) neural networks can be employed for crop identification purposes with SENTINEL 2A observations from large study areas and label information provided by local authorities. We compare these temporal neural network models, i.e., LSTM and recurrent neural network (RNN), with a classical non-temporal convolutional neural network (CNN) model and an additional support vector machine (SVM) baseline. With our rather straightforward LSTM variant, we exceeded state-of-the-art classification performance, thus opening promising potential for further research.

  9. MULTI-TEMPORAL LAND COVER CLASSIFICATION WITH LONG SHORT-TERM MEMORY NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    M. Rußwurm

    2017-05-01

    Full Text Available Land cover classification (LCC is a central and wide field of research in earth observation and has already put forth a variety of classification techniques. Many approaches are based on classification techniques considering observation at certain points in time. However, some land cover classes, such as crops, change their spectral characteristics due to environmental influences and can thus not be monitored effectively with classical mono-temporal approaches. Nevertheless, these temporal observations should be utilized to benefit the classification process. After extensive research has been conducted on modeling temporal dynamics by spectro-temporal profiles using vegetation indices, we propose a deep learning approach to utilize these temporal characteristics for classification tasks. In this work, we show how long short-term memory (LSTM neural networks can be employed for crop identification purposes with SENTINEL 2A observations from large study areas and label information provided by local authorities. We compare these temporal neural network models, i.e., LSTM and recurrent neural network (RNN, with a classical non-temporal convolutional neural network (CNN model and an additional support vector machine (SVM baseline. With our rather straightforward LSTM variant, we exceeded state-of-the-art classification performance, thus opening promising potential for further research.

  10. Classification of the web

    DEFF Research Database (Denmark)

    Mai, Jens Erik

    2004-01-01

    This paper discusses the challenges faced by investigations into the classification of the Web and outlines inquiries that are needed to use principles for bibliographic classification to construct classifications of the Web. This paper suggests that the classification of the Web meets challenges...... that call for inquiries into the theoretical foundation of bibliographic classification theory....

  11. Security classification of information

    Energy Technology Data Exchange (ETDEWEB)

    Quist, A.S.

    1993-04-01

    This document is the second of a planned four-volume work that comprehensively discusses the security classification of information. The main focus of Volume 2 is on the principles for classification of information. Included herein are descriptions of the two major types of information that governments classify for national security reasons (subjective and objective information), guidance to use when determining whether information under consideration for classification is controlled by the government (a necessary requirement for classification to be effective), information disclosure risks and benefits (the benefits and costs of classification), standards to use when balancing information disclosure risks and benefits, guidance for assigning classification levels (Top Secret, Secret, or Confidential) to classified information, guidance for determining how long information should be classified (classification duration), classification of associations of information, classification of compilations of information, and principles for declassifying and downgrading information. Rules or principles of certain areas of our legal system (e.g., trade secret law) are sometimes mentioned to .provide added support to some of those classification principles.

  12. Effects of uncertainty and variability on population declines and IUCN Red List classifications.

    Science.gov (United States)

    Rueda-Cediel, Pamela; Anderson, Kurt E; Regan, Tracey J; Regan, Helen M

    2018-01-22

    The International Union for Conservation of Nature (IUCN) Red List Categories and Criteria is a quantitative framework for classifying species according to extinction risk. Population models may be used to estimate extinction risk or population declines. Uncertainty and variability arise in threat classifications through measurement and process error in empirical data and uncertainty in the models used to estimate extinction risk and population declines. Furthermore, species traits are known to affect extinction risk. We investigated the effects of measurement and process error, model type, population growth rate, and age at first reproduction on the reliability of risk classifications based on projected population declines on IUCN Red List classifications. We used an age-structured population model to simulate true population trajectories with different growth rates, reproductive ages and levels of variation, and subjected them to measurement error. We evaluated the ability of scalar and matrix models parameterized with these simulated time series to accurately capture the IUCN Red List classification generated with true population declines. Under all levels of measurement error tested and low process error, classifications were reasonably accurate; scalar and matrix models yielded roughly the same rate of misclassifications, but the distribution of errors differed; matrix models led to greater overestimation of extinction risk than underestimations; process error tended to contribute to misclassifications to a greater extent than measurement error; and more misclassifications occurred for fast, rather than slow, life histories. These results indicate that classifications of highly threatened taxa (i.e., taxa with low growth rates) under criterion A are more likely to be reliable than for less threatened taxa when assessed with population models. Greater scrutiny needs to be placed on data used to parameterize population models for species with high growth rates

  13. Strategies to reduce the risk of drug-induced QT interval prolongation: a pharmaceutical company perspective.

    Science.gov (United States)

    Pollard, C E; Valentin, J-P; Hammond, T G

    2008-08-01

    Drug-induced prolongation of the QT interval is having a significant impact on the ability of the pharmaceutical industry to develop new drugs. The development implications for a compound causing a significant effect in the 'Thorough QT/QTc Study' -- as defined in the clinical regulatory guidance (ICH E14) -- are substantial. In view of this, and the fact that QT interval prolongation is linked to direct inhibition of the hERG channel, in the early stages of drug discovery the focus is on testing for and screening out hERG activity. This has led to understanding of how to produce low potency hERG blockers whilst retaining desirable properties. Despite this, a number of factors mean that when an integrated risk assessment is generated towards the end of the discovery phase (by conducting at least an in vivo QT assessment) a QT interval prolongation risk is still often apparent; inhibition of hERG channel trafficking and partitioning into cardiac tissue are just two confounding factors. However, emerging information suggests that hERG safety margins have high predictive value and that when hERG and in vivo non-clinical data are combined, their predictive value to man, whilst not perfect, is >80%. Although understanding the anomalies is important and is being addressed, of greater importance is developing a better understanding of TdP, with the aim of being able to predict TdP rather than using an imperfect surrogate marker (QT interval prolongation). Without an understanding of how to predict TdP risk, high-benefit drugs for serious indications may never be marketed.

  14. On the relevance of spectral features for instrument classification

    DEFF Research Database (Denmark)

    Nielsen, Andreas Brinch; Sigurdsson, Sigurdur; Hansen, Lars Kai

    2007-01-01

    Automatic knowledge extraction from music signals is a key component for most music organization and music information retrieval systems. In this paper, we consider the problem of instrument modelling and instrument classification from the rough audio data. Existing systems for automatic instrument...... classification operate normally on a relatively large number of features, from which those related to the spectrum of the audio signal are particularly relevant. In this paper, we confront two different models about the spectral characterization of musical instruments. The first assumes a constant envelope...

  15. Hazard classification methodology

    International Nuclear Information System (INIS)

    Brereton, S.J.

    1996-01-01

    This document outlines the hazard classification methodology used to determine the hazard classification of the NIF LTAB, OAB, and the support facilities on the basis of radionuclides and chemicals. The hazard classification determines the safety analysis requirements for a facility

  16. Gas Classification Using Deep Convolutional Neural Networks

    Science.gov (United States)

    Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

    2018-01-01

    In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP). PMID:29316723

  17. Gas Classification Using Deep Convolutional Neural Networks.

    Science.gov (United States)

    Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

    2018-01-08

    In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP).

  18. Site effect classification based on microtremor data analysis using a concentration-area fractal model

    Science.gov (United States)

    Adib, A.; Afzal, P.; Heydarzadeh, K.

    2015-01-01

    The aim of this study is to classify the site effect using concentration-area (C-A) fractal model in Meybod city, central Iran, based on microtremor data analysis. Log-log plots of the frequency, amplification and vulnerability index (k-g) indicate a multifractal nature for the parameters in the area. The results obtained from the C-A fractal modelling reveal that proper soil types are located around the central city. The results derived via the fractal modelling were utilized to improve the Nogoshi and Igarashi (1970, 1971) classification results in the Meybod city. The resulting categories are: (1) hard soil and weak rock with frequency of 6.2 to 8 Hz, (2) stiff soil with frequency of about 4.9 to 6.2 Hz, (3) moderately soft soil with the frequency of 2.4 to 4.9 Hz, and (4) soft soil with the frequency lower than 2.4 Hz.

  19. Simulation Modeling by Classification of Problems: A Case of Cellular Manufacturing

    International Nuclear Information System (INIS)

    Afiqah, K N; Mahayuddin, Z R

    2016-01-01

    Cellular manufacturing provides good solution approach to manufacturing area by applying Group Technology concept. The evolution of cellular manufacturing can enhance performance of the cell and to increase the quality of the product manufactured but it triggers other problem. Generally, this paper highlights factors and problems which emerge commonly in cellular manufacturing. The aim of the research is to develop a thorough understanding of common problems in cellular manufacturing. A part from that, in order to find a solution to the problems exist using simulation technique, this classification framework is very useful to be adapted during model building. Biology evolution tool was used in the research in order to classify the problems emerge. The result reveals 22 problems and 25 factors using cladistic technique. In this research, the expected result is the cladogram established based on the problems in cellular manufacturing gathered. (paper)

  20. Aphasia Classification Using Neural Networks

    DEFF Research Database (Denmark)

    Axer, H.; Jantzen, Jan; Berks, G.

    2000-01-01

    A web-based software model (http://fuzzy.iau.dtu.dk/aphasia.nsf) was developed as an example for classification of aphasia using neural networks. Two multilayer perceptrons were used to classify the type of aphasia (Broca, Wernicke, anomic, global) according to the results in some subtests...

  1. Establishment of land model at the Shika Nuclear Power Plant. Mainly, on rock board classification

    International Nuclear Information System (INIS)

    Katagawa, Hideki; Hashimoto, Toru; Hirano, Shuji

    1999-01-01

    In order to grasp engineering properties of basic land of constructions, there is rock board classification as a method to classify whole of rock board to some groups considerable to be nearly equal on its properties. Among the method, various methods in response to its aim and characteristics are devised, and for a classification to hard rock board, the Denken type rock board classification considering degree of weathering to its main element and so forth are well known. The basic rock board of the Shika Nuclear Power Plant is composed of middle and hard types of rock, and its weathering is limited to its shallow portion, most of which are held at fresh condition. For such land, a new classification standard in response to characteristics of land was established. Here were introduced on a progress to establish a new classification standard, its application results and rock board properties. (G.K.)

  2. Failure diagnosis using deep belief learning based health state classification

    International Nuclear Information System (INIS)

    Tamilselvan, Prasanna; Wang, Pingfeng

    2013-01-01

    Effective health diagnosis provides multifarious benefits such as improved safety, improved reliability and reduced costs for operation and maintenance of complex engineered systems. This paper presents a novel multi-sensor health diagnosis method using deep belief network (DBN). DBN has recently become a popular approach in machine learning for its promised advantages such as fast inference and the ability to encode richer and higher order network structures. The DBN employs a hierarchical structure with multiple stacked restricted Boltzmann machines and works through a layer by layer successive learning process. The proposed multi-sensor health diagnosis methodology using DBN based state classification can be structured in three consecutive stages: first, defining health states and preprocessing sensory data for DBN training and testing; second, developing DBN based classification models for diagnosis of predefined health states; third, validating DBN classification models with testing sensory dataset. Health diagnosis using DBN based health state classification technique is compared with four existing diagnosis techniques. Benchmark classification problems and two engineering health diagnosis applications: aircraft engine health diagnosis and electric power transformer health diagnosis are employed to demonstrate the efficacy of the proposed approach

  3. Anti-addiction drug ibogaine inhibits voltage-gated ionic currents: A study to assess the drug's cardiac ion channel profile

    Energy Technology Data Exchange (ETDEWEB)

    Koenig, Xaver; Kovar, Michael; Rubi, Lena; Mike, Agnes K.; Lukacs, Peter; Gawali, Vaibhavkumar S.; Todt, Hannes [Center for Physiology and Pharmacology, Department of Neurophysiology and -pharmacology, Medical University of Vienna, 1090 Vienna (Austria); Hilber, Karlheinz, E-mail: karlheinz.hilber@meduniwien.ac.at [Center for Physiology and Pharmacology, Department of Neurophysiology and -pharmacology, Medical University of Vienna, 1090 Vienna (Austria); Sandtner, Walter [Center for Physiology and Pharmacology, Institute of Pharmacology, Medical University of Vienna, 1090 Vienna (Austria)

    2013-12-01

    The plant alkaloid ibogaine has promising anti-addictive properties. Albeit not licenced as a therapeutic drug, and despite hints that ibogaine may perturb the heart rhythm, this alkaloid is used to treat drug addicts. We have recently reported that ibogaine inhibits human ERG (hERG) potassium channels at concentrations similar to the drugs affinity for several of its known brain targets. Thereby the drug may disturb the heart's electrophysiology. Here, to assess the drug's cardiac ion channel profile in more detail, we studied the effects of ibogaine and its congener 18-Methoxycoronaridine (18-MC) on various cardiac voltage-gated ion channels. We confirmed that heterologously expressed hERG currents are reduced by ibogaine in low micromolar concentrations. Moreover, at higher concentrations, the drug also reduced human Na{sub v}1.5 sodium and Ca{sub v}1.2 calcium currents. Ion currents were as well reduced by 18-MC, yet with diminished potency. Unexpectedly, although blocking hERG channels, ibogaine did not prolong the action potential (AP) in guinea pig cardiomyocytes at low micromolar concentrations. Higher concentrations (≥ 10 μM) even shortened the AP. These findings can be explained by the drug's calcium channel inhibition, which counteracts the AP-prolonging effect generated by hERG blockade. Implementation of ibogaine's inhibitory effects on human ion channels in a computer model of a ventricular cardiomyocyte, on the other hand, suggested that ibogaine does prolong the AP in the human heart. We conclude that therapeutic concentrations of ibogaine have the propensity to prolong the QT interval of the electrocardiogram in humans. In some cases this may lead to cardiac arrhythmias. - Highlights: • We study effects of anti-addiction drug ibogaine on ionic currents in cardiomyocytes. • We assess the cardiac ion channel profile of ibogaine. • Ibogaine inhibits hERG potassium, sodium and calcium channels. • Ibogaine’s effects on

  4. Anti-addiction drug ibogaine inhibits voltage-gated ionic currents: A study to assess the drug's cardiac ion channel profile

    International Nuclear Information System (INIS)

    Koenig, Xaver; Kovar, Michael; Rubi, Lena; Mike, Agnes K.; Lukacs, Peter; Gawali, Vaibhavkumar S.; Todt, Hannes; Hilber, Karlheinz; Sandtner, Walter

    2013-01-01

    The plant alkaloid ibogaine has promising anti-addictive properties. Albeit not licenced as a therapeutic drug, and despite hints that ibogaine may perturb the heart rhythm, this alkaloid is used to treat drug addicts. We have recently reported that ibogaine inhibits human ERG (hERG) potassium channels at concentrations similar to the drugs affinity for several of its known brain targets. Thereby the drug may disturb the heart's electrophysiology. Here, to assess the drug's cardiac ion channel profile in more detail, we studied the effects of ibogaine and its congener 18-Methoxycoronaridine (18-MC) on various cardiac voltage-gated ion channels. We confirmed that heterologously expressed hERG currents are reduced by ibogaine in low micromolar concentrations. Moreover, at higher concentrations, the drug also reduced human Na v 1.5 sodium and Ca v 1.2 calcium currents. Ion currents were as well reduced by 18-MC, yet with diminished potency. Unexpectedly, although blocking hERG channels, ibogaine did not prolong the action potential (AP) in guinea pig cardiomyocytes at low micromolar concentrations. Higher concentrations (≥ 10 μM) even shortened the AP. These findings can be explained by the drug's calcium channel inhibition, which counteracts the AP-prolonging effect generated by hERG blockade. Implementation of ibogaine's inhibitory effects on human ion channels in a computer model of a ventricular cardiomyocyte, on the other hand, suggested that ibogaine does prolong the AP in the human heart. We conclude that therapeutic concentrations of ibogaine have the propensity to prolong the QT interval of the electrocardiogram in humans. In some cases this may lead to cardiac arrhythmias. - Highlights: • We study effects of anti-addiction drug ibogaine on ionic currents in cardiomyocytes. • We assess the cardiac ion channel profile of ibogaine. • Ibogaine inhibits hERG potassium, sodium and calcium channels. • Ibogaine’s effects on ion channels are a potential

  5. Modified Mahalanobis Taguchi System for Imbalance Data Classification

    Directory of Open Access Journals (Sweden)

    Mahmoud El-Banna

    2017-01-01

    Full Text Available The Mahalanobis Taguchi System (MTS is considered one of the most promising binary classification algorithms to handle imbalance data. Unfortunately, MTS lacks a method for determining an efficient threshold for the binary classification. In this paper, a nonlinear optimization model is formulated based on minimizing the distance between MTS Receiver Operating Characteristics (ROC curve and the theoretical optimal point named Modified Mahalanobis Taguchi System (MMTS. To validate the MMTS classification efficacy, it has been benchmarked with Support Vector Machines (SVMs, Naive Bayes (NB, Probabilistic Mahalanobis Taguchi Systems (PTM, Synthetic Minority Oversampling Technique (SMOTE, Adaptive Conformal Transformation (ACT, Kernel Boundary Alignment (KBA, Hidden Naive Bayes (HNB, and other improved Naive Bayes algorithms. MMTS outperforms the benchmarked algorithms especially when the imbalance ratio is greater than 400. A real life case study on manufacturing sector is used to demonstrate the applicability of the proposed model and to compare its performance with Mahalanobis Genetic Algorithm (MGA.

  6. Modified Mahalanobis Taguchi System for Imbalance Data Classification

    Science.gov (United States)

    2017-01-01

    The Mahalanobis Taguchi System (MTS) is considered one of the most promising binary classification algorithms to handle imbalance data. Unfortunately, MTS lacks a method for determining an efficient threshold for the binary classification. In this paper, a nonlinear optimization model is formulated based on minimizing the distance between MTS Receiver Operating Characteristics (ROC) curve and the theoretical optimal point named Modified Mahalanobis Taguchi System (MMTS). To validate the MMTS classification efficacy, it has been benchmarked with Support Vector Machines (SVMs), Naive Bayes (NB), Probabilistic Mahalanobis Taguchi Systems (PTM), Synthetic Minority Oversampling Technique (SMOTE), Adaptive Conformal Transformation (ACT), Kernel Boundary Alignment (KBA), Hidden Naive Bayes (HNB), and other improved Naive Bayes algorithms. MMTS outperforms the benchmarked algorithms especially when the imbalance ratio is greater than 400. A real life case study on manufacturing sector is used to demonstrate the applicability of the proposed model and to compare its performance with Mahalanobis Genetic Algorithm (MGA). PMID:28811820

  7. Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System.

    Science.gov (United States)

    de Moura, Karina de O A; Balbinot, Alexandre

    2018-05-01

    A few prosthetic control systems in the scientific literature obtain pattern recognition algorithms adapted to changes that occur in the myoelectric signal over time and, frequently, such systems are not natural and intuitive. These are some of the several challenges for myoelectric prostheses for everyday use. The concept of the virtual sensor, which has as its fundamental objective to estimate unavailable measures based on other available measures, is being used in other fields of research. The virtual sensor technique applied to surface electromyography can help to minimize these problems, typically related to the degradation of the myoelectric signal that usually leads to a decrease in the classification accuracy of the movements characterized by computational intelligent systems. This paper presents a virtual sensor in a new extensive fault-tolerant classification system to maintain the classification accuracy after the occurrence of the following contaminants: ECG interference, electrode displacement, movement artifacts, power line interference, and saturation. The Time-Varying Autoregressive Moving Average (TVARMA) and Time-Varying Kalman filter (TVK) models are compared to define the most robust model for the virtual sensor. Results of movement classification were presented comparing the usual classification techniques with the method of the degraded signal replacement and classifier retraining. The experimental results were evaluated for these five noise types in 16 surface electromyography (sEMG) channel degradation case studies. The proposed system without using classifier retraining techniques recovered of mean classification accuracy was of 4% to 38% for electrode displacement, movement artifacts, and saturation noise. The best mean classification considering all signal contaminants and channel combinations evaluated was the classification using the retraining method, replacing the degraded channel by the virtual sensor TVARMA model. This method

  8. ACCUWIND - Methods for classification of cup anemometers

    Energy Technology Data Exchange (ETDEWEB)

    Dahlberg, J.Aa.; Friis Pedersen, T.; Busche, P.

    2006-05-15

    Errors associated with the measurement of wind speed are the major sources of uncertainties in power performance testing of wind turbines. Field comparisons of well-calibrated anemometers show significant and not acceptable difference. The European CLASSCUP project posed the objectives to quantify the errors associated with the use of cup anemometers, and to develop a classification system for quantification of systematic errors of cup anemometers. This classification system has now been implemented in the IEC 61400-12-1 standard on power performance measurements in annex I and J. The classification of cup anemometers requires general external climatic operational ranges to be applied for the analysis of systematic errors. A Class A category classification is connected to reasonably flat sites, and another Class B category is connected to complex terrain, General classification indices are the result of assessment of systematic deviations. The present report focuses on methods that can be applied for assessment of such systematic deviations. A new alternative method for torque coefficient measurements at inclined flow have been developed, which have then been applied and compared to the existing methods developed in the CLASSCUP project and earlier. A number of approaches including the use of two cup anemometer models, two methods of torque coefficient measurement, two angular response measurements, and inclusion and exclusion of influence of friction have been implemented in the classification process in order to assess the robustness of methods. The results of the analysis are presented as classification indices, which are compared and discussed. (au)

  9. Bearing Fault Classification Based on Conditional Random Field

    Directory of Open Access Journals (Sweden)

    Guofeng Wang

    2013-01-01

    Full Text Available Condition monitoring of rolling element bearing is paramount for predicting the lifetime and performing effective maintenance of the mechanical equipment. To overcome the drawbacks of the hidden Markov model (HMM and improve the diagnosis accuracy, conditional random field (CRF model based classifier is proposed. In this model, the feature vectors sequences and the fault categories are linked by an undirected graphical model in which their relationship is represented by a global conditional probability distribution. In comparison with the HMM, the main advantage of the CRF model is that it can depict the temporal dynamic information between the observation sequences and state sequences without assuming the independence of the input feature vectors. Therefore, the interrelationship between the adjacent observation vectors can also be depicted and integrated into the model, which makes the classifier more robust and accurate than the HMM. To evaluate the effectiveness of the proposed method, four kinds of bearing vibration signals which correspond to normal, inner race pit, outer race pit and roller pit respectively are collected from the test rig. And the CRF and HMM models are built respectively to perform fault classification by taking the sub band energy features of wavelet packet decomposition (WPD as the observation sequences. Moreover, K-fold cross validation method is adopted to improve the evaluation accuracy of the classifier. The analysis and comparison under different fold times show that the accuracy rate of classification using the CRF model is higher than the HMM. This method brings some new lights on the accurate classification of the bearing faults.

  10. The Pediatric Home Care/Expenditure Classification Model (P/ECM): A Home Care Case-Mix Model for Children Facing Special Health Care Challenges

    OpenAIRE

    Phillips, Charles D.

    2015-01-01

    Case-mix classification and payment systems help assure that persons with similar needs receive similar amounts of care resources, which is a major equity concern for consumers, providers, and programs. Although health service programs for adults regularly use case-mix payment systems, programs providing health services to children and youth rarely use such models. This research utilized Medicaid home care expenditures and assessment data on 2,578 children receiving home care in one large sta...

  11. Memristive Perceptron for Combinational Logic Classification

    Directory of Open Access Journals (Sweden)

    Lidan Wang

    2013-01-01

    Full Text Available The resistance of the memristor depends upon the past history of the input current or voltage; so it can function as synapse in neural networks. In this paper, a novel perceptron combined with the memristor is proposed to implement the combinational logic classification. The relationship between the memristive conductance change and the synapse weight update is deduced, and the memristive perceptron model and its synaptic weight update rule are explored. The feasibility of the novel memristive perceptron for implementing the combinational logic classification (NAND, NOR, XOR, and NXOR is confirmed by MATLAB simulation.

  12. Classification, disease, and diagnosis.

    Science.gov (United States)

    Jutel, Annemarie

    2011-01-01

    Classification shapes medicine and guides its practice. Understanding classification must be part of the quest to better understand the social context and implications of diagnosis. Classifications are part of the human work that provides a foundation for the recognition and study of illness: deciding how the vast expanse of nature can be partitioned into meaningful chunks, stabilizing and structuring what is otherwise disordered. This article explores the aims of classification, their embodiment in medical diagnosis, and the historical traditions of medical classification. It provides a brief overview of the aims and principles of classification and their relevance to contemporary medicine. It also demonstrates how classifications operate as social framing devices that enable and disable communication, assert and refute authority, and are important items for sociological study.

  13. Cost-sensitive classification problem (Poster)

    NARCIS (Netherlands)

    Calders, T.G.K.; Pechenizkiy, M.

    2012-01-01

    In practical situations almost all classification problems are cost-sensitive or utility based one way or another. This exercise mimics a real situation in which students first have to translate a description into a datamining workflow, learn a prediction model, apply it to new data, and set up a

  14. A Model of Classification of Phonemic and Phonetic Negative Transfer: The case of Turkish –English Interlanguage with Pedagogical Applications

    Directory of Open Access Journals (Sweden)

    Sinan Bayraktaroğlu

    2011-04-01

    Full Text Available This article introduces a model of classification of phonemic and phonetic negative- transfer based on an empirical study of Turkish-English Interlanguage. The model sets out a hierarchy of difficulties, starting from the most crucial phonemic features affecting “intelligibility”, down to other distributional, phonetic, and allophonic features which need to be acquired if a “near-native” level of phonological competence is aimed at. Unlike previous theoretical studies of predictions of classification of phonemic and phonetic L1 interference (Moulton 1962a 1962b; Wiik 1965, this model is based on an empirical study of the recorded materials of Turkish-English IL speakers transcribed allophonically using the IPA Alphabet and diacritics. For different categories of observed systematic negative- transfer and their avoidance of getting “fossilized” in the IL process, remedial exercises are recommended for the teaching and learning BBC Pronunciation. In conclusıon, few methodological phonetic techniques, approaches, and specifications are put forward for their use in designing the curriculum and syllabus content of teaching L2 pronunciation.

  15. Track classification within wireless sensor network

    Science.gov (United States)

    Doumerc, Robin; Pannetier, Benjamin; Moras, Julien; Dezert, Jean; Canevet, Loic

    2017-05-01

    In this paper, we present our study on track classification by taking into account environmental information and target estimated states. The tracker uses several motion model adapted to different target dynamics (pedestrian, ground vehicle and SUAV, i.e. small unmanned aerial vehicle) and works in centralized architecture. The main idea is to explore both: classification given by heterogeneous sensors and classification obtained with our fusion module. The fusion module, presented in his paper, provides a class on each track according to track location, velocity and associated uncertainty. To model the likelihood on each class, a fuzzy approach is used considering constraints on target capability to move in the environment. Then the evidential reasoning approach based on Dempster-Shafer Theory (DST) is used to perform a time integration of this classifier output. The fusion rules are tested and compared on real data obtained with our wireless sensor network.In order to handle realistic ground target tracking scenarios, we use an autonomous smart computer deposited in the surveillance area. After the calibration step of the heterogeneous sensor network, our system is able to handle real data from a wireless ground sensor network. The performance of this system is evaluated in a real exercise for intelligence operation ("hunter hunt" scenario).

  16. A dynamical classification of the cosmic web

    Science.gov (United States)

    Forero-Romero, J. E.; Hoffman, Y.; Gottlöber, S.; Klypin, A.; Yepes, G.

    2009-07-01

    In this paper, we propose a new dynamical classification of the cosmic web. Each point in space is classified in one of four possible web types: voids, sheets, filaments and knots. The classification is based on the evaluation of the deformation tensor (i.e. the Hessian of the gravitational potential) on a grid. The classification is based on counting the number of eigenvalues above a certain threshold, λth, at each grid point, where the case of zero, one, two or three such eigenvalues corresponds to void, sheet, filament or a knot grid point. The collection of neighbouring grid points, friends of friends, of the same web type constitutes voids, sheets, filaments and knots as extended web objects. A simple dynamical consideration of the emergence of the web suggests that the threshold should not be null, as in previous implementations of the algorithm. A detailed dynamical analysis would have found different threshold values for the collapse of sheets, filaments and knots. Short of such an analysis a phenomenological approach has been opted for, looking for a single threshold to be determined by analysing numerical simulations. Our cosmic web classification has been applied and tested against a suite of large (dark matter only) cosmological N-body simulations. In particular, the dependence of the volume and mass filling fractions on λth and on the resolution has been calculated for the four web types. We also study the percolation properties of voids and filaments. Our main findings are as follows. (i) Already at λth = 0.1 the resulting web classification reproduces the visual impression of the cosmic web. (ii) Between 0.2 net of interconnected filaments. This suggests a reasonable choice for λth as the parameter that defines the cosmic web. (iii) The dynamical nature of the suggested classification provides a robust framework for incorporating environmental information into galaxy formation models, and in particular to semi-analytical models.

  17. Seafloor backscatter signal simulation and classification

    Digital Repository Service at National Institute of Oceanography (India)

    Mahale, V.; El Dine, W.G.; Chakraborty, B.

    . In this model a smooth echo envelope is generated then mixed up with multiplicative and additive noise. Several such echo signals were simulated for three types of seafloor. An Artificial Neural Network based classification technique is conceived to classify...

  18. Standard classification: Physics

    International Nuclear Information System (INIS)

    1977-01-01

    This is a draft standard classification of physics. The conception is based on the physics part of the systematic catalogue of the Bayerische Staatsbibliothek and on the classification given in standard textbooks. The ICSU-AB classification now used worldwide by physics information services was not taken into account. (BJ) [de

  19. Recharge and discharge of near-surface groundwater in Forsmark. Comparison of classification methods

    International Nuclear Information System (INIS)

    Werner, Kent; Johansson, Per-Olof; Brydsten, Lars; Bosson, Emma; Berglund, Sten

    2007-03-01

    This report presents and compares data and models for identification of near-surface groundwater recharge and discharge (RD) areas in Forsmark. The general principles of groundwater recharge and discharge are demonstrated and applied to interpret hydrological and hydrogeological observations made in the Forsmark area. 'Continuous' RD classification methods considered in the study include topographical modelling, map overlays, and hydrological-hydrogeological flow modelling. 'Discrete' (point) methods include field-based and hydrochemistry-based RD classifications of groundwater monitoring well locations. The topographical RD modelling uses the digital elevation model as the only input. The map overlays use background maps of Quaternary deposits, soils, and ground- and field layers of the vegetation/land use map. Further, the hydrological-hydrogeological modelling is performed using the MIKE SHE-MIKE 11 software packages, taking into account e.g. topography, meteorology, hydrogeology, and geometry of watercourses and lakes. The best between-model agreement is found for the topography-based model and the MIKE SHE-MIKE 11 model. The agreement between the topographical model and the map overlays is less good. The agreement between the map overlays on the one hand, and the MIKE SHE and field-based RD classifications on the other, is thought to be less good, as inferred from the comparison made with the topography-based model. However, much improvement of the map overlays can likely be obtained, e.g. by using 'weights' and calibration (such exercises were outside the scope of the present study). For field-classified 'recharge wells', there is a good agreement to the hydrochemistry-based (Piper plot) well classification, but less good for the field-classified 'discharge wells'. In addition, the concentration of the age-dating parameter tritium shows low variability among recharge wells, but a large spread among discharge wells. The usefulness of hydrochemistry-based RD

  20. Key-phrase based classification of public health web pages.

    Science.gov (United States)

    Dolamic, Ljiljana; Boyer, Célia

    2013-01-01

    This paper describes and evaluates the public health web pages classification model based on key phrase extraction and matching. Easily extendible both in terms of new classes as well as the new language this method proves to be a good solution for text classification faced with the total lack of training data. To evaluate the proposed solution we have used a small collection of public health related web pages created by a double blind manual classification. Our experiments have shown that by choosing the adequate threshold value the desired value for either precision or recall can be achieved.

  1. Waste Classification based on Waste Form Heat Generation in Advanced Nuclear Fuel Cycles Using the Fuel-Cycle Integration and Tradeoffs (FIT) Model

    Energy Technology Data Exchange (ETDEWEB)

    Denia Djokic; Steven J. Piet; Layne F. Pincock; Nick R. Soelberg

    2013-02-01

    This study explores the impact of wastes generated from potential future fuel cycles and the issues presented by classifying these under current classification criteria, and discusses the possibility of a comprehensive and consistent characteristics-based classification framework based on new waste streams created from advanced fuel cycles. A static mass flow model, Fuel-Cycle Integration and Tradeoffs (FIT), was used to calculate the composition of waste streams resulting from different nuclear fuel cycle choices. This analysis focuses on the impact of waste form heat load on waste classification practices, although classifying by metrics of radiotoxicity, mass, and volume is also possible. The value of separation of heat-generating fission products and actinides in different fuel cycles is discussed. It was shown that the benefits of reducing the short-term fission-product heat load of waste destined for geologic disposal are neglected under the current source-based radioactive waste classification system , and that it is useful to classify waste streams based on how favorable the impact of interim storage is in increasing repository capacity.

  2. Improved classification and visualization of healthy and pathological hard dental tissues by modeling specular reflections in NIR hyperspectral images

    Science.gov (United States)

    Usenik, Peter; Bürmen, Miran; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan

    2012-03-01

    Despite major improvements in dental healthcare and technology, dental caries remains one of the most prevalent chronic diseases of modern society. The initial stages of dental caries are characterized by demineralization of enamel crystals, commonly known as white spots, which are difficult to diagnose. Near-infrared (NIR) hyperspectral imaging is a new promising technique for early detection of demineralization which can classify healthy and pathological dental tissues. However, due to non-ideal illumination of the tooth surface the hyperspectral images can exhibit specular reflections, in particular around the edges and the ridges of the teeth. These reflections significantly affect the performance of automated classification and visualization methods. Cross polarized imaging setup can effectively remove the specular reflections, however is due to the complexity and other imaging setup limitations not always possible. In this paper, we propose an alternative approach based on modeling the specular reflections of hard dental tissues, which significantly improves the classification accuracy in the presence of specular reflections. The method was evaluated on five extracted human teeth with corresponding gold standard for 6 different healthy and pathological hard dental tissues including enamel, dentin, calculus, dentin caries, enamel caries and demineralized regions. Principal component analysis (PCA) was used for multivariate local modeling of healthy and pathological dental tissues. The classification was performed by employing multiple discriminant analysis. Based on the obtained results we believe the proposed method can be considered as an effective alternative to the complex cross polarized imaging setups.

  3. Classification system for reporting events involving human malfunctions

    DEFF Research Database (Denmark)

    Rasmussen, Jens; Pedersen, O.M.; Mancini, G.

    1981-01-01

    The report describes a set of categories for reporting indus-trial incidents and events involving human malfunction. The classification system aims at ensuring information adequate for improvement of human work situations and man-machine interface systems and for attempts to quantify "human error......" rates. The classification system has a multifacetted non-hierarchical struc-ture and its compatibility with Isprals ERDS classification is described. The collection of the information in general and for quantification purposes are discussed. 24 categories, 12 of which being human factors oriented......, are listed with their respective subcategories, and comments are given. Underlying models of human data processes and their typical malfunc-tions and of a human decision sequence are described....

  4. Classification system for reporting events involving human malfunctions

    International Nuclear Information System (INIS)

    Rasmussen, J.; Pedersen, O.M.; Mancini, G.; Carnino, A.; Griffon, M.; Gagnolet, P.

    1981-03-01

    The report describes a set of categories for reporting industrial incidents and events involving human malfunction. The classification system aims at ensuring information adequate for improvement of human work situations and man-machine interface systems and for attempts to quantify ''human error'' rates. The classification system has a multifacetted non-hierarchial structure and its compatibility with Ispra's ERDS classification is described. The collection of the information in general and for quantification purposes are discussed. 24 categories, 12 of which being human factors oriented, are listed with their respective subcategories, and comments are given. Underlying models of human data processes and their typical malfunctions and of a human decision sequence are described. (author)

  5. Risk classification and cream skimming on the deregulated German insurance market

    OpenAIRE

    Beschorner, Patrick F. E.

    2003-01-01

    In a two-stage model insurance companies first decide upon risk classification and then compete in prices. I show that the observed heterogeneous behavior of similar firms is compatible with rational behavior. On the deregulated German insurance market individual application of classification schemes induces welfare losses due to cream skimming. Classification costs and pricing above marginal cost can be prevented by common industry-wide loss statistics which already exist to a rudimentary ex...

  6. A Directed Acyclic Graph-Large Margin Distribution Machine Model for Music Symbol Classification.

    Science.gov (United States)

    Wen, Cuihong; Zhang, Jing; Rebelo, Ana; Cheng, Fanyong

    2016-01-01

    Optical Music Recognition (OMR) has received increasing attention in recent years. In this paper, we propose a classifier based on a new method named Directed Acyclic Graph-Large margin Distribution Machine (DAG-LDM). The DAG-LDM is an improvement of the Large margin Distribution Machine (LDM), which is a binary classifier that optimizes the margin distribution by maximizing the margin mean and minimizing the margin variance simultaneously. We modify the LDM to the DAG-LDM to solve the multi-class music symbol classification problem. Tests are conducted on more than 10000 music symbol images, obtained from handwritten and printed images of music scores. The proposed method provides superior classification capability and achieves much higher classification accuracy than the state-of-the-art algorithms such as Support Vector Machines (SVMs) and Neural Networks (NNs).

  7. Structure based classification for bile salt export pump (BSEP) inhibitors using comparative structural modeling of human BSEP

    Science.gov (United States)

    Jain, Sankalp; Grandits, Melanie; Richter, Lars; Ecker, Gerhard F.

    2017-06-01

    The bile salt export pump (BSEP) actively transports conjugated monovalent bile acids from the hepatocytes into the bile. This facilitates the formation of micelles and promotes digestion and absorption of dietary fat. Inhibition of BSEP leads to decreased bile flow and accumulation of cytotoxic bile salts in the liver. A number of compounds have been identified to interact with BSEP, which results in drug-induced cholestasis or liver injury. Therefore, in silico approaches for flagging compounds as potential BSEP inhibitors would be of high value in the early stage of the drug discovery pipeline. Up to now, due to the lack of a high-resolution X-ray structure of BSEP, in silico based identification of BSEP inhibitors focused on ligand-based approaches. In this study, we provide a homology model for BSEP, developed using the corrected mouse P-glycoprotein structure (PDB ID: 4M1M). Subsequently, the model was used for docking-based classification of a set of 1212 compounds (405 BSEP inhibitors, 807 non-inhibitors). Using the scoring function ChemScore, a prediction accuracy of 81% on the training set and 73% on two external test sets could be obtained. In addition, the applicability domain of the models was assessed based on Euclidean distance. Further, analysis of the protein-ligand interaction fingerprints revealed certain functional group-amino acid residue interactions that could play a key role for ligand binding. Though ligand-based models, due to their high speed and accuracy, remain the method of choice for classification of BSEP inhibitors, structure-assisted docking models demonstrate reasonably good prediction accuracies while additionally providing information about putative protein-ligand interactions.

  8. TEXT CLASSIFICATION USING NAIVE BAYES UPDATEABLE ALGORITHM IN SBMPTN TEST QUESTIONS

    Directory of Open Access Journals (Sweden)

    Ristu Saptono

    2017-01-01

    Full Text Available Document classification is a growing interest in the research of text mining. Classification can be done based on the topics, languages, and so on. This study was conducted to determine how Naive Bayes Updateable performs in classifying the SBMPTN exam questions based on its theme. Increment model of one classification algorithm often used in text classification Naive Bayes classifier has the ability to learn from new data introduces with the system even after the classifier has been produced with the existing data. Naive Bayes Classifier classifies the exam questions based on the theme of the field of study by analyzing keywords that appear on the exam questions. One of feature selection method DF-Thresholding is implemented for improving the classification performance. Evaluation of the classification with Naive Bayes classifier algorithm produces 84,61% accuracy.

  9. Comparison of feature selection and classification for MALDI-MS data

    Directory of Open Access Journals (Sweden)

    Yang Mary

    2009-07-01

    Full Text Available Abstract Introduction In the classification of Mass Spectrometry (MS proteomics data, peak detection, feature selection, and learning classifiers are critical to classification accuracy. To better understand which methods are more accurate when classifying data, some publicly available peak detection algorithms for Matrix assisted Laser Desorption Ionization Mass Spectrometry (MALDI-MS data were recently compared; however, the issue of different feature selection methods and different classification models as they relate to classification performance has not been addressed. With the application of intelligent computing, much progress has been made in the development of feature selection methods and learning classifiers for the analysis of high-throughput biological data. The main objective of this paper is to compare the methods of feature selection and different learning classifiers when applied to MALDI-MS data and to provide a subsequent reference for the analysis of MS proteomics data. Results We compared a well-known method of feature selection, Support Vector Machine Recursive Feature Elimination (SVMRFE, and a recently developed method, Gradient based Leave-one-out Gene Selection (GLGS that effectively performs microarray data analysis. We also compared several learning classifiers including K-Nearest Neighbor Classifier (KNNC, Naïve Bayes Classifier (NBC, Nearest Mean Scaled Classifier (NMSC, uncorrelated normal based quadratic Bayes Classifier recorded as UDC, Support Vector Machines, and a distance metric learning for Large Margin Nearest Neighbor classifier (LMNN based on Mahanalobis distance. To compare, we conducted a comprehensive experimental study using three types of MALDI-MS data. Conclusion Regarding feature selection, SVMRFE outperformed GLGS in classification. As for the learning classifiers, when classification models derived from the best training were compared, SVMs performed the best with respect to the expected testing

  10. Quality Evaluation of Land-Cover Classification Using Convolutional Neural Network

    Science.gov (United States)

    Dang, Y.; Zhang, J.; Zhao, Y.; Luo, F.; Ma, W.; Yu, F.

    2018-04-01

    Land-cover classification is one of the most important products of earth observation, which focuses mainly on profiling the physical characters of the land surface with temporal and distribution attributes and contains the information of both natural and man-made coverage elements, such as vegetation, soil, glaciers, rivers, lakes, marsh wetlands and various man-made structures. In recent years, the amount of high-resolution remote sensing data has increased sharply. Accordingly, the volume of land-cover classification products increases, as well as the need to evaluate such frequently updated products that is a big challenge. Conventionally, the automatic quality evaluation of land-cover classification is made through pixel-based classifying algorithms, which lead to a much trickier task and consequently hard to keep peace with the required updating frequency. In this paper, we propose a novel quality evaluation approach for evaluating the land-cover classification by a scene classification method Convolutional Neural Network (CNN) model. By learning from remote sensing data, those randomly generated kernels that serve as filter matrixes evolved to some operators that has similar functions to man-crafted operators, like Sobel operator or Canny operator, and there are other kernels learned by the CNN model that are much more complex and can't be understood as existing filters. The method using CNN approach as the core algorithm serves quality-evaluation tasks well since it calculates a bunch of outputs which directly represent the image's membership grade to certain classes. An automatic quality evaluation approach for the land-cover DLG-DOM coupling data (DLG for Digital Line Graphic, DOM for Digital Orthophoto Map) will be introduced in this paper. The CNN model as an robustness method for image evaluation, then brought out the idea of an automatic quality evaluation approach for land-cover classification. Based on this experiment, new ideas of quality evaluation

  11. The paradox of atheoretical classification

    DEFF Research Database (Denmark)

    Hjørland, Birger

    2016-01-01

    A distinction can be made between “artificial classifications” and “natural classifications,” where artificial classifications may adequately serve some limited purposes, but natural classifications are overall most fruitful by allowing inference and thus many different purposes. There is strong...... support for the view that a natural classification should be based on a theory (and, of course, that the most fruitful theory provides the most fruitful classification). Nevertheless, atheoretical (or “descriptive”) classifications are often produced. Paradoxically, atheoretical classifications may...... be very successful. The best example of a successful “atheoretical” classification is probably the prestigious Diagnostic and Statistical Manual of Mental Disorders (DSM) since its third edition from 1980. Based on such successes one may ask: Should the claim that classifications ideally are natural...

  12. An application to pulmonary emphysema classification based on model of texton learning by sparse representation

    Science.gov (United States)

    Zhang, Min; Zhou, Xiangrong; Goshima, Satoshi; Chen, Huayue; Muramatsu, Chisako; Hara, Takeshi; Yokoyama, Ryojiro; Kanematsu, Masayuki; Fujita, Hiroshi

    2012-03-01

    We aim at using a new texton based texture classification method in the classification of pulmonary emphysema in computed tomography (CT) images of the lungs. Different from conventional computer-aided diagnosis (CAD) pulmonary emphysema classification methods, in this paper, firstly, the dictionary of texton is learned via applying sparse representation(SR) to image patches in the training dataset. Then the SR coefficients of the test images over the dictionary are used to construct the histograms for texture presentations. Finally, classification is performed by using a nearest neighbor classifier with a histogram dissimilarity measure as distance. The proposed approach is tested on 3840 annotated regions of interest consisting of normal tissue and mild, moderate and severe pulmonary emphysema of three subtypes. The performance of the proposed system, with an accuracy of about 88%, is comparably higher than state of the art method based on the basic rotation invariant local binary pattern histograms and the texture classification method based on texton learning by k-means, which performs almost the best among other approaches in the literature.

  13. Age group classification and gender detection based on forced expiratory spirometry.

    Science.gov (United States)

    Cosgun, Sema; Ozbek, I Yucel

    2015-08-01

    This paper investigates the utility of forced expiratory spirometry (FES) test with efficient machine learning algorithms for the purpose of gender detection and age group classification. The proposed method has three main stages: feature extraction, training of the models and detection. In the first stage, some features are extracted from volume-time curve and expiratory flow-volume loop obtained from FES test. In the second stage, the probabilistic models for each gender and age group are constructed by training Gaussian mixture models (GMMs) and Support vector machine (SVM) algorithm. In the final stage, the gender (or age group) of test subject is estimated by using the trained GMM (or SVM) model. Experiments have been evaluated on a large database from 4571 subjects. The experimental results show that average correct classification rate performance of both GMM and SVM methods based on the FES test is more than 99.3 % and 96.8 % for gender and age group classification, respectively.

  14. Transporter Classification Database (TCDB)

    Data.gov (United States)

    U.S. Department of Health & Human Services — The Transporter Classification Database details a comprehensive classification system for membrane transport proteins known as the Transporter Classification (TC)...

  15. Investigation of hydrometeor classification uncertainties through the POLARRIS polarimetric radar simulator

    Science.gov (United States)

    Dolan, B.; Rutledge, S. A.; Barnum, J. I.; Matsui, T.; Tao, W. K.; Iguchi, T.

    2017-12-01

    POLarimetric Radar Retrieval and Instrument Simulator (POLARRIS) is a framework that has been developed to simulate radar observations from cloud resolving model (CRM) output and subject model data and observations to the same retrievals, analysis and visualization. This framework not only enables validation of bulk microphysical model simulated properties, but also offers an opportunity to study the uncertainties associated with retrievals such as hydrometeor classification (HID). For the CSU HID, membership beta functions (MBFs) are built using a set of simulations with realistic microphysical assumptions about axis ratio, density, canting angles, size distributions for each of ten hydrometeor species. These assumptions are tested using POLARRIS to understand their influence on the resulting simulated polarimetric data and final HID classification. Several of these parameters (density, size distributions) are set by the model microphysics, and therefore the specific assumptions of axis ratio and canting angle are carefully studied. Through these sensitivity studies, we hope to be able to provide uncertainties in retrieved polarimetric variables and HID as applied to CRM output. HID retrievals assign a classification to each point by determining the highest score, thereby identifying the dominant hydrometeor type within a volume. However, in nature, there is rarely just one a single hydrometeor type at a particular point. Models allow for mixing ratios of different hydrometeors within a grid point. We use the mixing ratios from CRM output in concert with the HID scores and classifications to understand how the HID algorithm can provide information about mixtures within a volume, as well as calculate a confidence in the classifications. We leverage the POLARRIS framework to additionally probe radar wavelength differences toward the possibility of a multi-wavelength HID which could utilize the strengths of different wavelengths to improve HID classifications. With

  16. Monitoring nanotechnology using patent classifications: an overview and comparison of nanotechnology classification schemes

    Energy Technology Data Exchange (ETDEWEB)

    Jürgens, Björn, E-mail: bjurgens@agenciaidea.es [Agency of Innovation and Development of Andalusia, CITPIA PATLIB Centre (Spain); Herrero-Solana, Victor, E-mail: victorhs@ugr.es [University of Granada, SCImago-UGR (SEJ036) (Spain)

    2017-04-15

    Patents are an essential information source used to monitor, track, and analyze nanotechnology. When it comes to search nanotechnology-related patents, a keyword search is often incomplete and struggles to cover such an interdisciplinary discipline. Patent classification schemes can reveal far better results since they are assigned by experts who classify the patent documents according to their technology. In this paper, we present the most important classifications to search nanotechnology patents and analyze how nanotechnology is covered in the main patent classification systems used in search systems nowadays: the International Patent Classification (IPC), the United States Patent Classification (USPC), and the Cooperative Patent Classification (CPC). We conclude that nanotechnology has a significantly better patent coverage in the CPC since considerable more nanotechnology documents were retrieved than by using other classifications, and thus, recommend its use for all professionals involved in nanotechnology patent searches.

  17. Monitoring nanotechnology using patent classifications: an overview and comparison of nanotechnology classification schemes

    International Nuclear Information System (INIS)

    Jürgens, Björn; Herrero-Solana, Victor

    2017-01-01

    Patents are an essential information source used to monitor, track, and analyze nanotechnology. When it comes to search nanotechnology-related patents, a keyword search is often incomplete and struggles to cover such an interdisciplinary discipline. Patent classification schemes can reveal far better results since they are assigned by experts who classify the patent documents according to their technology. In this paper, we present the most important classifications to search nanotechnology patents and analyze how nanotechnology is covered in the main patent classification systems used in search systems nowadays: the International Patent Classification (IPC), the United States Patent Classification (USPC), and the Cooperative Patent Classification (CPC). We conclude that nanotechnology has a significantly better patent coverage in the CPC since considerable more nanotechnology documents were retrieved than by using other classifications, and thus, recommend its use for all professionals involved in nanotechnology patent searches.

  18. Manifold regularized multitask learning for semi-supervised multilabel image classification.

    Science.gov (United States)

    Luo, Yong; Tao, Dacheng; Geng, Bo; Xu, Chao; Maybank, Stephen J

    2013-02-01

    It is a significant challenge to classify images with multiple labels by using only a small number of labeled samples. One option is to learn a binary classifier for each label and use manifold regularization to improve the classification performance by exploring the underlying geometric structure of the data distribution. However, such an approach does not perform well in practice when images from multiple concepts are represented by high-dimensional visual features. Thus, manifold regularization is insufficient to control the model complexity. In this paper, we propose a manifold regularized multitask learning (MRMTL) algorithm. MRMTL learns a discriminative subspace shared by multiple classification tasks by exploiting the common structure of these tasks. It effectively controls the model complexity because different tasks limit one another's search volume, and the manifold regularization ensures that the functions in the shared hypothesis space are smooth along the data manifold. We conduct extensive experiments, on the PASCAL VOC'07 dataset with 20 classes and the MIR dataset with 38 classes, by comparing MRMTL with popular image classification algorithms. The results suggest that MRMTL is effective for image classification.

  19. Convolutional neural network with transfer learning for rice type classification

    Science.gov (United States)

    Patel, Vaibhav Amit; Joshi, Manjunath V.

    2018-04-01

    Presently, rice type is identified manually by humans, which is time consuming and error prone. Therefore, there is a need to do this by machine which makes it faster with greater accuracy. This paper proposes a deep learning based method for classification of rice types. We propose two methods to classify the rice types. In the first method, we train a deep convolutional neural network (CNN) using the given segmented rice images. In the second method, we train a combination of a pretrained VGG16 network and the proposed method, while using transfer learning in which the weights of a pretrained network are used to achieve better accuracy. Our approach can also be used for classification of rice grain as broken or fine. We train a 5-class model for classifying rice types using 4000 training images and another 2- class model for the classification of broken and normal rice using 1600 training images. We observe that despite having distinct rice images, our architecture, pretrained on ImageNet data boosts classification accuracy significantly.

  20. Evaluation of Current Approaches to Stream Classification and a Heuristic Guide to Developing Classifications of Integrated Aquatic Networks

    Science.gov (United States)

    Melles, S. J.; Jones, N. E.; Schmidt, B. J.

    2014-03-01

    Conservation and management of fresh flowing waters involves evaluating and managing effects of cumulative impacts on the aquatic environment from disturbances such as: land use change, point and nonpoint source pollution, the creation of dams and reservoirs, mining, and fishing. To assess effects of these changes on associated biotic communities it is necessary to monitor and report on the status of lotic ecosystems. A variety of stream classification methods are available to assist with these tasks, and such methods attempt to provide a systematic approach to modeling and understanding complex aquatic systems at various spatial and temporal scales. Of the vast number of approaches that exist, it is useful to group them into three main types. The first involves modeling longitudinal species turnover patterns within large drainage basins and relating these patterns to environmental predictors collected at reach and upstream catchment scales; the second uses regionalized hierarchical classification to create multi-scale, spatially homogenous aquatic ecoregions by grouping adjacent catchments together based on environmental similarities; and the third approach groups sites together on the basis of similarities in their environmental conditions both within and between catchments, independent of their geographic location. We review the literature with a focus on more recent classifications to examine the strengths and weaknesses of the different approaches. We identify gaps or problems with the current approaches, and we propose an eight-step heuristic process that may assist with development of more flexible and integrated aquatic classifications based on the current understanding, network thinking, and theoretical underpinnings.

  1. Adaptive SVM for Data Stream Classification

    Directory of Open Access Journals (Sweden)

    Isah A. Lawal

    2017-07-01

    Full Text Available In this paper, we address the problem of learning an adaptive classifier for the classification of continuous streams of data. We present a solution based on incremental extensions of the Support Vector Machine (SVM learning paradigm that updates an existing SVM whenever new training data are acquired. To ensure that the SVM effectiveness is guaranteed while exploiting the newly gathered data, we introduce an on-line model selection approach in the incremental learning process. We evaluated the proposed method on real world applications including on-line spam email filtering and human action classification from videos. Experimental results show the effectiveness and the potential of the proposed approach.

  2. Automatic Classification Using Supervised Learning in a Medical Document Filtering Application.

    Science.gov (United States)

    Mostafa, J.; Lam, W.

    2000-01-01

    Presents a multilevel model of the information filtering process that permits document classification. Evaluates a document classification approach based on a supervised learning algorithm, measures the accuracy of the algorithm in a neural network that was trained to classify medical documents on cell biology, and discusses filtering…

  3. Adaptive phase k-means algorithm for waveform classification

    Science.gov (United States)

    Song, Chengyun; Liu, Zhining; Wang, Yaojun; Xu, Feng; Li, Xingming; Hu, Guangmin

    2018-01-01

    Waveform classification is a powerful technique for seismic facies analysis that describes the heterogeneity and compartments within a reservoir. Horizon interpretation is a critical step in waveform classification. However, the horizon often produces inconsistent waveform phase, and thus results in an unsatisfied classification. To alleviate this problem, an adaptive phase waveform classification method called the adaptive phase k-means is introduced in this paper. Our method improves the traditional k-means algorithm using an adaptive phase distance for waveform similarity measure. The proposed distance is a measure with variable phases as it moves from sample to sample along the traces. Model traces are also updated with the best phase interference in the iterative process. Therefore, our method is robust to phase variations caused by the interpretation horizon. We tested the effectiveness of our algorithm by applying it to synthetic and real data. The satisfactory results reveal that the proposed method tolerates certain waveform phase variation and is a good tool for seismic facies analysis.

  4. PROGRESSIVE DENSIFICATION AND REGION GROWING METHODS FOR LIDAR DATA CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    J. L. Pérez-García

    2012-07-01

    Full Text Available At present, airborne laser scanner systems are one of the most frequent methods used to obtain digital terrain elevation models. While having the advantage of direct measurement on the object, the point cloud obtained has the need for classification of their points according to its belonging to the ground. This need for classification of raw data has led to appearance of multiple filters focused LiDAR classification information. According this approach, this paper presents a classification method that combines LiDAR data segmentation techniques and progressive densification to carry out the location of the points belonging to the ground. The proposed methodology is tested on several datasets with different terrain characteristics and data availability. In all case, we analyze the advantages and disadvantages that have been obtained compared with the individual techniques application and, in a special way, the benefits derived from the integration of both classification techniques. In order to provide a more comprehensive quality control of the classification process, the obtained results have been compared with the derived from a manual procedure, which is used as reference classification. The results are also compared with other automatic classification methodologies included in some commercial software packages, highly contrasted by users for LiDAR data treatment.

  5. A Directed Acyclic Graph-Large Margin Distribution Machine Model for Music Symbol Classification.

    Directory of Open Access Journals (Sweden)

    Cuihong Wen

    Full Text Available Optical Music Recognition (OMR has received increasing attention in recent years. In this paper, we propose a classifier based on a new method named Directed Acyclic Graph-Large margin Distribution Machine (DAG-LDM. The DAG-LDM is an improvement of the Large margin Distribution Machine (LDM, which is a binary classifier that optimizes the margin distribution by maximizing the margin mean and minimizing the margin variance simultaneously. We modify the LDM to the DAG-LDM to solve the multi-class music symbol classification problem. Tests are conducted on more than 10000 music symbol images, obtained from handwritten and printed images of music scores. The proposed method provides superior classification capability and achieves much higher classification accuracy than the state-of-the-art algorithms such as Support Vector Machines (SVMs and Neural Networks (NNs.

  6. Artificial neural network classification using a minimal training set - Comparison to conventional supervised classification

    Science.gov (United States)

    Hepner, George F.; Logan, Thomas; Ritter, Niles; Bryant, Nevin

    1990-01-01

    Recent research has shown an artificial neural network (ANN) to be capable of pattern recognition and the classification of image data. This paper examines the potential for the application of neural network computing to satellite image processing. A second objective is to provide a preliminary comparison and ANN classification. An artificial neural network can be trained to do land-cover classification of satellite imagery using selected sites representative of each class in a manner similar to conventional supervised classification. One of the major problems associated with recognition and classifications of pattern from remotely sensed data is the time and cost of developing a set of training sites. This reseach compares the use of an ANN back propagation classification procedure with a conventional supervised maximum likelihood classification procedure using a minimal training set. When using a minimal training set, the neural network is able to provide a land-cover classification superior to the classification derived from the conventional classification procedure. This research is the foundation for developing application parameters for further prototyping of software and hardware implementations for artificial neural networks in satellite image and geographic information processing.

  7. Small-scale classification schemes

    DEFF Research Database (Denmark)

    Hertzum, Morten

    2004-01-01

    Small-scale classification schemes are used extensively in the coordination of cooperative work. This study investigates the creation and use of a classification scheme for handling the system requirements during the redevelopment of a nation-wide information system. This requirements...... classification inherited a lot of its structure from the existing system and rendered requirements that transcended the framework laid out by the existing system almost invisible. As a result, the requirements classification became a defining element of the requirements-engineering process, though its main...... effects remained largely implicit. The requirements classification contributed to constraining the requirements-engineering process by supporting the software engineers in maintaining some level of control over the process. This way, the requirements classification provided the software engineers...

  8. Classification of Brazilian and foreign gasolines adulterated with alcohol using infrared spectroscopy.

    Science.gov (United States)

    da Silva, Neirivaldo C; Pimentel, Maria Fernanda; Honorato, Ricardo S; Talhavini, Marcio; Maldaner, Adriano O; Honorato, Fernanda A

    2015-08-01

    The smuggling of products across the border regions of many countries is a practice to be fought. Brazilian authorities are increasingly worried about the illicit trade of fuels along the frontiers of the country. In order to confirm this as a crime, the Federal Police must have a means of identifying the origin of the fuel. This work describes the development of a rapid and nondestructive methodology to classify gasoline as to its origin (Brazil, Venezuela and Peru), using infrared spectroscopy and multivariate classification. Partial Least Squares Discriminant Analysis (PLS-DA) and Soft Independent Modeling Class Analogy (SIMCA) models were built. Direct standardization (DS) was employed aiming to standardize the spectra obtained in different laboratories of the border units of the Federal Police. Two approaches were considered in this work: (1) local and (2) global classification models. When using Approach 1, the PLS-DA achieved 100% correct classification, and the deviation of the predicted values for the secondary instrument considerably decreased after performing DS. In this case, SIMCA models were not efficient in the classification, even after standardization. Using a global model (Approach 2), both PLS-DA and SIMCA techniques were effective after performing DS. Considering that real situations may involve questioned samples from other nations (such as Peru), the SIMCA method developed according to Approach 2 is a more adequate, since the sample will be classified neither as Brazil nor Venezuelan. This methodology could be applied to other forensic problems involving the chemical classification of a product, provided that a specific modeling is performed. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  9. Cheese Classification, Characterization, and Categorization: A Global Perspective.

    Science.gov (United States)

    Almena-Aliste, Montserrat; Mietton, Bernard

    2014-02-01

    Cheese is one of the most fascinating, complex, and diverse foods enjoyed today. Three elements constitute the cheese ecosystem: ripening agents, consisting of enzymes and microorganisms; the composition of the fresh cheese; and the environmental conditions during aging. These factors determine and define not only the sensory quality of the final cheese product but also the vast diversity of cheeses produced worldwide. How we define and categorize cheese is a complicated matter. There are various approaches to cheese classification, and a global approach for classification and characterization is needed. We review current cheese classification schemes and the limitations inherent in each of the schemes described. While some classification schemes are based on microbiological criteria, others rely on descriptions of the technologies used for cheese production. The goal of this review is to present an overview of comprehensive and practical integrative classification models in order to better describe cheese diversity and the fundamental differences within cheeses, as well as to connect fundamental technological, microbiological, chemical, and sensory characteristics to contribute to an overall characterization of the main families of cheese, including the expanding world of American artisanal cheeses.

  10. Joint Feature Selection and Classification for Multilabel Learning.

    Science.gov (United States)

    Huang, Jun; Li, Guorong; Huang, Qingming; Wu, Xindong

    2018-03-01

    Multilabel learning deals with examples having multiple class labels simultaneously. It has been applied to a variety of applications, such as text categorization and image annotation. A large number of algorithms have been proposed for multilabel learning, most of which concentrate on multilabel classification problems and only a few of them are feature selection algorithms. Current multilabel classification models are mainly built on a single data representation composed of all the features which are shared by all the class labels. Since each class label might be decided by some specific features of its own, and the problems of classification and feature selection are often addressed independently, in this paper, we propose a novel method which can perform joint feature selection and classification for multilabel learning, named JFSC. Different from many existing methods, JFSC learns both shared features and label-specific features by considering pairwise label correlations, and builds the multilabel classifier on the learned low-dimensional data representations simultaneously. A comparative study with state-of-the-art approaches manifests a competitive performance of our proposed method both in classification and feature selection for multilabel learning.

  11. Recharge and discharge of near-surface groundwater in Forsmark. Comparison of classification methods

    Energy Technology Data Exchange (ETDEWEB)

    Werner, Kent [Golder Associates AB, Uppsala (Sweden); Johansson, Per-Olof [Artesia Grundvattenkonsult AB, Taeby (Sweden); Brydsten, Lars [Umeaa University, Dept. of Ecology and Environmental Science (Sweden); Bosson, Emma; Berglund, Sten [Swedish Nuclear Fuel and Waste Management Co., Stockholm (Sweden)

    2007-03-15

    This report presents and compares data and models for identification of near-surface groundwater recharge and discharge (RD) areas in Forsmark. The general principles of groundwater recharge and discharge are demonstrated and applied to interpret hydrological and hydrogeological observations made in the Forsmark area. 'Continuous' RD classification methods considered in the study include topographical modelling, map overlays, and hydrological-hydrogeological flow modelling. 'Discrete' (point) methods include field-based and hydrochemistry-based RD classifications of groundwater monitoring well locations. The topographical RD modelling uses the digital elevation model as the only input. The map overlays use background maps of Quaternary deposits, soils, and ground- and field layers of the vegetation/land use map. Further, the hydrological-hydrogeological modelling is performed using the MIKE SHE-MIKE 11 software packages, taking into account e.g. topography, meteorology, hydrogeology, and geometry of watercourses and lakes. The best between-model agreement is found for the topography-based model and the MIKE SHE-MIKE 11 model. The agreement between the topographical model and the map overlays is less good. The agreement between the map overlays on the one hand, and the MIKE SHE and field-based RD classifications on the other, is thought to be less good, as inferred from the comparison made with the topography-based model. However, much improvement of the map overlays can likely be obtained, e.g. by using 'weights' and calibration (such exercises were outside the scope of the present study). For field-classified 'recharge wells', there is a good agreement to the hydrochemistry-based (Piper plot) well classification, but less good for the field-classified 'discharge wells'. In addition, the concentration of the age-dating parameter tritium shows low variability among recharge wells, but a large spread among discharge

  12. Music genre classification using temporal domain features

    Science.gov (United States)

    Shiu, Yu; Kuo, C.-C. Jay

    2004-10-01

    Music genre provides an efficient way to index songs in the music database, and can be used as an effective means to retrieval music of a similar type, i.e. content-based music retrieval. In addition to other features, the temporal domain features of a music signal are exploited so as to increase the classification rate in this research. Three temporal techniques are examined in depth. First, the hidden Markov model (HMM) is used to emulate the time-varying properties of music signals. Second, to further increase the classification rate, we propose another feature set that focuses on the residual part of music signals. Third, the overall classification rate is enhanced by classifying smaller segments from a test material individually and making decision via majority voting. Experimental results are given to demonstrate the performance of the proposed techniques.

  13. Transportation Modes Classification Using Sensors on Smartphones

    Directory of Open Access Journals (Sweden)

    Shih-Hau Fang

    2016-08-01

    Full Text Available This paper investigates the transportation and vehicular modes classification by using big data from smartphone sensors. The three types of sensors used in this paper include the accelerometer, magnetometer, and gyroscope. This study proposes improved features and uses three machine learning algorithms including decision trees, K-nearest neighbor, and support vector machine to classify the user’s transportation and vehicular modes. In the experiments, we discussed and compared the performance from different perspectives including the accuracy for both modes, the executive time, and the model size. Results show that the proposed features enhance the accuracy, in which the support vector machine provides the best performance in classification accuracy whereas it consumes the largest prediction time. This paper also investigates the vehicle classification mode and compares the results with that of the transportation modes.

  14. A fuzzy decision tree method for fault classification in the steam generator of a pressurized water reactor

    International Nuclear Information System (INIS)

    Zio, Enrico; Baraldi, Piero; Popescu, Irina Crenguta

    2009-01-01

    This paper extends a method previously introduced by the authors for building a transparent fault classification algorithm by combining the fuzzy clustering, fuzzy logic and decision trees techniques. The baseline method transforms an opaque, fuzzy clustering-based classification model into a fuzzy logic inference model based on linguistic rules which can be represented by a decision tree formalism. The classification model thereby obtained is transparent in that it allows direct interpretation and inspection of the model. An extension in the procedure for the development of the fuzzy logic inference model is introduced to allow the treatment of more complicated cases, e.g. splitted and overlapping clusters. The corresponding computational tool developed relies on a number of parameters which can be tuned by the user to optimally compromise the level of transparency of the classification process and its efficiency. A numerical application is presented with regards to the fault classification in the Steam Generator of a Pressurized Water Reactor.

  15. Assessment of an extended version of the Jenkinson-Collison classification on CMIP5 models over Europe

    Science.gov (United States)

    Otero, Noelia; Sillmann, Jana; Butler, Tim

    2018-03-01

    A gridded, geographically extended weather type classification has been developed based on the Jenkinson-Collison (JC) classification system and used to evaluate the representation of weather types over Europe in a suite of climate model simulations. To this aim, a set of models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) is compared with the circulation from two reanalysis products. Furthermore, we examine seasonal changes between simulated frequencies of weather types at present and future climate conditions. The models are in reasonably good agreement with the reanalyses, but some discrepancies occur in cyclonic days being overestimated over North, and underestimated over South Europe, while anticyclonic situations were overestimated over South, and underestimated over North Europe. Low flow conditions were generally underestimated, especially in summer over South Europe, and Westerly conditions were generally overestimated. The projected frequencies of weather types in the late twenty-first century suggest an increase of Anticyclonic days over South Europe in all seasons except summer, while Westerly days increase over North and Central Europe, particularly in winter. We find significant changes in the frequency of Low flow conditions and the Easterly type that become more frequent during the warmer seasons over Southeast and Southwest Europe, respectively. Our results indicate that in winter the Westerly type has significant impacts on positive anomalies of maximum and minimum temperature over most of Europe. Except in winter, the warmer temperatures are linked to Easterlies, Anticyclonic and Low Flow conditions, especially over the Mediterranean area. Furthermore, we show that changes in the frequency of weather types represent a minor contribution of the total change of European temperatures, which would be mainly driven by changes in the temperature anomalies associated with the weather types themselves.

  16. A reaction limited in vivo dissolution model for the study of drug absorption: Towards a new paradigm for the biopharmaceutic classification of drugs.

    Science.gov (United States)

    Macheras, Panos; Iliadis, Athanassios; Melagraki, Georgia

    2018-05-30

    The aim of this work is to develop a gastrointestinal (GI) drug absorption model based on a reaction limited model of dissolution and consider its impact on the biopharmaceutic classification of drugs. Estimates for the fraction of dose absorbed as a function of dose, solubility, reaction/dissolution rate constant and the stoichiometry of drug-GI fluids reaction/dissolution were derived by numerical solution of the model equations. The undissolved drug dose and the reaction/dissolution rate constant drive the dissolution rate and determine the extent of absorption when high-constant drug permeability throughout the gastrointestinal tract is assumed. Dose is an important element of drug-GI fluids reaction/dissolution while solubility exclusively acts as an upper limit for drug concentrations in the lumen. The 3D plots of fraction of dose absorbed as a function of dose and reaction/dissolution rate constant for highly soluble and low soluble drugs for different "stoichiometries" (0.7, 1.0, 2.0) of the drug-reaction/dissolution with the GI fluids revealed that high extent of absorption was found assuming high drug- reaction/dissolution rate constant and high drug solubility. The model equations were used to simulate in vivo supersaturation and precipitation phenomena. The model developed provides the theoretical basis for the interpretation of the extent of drug's absorption on the basis of the parameters associated with the drug-GI fluids reaction/dissolution. A new paradigm emerges for the biopharmaceutic classification of drugs, namely, a model independent biopharmaceutic classification scheme of four drug categories based on either the fulfillment or not of the current dissolution criteria and the high or low % drug metabolism. Copyright © 2018. Published by Elsevier B.V.

  17. Unsupervised classification of variable stars

    Science.gov (United States)

    Valenzuela, Lucas; Pichara, Karim

    2018-03-01

    During the past 10 years, a considerable amount of effort has been made to develop algorithms for automatic classification of variable stars. That has been primarily achieved by applying machine learning methods to photometric data sets where objects are represented as light curves. Classifiers require training sets to learn the underlying patterns that allow the separation among classes. Unfortunately, building training sets is an expensive process that demands a lot of human efforts. Every time data come from new surveys; the only available training instances are the ones that have a cross-match with previously labelled objects, consequently generating insufficient training sets compared with the large amounts of unlabelled sources. In this work, we present an algorithm that performs unsupervised classification of variable stars, relying only on the similarity among light curves. We tackle the unsupervised classification problem by proposing an untraditional approach. Instead of trying to match classes of stars with clusters found by a clustering algorithm, we propose a query-based method where astronomers can find groups of variable stars ranked by similarity. We also develop a fast similarity function specific for light curves, based on a novel data structure that allows scaling the search over the entire data set of unlabelled objects. Experiments show that our unsupervised model achieves high accuracy in the classification of different types of variable stars and that the proposed algorithm scales up to massive amounts of light curves.

  18. A Support Vector Machine Hydrometeor Classification Algorithm for Dual-Polarization Radar

    Directory of Open Access Journals (Sweden)

    Nicoletta Roberto

    2017-07-01

    Full Text Available An algorithm based on a support vector machine (SVM is proposed for hydrometeor classification. The training phase is driven by the output of a fuzzy logic hydrometeor classification algorithm, i.e., the most popular approach for hydrometer classification algorithms used for ground-based weather radar. The performance of SVM is evaluated by resorting to a weather scenario, generated by a weather model; the corresponding radar measurements are obtained by simulation and by comparing results of SVM classification with those obtained by a fuzzy logic classifier. Results based on the weather model and simulations show a higher accuracy of the SVM classification. Objective comparison of the two classifiers applied to real radar data shows that SVM classification maps are spatially more homogenous (textural indices, energy, and homogeneity increases by 21% and 12% respectively and do not present non-classified data. The improvements found by SVM classifier, even though it is applied pixel-by-pixel, can be attributed to its ability to learn from the entire hyperspace of radar measurements and to the accurate training. The reliability of results and higher computing performance make SVM attractive for some challenging tasks such as its implementation in Decision Support Systems for helping pilots to make optimal decisions about changes inthe flight route caused by unexpected adverse weather.

  19. Gynecomastia Classification for Surgical Management: A Systematic Review and Novel Classification System.

    Science.gov (United States)

    Waltho, Daniel; Hatchell, Alexandra; Thoma, Achilleas

    2017-03-01

    Gynecomastia is a common deformity of the male breast, where certain cases warrant surgical management. There are several surgical options, which vary depending on the breast characteristics. To guide surgical management, several classification systems for gynecomastia have been proposed. A systematic review was performed to (1) identify all classification systems for the surgical management of gynecomastia, and (2) determine the adequacy of these classification systems to appropriately categorize the condition for surgical decision-making. The search yielded 1012 articles, and 11 articles were included in the review. Eleven classification systems in total were ascertained, and a total of 10 unique features were identified: (1) breast size, (2) skin redundancy, (3) breast ptosis, (4) tissue predominance, (5) upper abdominal laxity, (6) breast tuberosity, (7) nipple malposition, (8) chest shape, (9) absence of sternal notch, and (10) breast skin elasticity. On average, classification systems included two or three of these features. Breast size and ptosis were the most commonly included features. Based on their review of the current classification systems, the authors believe the ideal classification system should be universal and cater to all causes of gynecomastia; be surgically useful and easy to use; and should include a comprehensive set of clinically appropriate patient-related features, such as breast size, breast ptosis, tissue predominance, and skin redundancy. None of the current classification systems appears to fulfill these criteria.

  20. Joint Probability-Based Neuronal Spike Train Classification

    Directory of Open Access Journals (Sweden)

    Yan Chen

    2009-01-01

    Full Text Available Neuronal spike trains are used by the nervous system to encode and transmit information. Euclidean distance-based methods (EDBMs have been applied to quantify the similarity between temporally-discretized spike trains and model responses. In this study, using the same discretization procedure, we developed and applied a joint probability-based method (JPBM to classify individual spike trains of slowly adapting pulmonary stretch receptors (SARs. The activity of individual SARs was recorded in anaesthetized, paralysed adult male rabbits, which were artificially-ventilated at constant rate and one of three different volumes. Two-thirds of the responses to the 600 stimuli presented at each volume were used to construct three response models (one for each stimulus volume consisting of a series of time bins, each with spike probabilities. The remaining one-third of the responses where used as test responses to be classified into one of the three model responses. This was done by computing the joint probability of observing the same series of events (spikes or no spikes, dictated by the test response in a given model and determining which probability of the three was highest. The JPBM generally produced better classification accuracy than the EDBM, and both performed well above chance. Both methods were similarly affected by variations in discretization parameters, response epoch duration, and two different response alignment strategies. Increasing bin widths increased classification accuracy, which also improved with increased observation time, but primarily during periods of increasing lung inflation. Thus, the JPBM is a simple and effective method performing spike train classification.

  1. Semi-supervised Probabilistic Distance Clustering and the Uncertainty of Classification

    Science.gov (United States)

    Iyigun, Cem; Ben-Israel, Adi

    Semi-supervised clustering is an attempt to reconcile clustering (unsupervised learning) and classification (supervised learning, using prior information on the data). These two modes of data analysis are combined in a parameterized model, the parameter θ ∈ [0, 1] is the weight attributed to the prior information, θ = 0 corresponding to clustering, and θ = 1 to classification. The results (cluster centers, classification rule) depend on the parameter θ, an insensitivity to θ indicates that the prior information is in agreement with the intrinsic cluster structure, and is otherwise redundant. This explains why some data sets (such as the Wisconsin breast cancer data, Merz and Murphy, UCI repository of machine learning databases, University of California, Irvine, CA) give good results for all reasonable classification methods. The uncertainty of classification is represented here by the geometric mean of the membership probabilities, shown to be an entropic distance related to the Kullback-Leibler divergence.

  2. Object-Oriented Semisupervised Classification of VHR Images by Combining MedLDA and a Bilateral Filter

    Directory of Open Access Journals (Sweden)

    Shi He

    2015-01-01

    Full Text Available A Bayesian hierarchical model is presented to classify very high resolution (VHR images in a semisupervised manner, in which both a maximum entropy discrimination latent Dirichlet allocation (MedLDA and a bilateral filter are combined into a novel application framework. The primary contribution of this paper is to nullify the disadvantages of traditional probabilistic topic models on pixel-level supervised information and to achieve the effective classification of VHR remote sensing images. This framework consists of the following two iterative steps. In the training stage, the model utilizes the central labeled pixel and its neighborhood, as a squared labeled image object, to train the classifiers. In the classification stage, each central unlabeled pixel with its neighborhood, as an unlabeled object, is classified as a user-provided geoobject class label with the maximum posterior probability. Gibbs sampling is adopted for model inference. The experimental results demonstrate that the proposed method outperforms two classical SVM-based supervised classification methods and probabilistic-topic-models-based classification methods.

  3. Training Small Networks for Scene Classification of Remote Sensing Images via Knowledge Distillation

    Directory of Open Access Journals (Sweden)

    Guanzhou Chen

    2018-05-01

    Full Text Available Scene classification, aiming to identify the land-cover categories of remotely sensed image patches, is now a fundamental task in the remote sensing image analysis field. Deep-learning-model-based algorithms are widely applied in scene classification and achieve remarkable performance, but these high-level methods are computationally expensive and time-consuming. Consequently in this paper, we introduce a knowledge distillation framework, currently a mainstream model compression method, into remote sensing scene classification to improve the performance of smaller and shallower network models. Our knowledge distillation training method makes the high-temperature softmax output of a small and shallow student model match the large and deep teacher model. In our experiments, we evaluate knowledge distillation training method for remote sensing scene classification on four public datasets: AID dataset, UCMerced dataset, NWPU-RESISC dataset, and EuroSAT dataset. Results show that our proposed training method was effective and increased overall accuracy (3% in AID experiments, 5% in UCMerced experiments, 1% in NWPU-RESISC and EuroSAT experiments for small and shallow models. We further explored the performance of the student model on small and unbalanced datasets. Our findings indicate that knowledge distillation can improve the performance of small network models on datasets with lower spatial resolution images, numerous categories, as well as fewer training samples.

  4. Information gathering for CLP classification

    Directory of Open Access Journals (Sweden)

    Ida Marcello

    2011-01-01

    Full Text Available Regulation 1272/2008 includes provisions for two types of classification: harmonised classification and self-classification. The harmonised classification of substances is decided at Community level and a list of harmonised classifications is included in the Annex VI of the classification, labelling and packaging Regulation (CLP. If a chemical substance is not included in the harmonised classification list it must be self-classified, based on available information, according to the requirements of Annex I of the CLP Regulation. CLP appoints that the harmonised classification will be performed for carcinogenic, mutagenic or toxic to reproduction substances (CMR substances and for respiratory sensitisers category 1 and for other hazard classes on a case-by-case basis. The first step of classification is the gathering of available and relevant information. This paper presents the procedure for gathering information and to obtain data. The data quality is also discussed.

  5. Completed Local Ternary Pattern for Rotation Invariant Texture Classification

    Directory of Open Access Journals (Sweden)

    Taha H. Rassem

    2014-01-01

    Full Text Available Despite the fact that the two texture descriptors, the completed modeling of Local Binary Pattern (CLBP and the Completed Local Binary Count (CLBC, have achieved a remarkable accuracy for invariant rotation texture classification, they inherit some Local Binary Pattern (LBP drawbacks. The LBP is sensitive to noise, and different patterns of LBP may be classified into the same class that reduces its discriminating property. Although, the Local Ternary Pattern (LTP is proposed to be more robust to noise than LBP, however, the latter’s weakness may appear with the LTP as well as with LBP. In this paper, a novel completed modeling of the Local Ternary Pattern (LTP operator is proposed to overcome both LBP drawbacks, and an associated completed Local Ternary Pattern (CLTP scheme is developed for rotation invariant texture classification. The experimental results using four different texture databases show that the proposed CLTP achieved an impressive classification accuracy as compared to the CLBP and CLBC descriptors.

  6. A combined reconstruction-classification method for diffuse optical tomography

    Energy Technology Data Exchange (ETDEWEB)

    Hiltunen, P [Department of Biomedical Engineering and Computational Science, Helsinki University of Technology, PO Box 3310, FI-02015 TKK (Finland); Prince, S J D; Arridge, S [Department of Computer Science, University College London, Gower Street London, WC1E 6B (United Kingdom)], E-mail: petri.hiltunen@tkk.fi, E-mail: s.prince@cs.ucl.ac.uk, E-mail: s.arridge@cs.ucl.ac.uk

    2009-11-07

    We present a combined classification and reconstruction algorithm for diffuse optical tomography (DOT). DOT is a nonlinear ill-posed inverse problem. Therefore, some regularization is needed. We present a mixture of Gaussians prior, which regularizes the DOT reconstruction step. During each iteration, the parameters of a mixture model are estimated. These associate each reconstructed pixel with one of several classes based on the current estimate of the optical parameters. This classification is exploited to form a new prior distribution to regularize the reconstruction step and update the optical parameters. The algorithm can be described as an iteration between an optimization scheme with zeroth-order variable mean and variance Tikhonov regularization and an expectation-maximization scheme for estimation of the model parameters. We describe the algorithm in a general Bayesian framework. Results from simulated test cases and phantom measurements show that the algorithm enhances the contrast of the reconstructed images with good spatial accuracy. The probabilistic classifications of each image contain only a few misclassified pixels.

  7. Catchment Classification: Connecting Climate, Structure and Function

    Science.gov (United States)

    Sawicz, K. A.; Wagener, T.; Sivapalan, M.; Troch, P. A.; Carrillo, G. A.

    2010-12-01

    Hydrology does not yet possess a generally accepted catchment classification framework. Such a classification framework needs to: [1] give names to things, i.e. the main classification step, [2] permit transfer of information, i.e. regionalization of information, [3] permit development of generalizations, i.e. to develop new theory, and [4] provide a first order environmental change impact assessment, i.e., the hydrologic implications of climate, land use and land cover change. One strategy is to create a catchment classification framework based on the notion of catchment functions (partitioning, storage, and release). Results of an empirical study presented here connects climate and structure to catchment function (in the form of select hydrologic signatures), based on analyzing over 300 US catchments. Initial results indicate a wide assortment of signature relationships with properties of climate, geology, and vegetation. The uncertainty in the different regionalized signatures varies widely, and therefore there is variability in the robustness of classifying ungauged basins. This research provides insight into the controls of hydrologic behavior of a catchment, and enables a classification framework applicable to gauged and ungauged across the study domain. This study sheds light on what we can expect to achieve in mapping climate, structure and function in a top-down manner. Results of this study complement work done using a bottom-up physically-based modeling framework to generalize this approach (Carrillo et al., this session).

  8. Extricating Manual and Non-Manual Features for Subunit Level Medical Sign Modelling in Automatic Sign Language Classification and Recognition.

    Science.gov (United States)

    R, Elakkiya; K, Selvamani

    2017-09-22

    Subunit segmenting and modelling in medical sign language is one of the important studies in linguistic-oriented and vision-based Sign Language Recognition (SLR). Many efforts were made in the precedent to focus the functional subunits from the view of linguistic syllables but the problem is implementing such subunit extraction using syllables is not feasible in real-world computer vision techniques. And also, the present recognition systems are designed in such a way that it can detect the signer dependent actions under restricted and laboratory conditions. This research paper aims at solving these two important issues (1) Subunit extraction and (2) Signer independent action on visual sign language recognition. Subunit extraction involved in the sequential and parallel breakdown of sign gestures without any prior knowledge on syllables and number of subunits. A novel Bayesian Parallel Hidden Markov Model (BPaHMM) is introduced for subunit extraction to combine the features of manual and non-manual parameters to yield better results in classification and recognition of signs. Signer independent action aims in using a single web camera for different signer behaviour patterns and for cross-signer validation. Experimental results have proved that the proposed signer independent subunit level modelling for sign language classification and recognition has shown improvement and variations when compared with other existing works.

  9. Deep learning application: rubbish classification with aid of an android device

    Science.gov (United States)

    Liu, Sijiang; Jiang, Bo; Zhan, Jie

    2017-06-01

    Deep learning is a very hot topic currently in pattern recognition and artificial intelligence researches. Aiming at the practical problem that people usually don't know correct classifications some rubbish should belong to, based on the powerful image classification ability of the deep learning method, we have designed a prototype system to help users to classify kinds of rubbish. Firstly the CaffeNet Model was adopted for our classification network training on the ImageNet dataset, and the trained network was deployed on a web server. Secondly an android app was developed for users to capture images of unclassified rubbish, upload images to the web server for analyzing backstage and retrieve the feedback, so that users can obtain the classification guide by an android device conveniently. Tests on our prototype system of rubbish classification show that: an image of one single type of rubbish with origin shape can be better used to judge its classification, while an image containing kinds of rubbish or rubbish with changed shape may fail to help users to decide rubbish's classification. However, the system still shows promising auxiliary function for rubbish classification if the network training strategy can be optimized further.

  10. Classification Accuracy Increase Using Multisensor Data Fusion

    Science.gov (United States)

    Makarau, A.; Palubinskas, G.; Reinartz, P.

    2011-09-01

    The practical use of very high resolution visible and near-infrared (VNIR) data is still growing (IKONOS, Quickbird, GeoEye-1, etc.) but for classification purposes the number of bands is limited in comparison to full spectral imaging. These limitations may lead to the confusion of materials such as different roofs, pavements, roads, etc. and therefore may provide wrong interpretation and use of classification products. Employment of hyperspectral data is another solution, but their low spatial resolution (comparing to multispectral data) restrict their usage for many applications. Another improvement can be achieved by fusion approaches of multisensory data since this may increase the quality of scene classification. Integration of Synthetic Aperture Radar (SAR) and optical data is widely performed for automatic classification, interpretation, and change detection. In this paper we present an approach for very high resolution SAR and multispectral data fusion for automatic classification in urban areas. Single polarization TerraSAR-X (SpotLight mode) and multispectral data are integrated using the INFOFUSE framework, consisting of feature extraction (information fission), unsupervised clustering (data representation on a finite domain and dimensionality reduction), and data aggregation (Bayesian or neural network). This framework allows a relevant way of multisource data combination following consensus theory. The classification is not influenced by the limitations of dimensionality, and the calculation complexity primarily depends on the step of dimensionality reduction. Fusion of single polarization TerraSAR-X, WorldView-2 (VNIR or full set), and Digital Surface Model (DSM) data allow for different types of urban objects to be classified into predefined classes of interest with increased accuracy. The comparison to classification results of WorldView-2 multispectral data (8 spectral bands) is provided and the numerical evaluation of the method in comparison to

  11. ASIST SIG/CR Classification Workshop 2000: Classification for User Support and Learning.

    Science.gov (United States)

    Soergel, Dagobert

    2001-01-01

    Reports on papers presented at the 62nd Annual Meeting of ASIST (American Society for Information Science and Technology) for the Special Interest Group in Classification Research (SIG/CR). Topics include types of knowledge; developing user-oriented classifications, including domain analysis; classification in the user interface; and automatic…

  12. Couinaud's classification v.s. Cho's classification. Their feasibility in the right hepatic lobe

    International Nuclear Information System (INIS)

    Shioyama, Yasukazu; Ikeda, Hiroaki; Sato, Motohito; Yoshimi, Fuyo; Kishi, Kazushi; Sato, Morio; Kimura, Masashi

    2008-01-01

    The objective of this study was to investigate if the new classification system proposed by Cho is feasible to clinical usage comparing with the classical Couinaud's one. One hundred consecutive cases of abdominal CT were studied using a 64 or an 8 slice multislice CT and created three dimensional portal vein images for analysis by the Workstation. We applied both Cho's classification and the classical Couinaud's one for each cases according to their definitions. Three diagnostic radiologists assessed their feasibility as category one (unable to classify) to five (clear to classify with total suit with the original classification criteria). And in each cases, we tried to judge whether Cho's or the classical Couinaud' classification could more easily transmit anatomical information. Analyzers could classified portal veins clearly (category 5) in 77 to 80% of cases and clearly (category 5) or almost clearly (category 4) in 86-93% along with both classifications. In the feasibility of classification, there was no statistically significant difference between two classifications. In 15 cases we felt that using Couinaud's classification is more convenient for us to transmit anatomical information to physicians than using Cho's one, because in these cases we noticed two large portal veins ramify from right main portal vein cranialy and caudaly and then we could not classify P5 as a branch of antero-ventral segment (AVS). Conversely in 17 cases we felt Cho's classification is more convenient because we could not divide right posterior branch as P6 and P7 and in these cases the right posterior portal vein ramified to several small branches. The anterior fissure vein was clearly noticed in only 60 cases. Comparing the classical Couinaud's classification and Cho's one in feasility of classification, there was no statistically significant difference. We propose we routinely report hepatic anatomy with the classical Couinauds classification and in the preoperative cases we

  13. GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting

    Directory of Open Access Journals (Sweden)

    Lintao Yang

    2018-01-01

    Full Text Available With the development of smart power grids, communication network technology and sensor technology, there has been an exponential growth in complex electricity load data. Irregular electricity load fluctuations caused by the weather and holiday factors disrupt the daily operation of the power companies. To deal with these challenges, this paper investigates a day-ahead electricity peak load interval forecasting problem. It transforms the conventional continuous forecasting problem into a novel interval forecasting problem, and then further converts the interval forecasting problem into the classification forecasting problem. In addition, an indicator system influencing the electricity load is established from three dimensions, namely the load series, calendar data, and weather data. A semi-supervised feature selection algorithm is proposed to address an electricity load classification forecasting issue based on the group method of data handling (GMDH technology. The proposed algorithm consists of three main stages: (1 training the basic classifier; (2 selectively marking the most suitable samples from the unclassified label data, and adding them to an initial training set; and (3 training the classification models on the final training set and classifying the test samples. An empirical analysis of electricity load dataset from four Chinese cities is conducted. Results show that the proposed model can address the electricity load classification forecasting problem more efficiently and effectively than the FW-Semi FS (forward semi-supervised feature selection and GMDH-U (GMDH-based semi-supervised feature selection for customer classification models.

  14. Applying deep neural networks to HEP job classification

    International Nuclear Information System (INIS)

    Wang, L; Shi, J; Yan, X

    2015-01-01

    The cluster of IHEP computing center is a middle-sized computing system which provides 10 thousands CPU cores, 5 PB disk storage, and 40 GB/s IO throughput. Its 1000+ users come from a variety of HEP experiments. In such a system, job classification is an indispensable task. Although experienced administrator can classify a HEP job by its IO pattern, it is unpractical to classify millions of jobs manually. We present how to solve this problem with deep neural networks in a supervised learning way. Firstly, we built a training data set of 320K samples by an IO pattern collection agent and a semi-automatic process of sample labelling. Then we implemented and trained DNNs models with Torch. During the process of model training, several meta-parameters was tuned with cross-validations. Test results show that a 5- hidden-layer DNNs model achieves 96% precision on the classification task. By comparison, it outperforms a linear model by 8% precision. (paper)

  15. When machine vision meets histology: A comparative evaluation of model architecture for classification of histology sections.

    Science.gov (United States)

    Zhong, Cheng; Han, Ju; Borowsky, Alexander; Parvin, Bahram; Wang, Yunfu; Chang, Hang

    2017-01-01

    Classification of histology sections in large cohorts, in terms of distinct regions of microanatomy (e.g., stromal) and histopathology (e.g., tumor, necrosis), enables the quantification of tumor composition, and the construction of predictive models of genomics and clinical outcome. To tackle the large technical variations and biological heterogeneities, which are intrinsic in large cohorts, emerging systems utilize either prior knowledge from pathologists or unsupervised feature learning for invariant representation of the underlying properties in the data. However, to a large degree, the architecture for tissue histology classification remains unexplored and requires urgent systematical investigation. This paper is the first attempt to provide insights into three fundamental questions in tissue histology classification: I. Is unsupervised feature learning preferable to human engineered features? II. Does cellular saliency help? III. Does the sparse feature encoder contribute to recognition? We show that (a) in I, both Cellular Morphometric Feature and features from unsupervised feature learning lead to superior performance when compared to SIFT and [Color, Texture]; (b) in II, cellular saliency incorporation impairs the performance for systems built upon pixel-/patch-level features; and (c) in III, the effect of the sparse feature encoder is correlated with the robustness of features, and the performance can be consistently improved by the multi-stage extension of systems built upon both Cellular Morphmetric Feature and features from unsupervised feature learning. These insights are validated with two cohorts of Glioblastoma Multiforme (GBM) and Kidney Clear Cell Carcinoma (KIRC). Copyright © 2016 Elsevier B.V. All rights reserved.

  16. Magnetic resonance imaging texture analysis classification of primary breast cancer

    International Nuclear Information System (INIS)

    Waugh, S.A.; Lerski, R.A.; Purdie, C.A.; Jordan, L.B.; Vinnicombe, S.; Martin, P.; Thompson, A.M.

    2016-01-01

    Patient-tailored treatments for breast cancer are based on histological and immunohistochemical (IHC) subtypes. Magnetic Resonance Imaging (MRI) texture analysis (TA) may be useful in non-invasive lesion subtype classification. Women with newly diagnosed primary breast cancer underwent pre-treatment dynamic contrast-enhanced breast MRI. TA was performed using co-occurrence matrix (COM) features, by creating a model on retrospective training data, then prospectively applying to a test set. Analyses were blinded to breast pathology. Subtype classifications were performed using a cross-validated k-nearest-neighbour (k = 3) technique, with accuracy relative to pathology assessed and receiver operator curve (AUROC) calculated. Mann-Whitney U and Kruskal-Wallis tests were used to assess raw entropy feature values. Histological subtype classifications were similar across training (n = 148 cancers) and test sets (n = 73 lesions) using all COM features (training: 75 %, AUROC = 0.816; test: 72.5 %, AUROC = 0.823). Entropy features were significantly different between lobular and ductal cancers (p < 0.001; Mann-Whitney U). IHC classifications using COM features were also similar for training and test data (training: 57.2 %, AUROC = 0.754; test: 57.0 %, AUROC = 0.750). Hormone receptor positive and negative cancers demonstrated significantly different entropy features. Entropy features alone were unable to create a robust classification model. Textural differences on contrast-enhanced MR images may reflect underlying lesion subtypes, which merits testing against treatment response. (orig.)

  17. Magnetic resonance imaging texture analysis classification of primary breast cancer

    Energy Technology Data Exchange (ETDEWEB)

    Waugh, S.A.; Lerski, R.A. [Ninewells Hospital and Medical School, Department of Medical Physics, Dundee (United Kingdom); Purdie, C.A.; Jordan, L.B. [Ninewells Hospital and Medical School, Department of Pathology, Dundee (United Kingdom); Vinnicombe, S. [University of Dundee, Division of Imaging and Technology, Ninewells Hospital and Medical School, Dundee (United Kingdom); Martin, P. [Ninewells Hospital and Medical School, Department of Clinical Radiology, Dundee (United Kingdom); Thompson, A.M. [University of Texas MD Anderson Cancer Center, Department of Surgical Oncology, Houston, TX (United States)

    2016-02-15

    Patient-tailored treatments for breast cancer are based on histological and immunohistochemical (IHC) subtypes. Magnetic Resonance Imaging (MRI) texture analysis (TA) may be useful in non-invasive lesion subtype classification. Women with newly diagnosed primary breast cancer underwent pre-treatment dynamic contrast-enhanced breast MRI. TA was performed using co-occurrence matrix (COM) features, by creating a model on retrospective training data, then prospectively applying to a test set. Analyses were blinded to breast pathology. Subtype classifications were performed using a cross-validated k-nearest-neighbour (k = 3) technique, with accuracy relative to pathology assessed and receiver operator curve (AUROC) calculated. Mann-Whitney U and Kruskal-Wallis tests were used to assess raw entropy feature values. Histological subtype classifications were similar across training (n = 148 cancers) and test sets (n = 73 lesions) using all COM features (training: 75 %, AUROC = 0.816; test: 72.5 %, AUROC = 0.823). Entropy features were significantly different between lobular and ductal cancers (p < 0.001; Mann-Whitney U). IHC classifications using COM features were also similar for training and test data (training: 57.2 %, AUROC = 0.754; test: 57.0 %, AUROC = 0.750). Hormone receptor positive and negative cancers demonstrated significantly different entropy features. Entropy features alone were unable to create a robust classification model. Textural differences on contrast-enhanced MR images may reflect underlying lesion subtypes, which merits testing against treatment response. (orig.)

  18. Differentiating Puerariae Lobatae Radix and Puerariae Thomsonii Radix using HPTLC coupled with multivariate classification analyses.

    Science.gov (United States)

    Wong, Ka H; Razmovski-Naumovski, Valentina; Li, Kong M; Li, George Q; Chan, Kelvin

    2014-07-01

    Puerariae Lobatae Radix (PLR), the root of Pueraria lobata, is a traditional Chinese medicine for treating diabetes and cardiovascular diseases. Puerariae Thomsonii Radix (PTR), the root of Pueraria thomsonii, is a closely related species to PLR and has been used as a PLR substitute in clinical practice. The aim of this study was to compare the classification accuracy of high performance thin-layer chromatography (HPTLC) with that of ultra-performance liquid chromatography (UPLC) in differentiating PLR from PTR. The Matlab functions were used to facilitate the digitalisation and pre-processing of the HPTLC plates. Seven multivariate classification methods were evaluated for the two chromatographic methods. The results demonstrated that the HPTLC classification models were comparable to the UPLC classification models. In particular, k-nearest neighbours, partial least square-discriminant analysis, principal component analysis-discriminant analysis and support vector machine-discriminant analysis showed the highest rate of correct species classification, whilst the lowest classification rate was obtained from soft independent modelling of class analogy. In conclusion, HPTLC combined with multivariate analysis is a promising technique for the quality control and differentiation of PLR and PTR. Copyright © 2014 Elsevier B.V. All rights reserved.

  19. Mechanisms underlying probucol-induced hERG-channel deficiency

    Directory of Open Access Journals (Sweden)

    Shi YQ

    2015-07-01

    Full Text Available Yuan-Qi Shi,1,* Cai-Chuan Yan,1,* Xiao Zhang,1 Meng Yan,1 Li-Rong Liu,1 Huai-Ze Geng,1 Lin Lv,1 Bao-Xin Li1,21Department of Pharmacology, Harbin Medical University, 2State-Province Key Laboratory of Biopharmaceutical Engineering, Harbin, Heilongjiang, People’s Republic of China*These authors contributed equally to this workAbstract: The hERG gene encodes the pore-forming α-subunit of the rapidly activating delayed rectifier potassium channel (IKr, which is important for cardiac repolarization. Reduction of IhERG due to genetic mutations or drug interferences causes long QT syndrome, leading to life-threatening cardiac arrhythmias (torsades de pointes or sudden death. Probucol is a cholesterol-lowering drug that could reduce hERG current by decreasing plasma membrane hERG protein expression and eventually cause long QT syndrome. Here, we investigated the mechanisms of probucol effects on IhERG and hERG-channel expression. Our data demonstrated that probucol reduces SGK1 expression, known as SGK isoform, in a concentration-dependent manner, resulting in downregulation of phosphorylated E3 ubiquitin ligase Nedd4-2 expression, but not the total level of Nedd4-2. As a result, the hERG protein reduces, due to the enhanced ubiquitination level. On the contrary, carbachol could enhance the phosphorylation level of Nedd4-2 as an alternative to SGK1, and thus rescue the ubiquitin-mediated degradation of hERG channels caused by probucol. These discoveries provide a novel mechanism of probucol-induced hERG-channel deficiency, and imply that carbachol or its analog may serve as potential therapeutic compounds for the handling of probucol cardiotoxicity.Keywords: long QT, hERG potassium channels, probucol, SGK1, Nedd4-2

  20. Subordinate-level object classification reexamined.

    Science.gov (United States)

    Biederman, I; Subramaniam, S; Bar, M; Kalocsai, P; Fiser, J

    1999-01-01

    The classification of a table as round rather than square, a car as a Mazda rather than a Ford, a drill bit as 3/8-inch rather than 1/4-inch, and a face as Tom have all been regarded as a single process termed "subordinate classification." Despite the common label, the considerable heterogeneity of the perceptual processing required to achieve such classifications requires, minimally, a more detailed taxonomy. Perceptual information relevant to subordinate-level shape classifications can be presumed to vary on continua of (a) the type of distinctive information that is present, nonaccidental or metric, (b) the size of the relevant contours or surfaces, and (c) the similarity of the to-be-discriminated features, such as whether a straight contour has to be distinguished from a contour of low curvature versus high curvature. We consider three, relatively pure cases. Case 1 subordinates may be distinguished by a representation, a geon structural description (GSD), specifying a nonaccidental characterization of an object's large parts and the relations among these parts, such as a round table versus a square table. Case 2 subordinates are also distinguished by GSDs, except that the distinctive GSDs are present at a small scale in a complex object so the location and mapping of the GSDs are contingent on an initial basic-level classification, such as when we use a logo to distinguish various makes of cars. Expertise for Cases 1 and 2 can be easily achieved through specification, often verbal, of the GSDs. Case 3 subordinates, which have furnished much of the grist for theorizing with "view-based" template models, require fine metric discriminations. Cases 1 and 2 account for the overwhelming majority of shape-based basic- and subordinate-level object classifications that people can and do make in their everyday lives. These classifications are typically made quickly, accurately, and with only modest costs of viewpoint changes. Whereas the activation of an array of