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Sample records for entropy-based automated classification

  1. An Entropy-based gene selection method for cancer classification using microarray data

    Directory of Open Access Journals (Sweden)

    Krishnan Arun

    2005-03-01

    Full Text Available Abstract Background Accurate diagnosis of cancer subtypes remains a challenging problem. Building classifiers based on gene expression data is a promising approach; yet the selection of non-redundant but relevant genes is difficult. The selected gene set should be small enough to allow diagnosis even in regular clinical laboratories and ideally identify genes involved in cancer-specific regulatory pathways. Here an entropy-based method is proposed that selects genes related to the different cancer classes while at the same time reducing the redundancy among the genes. Results The present study identifies a subset of features by maximizing the relevance and minimizing the redundancy of the selected genes. A merit called normalized mutual information is employed to measure the relevance and the redundancy of the genes. In order to find a more representative subset of features, an iterative procedure is adopted that incorporates an initial clustering followed by data partitioning and the application of the algorithm to each of the partitions. A leave-one-out approach then selects the most commonly selected genes across all the different runs and the gene selection algorithm is applied again to pare down the list of selected genes until a minimal subset is obtained that gives a satisfactory accuracy of classification. The algorithm was applied to three different data sets and the results obtained were compared to work done by others using the same data sets Conclusion This study presents an entropy-based iterative algorithm for selecting genes from microarray data that are able to classify various cancer sub-types with high accuracy. In addition, the feature set obtained is very compact, that is, the redundancy between genes is reduced to a large extent. This implies that classifiers can be built with a smaller subset of genes.

  2. An Entropy-Based Automated Cell Nuclei Segmentation and Quantification: Application in Analysis of Wound Healing Process

    Directory of Open Access Journals (Sweden)

    Varun Oswal

    2013-01-01

    Full Text Available The segmentation and quantification of cell nuclei are two very significant tasks in the analysis of histological images. Accurate results of cell nuclei segmentation are often adapted to a variety of applications such as the detection of cancerous cell nuclei and the observation of overlapping cellular events occurring during wound healing process in the human body. In this paper, an automated entropy-based thresholding system for segmentation and quantification of cell nuclei from histologically stained images has been presented. The proposed translational computation system aims to integrate clinical insight and computational analysis by identifying and segmenting objects of interest within histological images. Objects of interest and background regions are automatically distinguished by dynamically determining 3 optimal threshold values for the 3 color components of an input image. The threshold values are determined by means of entropy computations that are based on probability distributions of the color intensities of pixels and the spatial similarity of pixel intensities within neighborhoods. The effectiveness of the proposed system was tested over 21 histologically stained images containing approximately 1800 cell nuclei, and the overall performance of the algorithm was found to be promising, with high accuracy and precision values.

  3. An automated cirrus classification

    Science.gov (United States)

    Gryspeerdt, Edward; Quaas, Johannes; Sourdeval, Odran; Goren, Tom

    2017-04-01

    Cirrus clouds play an important role in determining the radiation budget of the earth, but our understanding of the lifecycle and controls on cirrus clouds remains incomplete. Cirrus clouds can have very different properties and development depending on their environment, particularly during their formation. However, the relevant factors often cannot be distinguished using commonly retrieved satellite data products (such as cloud optical depth). In particular, the initial cloud phase has been identified as an important factor in cloud development, but although back-trajectory based methods can provide information on the initial cloud phase, they are computationally expensive and depend on the cloud parametrisations used in re-analysis products. In this work, a classification system (Identification and Classification of Cirrus, IC-CIR) is introduced. Using re-analysis and satellite data, cirrus clouds are separated in four main types: frontal, convective, orographic and in-situ. The properties of these classes show that this classification is able to provide useful information on the properties and initial phase of cirrus clouds, information that could not be provided by instantaneous satellite retrieved cloud properties alone. This classification is designed to be easily implemented in global climate models, helping to improve future comparisons between observations and models and reducing the uncertainty in cirrus clouds properties, leading to improved cloud parametrisations.

  4. Automated spectral classification and the GAIA project

    Science.gov (United States)

    Lasala, Jerry; Kurtz, Michael J.

    1995-01-01

    Two dimensional spectral types for each of the stars observed in the global astrometric interferometer for astrophysics (GAIA) mission would provide additional information for the galactic structure and stellar evolution studies, as well as helping in the identification of unusual objects and populations. The classification of the large quantity generated spectra requires that automated techniques are implemented. Approaches for the automatic classification are reviewed, and a metric-distance method is discussed. In tests, the metric-distance method produced spectral types with mean errors comparable to those of human classifiers working at similar resolution. Data and equipment requirements for an automated classification survey, are discussed. A program of auxiliary observations is proposed to yield spectral types and radial velocities for the GAIA-observed stars.

  5. Automated source classification of new transient sources

    Science.gov (United States)

    Oertel, M.; Kreikenbohm, A.; Wilms, J.; DeLuca, A.

    2017-10-01

    The EXTraS project harvests the hitherto unexplored temporal domain information buried in the serendipitous data collected by the European Photon Imaging Camera (EPIC) onboard the ESA XMM-Newton mission since its launch. This includes a search for fast transients, missed by standard image analysis, and a search and characterization of variability in hundreds of thousands of sources. We present an automated classification scheme for new transient sources in the EXTraS project. The method is as follows: source classification features of a training sample are used to train machine learning algorithms (performed in R; randomForest (Breiman, 2001) in supervised mode) which are then tested on a sample of known source classes and used for classification.

  6. Automated compound classification using a chemical ontology

    Directory of Open Access Journals (Sweden)

    Bobach Claudia

    2012-12-01

    Full Text Available Abstract Background Classification of chemical compounds into compound classes by using structure derived descriptors is a well-established method to aid the evaluation and abstraction of compound properties in chemical compound databases. MeSH and recently ChEBI are examples of chemical ontologies that provide a hierarchical classification of compounds into general compound classes of biological interest based on their structural as well as property or use features. In these ontologies, compounds have been assigned manually to their respective classes. However, with the ever increasing possibilities to extract new compounds from text documents using name-to-structure tools and considering the large number of compounds deposited in databases, automated and comprehensive chemical classification methods are needed to avoid the error prone and time consuming manual classification of compounds. Results In the present work we implement principles and methods to construct a chemical ontology of classes that shall support the automated, high-quality compound classification in chemical databases or text documents. While SMARTS expressions have already been used to define chemical structure class concepts, in the present work we have extended the expressive power of such class definitions by expanding their structure-based reasoning logic. Thus, to achieve the required precision and granularity of chemical class definitions, sets of SMARTS class definitions are connected by OR and NOT logical operators. In addition, AND logic has been implemented to allow the concomitant use of flexible atom lists and stereochemistry definitions. The resulting chemical ontology is a multi-hierarchical taxonomy of concept nodes connected by directed, transitive relationships. Conclusions A proposal for a rule based definition of chemical classes has been made that allows to define chemical compound classes more precisely than before. The proposed structure-based reasoning

  7. Automated compound classification using a chemical ontology.

    Science.gov (United States)

    Bobach, Claudia; Böhme, Timo; Laube, Ulf; Püschel, Anett; Weber, Lutz

    2012-12-29

    Classification of chemical compounds into compound classes by using structure derived descriptors is a well-established method to aid the evaluation and abstraction of compound properties in chemical compound databases. MeSH and recently ChEBI are examples of chemical ontologies that provide a hierarchical classification of compounds into general compound classes of biological interest based on their structural as well as property or use features. In these ontologies, compounds have been assigned manually to their respective classes. However, with the ever increasing possibilities to extract new compounds from text documents using name-to-structure tools and considering the large number of compounds deposited in databases, automated and comprehensive chemical classification methods are needed to avoid the error prone and time consuming manual classification of compounds. In the present work we implement principles and methods to construct a chemical ontology of classes that shall support the automated, high-quality compound classification in chemical databases or text documents. While SMARTS expressions have already been used to define chemical structure class concepts, in the present work we have extended the expressive power of such class definitions by expanding their structure-based reasoning logic. Thus, to achieve the required precision and granularity of chemical class definitions, sets of SMARTS class definitions are connected by OR and NOT logical operators. In addition, AND logic has been implemented to allow the concomitant use of flexible atom lists and stereochemistry definitions. The resulting chemical ontology is a multi-hierarchical taxonomy of concept nodes connected by directed, transitive relationships. A proposal for a rule based definition of chemical classes has been made that allows to define chemical compound classes more precisely than before. The proposed structure-based reasoning logic allows to translate chemistry expert knowledge into a

  8. Automated Sunspot Detection and Classification Using SOHO/MDI Imagery

    Science.gov (United States)

    2015-03-01

    AUTOMATED SUNSPOT DETECTION AND CLASSIFICATION USING SOHO/MDI IMAGERY THESIS Samantha R. Howard, 1st Lieutenant, USAF AFIT-ENP-MS-15-M-078 DEPARTMENT...Government and is not subject to copyright protection in the United States. AFIT-ENP-MS-15-M-078 AUTOMATED SUNSPOT DETECTION AND CLASSIFICATION USING SOHO...MS-15-M-078 AUTOMATED SUNSPOT DETECTION AND CLASSIFICATION USING SOHO/MDI IMAGERY Samantha R. Howard, B.S. 1st Lieutenant, USAF Committee Membership

  9. Automated feature extraction and classification from image sources

    Science.gov (United States)

    ,

    1995-01-01

    The U.S. Department of the Interior, U.S. Geological Survey (USGS), and Unisys Corporation have completed a cooperative research and development agreement (CRADA) to explore automated feature extraction and classification from image sources. The CRADA helped the USGS define the spectral and spatial resolution characteristics of airborne and satellite imaging sensors necessary to meet base cartographic and land use and land cover feature classification requirements and help develop future automated geographic and cartographic data production capabilities. The USGS is seeking a new commercial partner to continue automated feature extraction and classification research and development.

  10. Automated structural classification of lipids by machine learning.

    Science.gov (United States)

    Taylor, Ryan; Miller, Ryan H; Miller, Ryan D; Porter, Michael; Dalgleish, James; Prince, John T

    2015-03-01

    Modern lipidomics is largely dependent upon structural ontologies because of the great diversity exhibited in the lipidome, but no automated lipid classification exists to facilitate this partitioning. The size of the putative lipidome far exceeds the number currently classified, despite a decade of work. Automated classification would benefit ongoing classification efforts by decreasing the time needed and increasing the accuracy of classification while providing classifications for mass spectral identification algorithms. We introduce a tool that automates classification into the LIPID MAPS ontology of known lipids with >95% accuracy and novel lipids with 63% accuracy. The classification is based upon simple chemical characteristics and modern machine learning algorithms. The decision trees produced are intelligible and can be used to clarify implicit assumptions about the current LIPID MAPS classification scheme. These characteristics and decision trees are made available to facilitate alternative implementations. We also discovered many hundreds of lipids that are currently misclassified in the LIPID MAPS database, strongly underscoring the need for automated classification. Source code and chemical characteristic lists as SMARTS search strings are available under an open-source license at https://www.github.com/princelab/lipid_classifier. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  11. Automated Classification of Seedlings Using Computer Vision

    DEFF Research Database (Denmark)

    Dyrmann, Mads; Christiansen, Peter

    The objective of this project is to investigate the possibilities of recognizing plant species at multiple growth stages based on RGB images. Plants and leaves are initially segmented from a database through a partly automated procedure providing samples of 2438 plants and 4767 leaves distributed...

  12. Automated classification of computer network attacks

    CSIR Research Space (South Africa)

    Van Heerden, R

    2013-11-01

    Full Text Available In this paper we demonstrate how an automated reasoner, HermiT, is used to classify instances of computer network based attacks in conjunction with a network attack ontology. The ontology describes different types of network attacks through classes...

  13. Improved Automated Classification of Alcoholics and Non-alcoholics

    OpenAIRE

    Ramaswamy Palaniappan

    2008-01-01

    In this paper, several improvements are proposed to previous work of automated classification of alcoholics and nonalcoholics. In the previous paper, multiplayer-perceptron neural network classifying energy of gamma band Visual Evoked Potential (VEP) signals gave the best classification performance using 800 VEP signals from 10 alcoholics and 10 non-alcoholics. Here, the dataset is extended to include 3560 VEP signals from 102 subjects: 62 alcoholics and 40 non-alcoholics...

  14. Feature Selection with Neighborhood Entropy-Based Cooperative Game Theory

    Directory of Open Access Journals (Sweden)

    Kai Zeng

    2014-01-01

    Full Text Available Feature selection plays an important role in machine learning and data mining. In recent years, various feature measurements have been proposed to select significant features from high-dimensional datasets. However, most traditional feature selection methods will ignore some features which have strong classification ability as a group but are weak as individuals. To deal with this problem, we redefine the redundancy, interdependence, and independence of features by using neighborhood entropy. Then the neighborhood entropy-based feature contribution is proposed under the framework of cooperative game. The evaluative criteria of features can be formalized as the product of contribution and other classical feature measures. Finally, the proposed method is tested on several UCI datasets. The results show that neighborhood entropy-based cooperative game theory model (NECGT yield better performance than classical ones.

  15. Intelligent Computer Vision System for Automated Classification

    International Nuclear Information System (INIS)

    Jordanov, Ivan; Georgieva, Antoniya

    2010-01-01

    In this paper we investigate an Intelligent Computer Vision System applied for recognition and classification of commercially available cork tiles. The system is capable of acquiring and processing gray images using several feature generation and analysis techniques. Its functionality includes image acquisition, feature extraction and preprocessing, and feature classification with neural networks (NN). We also discuss system test and validation results from the recognition and classification tasks. The system investigation also includes statistical feature processing (features number and dimensionality reduction techniques) and classifier design (NN architecture, target coding, learning complexity and performance, and training with our own metaheuristic optimization method). The NNs trained with our genetic low-discrepancy search method (GLPτS) for global optimisation demonstrated very good generalisation abilities. In our view, the reported testing success rate of up to 95% is due to several factors: combination of feature generation techniques; application of Analysis of Variance (ANOVA) and Principal Component Analysis (PCA), which appeared to be very efficient for preprocessing the data; and use of suitable NN design and learning method.

  16. Automated lung nodule classification following automated nodule detection on CT: A serial approach

    International Nuclear Information System (INIS)

    Armato, Samuel G. III; Altman, Michael B.; Wilkie, Joel; Sone, Shusuke; Li, Feng; Doi, Kunio; Roy, Arunabha S.

    2003-01-01

    We have evaluated the performance of an automated classifier applied to the task of differentiating malignant and benign lung nodules in low-dose helical computed tomography (CT) scans acquired as part of a lung cancer screening program. The nodules classified in this manner were initially identified by our automated lung nodule detection method, so that the output of automated lung nodule detection was used as input to automated lung nodule classification. This study begins to narrow the distinction between the 'detection task' and the 'classification task'. Automated lung nodule detection is based on two- and three-dimensional analyses of the CT image data. Gray-level-thresholding techniques are used to identify initial lung nodule candidates, for which morphological and gray-level features are computed. A rule-based approach is applied to reduce the number of nodule candidates that correspond to non-nodules, and the features of remaining candidates are merged through linear discriminant analysis to obtain final detection results. Automated lung nodule classification merges the features of the lung nodule candidates identified by the detection algorithm that correspond to actual nodules through another linear discriminant classifier to distinguish between malignant and benign nodules. The automated classification method was applied to the computerized detection results obtained from a database of 393 low-dose thoracic CT scans containing 470 confirmed lung nodules (69 malignant and 401 benign nodules). Receiver operating characteristic (ROC) analysis was used to evaluate the ability of the classifier to differentiate between nodule candidates that correspond to malignant nodules and nodule candidates that correspond to benign lesions. The area under the ROC curve for this classification task attained a value of 0.79 during a leave-one-out evaluation

  17. “The Naming of Cats”: Automated Genre Classification

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    Yunhyong Kim

    2007-07-01

    Full Text Available This paper builds on the work presented at the ECDL 2006 in automated genre classification as a step toward automating metadata extraction from digital documents for ingest into digital repositories such as those run by archives, libraries and eprint services (Kim & Ross, 2006b. We have previously proposed dividing features of a document into five types (features for visual layout, language model features, stylometric features, features for semantic structure, and contextual features as an object linked to previously classified objects and other external sources and have examined visual and language model features. The current paper compares results from testing classifiers based on image and stylometric features in a binary classification to show that certain genres have strong image features which enable effective separation of documents belonging to the genre from a large pool of other documents.

  18. Automated cell type discovery and classification through knowledge transfer

    Science.gov (United States)

    Lee, Hao-Chih; Kosoy, Roman; Becker, Christine E.

    2017-01-01

    Abstract Motivation: Recent advances in mass cytometry allow simultaneous measurements of up to 50 markers at single-cell resolution. However, the high dimensionality of mass cytometry data introduces computational challenges for automated data analysis and hinders translation of new biological understanding into clinical applications. Previous studies have applied machine learning to facilitate processing of mass cytometry data. However, manual inspection is still inevitable and becoming the barrier to reliable large-scale analysis. Results: We present a new algorithm called Automated Cell-type Discovery and Classification (ACDC) that fully automates the classification of canonical cell populations and highlights novel cell types in mass cytometry data. Evaluations on real-world data show ACDC provides accurate and reliable estimations compared to manual gating results. Additionally, ACDC automatically classifies previously ambiguous cell types to facilitate discovery. Our findings suggest that ACDC substantially improves both reliability and interpretability of results obtained from high-dimensional mass cytometry profiling data. Availability and Implementation: A Python package (Python 3) and analysis scripts for reproducing the results are availability on https://bitbucket.org/dudleylab/acdc. Contact: brian.kidd@mssm.edu or joel.dudley@mssm.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28158442

  19. Automated Classification of Asteroids into Families at Work

    Science.gov (United States)

    Knežević, Zoran; Milani, Andrea; Cellino, Alberto; Novaković, Bojan; Spoto, Federica; Paolicchi, Paolo

    2014-07-01

    We have recently proposed a new approach to the asteroid family classification by combining the classical HCM method with an automated procedure to add newly discovered members to existing families. This approach is specifically intended to cope with ever increasing asteroid data sets, and consists of several steps to segment the problem and handle the very large amount of data in an efficient and accurate manner. We briefly present all these steps and show the results from three subsequent updates making use of only the automated step of attributing the newly numbered asteroids to the known families. We describe the changes of the individual families membership, as well as the evolution of the classification due to the newly added intersections between the families, resolved candidate family mergers, and emergence of the new candidates for the mergers. We thus demonstrate how by the new approach the asteroid family classification becomes stable in general terms (converging towards a permanent list of confirmed families), and in the same time evolving in details (to account for the newly discovered asteroids) at each update.

  20. Automated Feature Identification and Classification Using Automated Feature Weighted Self Organizing Map (FWSOM)

    Science.gov (United States)

    Starkey, Andrew; Usman Ahmad, Aliyu; Hamdoun, Hassan

    2017-10-01

    This paper investigates the application of a novel method for classification called Feature Weighted Self Organizing Map (FWSOM) that analyses the topology information of a converged standard Self Organizing Map (SOM) to automatically guide the selection of important inputs during training for improved classification of data with redundant inputs, examined against two traditional approaches namely neural networks and Support Vector Machines (SVM) for the classification of EEG data as presented in previous work. In particular, the novel method looks to identify the features that are important for classification automatically, and in this way the important features can be used to improve the diagnostic ability of any of the above methods. The paper presents the results and shows how the automated identification of the important features successfully identified the important features in the dataset and how this results in an improvement of the classification results for all methods apart from linear discriminatory methods which cannot separate the underlying nonlinear relationship in the data. The FWSOM in addition to achieving higher classification accuracy has given insights into what features are important in the classification of each class (left and right-hand movements), and these are corroborated by already published work in this area.

  1. Literature classification for semi-automated updating of biological knowledgebases

    DEFF Research Database (Denmark)

    Olsen, Lars Rønn; Kudahl, Ulrich Johan; Winther, Ole

    2013-01-01

    abstracts yielded classification accuracy of 0.95, thus showing significant value in support of data extraction from the literature. Conclusion: We here propose a conceptual framework for semi-automated extraction of epitope data embedded in scientific literature using principles from text mining...... types of biological data, such as sequence data, are extensively stored in biological databases, functional annotations, such as immunological epitopes, are found primarily in semi-structured formats or free text embedded in primary scientific literature. Results: We defined and applied a machine...... learning approach for literature classification to support updating of TANTIGEN, a knowledgebase of tumor T-cell antigens. Abstracts from PubMed were downloaded and classified as either "relevant" or "irrelevant" for database update. Training and five-fold cross-validation of a k-NN classifier on 310...

  2. Automated tissue classification framework for reproducible chronic wound assessment.

    Science.gov (United States)

    Mukherjee, Rashmi; Manohar, Dhiraj Dhane; Das, Dev Kumar; Achar, Arun; Mitra, Analava; Chakraborty, Chandan

    2014-01-01

    The aim of this paper was to develop a computer assisted tissue classification (granulation, necrotic, and slough) scheme for chronic wound (CW) evaluation using medical image processing and statistical machine learning techniques. The red-green-blue (RGB) wound images grabbed by normal digital camera were first transformed into HSI (hue, saturation, and intensity) color space and subsequently the "S" component of HSI color channels was selected as it provided higher contrast. Wound areas from 6 different types of CW were segmented from whole images using fuzzy divergence based thresholding by minimizing edge ambiguity. A set of color and textural features describing granulation, necrotic, and slough tissues in the segmented wound area were extracted using various mathematical techniques. Finally, statistical learning algorithms, namely, Bayesian classification and support vector machine (SVM), were trained and tested for wound tissue classification in different CW images. The performance of the wound area segmentation protocol was further validated by ground truth images labeled by clinical experts. It was observed that SVM with 3rd order polynomial kernel provided the highest accuracies, that is, 86.94%, 90.47%, and 75.53%, for classifying granulation, slough, and necrotic tissues, respectively. The proposed automated tissue classification technique achieved the highest overall accuracy, that is, 87.61%, with highest kappa statistic value (0.793).

  3. Automated Tissue Classification Framework for Reproducible Chronic Wound Assessment

    Directory of Open Access Journals (Sweden)

    Rashmi Mukherjee

    2014-01-01

    Full Text Available The aim of this paper was to develop a computer assisted tissue classification (granulation, necrotic, and slough scheme for chronic wound (CW evaluation using medical image processing and statistical machine learning techniques. The red-green-blue (RGB wound images grabbed by normal digital camera were first transformed into HSI (hue, saturation, and intensity color space and subsequently the “S” component of HSI color channels was selected as it provided higher contrast. Wound areas from 6 different types of CW were segmented from whole images using fuzzy divergence based thresholding by minimizing edge ambiguity. A set of color and textural features describing granulation, necrotic, and slough tissues in the segmented wound area were extracted using various mathematical techniques. Finally, statistical learning algorithms, namely, Bayesian classification and support vector machine (SVM, were trained and tested for wound tissue classification in different CW images. The performance of the wound area segmentation protocol was further validated by ground truth images labeled by clinical experts. It was observed that SVM with 3rd order polynomial kernel provided the highest accuracies, that is, 86.94%, 90.47%, and 75.53%, for classifying granulation, slough, and necrotic tissues, respectively. The proposed automated tissue classification technique achieved the highest overall accuracy, that is, 87.61%, with highest kappa statistic value (0.793.

  4. Empirical Analysis and Automated Classification of Security Bug Reports

    Science.gov (United States)

    Tyo, Jacob P.

    2016-01-01

    With the ever expanding amount of sensitive data being placed into computer systems, the need for effective cybersecurity is of utmost importance. However, there is a shortage of detailed empirical studies of security vulnerabilities from which cybersecurity metrics and best practices could be determined. This thesis has two main research goals: (1) to explore the distribution and characteristics of security vulnerabilities based on the information provided in bug tracking systems and (2) to develop data analytics approaches for automatic classification of bug reports as security or non-security related. This work is based on using three NASA datasets as case studies. The empirical analysis showed that the majority of software vulnerabilities belong only to a small number of types. Addressing these types of vulnerabilities will consequently lead to cost efficient improvement of software security. Since this analysis requires labeling of each bug report in the bug tracking system, we explored using machine learning to automate the classification of each bug report as a security or non-security related (two-class classification), as well as each security related bug report as specific security type (multiclass classification). In addition to using supervised machine learning algorithms, a novel unsupervised machine learning approach is proposed. An ac- curacy of 92%, recall of 96%, precision of 92%, probability of false alarm of 4%, F-Score of 81% and G-Score of 90% were the best results achieved during two-class classification. Furthermore, an accuracy of 80%, recall of 80%, precision of 94%, and F-score of 85% were the best results achieved during multiclass classification.

  5. An entropy-based improved k-top scoring pairs (TSP) method for ...

    African Journals Online (AJOL)

    An entropy-based improved k-top scoring pairs (TSP) (Ik-TSP) method was presented in this study for the classification and prediction of human cancers based on gene-expression data. We compared Ik-TSP classifiers with 5 different machine learning methods and the k-TSP method based on 3 different feature selection ...

  6. Automated segmentation of atherosclerotic histology based on pattern classification

    Directory of Open Access Journals (Sweden)

    Arna van Engelen

    2013-01-01

    Full Text Available Background: Histology sections provide accurate information on atherosclerotic plaque composition, and are used in various applications. To our knowledge, no automated systems for plaque component segmentation in histology sections currently exist. Materials and Methods: We perform pixel-wise classification of fibrous, lipid, and necrotic tissue in Elastica Von Gieson-stained histology sections, using features based on color channel intensity and local image texture and structure. We compare an approach where we train on independent data to an approach where we train on one or two sections per specimen in order to segment the remaining sections. We evaluate the results on segmentation accuracy in histology, and we use the obtained histology segmentations to train plaque component classification methods in ex vivo Magnetic resonance imaging (MRI and in vivo MRI and computed tomography (CT. Results: In leave-one-specimen-out experiments on 176 histology slices of 13 plaques, a pixel-wise accuracy of 75.7 ± 6.8% was obtained. This increased to 77.6 ± 6.5% when two manually annotated slices of the specimen to be segmented were used for training. Rank correlations of relative component volumes with manually annotated volumes were high in this situation (P = 0.82-0.98. Using the obtained histology segmentations to train plaque component classification methods in ex vivo MRI and in vivo MRI and CT resulted in similar image segmentations for training on the automated histology segmentations as for training on a fully manual ground truth. The size of the lipid-rich necrotic core was significantly smaller when training on fully automated histology segmentations than when manually annotated histology sections were used. This difference was reduced and not statistically significant when one or two slices per section were manually annotated for histology segmentation. Conclusions: Good histology segmentations can be obtained by automated segmentation

  7. One step automated unpatterned wafer defect detection and classification

    International Nuclear Information System (INIS)

    Dou Lie; Kesler, Daniel; Bruno, William; Monjak, Charles; Hunt, Jim

    1998-01-01

    Automated detection and classification of crystalline defects on micro-grade silicon wafers is extremely important for integrated circuit (IC) device yield. High training cost, limited capability of classifying defects, increasing possibility of contamination, and unexpected human mistakes necessitate the need to replace the human visual inspection with automated defect inspection. The Laser Scanning Surface Inspection Systems (SSISs) equipped with the Reconvergent Specular Detection (RSD) apparatus are widely used for final wafer inspection. RSD, more commonly known as light channel detection (LC), is capable of detecting and classifying material defects by analyzing information from two independent phenomena, light scattering and reflecting. This paper presents a new technique including a new type of light channel detector to detect and classify wafer surface defects such as slipline dislocation, Epi spikes, Pits, and dimples. The optical system to study this technique consists of a particle scanner to detect and quantify light scattering events from contaminants on the wafer surface and a RSD apparatus (silicon photo detector). Compared with the light channel detector presently used in the wafer fabs, this new light channel technique provides higher sensitivity for small defect detection and more defect scattering signatures for defect classification. Epi protrusions (mounds and spikes), slip dislocations, voids, dimples, and some other common defect features and contamination on silicon wafers are studied using this equipment. The results are compared quantitatively with that of human visual inspection and confirmed by microscope or AFM. This new light channel technology could provide the real future solution to the wafer manufacturing industry for fully automated wafer inspection and defect characterization

  8. Automated noninvasive classification of renal cancer on multiphase CT

    Energy Technology Data Exchange (ETDEWEB)

    Linguraru, Marius George; Wang, Shijun; Shah, Furhawn; Gautam, Rabindra; Peterson, James; Linehan, W. Marston; Summers, Ronald M. [Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, Maryland 20892 (United States); Urologic Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892 (United States); Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, Maryland 20892 (United States)

    2011-10-15

    Purpose: To explore the added value of the shape of renal lesions for classifying renal neoplasms. To investigate the potential of computer-aided analysis of contrast-enhanced computed-tomography (CT) to quantify and classify renal lesions. Methods: A computer-aided clinical tool based on adaptive level sets was employed to analyze 125 renal lesions from contrast-enhanced abdominal CT studies of 43 patients. There were 47 cysts and 78 neoplasms: 22 Von Hippel-Lindau (VHL), 16 Birt-Hogg-Dube (BHD), 19 hereditary papillary renal carcinomas (HPRC), and 21 hereditary leiomyomatosis and renal cell cancers (HLRCC). The technique quantified the three-dimensional size and enhancement of lesions. Intrapatient and interphase registration facilitated the study of lesion serial enhancement. The histograms of curvature-related features were used to classify the lesion types. The areas under the curve (AUC) were calculated for receiver operating characteristic curves. Results: Tumors were robustly segmented with 0.80 overlap (0.98 correlation) between manual and semi-automated quantifications. The method further identified morphological discrepancies between the types of lesions. The classification based on lesion appearance, enhancement and morphology between cysts and cancers showed AUC = 0.98; for BHD + VHL (solid cancers) vs. HPRC + HLRCC AUC = 0.99; for VHL vs. BHD AUC = 0.82; and for HPRC vs. HLRCC AUC = 0.84. All semi-automated classifications were statistically significant (p < 0.05) and superior to the analyses based solely on serial enhancement. Conclusions: The computer-aided clinical tool allowed the accurate quantification of cystic, solid, and mixed renal tumors. Cancer types were classified into four categories using their shape and enhancement. Comprehensive imaging biomarkers of renal neoplasms on abdominal CT may facilitate their noninvasive classification, guide clinical management, and monitor responses to drugs or interventions.

  9. Applying machine learning classification techniques to automate sky object cataloguing

    Science.gov (United States)

    Fayyad, Usama M.; Doyle, Richard J.; Weir, W. Nick; Djorgovski, Stanislav

    1993-08-01

    We describe the application of an Artificial Intelligence machine learning techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Mt. Palomar Northern Sky Survey is nearly completed. This survey provides comprehensive coverage of the northern celestial hemisphere in the form of photographic plates. The plates are being transformed into digitized images whose quality will probably not be surpassed in the next ten to twenty years. The images are expected to contain on the order of 107 galaxies and 108 stars. Astronomers wish to determine which of these sky objects belong to various classes of galaxies and stars. Unfortunately, the size of this data set precludes analysis in an exclusively manual fashion. Our approach is to develop a software system which integrates the functions of independently developed techniques for image processing and data classification. Digitized sky images are passed through image processing routines to identify sky objects and to extract a set of features for each object. These routines are used to help select a useful set of attributes for classifying sky objects. Then GID3 (Generalized ID3) and O-B Tree, two inductive learning techniques, learns classification decision trees from examples. These classifiers will then be applied to new data. These developmnent process is highly interactive, with astronomer input playing a vital role. Astronomers refine the feature set used to construct sky object descriptions, and evaluate the performance of the automated classification technique on new data. This paper gives an overview of the machine learning techniques with an emphasis on their general applicability, describes the details of our specific application, and reports the initial encouraging results. The results indicate that our machine learning approach is well-suited to the problem. The primary benefit of the approach is increased data reduction throughput. Another benefit is

  10. Automated recognition system for ELM classification in JET

    International Nuclear Information System (INIS)

    Duro, N.; Dormido, R.; Vega, J.; Dormido-Canto, S.; Farias, G.; Sanchez, J.; Vargas, H.; Murari, A.

    2009-01-01

    Edge localized modes (ELMs) are instabilities occurring in the edge of H-mode plasmas. Considerable efforts are being devoted to understanding the physics behind this non-linear phenomenon. A first characterization of ELMs is usually their identification as type I or type III. An automated pattern recognition system has been developed in JET for off-line ELM recognition and classification. The empirical method presented in this paper analyzes each individual ELM instead of starting from a temporal segment containing many ELM bursts. The ELM recognition and isolation is carried out using three signals: Dα, line integrated electron density and stored diamagnetic energy. A reduced set of characteristics (such as diamagnetic energy drop, ELM period or Dα shape) has been extracted to build supervised and unsupervised learning systems for classification purposes. The former are based on support vector machines (SVM). The latter have been developed with hierarchical and K-means clustering methods. The success rate of the classification systems is about 98% for a database of almost 300 ELMs.

  11. Automated recognition system for ELM classification in JET

    Energy Technology Data Exchange (ETDEWEB)

    Duro, N. [Dpto. de Informatica y Automatica - UNED, C/ Juan del Rosal 16, 28040 Madrid (Spain)], E-mail: nduro@dia.uned.es; Dormido, R. [Dpto. de Informatica y Automatica - UNED, C/ Juan del Rosal 16, 28040 Madrid (Spain); Vega, J. [Asociacion EURATOM/CIEMAT para Fusion, Avd. Complutense 22, 28040 Madrid (Spain); Dormido-Canto, S.; Farias, G.; Sanchez, J.; Vargas, H. [Dpto. de Informatica y Automatica - UNED, C/ Juan del Rosal 16, 28040 Madrid (Spain); Murari, A. [Consorzio RFX-Associazione EURATOM ENEA per la Fusione, I-35127 Padua (Italy)

    2009-06-15

    Edge localized modes (ELMs) are instabilities occurring in the edge of H-mode plasmas. Considerable efforts are being devoted to understanding the physics behind this non-linear phenomenon. A first characterization of ELMs is usually their identification as type I or type III. An automated pattern recognition system has been developed in JET for off-line ELM recognition and classification. The empirical method presented in this paper analyzes each individual ELM instead of starting from a temporal segment containing many ELM bursts. The ELM recognition and isolation is carried out using three signals: D{alpha}, line integrated electron density and stored diamagnetic energy. A reduced set of characteristics (such as diamagnetic energy drop, ELM period or D{alpha} shape) has been extracted to build supervised and unsupervised learning systems for classification purposes. The former are based on support vector machines (SVM). The latter have been developed with hierarchical and K-means clustering methods. The success rate of the classification systems is about 98% for a database of almost 300 ELMs.

  12. Towards automated classification of intensive care nursing narratives.

    Science.gov (United States)

    Hiissa, Marketta; Pahikkala, Tapio; Suominen, Hanna; Lehtikunnas, Tuija; Back, Barbro; Karsten, Helena; Salanterä, Sanna; Salakoski, Tapio

    2006-01-01

    Nursing narratives are an important part of patient documentation, but the possibilities to utilize them in the direct care process are limited due to the lack of proper tools. One solution to facilitate the utilization of narrative data could be to classify them according to their content. In this paper, we addressed two issues related to designing an automated classifier: domain experts' agreement on the content of the classes into which the data are to be classified, and the ability of the machine-learning algorithm to perform the classification on an acceptable level. The data we used were a set of Finnish intensive care nursing narratives. By using Cohen's kappa, we assessed the agreement of three nurses on the content of the classes Breathing, Blood Circulation and Pain, and by using the area under ROC curve (AUC), we measured the ability of the Least Squares Support Vector Machine (LS-SVM) algorithm to learn the classification patterns of the nurses. On average, the values of kappa were around 0.8. The agreement was highest in the class Blood Circulation, and lowest in the class Breathing. The LS-SVM algorithm was able to learn the classification patterns of the three nurses on an acceptable level; the values of AUC were generally around 0.85. Our results indicate that one way to develop electronic patient records could be tools that handle the free text in nursing documentation.

  13. Towards automated classification of intensive care nursing narratives.

    Science.gov (United States)

    Hiissa, Marketta; Pahikkala, Tapio; Suominen, Hanna; Lehtikunnas, Tuija; Back, Barbro; Karsten, Helena; Salanterä, Sanna; Salakoski, Tapio

    2007-12-01

    Nursing narratives are an important part of patient documentation, but the possibilities to utilize them in the direct care process are limited due to the lack of proper tools. One solution to facilitate the utilization of narrative data could be to classify them according to their content. Our objective is to address two issues related to designing an automated classifier: domain experts' agreement on the content of classes Breathing, Blood Circulation and Pain, as well as the ability of a machine-learning-based classifier to learn the classification patterns of the nurses. The data we used were a set of Finnish intensive care nursing narratives, and we used the regularized least-squares (RLS) algorithm for the automatic classification. The agreement of the nurses was assessed by using Cohen's kappa, and the performance of the algorithm was measured using area under ROC curve (AUC). On average, the values of kappa were around 0.8. The agreement was highest in the class Blood Circulation, and lowest in the class Breathing. The RLS algorithm was able to learn the classification patterns of the three nurses on an acceptable level; the values of AUC were generally around 0.85. Our results indicate that the free text in nursing documentation can be automatically classified and this can offer a way to develop electronic patient records.

  14. Automated Classification of ROSAT Sources Using Heterogeneous Multiwavelength Source Catalogs

    Science.gov (United States)

    McGlynn, Thomas; Suchkov, A. A.; Winter, E. L.; Hanisch, R. J.; White, R. L.; Ochsenbein, F.; Derriere, S.; Voges, W.; Corcoran, M. F.

    2004-01-01

    We describe an on-line system for automated classification of X-ray sources, ClassX, and present preliminary results of classification of the three major catalogs of ROSAT sources, RASS BSC, RASS FSC, and WGACAT, into six class categories: stars, white dwarfs, X-ray binaries, galaxies, AGNs, and clusters of galaxies. ClassX is based on a machine learning technology. It represents a system of classifiers, each classifier consisting of a considerable number of oblique decision trees. These trees are built as the classifier is 'trained' to recognize various classes of objects using a training sample of sources of known object types. Each source is characterized by a preselected set of parameters, or attributes; the same set is then used as the classifier conducts classification of sources of unknown identity. The ClassX pipeline features an automatic search for X-ray source counterparts among heterogeneous data sets in on-line data archives using Virtual Observatory protocols; it retrieves from those archives all the attributes required by the selected classifier and inputs them to the classifier. The user input to ClassX is typically a file with target coordinates, optionally complemented with target IDs. The output contains the class name, attributes, and class probabilities for all classified targets. We discuss ways to characterize and assess the classifier quality and performance and present the respective validation procedures. Based on both internal and external validation, we conclude that the ClassX classifiers yield reasonable and reliable classifications for ROSAT sources and have the potential to broaden class representation significantly for rare object types.

  15. Automated vegetation classification using Thematic Mapper Simulation data

    Science.gov (United States)

    Nedelman, K. S.; Cate, R. B.; Bizzell, R. M.

    1983-01-01

    The present investigation is concerned with the results of a study of Thematic Mapper Simulation (TMS) data. One of the objectives of the study was related to an evaluation of the usefulness of the Thematic Mapper's (TM) improved spatial resolution and spectral coverage. The study was undertaken as part of a preparation for the efficient incorporation of Landsat 4 data into ongoing technology development in remote sensing. The study included an application of automated Landsat vegetation classification technology to TMS data. Results of comparing TMS data to Multispectral Scanner (MSS) data were found to indicate that all field definition, crop type discrimination, and subsequent proportion estimation may be greatly increased with the availability of TM data.

  16. Automated detection and classification for craters based on geometric matching

    Science.gov (United States)

    Chen, Jian-qing; Cui, Ping-yuan; Cui, Hui-tao

    2011-08-01

    Crater detection and classification are critical elements for planetary mission preparations and landing site selection. This paper presents a methodology for the automated detection and matching of craters on images of planetary surface such as Moon, Mars and asteroids. For craters usually are bowl shaped depression, craters can be figured as circles or circular arc during landing phase. Based on the hypothesis that detected crater edges is related to craters in a template by translation, rotation and scaling, the proposed matching method use circles to fitting craters edge, and align circular arc edges from the image of the target body with circular features contained in a model. The approach includes edge detection, edge grouping, reference point detection and geometric circle model matching. Finally we simulate planetary surface to test the reasonableness and effectiveness of the proposed method.

  17. Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification

    Science.gov (United States)

    Juan-Albarracín, Javier; Fuster-Garcia, Elies; Manjón, José V.; Robles, Montserrat; Aparici, F.; Martí-Bonmatí, L.; García-Gómez, Juan M.

    2015-01-01

    Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation. PMID:25978453

  18. Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification.

    Science.gov (United States)

    Juan-Albarracín, Javier; Fuster-Garcia, Elies; Manjón, José V; Robles, Montserrat; Aparici, F; Martí-Bonmatí, L; García-Gómez, Juan M

    2015-01-01

    Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation.

  19. Fully Automated Sunspot Detection and Classification Using SDO HMI Imagery in MATLAB

    Science.gov (United States)

    2014-03-27

    FULLY AUTOMATED SUNSPOT DETECTION AND CLASSIFICATION USING SDO HMI IMAGERY IN MATLAB THESIS Gordon M. Spahr, Second Lieutenant, USAF AFIT-ENP-14-M-34...the U.S. Government and is not subject to copyright protection in the United States. AFIT-ENP-14-M-34 FULLY AUTOMATED SUNSPOT DETECTION AND...DISTRIUBUTION UNLIMITED. AFIT-ENP-14-M-34 FULLY AUTOMATED SUNSPOT DETECTION AND CLASSIFICATION USING SDO HMI IMAGERY IN MATLAB Gordon M. Spahr, BS Second

  20. Automated cell type discovery and classification through knowledge transfer.

    Science.gov (United States)

    Lee, Hao-Chih; Kosoy, Roman; Becker, Christine E; Dudley, Joel T; Kidd, Brian A

    2017-06-01

    Recent advances in mass cytometry allow simultaneous measurements of up to 50 markers at single-cell resolution. However, the high dimensionality of mass cytometry data introduces computational challenges for automated data analysis and hinders translation of new biological understanding into clinical applications. Previous studies have applied machine learning to facilitate processing of mass cytometry data. However, manual inspection is still inevitable and becoming the barrier to reliable large-scale analysis. We present a new algorithm called utomated ell-type iscovery and lassification (ACDC) that fully automates the classification of canonical cell populations and highlights novel cell types in mass cytometry data. Evaluations on real-world data show ACDC provides accurate and reliable estimations compared to manual gating results. Additionally, ACDC automatically classifies previously ambiguous cell types to facilitate discovery. Our findings suggest that ACDC substantially improves both reliability and interpretability of results obtained from high-dimensional mass cytometry profiling data. A Python package (Python 3) and analysis scripts for reproducing the results are availability on https://bitbucket.org/dudleylab/acdc . brian.kidd@mssm.edu or joel.dudley@mssm.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press.

  1. Automated Terrestrial EMI Emitter Detection, Classification, and Localization

    Science.gov (United States)

    Stottler, R.; Ong, J.; Gioia, C.; Bowman, C.; Bhopale, A.

    Clear operating spectrum at ground station antenna locations is critically important for communicating with, commanding, controlling, and maintaining the health of satellites. Electro Magnetic Interference (EMI) can interfere with these communications, so it is extremely important to track down and eliminate sources of EMI. The Terrestrial RFI-locating Automation with CasE based Reasoning (TRACER) system is being implemented to automate terrestrial EMI emitter localization and identification to improve space situational awareness, reduce manpower requirements, dramatically shorten EMI response time, enable the system to evolve without programmer involvement, and support adversarial scenarios such as jamming. The operational version of TRACER is being implemented and applied with real data (power versus frequency over time) for both satellite communication antennas and sweeping Direction Finding (DF) antennas located near them. This paper presents the design and initial implementation of TRACER’s investigation data management, automation, and data visualization capabilities. TRACER monitors DF antenna signals and detects and classifies EMI using neural network technology, trained on past cases of both normal communications and EMI events. When EMI events are detected, an Investigation Object is created automatically. The user interface facilitates the management of multiple investigations simultaneously. Using a variant of the Friis transmission equation, emissions data is used to estimate and plot the emitter’s locations over time for comparison with current flights. The data is also displayed on a set of five linked graphs to aid in the perception of patterns spanning power, time, frequency, and bearing. Based on details of the signal (its classification, direction, and strength, etc.), TRACER retrieves one or more cases of EMI investigation methodologies which are represented as graphical behavior transition networks (BTNs). These BTNs can be edited easily

  2. Automated authorship attribution using advanced signal classification techniques.

    Directory of Open Access Journals (Sweden)

    Maryam Ebrahimpour

    Full Text Available In this paper, we develop two automated authorship attribution schemes, one based on Multiple Discriminant Analysis (MDA and the other based on a Support Vector Machine (SVM. The classification features we exploit are based on word frequencies in the text. We adopt an approach of preprocessing each text by stripping it of all characters except a-z and space. This is in order to increase the portability of the software to different types of texts. We test the methodology on a corpus of undisputed English texts, and use leave-one-out cross validation to demonstrate classification accuracies in excess of 90%. We further test our methods on the Federalist Papers, which have a partly disputed authorship and a fair degree of scholarly consensus. And finally, we apply our methodology to the question of the authorship of the Letter to the Hebrews by comparing it against a number of original Greek texts of known authorship. These tests identify where some of the limitations lie, motivating a number of open questions for future work. An open source implementation of our methodology is freely available for use at https://github.com/matthewberryman/author-detection.

  3. Automated classification of Acid Rock Drainage potential from Corescan drill core imagery

    Science.gov (United States)

    Cracknell, M. J.; Jackson, L.; Parbhakar-Fox, A.; Savinova, K.

    2017-12-01

    Classification of the acid forming potential of waste rock is important for managing environmental hazards associated with mining operations. Current methods for the classification of acid rock drainage (ARD) potential usually involve labour intensive and subjective assessment of drill core and/or hand specimens. Manual methods are subject to operator bias, human error and the amount of material that can be assessed within a given time frame is limited. The automated classification of ARD potential documented here is based on the ARD Index developed by Parbhakar-Fox et al. (2011). This ARD Index involves the combination of five indicators: A - sulphide content; B - sulphide alteration; C - sulphide morphology; D - primary neutraliser content; and E - sulphide mineral association. Several components of the ARD Index require accurate identification of sulphide minerals. This is achieved by classifying Corescan Red-Green-Blue true colour images into the presence or absence of sulphide minerals using supervised classification. Subsequently, sulphide classification images are processed and combined with Corescan SWIR-based mineral classifications to obtain information on sulphide content, indices representing sulphide textures (disseminated versus massive and degree of veining), and spatially associated minerals. This information is combined to calculate ARD Index indicator values that feed into the classification of ARD potential. Automated ARD potential classifications of drill core samples associated with a porphyry Cu-Au deposit are compared to manually derived classifications and those obtained by standard static geochemical testing and X-ray diffractometry analyses. Results indicate a high degree of similarity between automated and manual ARD potential classifications. Major differences between approaches are observed in sulphide and neutraliser mineral percentages, likely due to the subjective nature of manual estimates of mineral content. The automated approach

  4. Comparison of an automated classification system with an empirical classification of circulation patterns over the Pannonian basin, Central Europe

    Science.gov (United States)

    Maheras, Panagiotis; Tolika, Konstantia; Tegoulias, Ioannis; Anagnostopoulou, Christina; Szpirosz, Klicász; Károssy, Csaba; Makra, László

    2018-04-01

    The aim of the study is to compare the performance of the two classification methods, based on the atmospheric circulation types over the Pannonian basin in Central Europe. Moreover, relationships including seasonal occurrences and correlation coefficients, as well as comparative diagrams of the seasonal occurrences of the circulation types of the two classification systems are presented. When comparing of the automated (objective) and empirical (subjective) classification methods, it was found that the frequency of the empirical anticyclonic (cyclonic) types is much higher (lower) than that of the automated anticyclonic (cyclonic) types both on an annual and seasonal basis. The highest and statistically significant correlations between the circulation types of the two classification systems, as well as those between the cumulated seasonal anticyclonic and cyclonic types occur in winter for both classifications, since the weather-influencing effect of the atmospheric circulation in this season is the most prevalent. Precipitation amounts in Budapest display a decreasing trend in accordance with the decrease in the occurrence of the automated cyclonic types. In contrast, the occurrence of the empirical cyclonic types displays an increasing trend. There occur types in a given classification that are usually accompanied by high ratios of certain types in the other classification.

  5. Semi-Automated Classification of Seafloor Data Collected on the Delmarva Inner Shelf

    Science.gov (United States)

    Sweeney, E. M.; Pendleton, E. A.; Brothers, L. L.; Mahmud, A.; Thieler, E. R.

    2017-12-01

    We tested automated classification methods on acoustic bathymetry and backscatter data collected by the U.S. Geological Survey (USGS) and National Oceanic and Atmospheric Administration (NOAA) on the Delmarva inner continental shelf to efficiently and objectively identify sediment texture and geomorphology. Automated classification techniques are generally less subjective and take significantly less time than manual classification methods. We used a semi-automated process combining unsupervised and supervised classification techniques to characterize seafloor based on bathymetric slope and relative backscatter intensity. Statistical comparison of our automated classification results with those of a manual classification conducted on a subset of the acoustic imagery indicates that our automated method was highly accurate (95% total accuracy and 93% Kappa). Our methods resolve sediment ridges, zones of flat seafloor and areas of high and low backscatter. We compared our classification scheme with mean grain size statistics of samples collected in the study area and found that strong correlations between backscatter intensity and sediment texture exist. High backscatter zones are associated with the presence of gravel and shells mixed with sand, and low backscatter areas are primarily clean sand or sand mixed with mud. Slope classes further elucidate textural and geomorphologic differences in the seafloor, such that steep slopes (>0.35°) with high backscatter are most often associated with the updrift side of sand ridges and bedforms, whereas low slope with high backscatter correspond to coarse lag or shell deposits. Low backscatter and high slopes are most often found on the downdrift side of ridges and bedforms, and low backscatter and low slopes identify swale areas and sand sheets. We found that poor acoustic data quality was the most significant cause of inaccurate classification results, which required additional user input to mitigate. Our method worked well

  6. Simple Fully Automated Group Classification on Brain fMRI

    Energy Technology Data Exchange (ETDEWEB)

    Honorio, J.; Goldstein, R.; Honorio, J.; Samaras, D.; Tomasi, D.; Goldstein, R.Z.

    2010-04-14

    We propose a simple, well grounded classification technique which is suited for group classification on brain fMRI data sets that have high dimensionality, small number of subjects, high noise level, high subject variability, imperfect registration and capture subtle cognitive effects. We propose threshold-split region as a new feature selection method and majority voteas the classification technique. Our method does not require a predefined set of regions of interest. We use average acros ssessions, only one feature perexperimental condition, feature independence assumption, and simple classifiers. The seeming counter-intuitive approach of using a simple design is supported by signal processing and statistical theory. Experimental results in two block design data sets that capture brain function under distinct monetary rewards for cocaine addicted and control subjects, show that our method exhibits increased generalization accuracy compared to commonly used feature selection and classification techniques.

  7. Simple Fully Automated Group Classification on Brain fMRI

    International Nuclear Information System (INIS)

    Honorio, J.; Goldstein, R.; Samaras, D.; Tomasi, D.; Goldstein, R.Z.

    2010-01-01

    We propose a simple, well grounded classification technique which is suited for group classification on brain fMRI data sets that have high dimensionality, small number of subjects, high noise level, high subject variability, imperfect registration and capture subtle cognitive effects. We propose threshold-split region as a new feature selection method and majority voteas the classification technique. Our method does not require a predefined set of regions of interest. We use average acros ssessions, only one feature perexperimental condition, feature independence assumption, and simple classifiers. The seeming counter-intuitive approach of using a simple design is supported by signal processing and statistical theory. Experimental results in two block design data sets that capture brain function under distinct monetary rewards for cocaine addicted and control subjects, show that our method exhibits increased generalization accuracy compared to commonly used feature selection and classification techniques.

  8. Automated Diatom Classification (Part B: A Deep Learning Approach

    Directory of Open Access Journals (Sweden)

    Anibal Pedraza

    2017-05-01

    Full Text Available Diatoms, a kind of algae microorganisms with several species, are quite useful for water quality determination, one of the hottest topics in applied biology nowadays. At the same time, deep learning and convolutional neural networks (CNN are becoming an extensively used technique for image classification in a variety of problems. This paper approaches diatom classification with this technique, in order to demonstrate whether it is suitable for solving the classification problem. An extensive dataset was specifically collected (80 types, 100 samples/type for this study. The dataset covers different illumination conditions and it was computationally augmented to more than 160,000 samples. After that, CNNs were applied over datasets pre-processed with different image processing techniques. An overall accuracy of 99% is obtained for the 80-class problem and different kinds of images (brightfield, normalized. Results were compared to previous presented classification techniques with different number of samples. As far as the authors know, this is the first time that CNNs are applied to diatom classification.

  9. Generating Clustered Journal Maps : An Automated System for Hierarchical Classification

    NARCIS (Netherlands)

    Leydesdorff, L.; Bornmann, L.; Wagner, C.S.

    2017-01-01

    Journal maps and classifications for 11,359 journals listed in the combined Journal Citation Reports 2015 of the Science and Social Sciences Citation Indexes are provided at https://leydesdorff.github.io/journals/ and http://www.leydesdorff.net/jcr15. A routine using VOSviewer for integrating the

  10. How automated image analysis techniques help scientists in species identification and classification?

    Science.gov (United States)

    Yousef Kalafi, Elham; Town, Christopher; Kaur Dhillon, Sarinder

    2017-09-04

    Identification of taxonomy at a specific level is time consuming and reliant upon expert ecologists. Hence the demand for automated species identification increased over the last two decades. Automation of data classification is primarily focussed on images, incorporating and analysing image data has recently become easier due to developments in computational technology. Research efforts in identification of species include specimens' image processing, extraction of identical features, followed by classifying them into correct categories. In this paper, we discuss recent automated species identification systems, categorizing and evaluating their methods. We reviewed and compared different methods in step by step scheme of automated identification and classification systems of species images. The selection of methods is influenced by many variables such as level of classification, number of training data and complexity of images. The aim of writing this paper is to provide researchers and scientists an extensive background study on work related to automated species identification, focusing on pattern recognition techniques in building such systems for biodiversity studies.

  11. Automated Heuristic Defect Classification (AHDC) for haze-induced defect growth management and mask requalification

    Science.gov (United States)

    Munir, Saghir; Qidwai, Gul

    2012-03-01

    This article presents results from a heuristic automated defect classification algorithm for reticle inspection that mimics the classification rules. AHDC does not require CAD data, thus it can be rapidly deployed in a high volume production environment without the need for extensive design data management. To ensure classification consistency a software framework tracks every defect in repeated inspections. Through its various image based derived metrics it is shown that such a system manages and tracks repeated defects in applications such as haze induced defect growth.

  12. Automated otolith image classification with multiple views: an evaluation on Sciaenidae.

    Science.gov (United States)

    Wong, J Y; Chu, C; Chong, V C; Dhillon, S K; Loh, K H

    2016-08-01

    Combined multiple 2D views (proximal, anterior and ventral aspects) of the sagittal otolith are proposed here as a method to capture shape information for fish classification. Classification performance of single view compared with combined 2D views show improved classification accuracy of the latter, for nine species of Sciaenidae. The effects of shape description methods (shape indices, Procrustes analysis and elliptical Fourier analysis) on classification performance were evaluated. Procrustes analysis and elliptical Fourier analysis perform better than shape indices when single view is considered, but all perform equally well with combined views. A generic content-based image retrieval (CBIR) system that ranks dissimilarity (Procrustes distance) of otolith images was built to search query images without the need for detailed information of side (left or right), aspect (proximal or distal) and direction (positive or negative) of the otolith. Methods for the development of this automated classification system are discussed. © 2016 The Fisheries Society of the British Isles.

  13. Automated Tissue Classification Framework for Reproducible Chronic Wound Assessment

    OpenAIRE

    Mukherjee, Rashmi; Manohar, Dhiraj Dhane; Das, Dev Kumar; Achar, Arun; Mitra, Analava; Chakraborty, Chandan

    2014-01-01

    The aim of this paper was to develop a computer assisted tissue classification (granulation, necrotic, and slough) scheme for chronic wound (CW) evaluation using medical image processing and statistical machine learning techniques. The red-green-blue (RGB) wound images grabbed by normal digital camera were first transformed into HSI (hue, saturation, and intensity) color space and subsequently the “S” component of HSI color channels was selected as it provided higher contrast. Wound areas fro...

  14. Automated morphological classification of galaxies based on projection gradient nonnegative matrix factorization algorithm

    Science.gov (United States)

    Selim, I. M.; Abd El Aziz, Mohamed

    2017-04-01

    The development of automated morphological classification schemes can successfully distinguish between morphological types of galaxies and can be used for studies of the formation and subsequent evolution of galaxies in our universe. In this paper, we present a new automated machine supervised learning astronomical classification scheme based on the Nonnegative Matrix Factorization algorithm. This scheme is making distinctions between all types roughly corresponding to Hubble types such as elliptical, lenticulars, spiral, and irregular galaxies. The proposed algorithm is performed on two examples with different number of image (small dataset contains 110 image and large dataset contains 700 images). The experimental results show that galaxy images from EFIGI catalog can be classified automatically with an accuracy of ˜93% for small and ˜92% for large number. These results are in good agreement when compared with the visual classifications.

  15. Automated Classification of Phonological Errors in Aphasic Language

    Science.gov (United States)

    Ahuja, Sanjeev B.; Reggia, James A.; Berndt, Rita S.

    1984-01-01

    Using heuristically-guided state space search, a prototype program has been developed to simulate and classify phonemic errors occurring in the speech of neurologically-impaired patients. Simulations are based on an interchangeable rule/operator set of elementary errors which represent a theory of phonemic processing faults. This work introduces and evaluates a novel approach to error simulation and classification, it provides a prototype simulation tool for neurolinguistic research, and it forms the initial phase of a larger research effort involving computer modelling of neurolinguistic processes.

  16. Automated functional classification of experimental and predicted protein structures

    Directory of Open Access Journals (Sweden)

    Samudrala Ram

    2006-06-01

    Full Text Available Abstract Background Proteins that are similar in sequence or structure may perform different functions in nature. In such cases, function cannot be inferred from sequence or structural similarity. Results We analyzed experimental structures belonging to the Structural Classification of Proteins (SCOP database and showed that about half of them belong to multi-functional fold families for which protein similarity alone is not adequate to assign function. We also analyzed predicted structures from the LiveBench and the PDB-CAFASP experiments and showed that accurate homology-based functional assignments cannot be achieved approximately one third of the time, when the protein is a member of a multi-functional fold family. We then conducted extended performance evaluation and comparisons on both experimental and predicted structures using our Functional Signatures from Structural Alignments (FSSA algorithm that we previously developed to handle the problem of classifying proteins belonging to multi-functional fold families. Conclusion The results indicate that the FSSA algorithm has better accuracy when compared to homology-based approaches for functional classification of both experimental and predicted protein structures, in part due to its use of local, as opposed to global, information for classifying function. The FSSA algorithm has also been implemented as a webserver and is available at http://protinfo.compbio.washington.edu/fssa.

  17. Automated classification of tailed bacteriophages according to their neck organization.

    Science.gov (United States)

    Lopes, Anne; Tavares, Paulo; Petit, Marie-Agnès; Guérois, Raphaël; Zinn-Justin, Sophie

    2014-11-27

    The genetic diversity observed among bacteriophages remains a major obstacle for the identification of homologs and the comparison of their functional modules. In the structural module, although several classes of homologous proteins contributing to the head and tail structure can be detected, proteins of the head-to-tail connection (or neck) are generally more divergent. Yet, molecular analyses of a few tailed phages belonging to different morphological classes suggested that only a limited number of structural solutions are used in order to produce a functional virion. To challenge this hypothesis and analyze proteins diversity at the virion neck, we developed a specific computational strategy to cope with sequence divergence in phage proteins. We searched for homologs of a set of proteins encoded in the structural module using a phage learning database. We show that using a combination of iterative profile-profile comparison and gene context analyses, we can identify a set of head, neck and tail proteins in most tailed bacteriophages of our database. Classification of phages based on neck protein sequences delineates 4 Types corresponding to known morphological subfamilies. Further analysis of the most abundant Type 1 yields 10 Clusters characterized by consistent sets of head, neck and tail proteins. We developed Virfam, a webserver that automatically identifies proteins of the phage head-neck-tail module and assign phages to the most closely related cluster of phages. This server was tested against 624 new phages from the NCBI database. 93% of the tailed and unclassified phages could be assigned to our head-neck-tail based categories, thus highlighting the large representativeness of the identified virion architectures. Types and Clusters delineate consistent subgroups of Caudovirales, which correlate with several virion properties. Our method and webserver have the capacity to automatically classify most tailed phages, detect their structural module, assign a

  18. A package for the automated classification of periodic variable stars

    Science.gov (United States)

    Kim, Dae-Won; Bailer-Jones, Coryn A. L.

    2016-03-01

    We present a machine learning package for the classification of periodic variable stars. Our package is intended to be general: it can classify any single band optical light curve comprising at least a few tens of observations covering durations from weeks to years with arbitrary time sampling. We use light curves of periodic variable stars taken from OGLE and EROS-2 to train the model. To make our classifier relatively survey-independent, it is trained on 16 features extracted from the light curves (e.g., period, skewness, Fourier amplitude ratio). The model classifies light curves into one of seven superclasses - δ Scuti, RR Lyrae, Cepheid, Type II Cepheid, eclipsing binary, long-period variable, non-variable - as well as subclasses of these, such as ab, c, d, and e types for RR Lyraes. When trained to give only superclasses, our model achieves 0.98 for both recall and precision as measured on an independent validation dataset (on a scale of 0 to 1). When trained to give subclasses, it achieves 0.81 for both recall and precision. The majority of misclassifications of the subclass model is caused by confusion within a superclass rather than between superclasses. To assess classification performance of the subclass model, we applied it to the MACHO, LINEAR, and ASAS periodic variables, which gave recall/precision of 0.92/0.98, 0.89/0.96, and 0.84/0.88, respectively. We also applied the subclass model to Hipparcos periodic variable stars of many other variability types that do not exist in our training set, in order to examine how much those types degrade the classification performance of our target classes. In addition, we investigate how the performance varies with the number of data points and duration of observations. We find that recall and precision do not vary significantly if there are more than 80 data points and the duration is more than a few weeks. The classifier software of the subclass model is available (in Python) from the GitHub repository (http

  19. Automated validation of patient safety clinical incident classification: macro analysis.

    Science.gov (United States)

    Gupta, Jaiprakash; Patrick, Jon

    2013-01-01

    Patient safety is the buzz word in healthcare. Incident Information Management System (IIMS) is electronic software that stores clinical mishaps narratives in places where patients are treated. It is estimated that in one state alone over one million electronic text documents are available in IIMS. In this paper we investigate the data density available in the fields entered to notify an incident and the validity of the built in classification used by clinician to categories the incidents. Waikato Environment for Knowledge Analysis (WEKA) software was used to test the classes. Four statistical classifier based on J48, Naïve Bayes (NB), Naïve Bayes Multinominal (NBM) and Support Vector Machine using radial basis function (SVM_RBF) algorithms were used to validate the classes. The data pool was 10,000 clinical incidents drawn from 7 hospitals in one state in Australia. In first part of the study 1000 clinical incidents were selected to determine type and number of fields worth investigating and in the second part another 5448 clinical incidents were randomly selected to validate 13 clinical incident types. Result shows 74.6% of the cells were empty and only 23 fields had content over 70% of the time. The percentage correctly classified classes on four algorithms using categorical dataset ranged from 42 to 49%, using free-text datasets from 65% to 77% and using both datasets from 72% to 79%. Kappa statistic ranged from 0.36 to 0.4. for categorical data, from 0.61 to 0.74. for free-text and from 0.67 to 0.77 for both datasets. Similar increases in performance in the 3 experiments was noted on true positive rate, precision, F-measure and area under curve (AUC) of receiver operating characteristics (ROC) scores. The study demonstrates only 14 of 73 fields in IIMS have data that is usable for machine learning experiments. Irrespective of the type of algorithms used when all datasets are used performance was better. Classifier NBM showed best performance. We think the

  20. Automated classification of cell morphology by coherence-controlled holographic microscopy.

    Science.gov (United States)

    Strbkova, Lenka; Zicha, Daniel; Vesely, Pavel; Chmelik, Radim

    2017-08-01

    In the last few years, classification of cells by machine learning has become frequently used in biology. However, most of the approaches are based on morphometric (MO) features, which are not quantitative in terms of cell mass. This may result in poor classification accuracy. Here, we study the potential contribution of coherence-controlled holographic microscopy enabling quantitative phase imaging for the classification of cell morphologies. We compare our approach with the commonly used method based on MO features. We tested both classification approaches in an experiment with nutritionally deprived cancer tissue cells, while employing several supervised machine learning algorithms. Most of the classifiers provided higher performance when quantitative phase features were employed. Based on the results, it can be concluded that the quantitative phase features played an important role in improving the performance of the classification. The methodology could be valuable help in refining the monitoring of live cells in an automated fashion. We believe that coherence-controlled holographic microscopy, as a tool for quantitative phase imaging, offers all preconditions for the accurate automated analysis of live cell behavior while enabling noninvasive label-free imaging with sufficient contrast and high-spatiotemporal phase sensitivity. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

  1. Automated database-guided expert-supervised orientation for immunophenotypic diagnosis and classification of acute leukemia.

    Science.gov (United States)

    Lhermitte, L; Mejstrikova, E; van der Sluijs-Gelling, A J; Grigore, G E; Sedek, L; Bras, A E; Gaipa, G; Sobral da Costa, E; Novakova, M; Sonneveld, E; Buracchi, C; de Sá Bacelar, T; Te Marvelde, J G; Trinquand, A; Asnafi, V; Szczepanski, T; Matarraz, S; Lopez, A; Vidriales, B; Bulsa, J; Hrusak, O; Kalina, T; Lecrevisse, Q; Martin Ayuso, M; Brüggemann, M; Verde, J; Fernandez, P; Burgos, L; Paiva, B; Pedreira, C E; van Dongen, J J M; Orfao, A; van der Velden, V H J

    2017-11-01

    Precise classification of acute leukemia (AL) is crucial for adequate treatment. EuroFlow has previously designed an AL orientation tube (ALOT) to guide towards the relevant classification panel (T-cell acute lymphoblastic leukemia (T-ALL), B-cell precursor (BCP)-ALL and/or acute myeloid leukemia (AML)) and final diagnosis. Now we built a reference database with 656 typical AL samples (145 T-ALL, 377 BCP-ALL, 134 AML), processed and analyzed via standardized protocols. Using principal component analysis (PCA)-based plots and automated classification algorithms for direct comparison of single-cells from individual patients against the database, another 783 cases were subsequently evaluated. Depending on the database-guided results, patients were categorized as: (i) typical T, B or Myeloid without or; (ii) with a transitional component to another lineage; (iii) atypical; or (iv) mixed-lineage. Using this automated algorithm, in 781/783 cases (99.7%) the right panel was selected, and data comparable to the final WHO-diagnosis was already provided in >93% of cases (85% T-ALL, 97% BCP-ALL, 95% AML and 87% mixed-phenotype AL patients), even without data on the full-characterization panels. Our results show that database-guided analysis facilitates standardized interpretation of ALOT results and allows accurate selection of the relevant classification panels, hence providing a solid basis for designing future WHO AL classifications.Leukemia advance online publication, 1 December 2017; doi:10.1038/leu.2017.313.

  2. Automated classification of cell morphology by coherence-controlled holographic microscopy

    Science.gov (United States)

    Strbkova, Lenka; Zicha, Daniel; Vesely, Pavel; Chmelik, Radim

    2017-08-01

    In the last few years, classification of cells by machine learning has become frequently used in biology. However, most of the approaches are based on morphometric (MO) features, which are not quantitative in terms of cell mass. This may result in poor classification accuracy. Here, we study the potential contribution of coherence-controlled holographic microscopy enabling quantitative phase imaging for the classification of cell morphologies. We compare our approach with the commonly used method based on MO features. We tested both classification approaches in an experiment with nutritionally deprived cancer tissue cells, while employing several supervised machine learning algorithms. Most of the classifiers provided higher performance when quantitative phase features were employed. Based on the results, it can be concluded that the quantitative phase features played an important role in improving the performance of the classification. The methodology could be valuable help in refining the monitoring of live cells in an automated fashion. We believe that coherence-controlled holographic microscopy, as a tool for quantitative phase imaging, offers all preconditions for the accurate automated analysis of live cell behavior while enabling noninvasive label-free imaging with sufficient contrast and high-spatiotemporal phase sensitivity.

  3. Automated color classification of urine dipstick image in urine examination

    Science.gov (United States)

    Rahmat, R. F.; Royananda; Muchtar, M. A.; Taqiuddin, R.; Adnan, S.; Anugrahwaty, R.; Budiarto, R.

    2018-03-01

    Urine examination using urine dipstick has long been used to determine the health status of a person. The economical and convenient use of urine dipstick is one of the reasons urine dipstick is still used to check people health status. The real-life implementation of urine dipstick is done manually, in general, that is by comparing it with the reference color visually. This resulted perception differences in the color reading of the examination results. In this research, authors used a scanner to obtain the urine dipstick color image. The use of scanner can be one of the solutions in reading the result of urine dipstick because the light produced is consistent. A method is required to overcome the problems of urine dipstick color matching and the test reference color that have been conducted manually. The method proposed by authors is Euclidean Distance, Otsu along with RGB color feature extraction method to match the colors on the urine dipstick with the standard reference color of urine examination. The result shows that the proposed approach was able to classify the colors on a urine dipstick with an accuracy of 95.45%. The accuracy of color classification on urine dipstick against the standard reference color is influenced by the level of scanner resolution used, the higher the scanner resolution level, the higher the accuracy.

  4. Improving automated case finding for ectopic pregnancy using a classification algorithm

    Science.gov (United States)

    Scholes, D.; Yu, O.; Raebel, M.A.; Trabert, B.; Holt, V.L.

    2011-01-01

    BACKGROUND Research and surveillance work addressing ectopic pregnancy often rely on diagnosis and procedure codes available from automated data sources. However, the use of these codes may result in misclassification of cases. Our aims were to evaluate the accuracy of standard ectopic pregnancy codes; and, through the use of additional automated data, to develop and validate a classification algorithm that could potentially improve the accuracy of ectopic pregnancy case identification. METHODS Using automated databases from two US managed-care plans, Group Health Cooperative (GH) and Kaiser Permanente Colorado (KPCO), we sampled women aged 15–44 with an ectopic pregnancy diagnosis or procedure code from 2001 to 2007 and verified their true case status through medical record review. We calculated positive predictive values (PPV) for code-selected cases compared with true cases at both sites. Using additional variables from the automated databases and classification and regression tree (CART) analysis, we developed a case-finding algorithm at GH (n = 280), which was validated at KPCO (n = 500). RESULTS Compared with true cases, the PPV of code-selected cases was 68 and 81% at GH and KPCO, respectively. The case-finding algorithm identified three predictors: ≥2 visits with an ectopic pregnancy code within 180 days; International Classification of Diseases, 9th Revision, Clinical Modification codes for tubal pregnancy; and methotrexate treatment. Relative to true cases, performance measures for the development and validation sets, respectively, were: 93 and 95% sensitivity; 81 and 81% specificity; 91 and 96% PPV; 84 and 79% negative predictive value. Misclassification proportions were 32% in the development set and 19% in the validation set when using standard codes; they were 11 and 8%, respectively, when using the algorithm. CONCLUSIONS The ectopic pregnancy algorithm improved case-finding accuracy over use of standard codes alone and generalized well to a

  5. A Fully Automated Classification for Mapping the Annual Cropland Extent

    Science.gov (United States)

    Waldner, F.; Defourny, P.

    2015-12-01

    Mapping the global cropland extent is of paramount importance for food security. Indeed, accurate and reliable information on cropland and the location of major crop types is required to make future policy, investment, and logistical decisions, as well as production monitoring. Timely cropland information directly feed early warning systems such as GIEWS and, FEWS NET. In Africa, and particularly in the arid and semi-arid region, food security is center of debate (at least 10% of the population remains undernourished) and accurate cropland estimation is a challenge. Space borne Earth Observation provides opportunities for global cropland monitoring in a spatially explicit, economic, efficient, and objective fashion. In the both agriculture monitoring and climate modelling, cropland maps serve as mask to isolate agricultural land for (i) time-series analysis for crop condition monitoring and (ii) to investigate how the cropland is respond to climatic evolution. A large diversity of mapping strategies ranging from the local to the global scale and associated with various degrees of accuracy can be found in the literature. At the global scale, despite efforts, cropland is generally one of classes with the poorest accuracy which make difficult the use for agricultural. This research aims at improving the cropland delineation from the local scale to the regional and global scales as well as allowing near real time updates. To that aim, five temporal features were designed to target the key- characteristics of crop spectral-temporal behavior. To ensure a high degree of automation, training data is extracted from available baseline land cover maps. The method delivers cropland maps with a high accuracy over contrasted agro-systems in Ukraine, Argentina, China and Belgium. The accuracy reached are comparable to those obtained with classifiers trained with in-situ data. Besides, it was found that the cropland class is associated with a low uncertainty. The temporal features

  6. Automated processing of webcam images for phenological classification.

    Directory of Open Access Journals (Sweden)

    Ludwig Bothmann

    Full Text Available Along with the global climate change, there is an increasing interest for its effect on phenological patterns such as start and end of the growing season. Scientific digital webcams are used for this purpose taking every day one or more images from the same natural motive showing for example trees or grassland sites. To derive phenological patterns from the webcam images, regions of interest are manually defined on these images by an expert and subsequently a time series of percentage greenness is derived and analyzed with respect to structural changes. While this standard approach leads to satisfying results and allows to determine dates of phenological change points, it is associated with a considerable amount of manual work and is therefore constrained to a limited number of webcams only. In particular, this forbids to apply the phenological analysis to a large network of publicly accessible webcams in order to capture spatial phenological variation. In order to be able to scale up the analysis to several hundreds or thousands of webcams, we propose and evaluate two automated alternatives for the definition of regions of interest, allowing for efficient analyses of webcam images. A semi-supervised approach selects pixels based on the correlation of the pixels' time series of percentage greenness with a few prototype pixels. An unsupervised approach clusters pixels based on scores of a singular value decomposition. We show for a scientific webcam that the resulting regions of interest are at least as informative as those chosen by an expert with the advantage that no manual action is required. Additionally, we show that the methods can even be applied to publicly available webcams accessed via the internet yielding interesting partitions of the analyzed images. Finally, we show that the methods are suitable for the intended big data applications by analyzing 13988 webcams from the AMOS database. All developed methods are implemented in the

  7. Automated classification of inflammation in colon histological sections based on digital microscopy and advanced image analysis.

    Science.gov (United States)

    Ficsor, Levente; Varga, Viktor Sebestyén; Tagscherer, Attila; Tulassay, Zsolt; Molnar, Bela

    2008-03-01

    Automated and quantitative histological analysis can improve diagnostic efficacy in colon sections. Our objective was to develop a parameter set for automated classification of aspecific colitis, ulcerative colitis, and Crohn's disease using digital slides, tissue cytometric parameters, and virtual microscopy. Routinely processed hematoxylin-and-eosin-stained histological sections from specimens that showed normal mucosa (24 cases), aspecific colitis (11 cases), ulcerative colitis (25 cases), and Crohn's disease (9 cases) diagnosed by conventional optical microscopy were scanned and digitized in high resolution (0.24 mum/pixel). Thirty-eight cytometric parameters based on morphometry were determined on cells, glands, and superficial epithelium. Fourteen tissue cytometric parameters based on ratios of tissue compartments were counted as well. Leave-one-out discriminant analysis was used for classification of the samples groups. Cellular morphometric features showed no significant differences in these benign colon alterations. However, gland related morphological differences (Gland Shape) for normal mucosa, ulcerative colitis, and aspecific colitis were found (P parameters showed significant differences (P parameters were the ratio of cell number in glands and in the whole slide, biopsy/gland surface ratio. These differences resulted in 88% overall accuracy in the classification. Crohn's disease could be discriminated only in 56%. Automated virtual microscopy can be used to classify colon mucosa as normal, ulcerative colitis, and aspecific colitis with reasonable accuracy. Further developments of dedicated parameters are necessary to identify Crohn's disease on digital slides. Copyright 2008 International Society for Analytical Cytology.

  8. Methods for pattern selection, class-specific feature selection and classification for automated learning.

    Science.gov (United States)

    Roy, Asim; Mackin, Patrick D; Mukhopadhyay, Somnath

    2013-05-01

    This paper presents methods for training pattern (prototype) selection, class-specific feature selection and classification for automated learning. For training pattern selection, we propose a method of sampling that extracts a small number of representative training patterns (prototypes) from the dataset. The idea is to extract a set of prototype training patterns that represents each class region in a classification problem. In class-specific feature selection, we try to find a separate feature set for each class such that it is the best one to separate that class from the other classes. We then build a separate classifier for that class based on its own feature set. The paper also presents a new hypersphere classification algorithm. Hypersphere nets are similar to radial basis function (RBF) nets and belong to the group of kernel function nets. Polynomial time complexity of the methods is proven. Polynomial time complexity of learning algorithms is important to the field of neural networks. Computational results are provided for a number of well-known datasets. None of the parameters of the algorithm were fine tuned for any of the problems solved and this supports the idea of automation of learning methods. Automation of learning is crucial to wider deployment of learning technologies. Copyright © 2012 Elsevier Ltd. All rights reserved.

  9. An Entropy-Based Network Anomaly Detection Method

    Directory of Open Access Journals (Sweden)

    Przemysław Bereziński

    2015-04-01

    Full Text Available Data mining is an interdisciplinary subfield of computer science involving methods at the intersection of artificial intelligence, machine learning and statistics. One of the data mining tasks is anomaly detection which is the analysis of large quantities of data to identify items, events or observations which do not conform to an expected pattern. Anomaly detection is applicable in a variety of domains, e.g., fraud detection, fault detection, system health monitoring but this article focuses on application of anomaly detection in the field of network intrusion detection.The main goal of the article is to prove that an entropy-based approach is suitable to detect modern botnet-like malware based on anomalous patterns in network. This aim is achieved by realization of the following points: (i preparation of a concept of original entropy-based network anomaly detection method, (ii implementation of the method, (iii preparation of original dataset, (iv evaluation of the method.

  10. Improved reticle requalification accuracy and efficiency via simulation-powered automated defect classification

    Science.gov (United States)

    Paracha, Shazad; Eynon, Benjamin; Noyes, Ben F.; Nhiev, Anthony; Vacca, Anthony; Fiekowsky, Peter; Fiekowsky, Dan; Ham, Young Mog; Uzzel, Doug; Green, Michael; MacDonald, Susan; Morgan, John

    2014-04-01

    Advanced IC fabs must inspect critical reticles on a frequent basis to ensure high wafer yields. These necessary requalification inspections have traditionally carried high risk and expense. Manually reviewing sometimes hundreds of potentially yield-limiting detections is a very high-risk activity due to the likelihood of human error; the worst of which is the accidental passing of a real, yield-limiting defect. Painfully high cost is incurred as a result, but high cost is also realized on a daily basis while reticles are being manually classified on inspection tools since these tools often remain in a non-productive state during classification. An automatic defect analysis system (ADAS) has been implemented at a 20nm node wafer fab to automate reticle defect classification by simulating each defect's printability under the intended illumination conditions. In this paper, we have studied and present results showing the positive impact that an automated reticle defect classification system has on the reticle requalification process; specifically to defect classification speed and accuracy. To verify accuracy, detected defects of interest were analyzed with lithographic simulation software and compared to the results of both AIMS™ optical simulation and to actual wafer prints.

  11. Prototype semantic infrastructure for automated small molecule classification and annotation in lipidomics.

    Science.gov (United States)

    Chepelev, Leonid L; Riazanov, Alexandre; Kouznetsov, Alexandre; Low, Hong Sang; Dumontier, Michel; Baker, Christopher J O

    2011-07-26

    The development of high-throughput experimentation has led to astronomical growth in biologically relevant lipids and lipid derivatives identified, screened, and deposited in numerous online databases. Unfortunately, efforts to annotate, classify, and analyze these chemical entities have largely remained in the hands of human curators using manual or semi-automated protocols, leaving many novel entities unclassified. Since chemical function is often closely linked to structure, accurate structure-based classification and annotation of chemical entities is imperative to understanding their functionality. As part of an exploratory study, we have investigated the utility of semantic web technologies in automated chemical classification and annotation of lipids. Our prototype framework consists of two components: an ontology and a set of federated web services that operate upon it. The formal lipid ontology we use here extends a part of the LiPrO ontology and draws on the lipid hierarchy in the LIPID MAPS database, as well as literature-derived knowledge. The federated semantic web services that operate upon this ontology are deployed within the Semantic Annotation, Discovery, and Integration (SADI) framework. Structure-based lipid classification is enacted by two core services. Firstly, a structural annotation service detects and enumerates relevant functional groups for a specified chemical structure. A second service reasons over lipid ontology class descriptions using the attributes obtained from the annotation service and identifies the appropriate lipid classification. We extend the utility of these core services by combining them with additional SADI services that retrieve associations between lipids and proteins and identify publications related to specified lipid types. We analyze the performance of SADI-enabled eicosanoid classification relative to the LIPID MAPS classification and reflect on the contribution of our integrative methodology in the context of

  12. Prototype semantic infrastructure for automated small molecule classification and annotation in lipidomics

    Directory of Open Access Journals (Sweden)

    Dumontier Michel

    2011-07-01

    Full Text Available Abstract Background The development of high-throughput experimentation has led to astronomical growth in biologically relevant lipids and lipid derivatives identified, screened, and deposited in numerous online databases. Unfortunately, efforts to annotate, classify, and analyze these chemical entities have largely remained in the hands of human curators using manual or semi-automated protocols, leaving many novel entities unclassified. Since chemical function is often closely linked to structure, accurate structure-based classification and annotation of chemical entities is imperative to understanding their functionality. Results As part of an exploratory study, we have investigated the utility of semantic web technologies in automated chemical classification and annotation of lipids. Our prototype framework consists of two components: an ontology and a set of federated web services that operate upon it. The formal lipid ontology we use here extends a part of the LiPrO ontology and draws on the lipid hierarchy in the LIPID MAPS database, as well as literature-derived knowledge. The federated semantic web services that operate upon this ontology are deployed within the Semantic Annotation, Discovery, and Integration (SADI framework. Structure-based lipid classification is enacted by two core services. Firstly, a structural annotation service detects and enumerates relevant functional groups for a specified chemical structure. A second service reasons over lipid ontology class descriptions using the attributes obtained from the annotation service and identifies the appropriate lipid classification. We extend the utility of these core services by combining them with additional SADI services that retrieve associations between lipids and proteins and identify publications related to specified lipid types. We analyze the performance of SADI-enabled eicosanoid classification relative to the LIPID MAPS classification and reflect on the contribution of

  13. Automated classification of dolphin echolocation click types from the Gulf of Mexico.

    Science.gov (United States)

    Frasier, Kaitlin E; Roch, Marie A; Soldevilla, Melissa S; Wiggins, Sean M; Garrison, Lance P; Hildebrand, John A

    2017-12-01

    Delphinids produce large numbers of short duration, broadband echolocation clicks which may be useful for species classification in passive acoustic monitoring efforts. A challenge in echolocation click classification is to overcome the many sources of variability to recognize underlying patterns across many detections. An automated unsupervised network-based classification method was developed to simulate the approach a human analyst uses when categorizing click types: Clusters of similar clicks were identified by incorporating multiple click characteristics (spectral shape and inter-click interval distributions) to distinguish within-type from between-type variation, and identify distinct, persistent click types. Once click types were established, an algorithm for classifying novel detections using existing clusters was tested. The automated classification method was applied to a dataset of 52 million clicks detected across five monitoring sites over two years in the Gulf of Mexico (GOM). Seven distinct click types were identified, one of which is known to be associated with an acoustically identifiable delphinid (Risso's dolphin) and six of which are not yet identified. All types occurred at multiple monitoring locations, but the relative occurrence of types varied, particularly between continental shelf and slope locations. Automatically-identified click types from autonomous seafloor recorders without verifiable species identification were compared with clicks detected on sea-surface towed hydrophone arrays in the presence of visually identified delphinid species. These comparisons suggest potential species identities for the animals producing some echolocation click types. The network-based classification method presented here is effective for rapid, unsupervised delphinid click classification across large datasets in which the click types may not be known a priori.

  14. A Neural-Network-Based Semi-Automated Geospatial Classification Tool

    Science.gov (United States)

    Hale, R. G.; Herzfeld, U. C.

    2014-12-01

    North America's largest glacier system, the Bering Bagley Glacier System (BBGS) in Alaska, surged in 2011-2013, as shown by rapid mass transfer, elevation change, and heavy crevassing. Little is known about the physics controlling surge glaciers' semi-cyclic patterns; therefore, it is crucial to collect and analyze as much data as possible so that predictive models can be made. In addition, physical signs frozen in ice in the form of crevasses may help serve as a warning for future surges. The BBGS surge provided an opportunity to develop an automated classification tool for crevasse classification based on imagery collected from small aircraft. The classification allows one to link image classification to geophysical processes associated with ice deformation. The tool uses an approach that employs geostatistical functions and a feed-forward perceptron with error back-propagation. The connectionist-geostatistical approach uses directional experimental (discrete) variograms to parameterize images into a form that the Neural Network (NN) can recognize. In an application to preform analysis on airborne video graphic data from the surge of the BBGS, an NN was able to distinguish 18 different crevasse classes with 95 percent or higher accuracy, for over 3,000 images. Recognizing that each surge wave results in different crevasse types and that environmental conditions affect the appearance in imagery, we designed the tool's semi-automated pre-training algorithm to be adaptable. The tool can be optimized to specific settings and variables of image analysis: (airborne and satellite imagery, different camera types, observation altitude, number and types of classes, and resolution). The generalization of the classification tool brings three important advantages: (1) multiple types of problems in geophysics can be studied, (2) the training process is sufficiently formalized to allow non-experts in neural nets to perform the training process, and (3) the time required to

  15. Automated Classification of Lung Cancer Types from Cytological Images Using Deep Convolutional Neural Networks.

    Science.gov (United States)

    Teramoto, Atsushi; Tsukamoto, Tetsuya; Kiriyama, Yuka; Fujita, Hiroshi

    2017-01-01

    Lung cancer is a leading cause of death worldwide. Currently, in differential diagnosis of lung cancer, accurate classification of cancer types (adenocarcinoma, squamous cell carcinoma, and small cell carcinoma) is required. However, improving the accuracy and stability of diagnosis is challenging. In this study, we developed an automated classification scheme for lung cancers presented in microscopic images using a deep convolutional neural network (DCNN), which is a major deep learning technique. The DCNN used for classification consists of three convolutional layers, three pooling layers, and two fully connected layers. In evaluation experiments conducted, the DCNN was trained using our original database with a graphics processing unit. Microscopic images were first cropped and resampled to obtain images with resolution of 256 × 256 pixels and, to prevent overfitting, collected images were augmented via rotation, flipping, and filtering. The probabilities of three types of cancers were estimated using the developed scheme and its classification accuracy was evaluated using threefold cross validation. In the results obtained, approximately 71% of the images were classified correctly, which is on par with the accuracy of cytotechnologists and pathologists. Thus, the developed scheme is useful for classification of lung cancers from microscopic images.

  16. Leveraging Long-term Seismic Catalogs for Automated Real-time Event Classification

    Science.gov (United States)

    Linville, L.; Draelos, T.; Pankow, K. L.; Young, C. J.; Alvarez, S.

    2017-12-01

    We investigate the use of labeled event types available through reviewed seismic catalogs to produce automated event labels on new incoming data from the crustal region spanned by the cataloged events. Using events cataloged by the University of Utah Seismograph Stations between October, 2012 and June, 2017, we calculate the spectrogram for a time window that spans the duration of each event as seen on individual stations, resulting in 110k event spectrograms (50% local earthquakes examples, 50% quarry blasts examples). Using 80% of the randomized example events ( 90k), a classifier is trained to distinguish between local earthquakes and quarry blasts. We explore variations of deep learning classifiers, incorporating elements of convolutional and recurrent neural networks. Using a single-layer Long Short Term Memory recurrent neural network, we achieve 92% accuracy on the classification task on the remaining 20K test examples. Leveraging the decisions from a group of stations that detected the same event by using the median of all classifications in the group increases the model accuracy to 96%. Additional data with equivalent processing from 500 more recently cataloged events (July, 2017), achieves the same accuracy as our test data on both single-station examples and multi-station medians, suggesting that the model can maintain accurate and stable classification rates on real-time automated events local to the University of Utah Seismograph Stations, with potentially minimal levels of re-training through time.

  17. A Novel Method for the Separation of Overlapping Pollen Species for Automated Detection and Classification.

    Science.gov (United States)

    Tello-Mijares, Santiago; Flores, Francisco

    2016-01-01

    The identification of pollen in an automated way will accelerate different tasks and applications of palynology to aid in, among others, climate change studies, medical allergies calendar, and forensic science. The aim of this paper is to develop a system that automatically captures a hundred microscopic images of pollen and classifies them into the 12 different species from Lagunera Region, Mexico. Many times, the pollen is overlapping on the microscopic images, which increases the difficulty for its automated identification and classification. This paper focuses on a method to segment the overlapping pollen. First, the proposed method segments the overlapping pollen. Second, the method separates the pollen based on the mean shift process (100% segmentation) and erosion by H-minima based on the Fibonacci series. Thus, pollen is characterized by its shape, color, and texture for training and evaluating the performance of three classification techniques: random tree forest, multilayer perceptron, and Bayes net. Using the newly developed system, we obtained segmentation results of 100% and classification on top of 96.2% and 96.1% in recall and precision using multilayer perceptron in twofold cross validation.

  18. Automated image classification applied to reconstituted human corneal epithelium for the early detection of toxic damage

    Science.gov (United States)

    Crosta, Giovanni Franco; Urani, Chiara; De Servi, Barbara; Meloni, Marisa

    2010-02-01

    For a long time acute eye irritation has been assessed by means of the DRAIZE rabbit test, the limitations of which are known. Alternative tests based on in vitro models have been proposed. This work focuses on the "reconstituted human corneal epithelium" (R-HCE), which resembles the corneal epithelium of the human eye by thickness, morphology and marker expression. Testing a substance on R-HCE involves a variety of methods. Herewith quantitative morphological analysis is applied to optical microscope images of R-HCE cross sections resulting from exposure to benzalkonium chloride (BAK). The short term objectives and the first results are the analysis and classification of said images. Automated analysis relies on feature extraction by the spectrum-enhancement algorithm, which is made sensitive to anisotropic morphology, and classification based on principal components analysis. The winning strategy has been the separate analysis of the apical and basal layers, which carry morphological information of different types. R-HCE specimens have been ranked by gross damage. The onset of early damage has been detected and an R-HCE specimen exposed to a low BAK dose has been singled out from the negative and positive control. These results provide a proof of principle for the automated classification of the specimens of interest on a purely morphological basis by means of the spectrum enhancement algorithm.

  19. The use of eDR-71xx for DSA defect review and automated classification

    Science.gov (United States)

    Pathangi, Hari; Van Den Heuvel, Dieter; Bayana, Hareen; Bouckou, Loemba; Brown, Jim; Parisi, Paolo; Gosain, Rohan

    2015-03-01

    The Liu-Nealey (LiNe) chemo-epitaxy Directed Self Assembly flow has been screened thoroughly in the past years in terms of defects. Various types of DSA specific defects have been identified and best known methods have been developed to be able to get sufficient S/N for defect inspection to help understand the root causes for the various defect types and to reduce the defect levels to prepare the process for high volume manufacturing. Within this process development, SEM-review and defect classification play a key role. This paper provides an overview of the challenges that DSA brings also in this metrology aspect and we will provide successful solutions in terms of making the automated defect review. In addition, a new Real Time Automated Defect Classification (RT-ADC) will be introduced that can save up to 90% in the time required for manual defect classification. This will enable a much larger sampling for defect review, resulting in a better understanding of signatures and behaviors of various DSA specific defect types, such as dislocations, 1-period bridges and line wiggling.

  20. Automated Outcome Classification of Computed Tomography Imaging Reports for Pediatric Traumatic Brain Injury.

    Science.gov (United States)

    Yadav, Kabir; Sarioglu, Efsun; Choi, Hyeong Ah; Cartwright, Walter B; Hinds, Pamela S; Chamberlain, James M

    2016-02-01

    The authors have previously demonstrated highly reliable automated classification of free-text computed tomography (CT) imaging reports using a hybrid system that pairs linguistic (natural language processing) and statistical (machine learning) techniques. Previously performed for identifying the outcome of orbital fracture in unprocessed radiology reports from a clinical data repository, the performance has not been replicated for more complex outcomes. To validate automated outcome classification performance of a hybrid natural language processing (NLP) and machine learning system for brain CT imaging reports. The hypothesis was that our system has performance characteristics for identifying pediatric traumatic brain injury (TBI). This was a secondary analysis of a subset of 2,121 CT reports from the Pediatric Emergency Care Applied Research Network (PECARN) TBI study. For that project, radiologists dictated CT reports as free text, which were then deidentified and scanned as PDF documents. Trained data abstractors manually coded each report for TBI outcome. Text was extracted from the PDF files using optical character recognition. The data set was randomly split evenly for training and testing. Training patient reports were used as input to the Medical Language Extraction and Encoding (MedLEE) NLP tool to create structured output containing standardized medical terms and modifiers for negation, certainty, and temporal status. A random subset stratified by site was analyzed using descriptive quantitative content analysis to confirm identification of TBI findings based on the National Institute of Neurological Disorders and Stroke (NINDS) Common Data Elements project. Findings were coded for presence or absence, weighted by frequency of mentions, and past/future/indication modifiers were filtered. After combining with the manual reference standard, a decision tree classifier was created using data mining tools WEKA 3.7.5 and Salford Predictive Miner 7

  1. VizieR Online Data Catalog: SDSS automated morphology classification (Huertas-Company+, 2011)

    Science.gov (United States)

    Huertas-Company, M.; Aguerri, J. A. L.; Bernardi, M.; Mei, S.; Sanchez Almeida, J.

    2010-11-01

    We used all the SDSS DR7 spectroscopic sample as the starting base. Then, the selection of objects was based on Sanchez Almeida et al. (2010ApJ...714..487A) who performed an unsupervised automated classification of all the SDSS spectra. Basically, we chose galaxies with redshift below 0.25, and with good photometric data and clean spectra, meaning objects not too close to the edges, not saturated, or not properly deblended. The final catalog contains 698420 objects for which we estimate the morphology (also available at http://gepicom04.obspm.fr/sdssmorphology/Morphology2010.html ). (3 data files).

  2. Automated identification of dementia using medical imaging: a survey from a pattern classification perspective.

    Science.gov (United States)

    Zheng, Chuanchuan; Xia, Yong; Pan, Yongsheng; Chen, Jinhu

    2016-03-01

    In this review paper, we summarized the automated dementia identification algorithms in the literature from a pattern classification perspective. Since most of those algorithms consist of both feature extraction and classification, we provide a survey on three categories of feature extraction methods, including the voxel-, vertex- and ROI-based ones, and four categories of classifiers, including the linear discriminant analysis, Bayes classifiers, support vector machines, and artificial neural networks. We also compare the reported performance of many recently published dementia identification algorithms. Our comparison shows that many algorithms can differentiate the Alzheimer's disease (AD) from elderly normal with a largely satisfying accuracy, whereas distinguishing the mild cognitive impairment from AD or elderly normal still remains a major challenge.

  3. An automated approach to classification of duplex assay for digital droplet PCR.

    Science.gov (United States)

    Liu, Cong; Zhou, Wuping; Zhang, Tao; Jiang, Keming; Li, Haiwen; Dong, Wenfei

    2018-01-25

    In the digital polymerase chain reaction (dPCR) detection process, discriminating positive droplets from negative ones directly affects the final concentration and is one of the most important factors affecting accuracy. Current automated classification methods usually discuss single-channel detections, whereas duplex detection experiments are less discussed. In this paper, we designed a classification method by estimating the upper limit of the negative droplets. The right tail of the negative droplets is approximated using a generalized Pareto distribution. Furthermore, our method takes fluorescence compensation in duplex assays into account. We also demonstrate the method on Bio-Rad's mutant detection dataset. Experimental results show that the method provides similar or better accuracy than other algorithms reported over a wider dynamic range.

  4. Integrating image processing and classification technology into automated polarizing film defect inspection

    Science.gov (United States)

    Kuo, Chung-Feng Jeffrey; Lai, Chun-Yu; Kao, Chih-Hsiang; Chiu, Chin-Hsun

    2018-05-01

    In order to improve the current manual inspection and classification process for polarizing film on production lines, this study proposes a high precision automated inspection and classification system for polarizing film, which is used for recognition and classification of four common defects: dent, foreign material, bright spot, and scratch. First, the median filter is used to remove the impulse noise in the defect image of polarizing film. The random noise in the background is smoothed by the improved anisotropic diffusion, while the edge detail of the defect region is sharpened. Next, the defect image is transformed by Fourier transform to the frequency domain, combined with a Butterworth high pass filter to sharpen the edge detail of the defect region, and brought back by inverse Fourier transform to the spatial domain to complete the image enhancement process. For image segmentation, the edge of the defect region is found by Canny edge detector, and then the complete defect region is obtained by two-stage morphology processing. For defect classification, the feature values, including maximum gray level, eccentricity, the contrast, and homogeneity of gray level co-occurrence matrix (GLCM) extracted from the images, are used as the input of the radial basis function neural network (RBFNN) and back-propagation neural network (BPNN) classifier, 96 defect images are then used as training samples, and 84 defect images are used as testing samples to validate the classification effect. The result shows that the classification accuracy by using RBFNN is 98.9%. Thus, our proposed system can be used by manufacturing companies for a higher yield rate and lower cost. The processing time of one single image is 2.57 seconds, thus meeting the practical application requirement of an industrial production line.

  5. Deceiving entropy-based DoS detection

    Science.gov (United States)

    Özçelik, Ä.°lker; Brooks, Richard R.

    2014-06-01

    Denial of Service (DoS) attacks disable network services for legitimate users. A McAfee report shows that eight out of ten Critical Infrastructure Providers (CIPs) surveyed had a significant Distributed DoS (DDoS) attack in 2010.1 Researchers proposed many approaches for detecting these attacks in the past decade. Anomaly based DoS detection is the most common. In this approach, the detector uses statistical features; such as the entropy of incoming packet header fields like source IP addresses or protocol type. It calculates the observed statistical feature and triggers an alarm if an extreme deviation occurs. However, intrusion detection systems (IDS) using entropy based detection can be fooled by spoofing. An attacker can sniff the network to collect header field data of network packets coming from distributed nodes on the Internet and fuses them to calculate the entropy of normal background traffic. Then s/he can spoof attack packets to keep the entropy value in the expected range during the attack. In this study, we present a proof of concept entropy spoofing attack that deceives entropy based detection approaches. Our preliminary results show that spoofing attacks cause significant detection performance degradation.

  6. ClassyFire: automated chemical classification with a comprehensive, computable taxonomy.

    Science.gov (United States)

    Djoumbou Feunang, Yannick; Eisner, Roman; Knox, Craig; Chepelev, Leonid; Hastings, Janna; Owen, Gareth; Fahy, Eoin; Steinbeck, Christoph; Subramanian, Shankar; Bolton, Evan; Greiner, Russell; Wishart, David S

    2016-01-01

    Scientists have long been driven by the desire to describe, organize, classify, and compare objects using taxonomies and/or ontologies. In contrast to biology, geology, and many other scientific disciplines, the world of chemistry still lacks a standardized chemical ontology or taxonomy. Several attempts at chemical classification have been made; but they have mostly been limited to either manual, or semi-automated proof-of-principle applications. This is regrettable as comprehensive chemical classification and description tools could not only improve our understanding of chemistry but also improve the linkage between chemistry and many other fields. For instance, the chemical classification of a compound could help predict its metabolic fate in humans, its druggability or potential hazards associated with it, among others. However, the sheer number (tens of millions of compounds) and complexity of chemical structures is such that any manual classification effort would prove to be near impossible. We have developed a comprehensive, flexible, and computable, purely structure-based chemical taxonomy (ChemOnt), along with a computer program (ClassyFire) that uses only chemical structures and structural features to automatically assign all known chemical compounds to a taxonomy consisting of >4800 different categories. This new chemical taxonomy consists of up to 11 different levels (Kingdom, SuperClass, Class, SubClass, etc.) with each of the categories defined by unambiguous, computable structural rules. Furthermore each category is named using a consensus-based nomenclature and described (in English) based on the characteristic common structural properties of the compounds it contains. The ClassyFire webserver is freely accessible at http://classyfire.wishartlab.com/. Moreover, a Ruby API version is available at https://bitbucket.org/wishartlab/classyfire_api, which provides programmatic access to the ClassyFire server and database. ClassyFire has been used to

  7. Automated Classification of Severity in Cardiac Dyssynchrony Merging Clinical Data and Mechanical Descriptors

    Directory of Open Access Journals (Sweden)

    Alejandro Santos-Díaz

    2017-01-01

    Full Text Available Cardiac resynchronization therapy (CRT improves functional classification among patients with left ventricle malfunction and ventricular electric conduction disorders. However, a high percentage of subjects under CRT (20%–30% do not show any improvement. Nonetheless the presence of mechanical contraction dyssynchrony in ventricles has been proposed as an indicator of CRT response. This work proposes an automated classification model of severity in ventricular contraction dyssynchrony. The model includes clinical data such as left ventricular ejection fraction (LVEF, QRS and P-R intervals, and the 3 most significant factors extracted from the factor analysis of dynamic structures applied to a set of equilibrium radionuclide angiography images representing the mechanical behavior of cardiac contraction. A control group of 33 normal volunteers (28±5 years, LVEF of 59.7%±5.8% and a HF group of 42 subjects (53.12±15.05 years, LVEF < 35% were studied. The proposed classifiers had hit rates of 90%, 50%, and 80% to distinguish between absent, mild, and moderate-severe interventricular dyssynchrony, respectively. For intraventricular dyssynchrony, hit rates of 100%, 50%, and 90% were observed distinguishing between absent, mild, and moderate-severe, respectively. These results seem promising in using this automated method for clinical follow-up of patients undergoing CRT.

  8. A Framework to Support Automated Classification and Labeling of Brain Electromagnetic Patterns

    Directory of Open Access Journals (Sweden)

    Gwen A. Frishkoff

    2007-01-01

    Full Text Available This paper describes a framework for automated classification and labeling of patterns in electroencephalographic (EEG and magnetoencephalographic (MEG data. We describe recent progress on four goals: 1 specification of rules and concepts that capture expert knowledge of event-related potentials (ERP patterns in visual word recognition; 2 implementation of rules in an automated data processing and labeling stream; 3 data mining techniques that lead to refinement of rules; and 4 iterative steps towards system evaluation and optimization. This process combines top-down, or knowledge-driven, methods with bottom-up, or data-driven, methods. As illustrated here, these methods are complementary and can lead to development of tools for pattern classification and labeling that are robust and conceptually transparent to researchers. The present application focuses on patterns in averaged EEG (ERP data. We also describe efforts to extend our methods to represent patterns in MEG data, as well as EM patterns in source (anatomical space. The broader aim of this work is to design an ontology-based system to support cross-laboratory, cross-paradigm, and cross-modal integration of brain functional data. Tools developed for this project are implemented in MATLAB and are freely available on request.

  9. Robust automated classification of first-motion polarities for focal mechanism determination with machine learning

    Science.gov (United States)

    Ross, Z. E.; Meier, M. A.; Hauksson, E.

    2017-12-01

    Accurate first-motion polarities are essential for determining earthquake focal mechanisms, but are difficult to measure automatically because of picking errors and signal to noise issues. Here we develop an algorithm for reliable automated classification of first-motion polarities using machine learning algorithms. A classifier is designed to identify whether the first-motion polarity is up, down, or undefined by examining the waveform data directly. We first improve the accuracy of automatic P-wave onset picks by maximizing a weighted signal/noise ratio for a suite of candidate picks around the automatic pick. We then use the waveform amplitudes before and after the optimized pick as features for the classification. We demonstrate the method's potential by training and testing the classifier on tens of thousands of hand-made first-motion picks by the Southern California Seismic Network. The classifier assigned the same polarity as chosen by an analyst in more than 94% of the records. We show that the method is generalizable to a variety of learning algorithms, including neural networks and random forest classifiers. The method is suitable for automated processing of large seismic waveform datasets, and can potentially be used in real-time applications, e.g. for improving the source characterizations of earthquake early warning algorithms.

  10. Combined SPHARM-PDM and entropy-based particle systems shape analysis framework.

    Science.gov (United States)

    Paniagua, Beatriz; Bompard, Lucile; Cates, Josh; Whitaker, Ross; Datar, Manasi; Vachet, Clement; Styner, Martin

    2012-03-23

    The NA-MIC SPHARM-PDM Toolbox represents an automated set of tools for the computation of 3D structural statistical shape analysis. SPHARM-PDM solves the correspondence problem by defining a first order ellipsoid aligned, uniform spherical parameterization for each object with correspondence established at equivalently parameterized points. However, SPHARM correspondence has shown to be inadequate for some biological shapes that are not well described by a uniform spherical parameterization. Entropy-based particle systems compute correspondence by representing surfaces as discrete point sets that does not rely on any inherent parameterization. However, they are sensitive to initialization and have little ability to recover from initial errors. By combining both methodologies we compute reliable correspondences in topologically challenging biological shapes. Diverse subcortical structures cohorts were used, obtained from MR brain images. The SPHARM-PDM shape analysis toolbox was used to compute point based correspondent models that were then used as initializing particles for the entropy-based particle systems. The combined framework was implemented as a stand-alone Slicer3 module, which works as an end-to-end shape analysis module. The combined SPHARM-PDM-Particle framework has demonstrated to improve correspondence in the example dataset over the conventional SPHARM-PDM toolbox. The work presented in this paper demonstrates a two-sided improvement for the scientific community, being able to 1) find good correspondences among spherically topological shapes, that can be used in many morphometry studies 2) offer an end-to-end solution that will facilitate the access to shape analysis framework to users without computer expertise.

  11. Automated classification of tropical shrub species: a hybrid of leaf shape and machine learning approach.

    Science.gov (United States)

    Murat, Miraemiliana; Chang, Siow-Wee; Abu, Arpah; Yap, Hwa Jen; Yong, Kien-Thai

    2017-01-01

    Plants play a crucial role in foodstuff, medicine, industry, and environmental protection. The skill of recognising plants is very important in some applications, including conservation of endangered species and rehabilitation of lands after mining activities. However, it is a difficult task to identify plant species because it requires specialized knowledge. Developing an automated classification system for plant species is necessary and valuable since it can help specialists as well as the public in identifying plant species easily. Shape descriptors were applied on the myDAUN dataset that contains 45 tropical shrub species collected from the University of Malaya (UM), Malaysia. Based on literature review, this is the first study in the development of tropical shrub species image dataset and classification using a hybrid of leaf shape and machine learning approach. Four types of shape descriptors were used in this study namely morphological shape descriptors (MSD), Histogram of Oriented Gradients (HOG), Hu invariant moments (Hu) and Zernike moments (ZM). Single descriptor, as well as the combination of hybrid descriptors were tested and compared. The tropical shrub species are classified using six different classifiers, which are artificial neural network (ANN), random forest (RF), support vector machine (SVM), k-nearest neighbour (k-NN), linear discriminant analysis (LDA) and directed acyclic graph multiclass least squares twin support vector machine (DAG MLSTSVM). In addition, three types of feature selection methods were tested in the myDAUN dataset, Relief, Correlation-based feature selection (CFS) and Pearson's coefficient correlation (PCC). The well-known Flavia dataset and Swedish Leaf dataset were used as the validation dataset on the proposed methods. The results showed that the hybrid of all descriptors of ANN outperformed the other classifiers with an average classification accuracy of 98.23% for the myDAUN dataset, 95.25% for the Flavia dataset and 99

  12. A simple and robust method for automated photometric classification of supernovae using neural networks

    Science.gov (United States)

    Karpenka, N. V.; Feroz, F.; Hobson, M. P.

    2013-02-01

    A method is presented for automated photometric classification of supernovae (SNe) as Type Ia or non-Ia. A two-step approach is adopted in which (i) the SN light curve flux measurements in each observing filter are fitted separately to an analytical parametrized function that is sufficiently flexible to accommodate virtually all types of SNe and (ii) the fitted function parameters and their associated uncertainties, along with the number of flux measurements, the maximum-likelihood value of the fit and Bayesian evidence for the model, are used as the input feature vector to a classification neural network that outputs the probability that the SN under consideration is of Type Ia. The method is trained and tested using data released following the Supernova Photometric Classification Challenge (SNPCC), consisting of light curves for 20 895 SNe in total. We consider several random divisions of the data into training and testing sets: for instance, for our sample D_1 (D_4), a total of 10 (40) per cent of the data are involved in training the algorithm and the remainder used for blind testing of the resulting classifier; we make no selection cuts. Assigning a canonical threshold probability of pth = 0.5 on the network output to class an SN as Type Ia, for the sample D_1 (D_4) we obtain a completeness of 0.78 (0.82), purity of 0.77 (0.82) and SNPCC figure of merit of 0.41 (0.50). Including the SN host-galaxy redshift and its uncertainty as additional inputs to the classification network results in a modest 5-10 per cent increase in these values. We find that the quality of the classification does not vary significantly with SN redshift. Moreover, our probabilistic classification method allows one to calculate the expected completeness, purity and figure of merit (or other measures of classification quality) as a function of the threshold probability pth, without knowing the true classes of the SNe in the testing sample, as is the case in the classification of real SNe

  13. Automated arteriole and venule classification using deep learning for retinal images from the UK Biobank cohort.

    Science.gov (United States)

    Welikala, R A; Foster, P J; Whincup, P H; Rudnicka, A R; Owen, C G; Strachan, D P; Barman, S A

    2017-11-01

    The morphometric characteristics of the retinal vasculature are associated with future risk of many systemic and vascular diseases. However, analysis of data from large population based studies is needed to help resolve uncertainties in some of these associations. This requires automated systems that extract quantitative measures of vessel morphology from large numbers of retinal images. Associations between retinal vessel morphology and disease precursors/outcomes may be similar or opposing for arterioles and venules. Therefore, the accurate detection of the vessel type is an important element in such automated systems. This paper presents a deep learning approach for the automatic classification of arterioles and venules across the entire retinal image, including vessels located at the optic disc. This comprises of a convolutional neural network whose architecture contains six learned layers: three convolutional and three fully-connected. Complex patterns are automatically learnt from the data, which avoids the use of hand crafted features. The method is developed and evaluated using 835,914 centreline pixels derived from 100 retinal images selected from the 135,867 retinal images obtained at the UK Biobank (large population-based cohort study of middle aged and older adults) baseline examination. This is a challenging dataset in respect to image quality and hence arteriole/venule classification is required to be highly robust. The method achieves a significant increase in accuracy of 8.1% when compared to the baseline method, resulting in an arteriole/venule classification accuracy of 86.97% (per pixel basis) over the entire retinal image. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. Comparing automated classification and digitization approaches to detect change in eelgrass bed extent during restoration of a large river delta

    Science.gov (United States)

    Davenport, Anna Elizabeth; Davis, Jerry D.; Woo, Isa; Grossman, Eric; Barham, Jesse B.; Ellings, Christopher S.; Takekawa, John Y.

    2017-01-01

    Native eelgrass (Zostera marina) is an important contributor to ecosystem services that supplies cover for juvenile fish, supports a variety of invertebrate prey resources for fish and waterbirds, provides substrate for herring roe consumed by numerous fish and birds, helps stabilize sediment, and sequesters organic carbon. Seagrasses are in decline globally, and monitoring changes in their growth and extent is increasingly valuable to determine impacts from large-scale estuarine restoration and inform blue carbon mapping initiatives. Thus, we examined the efficacy of two remote sensing mapping methods with high-resolution (0.5 m pixel size) color near infrared imagery with ground validation to assess change following major tidal marsh restoration. Automated classification of false color aerial imagery and digitized polygons documented a slight decline in eelgrass area directly after restoration followed by an increase two years later. Classification of sparse and low to medium density eelgrass was confounded in areas with algal cover, however large dense patches of eelgrass were well delineated. Automated classification of aerial imagery from unsupervised and supervised methods provided reasonable accuracies of 73% and hand-digitizing polygons from the same imagery yielded similar results. Visual clues for hand digitizing from the high-resolution imagery provided as reliable a map of dense eelgrass extent as automated image classification. We found that automated classification had no advantages over manual digitization particularly because of the limitations of detecting eelgrass with only three bands of imagery and near infrared.

  15. Effective automated feature construction and selection for classification of biological sequences.

    Directory of Open Access Journals (Sweden)

    Uday Kamath

    Full Text Available Many open problems in bioinformatics involve elucidating underlying functional signals in biological sequences. DNA sequences, in particular, are characterized by rich architectures in which functional signals are increasingly found to combine local and distal interactions at the nucleotide level. Problems of interest include detection of regulatory regions, splice sites, exons, hypersensitive sites, and more. These problems naturally lend themselves to formulation as classification problems in machine learning. When classification is based on features extracted from the sequences under investigation, success is critically dependent on the chosen set of features.We present an algorithmic framework (EFFECT for automated detection of functional signals in biological sequences. We focus here on classification problems involving DNA sequences which state-of-the-art work in machine learning shows to be challenging and involve complex combinations of local and distal features. EFFECT uses a two-stage process to first construct a set of candidate sequence-based features and then select a most effective subset for the classification task at hand. Both stages make heavy use of evolutionary algorithms to efficiently guide the search towards informative features capable of discriminating between sequences that contain a particular functional signal and those that do not.To demonstrate its generality, EFFECT is applied to three separate problems of importance in DNA research: the recognition of hypersensitive sites, splice sites, and ALU sites. Comparisons with state-of-the-art algorithms show that the framework is both general and powerful. In addition, a detailed analysis of the constructed features shows that they contain valuable biological information about DNA architecture, allowing biologists and other researchers to directly inspect the features and potentially use the insights obtained to assist wet-laboratory studies on retainment or modification

  16. Automated classification of bone marrow cells in microscopic images for diagnosis of leukemia: a comparison of two classification schemes with respect to the segmentation quality

    Science.gov (United States)

    Krappe, Sebastian; Benz, Michaela; Wittenberg, Thomas; Haferlach, Torsten; Münzenmayer, Christian

    2015-03-01

    The morphological analysis of bone marrow smears is fundamental for the diagnosis of leukemia. Currently, the counting and classification of the different types of bone marrow cells is done manually with the use of bright field microscope. This is a time consuming, partly subjective and tedious process. Furthermore, repeated examinations of a slide yield intra- and inter-observer variances. For this reason an automation of morphological bone marrow analysis is pursued. This analysis comprises several steps: image acquisition and smear detection, cell localization and segmentation, feature extraction and cell classification. The automated classification of bone marrow cells is depending on the automated cell segmentation and the choice of adequate features extracted from different parts of the cell. In this work we focus on the evaluation of support vector machines (SVMs) and random forests (RFs) for the differentiation of bone marrow cells in 16 different classes, including immature and abnormal cell classes. Data sets of different segmentation quality are used to test the two approaches. Automated solutions for the morphological analysis for bone marrow smears could use such a classifier to pre-classify bone marrow cells and thereby shortening the examination duration.

  17. Developing and Integrating Advanced Movement Features Improves Automated Classification of Ciliate Species.

    Science.gov (United States)

    Soleymani, Ali; Pennekamp, Frank; Petchey, Owen L; Weibel, Robert

    2015-01-01

    Recent advances in tracking technologies such as GPS or video tracking systems describe the movement paths of individuals in unprecedented details and are increasingly used in different fields, including ecology. However, extracting information from raw movement data requires advanced analysis techniques, for instance to infer behaviors expressed during a certain period of the recorded trajectory, or gender or species identity in case data is obtained from remote tracking. In this paper, we address how different movement features affect the ability to automatically classify the species identity, using a dataset of unicellular microbes (i.e., ciliates). Previously, morphological attributes and simple movement metrics, such as speed, were used for classifying ciliate species. Here, we demonstrate that adding advanced movement features, in particular such based on discrete wavelet transform, to morphological features can improve classification. These results may have practical applications in automated monitoring of waste water facilities as well as environmental monitoring of aquatic systems.

  18. Developing and Integrating Advanced Movement Features Improves Automated Classification of Ciliate Species.

    Directory of Open Access Journals (Sweden)

    Ali Soleymani

    Full Text Available Recent advances in tracking technologies such as GPS or video tracking systems describe the movement paths of individuals in unprecedented details and are increasingly used in different fields, including ecology. However, extracting information from raw movement data requires advanced analysis techniques, for instance to infer behaviors expressed during a certain period of the recorded trajectory, or gender or species identity in case data is obtained from remote tracking. In this paper, we address how different movement features affect the ability to automatically classify the species identity, using a dataset of unicellular microbes (i.e., ciliates. Previously, morphological attributes and simple movement metrics, such as speed, were used for classifying ciliate species. Here, we demonstrate that adding advanced movement features, in particular such based on discrete wavelet transform, to morphological features can improve classification. These results may have practical applications in automated monitoring of waste water facilities as well as environmental monitoring of aquatic systems.

  19. Automated Defect Classification (ADC) and Progression Monitoring (DPM) in wafer fab reticle requalification

    Science.gov (United States)

    Yen, T. H.; Lai, Rick; Tuo, Laurent C.; Tolani, Vikram; Chen, Dongxue; Hu, Peter; Yu, Jiao; Hwa, George; Zheng, Yan; Lakkapragada, Suresh; Wang, Kechang; Peng, Danping; Wang, Bill; Chiang, Kaiming

    2013-09-01

    As optical lithography continues to extend into low-k1 regime, resolution of mask patterns continue to diminish, and so do mask defect requirements due to increasing MEEF. Post-inspection, mask defects have traditionally been classified by operators manually based on visual review. This approach may have worked down to 65/55nm node layers. However, starting 45nm and smaller nodes, visually reviewing 50 to sometimes 100s of defects on masks with complex modelbased OPC, SRAF, and ILT geometries, is error-prone and takes up valuable inspection tool capacity. Both these shortcomings in manual defect review are overcome by adoption of the computational solution called Automated Defect Classification (ADC) wherein mask defects are accurately classified within seconds and consistent to guidelines used by production technicians and engineers.

  20. a Fully Automated Pipeline for Classification Tasks with AN Application to Remote Sensing

    Science.gov (United States)

    Suzuki, K.; Claesen, M.; Takeda, H.; De Moor, B.

    2016-06-01

    Nowadays deep learning has been intensively in spotlight owing to its great victories at major competitions, which undeservedly pushed `shallow' machine learning methods, relatively naive/handy algorithms commonly used by industrial engineers, to the background in spite of their facilities such as small requisite amount of time/dataset for training. We, with a practical point of view, utilized shallow learning algorithms to construct a learning pipeline such that operators can utilize machine learning without any special knowledge, expensive computation environment, and a large amount of labelled data. The proposed pipeline automates a whole classification process, namely feature-selection, weighting features and the selection of the most suitable classifier with optimized hyperparameters. The configuration facilitates particle swarm optimization, one of well-known metaheuristic algorithms for the sake of generally fast and fine optimization, which enables us not only to optimize (hyper)parameters but also to determine appropriate features/classifier to the problem, which has conventionally been a priori based on domain knowledge and remained untouched or dealt with naïve algorithms such as grid search. Through experiments with the MNIST and CIFAR-10 datasets, common datasets in computer vision field for character recognition and object recognition problems respectively, our automated learning approach provides high performance considering its simple setting (i.e. non-specialized setting depending on dataset), small amount of training data, and practical learning time. Moreover, compared to deep learning the performance stays robust without almost any modification even with a remote sensing object recognition problem, which in turn indicates that there is a high possibility that our approach contributes to general classification problems.

  1. Automated Analysis and Classification of Histological Tissue Features by Multi-Dimensional Microscopic Molecular Profiling.

    Directory of Open Access Journals (Sweden)

    Daniel P Riordan

    Full Text Available Characterization of the molecular attributes and spatial arrangements of cells and features within complex human tissues provides a critical basis for understanding processes involved in development and disease. Moreover, the ability to automate steps in the analysis and interpretation of histological images that currently require manual inspection by pathologists could revolutionize medical diagnostics. Toward this end, we developed a new imaging approach called multidimensional microscopic molecular profiling (MMMP that can measure several independent molecular properties in situ at subcellular resolution for the same tissue specimen. MMMP involves repeated cycles of antibody or histochemical staining, imaging, and signal removal, which ultimately can generate information analogous to a multidimensional flow cytometry analysis on intact tissue sections. We performed a MMMP analysis on a tissue microarray containing a diverse set of 102 human tissues using a panel of 15 informative antibody and 5 histochemical stains plus DAPI. Large-scale unsupervised analysis of MMMP data, and visualization of the resulting classifications, identified molecular profiles that were associated with functional tissue features. We then directly annotated H&E images from this MMMP series such that canonical histological features of interest (e.g. blood vessels, epithelium, red blood cells were individually labeled. By integrating image annotation data, we identified molecular signatures that were associated with specific histological annotations and we developed statistical models for automatically classifying these features. The classification accuracy for automated histology labeling was objectively evaluated using a cross-validation strategy, and significant accuracy (with a median per-pixel rate of 77% per feature from 15 annotated samples for de novo feature prediction was obtained. These results suggest that high-dimensional profiling may advance the

  2. Automated annotation and classification of BI-RADS assessment from radiology reports.

    Science.gov (United States)

    Castro, Sergio M; Tseytlin, Eugene; Medvedeva, Olga; Mitchell, Kevin; Visweswaran, Shyam; Bekhuis, Tanja; Jacobson, Rebecca S

    2017-05-01

    The Breast Imaging Reporting and Data System (BI-RADS) was developed to reduce variation in the descriptions of findings. Manual analysis of breast radiology report data is challenging but is necessary for clinical and healthcare quality assurance activities. The objective of this study is to develop a natural language processing (NLP) system for automated BI-RADS categories extraction from breast radiology reports. We evaluated an existing rule-based NLP algorithm, and then we developed and evaluated our own method using a supervised machine learning approach. We divided the BI-RADS category extraction task into two specific tasks: (1) annotation of all BI-RADS category values within a report, (2) classification of the laterality of each BI-RADS category value. We used one algorithm for task 1 and evaluated three algorithms for task 2. Across all evaluations and model training, we used a total of 2159 radiology reports from 18 hospitals, from 2003 to 2015. Performance with the existing rule-based algorithm was not satisfactory. Conditional random fields showed a high performance for task 1 with an F-1 measure of 0.95. Rules from partial decision trees (PART) algorithm showed the best performance across classes for task 2 with a weighted F-1 measure of 0.91 for BIRADS 0-6, and 0.93 for BIRADS 3-5. Classification performance by class showed that performance improved for all classes from Naïve Bayes to Support Vector Machine (SVM), and also from SVM to PART. Our system is able to annotate and classify all BI-RADS mentions present in a single radiology report and can serve as the foundation for future studies that will leverage automated BI-RADS annotation, to provide feedback to radiologists as part of a learning health system loop. Copyright © 2017. Published by Elsevier Inc.

  3. Automated classification of mammographic microcalcifications by using artificial neural networks and ACR BI-RADS criteria

    Science.gov (United States)

    Hara, Takeshi; Yamada, Akitsugu; Fujita, Hiroshi; Iwase, Takuji; Endo, Tokiko

    2001-07-01

    We have been developing an automated detection scheme for mammographic microcalcifications as a part of computer-assisted diagnosis (CAD) system. The purpose of this study is to develop an automated classification technique for the detected microcalcifications. Types of distributions of calcifications are known to be significantly relevant to their probability of malignancy, and are described on ACR BI-RADS (Breast Imaging Reporting and Data System) , in which five typical types are illustrated as diffuse/scattered, regional, segmental, linear and clustered. Detected microcalcifications by our CAD system are classified automatically into one of their five types based on shape of grouped microcalcifications and the number of microcalcifications within the grouped area. The type of distribution and other general image feature values are analyzed by artificial neural networks (ANNs) and the probability of malignancy is indicated. Eighty mammograms with biopsy-proven microcalcifications were employed and digitized with a laser scanner at a pixel size of 0.1mm and 12-bit density depth. The sensitivity and specificity were 93% and 93%, respectively. The performance was significantly improved in comparison with the case that the five criteria in BI-RADS were not employed.

  4. Automated classification of self-grooming in mice using open-source software.

    Science.gov (United States)

    van den Boom, Bastijn J G; Pavlidi, Pavlina; Wolf, Casper J H; Mooij, Adriana H; Willuhn, Ingo

    2017-09-01

    Manual analysis of behavior is labor intensive and subject to inter-rater variability. Although considerable progress in automation of analysis has been made, complex behavior such as grooming still lacks satisfactory automated quantification. We trained a freely available, automated classifier, Janelia Automatic Animal Behavior Annotator (JAABA), to quantify self-grooming duration and number of bouts based on video recordings of SAPAP3 knockout mice (a mouse line that self-grooms excessively) and wild-type animals. We compared the JAABA classifier with human expert observers to test its ability to measure self-grooming in three scenarios: mice in an open field, mice on an elevated plus-maze, and tethered mice in an open field. In each scenario, the classifier identified both grooming and non-grooming with great accuracy and correlated highly with results obtained by human observers. Consistently, the JAABA classifier confirmed previous reports of excessive grooming in SAPAP3 knockout mice. Thus far, manual analysis was regarded as the only valid quantification method for self-grooming. We demonstrate that the JAABA classifier is a valid and reliable scoring tool, more cost-efficient than manual scoring, easy to use, requires minimal effort, provides high throughput, and prevents inter-rater variability. We introduce the JAABA classifier as an efficient analysis tool for the assessment of rodent self-grooming with expert quality. In our "how-to" instructions, we provide all information necessary to implement behavioral classification with JAABA. Copyright © 2017 Elsevier B.V. All rights reserved.

  5. Automated Classification of Heritage Buildings for As-Built Bim Using Machine Learning Techniques

    Science.gov (United States)

    Bassier, M.; Vergauwen, M.; Van Genechten, B.

    2017-08-01

    Semantically rich three dimensional models such as Building Information Models (BIMs) are increasingly used in digital heritage. They provide the required information to varying stakeholders during the different stages of the historic buildings life cyle which is crucial in the conservation process. The creation of as-built BIM models is based on point cloud data. However, manually interpreting this data is labour intensive and often leads to misinterpretations. By automatically classifying the point cloud, the information can be proccesed more effeciently. A key aspect in this automated scan-to-BIM process is the classification of building objects. In this research we look to automatically recognise elements in existing buildings to create compact semantic information models. Our algorithm efficiently extracts the main structural components such as floors, ceilings, roofs, walls and beams despite the presence of significant clutter and occlusions. More specifically, Support Vector Machines (SVM) are proposed for the classification. The algorithm is evaluated using real data of a variety of existing buildings. The results prove that the used classifier recognizes the objects with both high precision and recall. As a result, entire data sets are reliably labelled at once. The approach enables experts to better document and process heritage assets.

  6. Automated Tongue Feature Extraction for ZHENG Classification in Traditional Chinese Medicine

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    Ratchadaporn Kanawong

    2012-01-01

    Full Text Available ZHENG, Traditional Chinese Medicine syndrome, is an integral and essential part of Traditional Chinese Medicine theory. It defines the theoretical abstraction of the symptom profiles of individual patients and thus, used as a guideline in disease classification in Chinese medicine. For example, patients suffering from gastritis may be classified as Cold or Hot ZHENG, whereas patients with different diseases may be classified under the same ZHENG. Tongue appearance is a valuable diagnostic tool for determining ZHENG in patients. In this paper, we explore new modalities for the clinical characterization of ZHENG using various supervised machine learning algorithms. We propose a novel-color-space-based feature set, which can be extracted from tongue images of clinical patients to build an automated ZHENG classification system. Given that Chinese medical practitioners usually observe the tongue color and coating to determine a ZHENG type and to diagnose different stomach disorders including gastritis, we propose using machine-learning techniques to establish the relationship between the tongue image features and ZHENG by learning through examples. The experimental results obtained over a set of 263 gastritis patients, most of whom suffering Cold Zheng or Hot ZHENG, and a control group of 48 healthy volunteers demonstrate an excellent performance of our proposed system.

  7. An Automated Artificial Neural Network System for Land Use/Land Cover Classification from Landsat TM Imagery

    Directory of Open Access Journals (Sweden)

    Siamak Khorram

    2009-07-01

    Full Text Available This paper focuses on an automated ANN classification system consisting of two modules: an unsupervised Kohonen’s Self-Organizing Mapping (SOM neural network module, and a supervised Multilayer Perceptron (MLP neural network module using the Backpropagation (BP training algorithm. Two training algorithms were provided for the SOM network module: the standard SOM, and a refined SOM learning algorithm which incorporated Simulated Annealing (SA. The ability of our automated ANN system to perform Land-Use/Land-Cover (LU/LC classifications of a Landsat Thematic Mapper (TM image was tested using a supervised MLP network, an unsupervised SOM network, and a combination of SOM with SA network. Our case study demonstrated that the ANN classification system fulfilled the tasks of network training pattern creation, network training, and network generalization. The results from the three networks were assessed via a comparison with reference data derived from the high spatial resolution Digital Colour Infrared (CIR Digital Orthophoto Quarter Quad (DOQQ data. The supervised MLP network obtained the most accurate classification accuracy as compared to the two unsupervised SOM networks. Additionally, the classification performance of the refined SOM network was found to be significantly better than that of the standard SOM network essentially due to the incorporation of SA. This is mainly due to the SA-assisted classification utilizing the scheduling cooling scheme. It is concluded that our automated ANN classification system can be utilized for LU/LC applications and will be particularly useful when traditional statistical classification methods are not suitable due to a statistically abnormal distribution of the input data.

  8. Automated Segmentation and Classification of Coral using Fluid Lensing from Unmanned Airborne Platforms

    Science.gov (United States)

    Instrella, R.; Chirayath, V.

    2015-12-01

    In recent years, there has been a growing interest among biologists in monitoring the short and long term health of the world's coral reefs. The environmental impact of climate change poses a growing threat to these biologically diverse and fragile ecosystems, prompting scientists to use remote sensing platforms and computer vision algorithms to analyze shallow marine systems. In this study, we present a novel method for performing coral segmentation and classification from aerial data collected from small unmanned aerial vehicles (sUAV). Our method uses Fluid Lensing algorithms to remove and exploit strong optical distortions created along the air-fluid boundary to produce cm-scale resolution imagery of the ocean floor at depths up to 5 meters. A 3D model of the reef is reconstructed using structure from motion (SFM) algorithms, and the associated depth information is combined with multidimensional maximum a posteriori (MAP) estimation to separate organic from inorganic material and classify coral morphologies in the Fluid-Lensed transects. In this study, MAP estimation is performed using a set of manually classified 100 x 100 pixel training images to determine the most probable coral classification within an interrogated region of interest. Aerial footage of a coral reef was captured off the coast of American Samoa and used to test our proposed method. 90 x 20 meter transects of the Samoan coastline undergo automated classification and are manually segmented by a marine biologist for comparison, leading to success rates as high as 85%. This method has broad applications for coastal remote sensing, and will provide marine biologists access to large swaths of high resolution, segmented coral imagery.

  9. Automated Segmentation and Classification of Coral using Fluid Lensing from Unmanned Airborne Platforms

    Science.gov (United States)

    Instrella, Ron; Chirayath, Ved

    2016-01-01

    In recent years, there has been a growing interest among biologists in monitoring the short and long term health of the world's coral reefs. The environmental impact of climate change poses a growing threat to these biologically diverse and fragile ecosystems, prompting scientists to use remote sensing platforms and computer vision algorithms to analyze shallow marine systems. In this study, we present a novel method for performing coral segmentation and classification from aerial data collected from small unmanned aerial vehicles (sUAV). Our method uses Fluid Lensing algorithms to remove and exploit strong optical distortions created along the air-fluid boundary to produce cm-scale resolution imagery of the ocean floor at depths up to 5 meters. A 3D model of the reef is reconstructed using structure from motion (SFM) algorithms, and the associated depth information is combined with multidimensional maximum a posteriori (MAP) estimation to separate organic from inorganic material and classify coral morphologies in the Fluid-Lensed transects. In this study, MAP estimation is performed using a set of manually classified 100 x 100 pixel training images to determine the most probable coral classification within an interrogated region of interest. Aerial footage of a coral reef was captured off the coast of American Samoa and used to test our proposed method. 90 x 20 meter transects of the Samoan coastline undergo automated classification and are manually segmented by a marine biologist for comparison, leading to success rates as high as 85%. This method has broad applications for coastal remote sensing, and will provide marine biologists access to large swaths of high resolution, segmented coral imagery.

  10. Automated classification of tropical shrub species: a hybrid of leaf shape and machine learning approach

    Directory of Open Access Journals (Sweden)

    Miraemiliana Murat

    2017-09-01

    Full Text Available Plants play a crucial role in foodstuff, medicine, industry, and environmental protection. The skill of recognising plants is very important in some applications, including conservation of endangered species and rehabilitation of lands after mining activities. However, it is a difficult task to identify plant species because it requires specialized knowledge. Developing an automated classification system for plant species is necessary and valuable since it can help specialists as well as the public in identifying plant species easily. Shape descriptors were applied on the myDAUN dataset that contains 45 tropical shrub species collected from the University of Malaya (UM, Malaysia. Based on literature review, this is the first study in the development of tropical shrub species image dataset and classification using a hybrid of leaf shape and machine learning approach. Four types of shape descriptors were used in this study namely morphological shape descriptors (MSD, Histogram of Oriented Gradients (HOG, Hu invariant moments (Hu and Zernike moments (ZM. Single descriptor, as well as the combination of hybrid descriptors were tested and compared. The tropical shrub species are classified using six different classifiers, which are artificial neural network (ANN, random forest (RF, support vector machine (SVM, k-nearest neighbour (k-NN, linear discriminant analysis (LDA and directed acyclic graph multiclass least squares twin support vector machine (DAG MLSTSVM. In addition, three types of feature selection methods were tested in the myDAUN dataset, Relief, Correlation-based feature selection (CFS and Pearson’s coefficient correlation (PCC. The well-known Flavia dataset and Swedish Leaf dataset were used as the validation dataset on the proposed methods. The results showed that the hybrid of all descriptors of ANN outperformed the other classifiers with an average classification accuracy of 98.23% for the myDAUN dataset, 95.25% for the Flavia

  11. Entropy-Based Privacy against Profiling of User Mobility

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    Alicia Rodriguez-Carrion

    2015-06-01

    Full Text Available Location-based services (LBSs flood mobile phones nowadays, but their use poses an evident privacy risk. The locations accompanying the LBS queries can be exploited by the LBS provider to build the user profile of visited locations, which might disclose sensitive data, such as work or home locations. The classic concept of entropy is widely used to evaluate privacy in these scenarios, where the information is represented as a sequence of independent samples of categorized data. However, since the LBS queries might be sent very frequently, location profiles can be improved by adding temporal dependencies, thus becoming mobility profiles, where location samples are not independent anymore and might disclose the user’s mobility patterns. Since the time dimension is factored in, the classic entropy concept falls short of evaluating the real privacy level, which depends also on the time component. Therefore, we propose to extend the entropy-based privacy metric to the use of the entropy rate to evaluate mobility profiles. Then, two perturbative mechanisms are considered to preserve locations and mobility profiles under gradual utility constraints. We further use the proposed privacy metric and compare it to classic ones to evaluate both synthetic and real mobility profiles when the perturbative methods proposed are applied. The results prove the usefulness of the proposed metric for mobility profiles and the need for tailoring the perturbative methods to the features of mobility profiles in order to improve privacy without completely loosing utility.

  12. Entropy-based particle correspondence for shape populations.

    Science.gov (United States)

    Oguz, Ipek; Cates, Josh; Datar, Manasi; Paniagua, Beatriz; Fletcher, Thomas; Vachet, Clement; Styner, Martin; Whitaker, Ross

    2016-07-01

    Statistical shape analysis of anatomical structures plays an important role in many medical image analysis applications such as understanding the structural changes in anatomy in various stages of growth or disease. Establishing accurate correspondence across object populations is essential for such statistical shape analysis studies. In this paper, we present an entropy-based correspondence framework for computing point-based correspondence among populations of surfaces in a groupwise manner. This robust framework is parameterization-free and computationally efficient. We review the core principles of this method as well as various extensions to deal effectively with surfaces of complex geometry and application-driven correspondence metrics. We apply our method to synthetic and biological datasets to illustrate the concepts proposed and compare the performance of our framework to existing techniques. Through the numerous extensions and variations presented here, we create a very flexible framework that can effectively handle objects of various topologies, multi-object complexes, open surfaces, and objects of complex geometry such as high-curvature regions or extremely thin features.

  13. An Automated Algorithm to Screen Massive Training Samples for a Global Impervious Surface Classification

    Science.gov (United States)

    Tan, Bin; Brown de Colstoun, Eric; Wolfe, Robert E.; Tilton, James C.; Huang, Chengquan; Smith, Sarah E.

    2012-01-01

    An algorithm is developed to automatically screen the outliers from massive training samples for Global Land Survey - Imperviousness Mapping Project (GLS-IMP). GLS-IMP is to produce a global 30 m spatial resolution impervious cover data set for years 2000 and 2010 based on the Landsat Global Land Survey (GLS) data set. This unprecedented high resolution impervious cover data set is not only significant to the urbanization studies but also desired by the global carbon, hydrology, and energy balance researches. A supervised classification method, regression tree, is applied in this project. A set of accurate training samples is the key to the supervised classifications. Here we developed the global scale training samples from 1 m or so resolution fine resolution satellite data (Quickbird and Worldview2), and then aggregate the fine resolution impervious cover map to 30 m resolution. In order to improve the classification accuracy, the training samples should be screened before used to train the regression tree. It is impossible to manually screen 30 m resolution training samples collected globally. For example, in Europe only, there are 174 training sites. The size of the sites ranges from 4.5 km by 4.5 km to 8.1 km by 3.6 km. The amount training samples are over six millions. Therefore, we develop this automated statistic based algorithm to screen the training samples in two levels: site and scene level. At the site level, all the training samples are divided to 10 groups according to the percentage of the impervious surface within a sample pixel. The samples following in each 10% forms one group. For each group, both univariate and multivariate outliers are detected and removed. Then the screen process escalates to the scene level. A similar screen process but with a looser threshold is applied on the scene level considering the possible variance due to the site difference. We do not perform the screen process across the scenes because the scenes might vary due to

  14. Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach.

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    Andre F Marquand

    Full Text Available Progressive supranuclear palsy (PSP, multiple system atrophy (MSA and idiopathic Parkinson's disease (IPD can be clinically indistinguishable, especially in the early stages, despite distinct patterns of molecular pathology. Structural neuroimaging holds promise for providing objective biomarkers for discriminating these diseases at the single subject level but all studies to date have reported incomplete separation of disease groups. In this study, we employed multi-class pattern recognition to assess the value of anatomical patterns derived from a widely available structural neuroimaging sequence for automated classification of these disorders. To achieve this, 17 patients with PSP, 14 with IPD and 19 with MSA were scanned using structural MRI along with 19 healthy controls (HCs. An advanced probabilistic pattern recognition approach was employed to evaluate the diagnostic value of several pre-defined anatomical patterns for discriminating the disorders, including: (i a subcortical motor network; (ii each of its component regions and (iii the whole brain. All disease groups could be discriminated simultaneously with high accuracy using the subcortical motor network. The region providing the most accurate predictions overall was the midbrain/brainstem, which discriminated all disease groups from one another and from HCs. The subcortical network also produced more accurate predictions than the whole brain and all of its constituent regions. PSP was accurately predicted from the midbrain/brainstem, cerebellum and all basal ganglia compartments; MSA from the midbrain/brainstem and cerebellum and IPD from the midbrain/brainstem only. This study demonstrates that automated analysis of structural MRI can accurately predict diagnosis in individual patients with Parkinsonian disorders, and identifies distinct patterns of regional atrophy particularly useful for this process.

  15. Interpreting complex data by methods of recognition and classification in an automated system of aerogeophysical material processing

    Energy Technology Data Exchange (ETDEWEB)

    Koval' , L.A.; Dolgov, S.V.; Liokumovich, G.B.; Ovcharenko, A.V.; Priyezzhev, I.I.

    1984-01-01

    The system of automated processing of aerogeophysical data, ASOM-AGS/YeS, is equipped with complex interpretation of multichannel measurements. Algorithms of factor analysis, automatic classification and apparatus of a priori specified (selected) decisive rules are used. The areas of effect of these procedures can be initially limited to the specified geological information. The possibilities of the method are demonstrated by the results of automated processing of the aerogram-spectrometric measurements in the region of the known copper-porphyr manifestation in Kazakhstan. This ore deposit was clearly noted after processing by the method of main components by complex aureole of independent factors U (severe increase), Th (noticeable increase), K (decrease).

  16. Increasing reticle inspection efficiency and reducing wafer print-checks using automated defect classification and simulation

    Science.gov (United States)

    Ryu, Sung Jae; Lim, Sung Taek; Vacca, Anthony; Fiekowsky, Peter; Fiekowsky, Dan

    2013-09-01

    IC fabs inspect critical masks on a regular basis to ensure high wafer yields. These requalification inspections are costly for many reasons including the capital equipment, system maintenance, and labor costs. In addition, masks typically remain in the "requal" phase for extended, non-productive periods of time. The overall "requal" cycle time in which reticles remain non-productive is challenging to control. Shipping schedules can slip when wafer lots are put on hold until the master critical layer reticle is returned to production. Unfortunately, substituting backup critical layer reticles can significantly reduce an otherwise tightly controlled process window adversely affecting wafer yields. One major requal cycle time component is the disposition process of mask inspections containing hundreds of defects. Not only is precious non-productive time extended by reviewing hundreds of potentially yield-limiting detections, each additional classification increases the risk of manual review techniques accidentally passing real yield limiting defects. Even assuming all defects of interest are flagged by operators, how can any person's judgment be confident regarding lithographic impact of such defects? The time reticles spend away from scanners combined with potential yield loss due to lithographic uncertainty presents significant cycle time loss and increased production costs. Fortunately, a software program has been developed which automates defect classification with simulated printability measurement greatly reducing requal cycle time and improving overall disposition accuracy. This product, called ADAS (Auto Defect Analysis System), has been tested in both engineering and high-volume production environments with very successful results. In this paper, data is presented supporting significant reduction for costly wafer print checks, improved inspection area productivity, and minimized risk of misclassified yield limiting defects.

  17. Automated classification of radiology reports to facilitate retrospective study in radiology.

    Science.gov (United States)

    Zhou, Yihua; Amundson, Per K; Yu, Fang; Kessler, Marcus M; Benzinger, Tammie L S; Wippold, Franz J

    2014-12-01

    Retrospective research is an import tool in radiology. Identifying imaging examinations appropriate for a given research question from the unstructured radiology reports is extremely useful, but labor-intensive. Using the machine learning text-mining methods implemented in LingPipe [1], we evaluated the performance of the dynamic language model (DLM) and the Naïve Bayesian (NB) classifiers in classifying radiology reports to facilitate identification of radiological examinations for research projects. The training dataset consisted of 14,325 sentences from 11,432 radiology reports randomly selected from a database of 5,104,594 reports in all disciplines of radiology. The training sentences were categorized manually into six categories (Positive, Differential, Post Treatment, Negative, Normal, and History). A 10-fold cross-validation [2] was used to evaluate the performance of the models, which were tested in classification of radiology reports for cases of sellar or suprasellar masses and colloid cysts. The average accuracies for the DLM and NB classifiers were 88.5% with 95% confidence interval (CI) of 1.9% and 85.9% with 95% CI of 2.0%, respectively. The DLM performed slightly better and was used to classify 1,397 radiology reports containing the keywords "sellar or suprasellar mass", or "colloid cyst". The DLM model produced an accuracy of 88.2% with 95% CI of 2.1% for 959 reports that contain "sellar or suprasellar mass" and an accuracy of 86.3% with 95% CI of 2.5% for 437 reports of "colloid cyst". We conclude that automated classification of radiology reports using machine learning techniques can effectively facilitate the identification of cases suitable for retrospective research.

  18. A multiresolution approach to automated classification of protein subcellular location images

    Directory of Open Access Journals (Sweden)

    Srinivasa Gowri

    2007-06-01

    Full Text Available Abstract Background Fluorescence microscopy is widely used to determine the subcellular location of proteins. Efforts to determine location on a proteome-wide basis create a need for automated methods to analyze the resulting images. Over the past ten years, the feasibility of using machine learning methods to recognize all major subcellular location patterns has been convincingly demonstrated, using diverse feature sets and classifiers. On a well-studied data set of 2D HeLa single-cell images, the best performance to date, 91.5%, was obtained by including a set of multiresolution features. This demonstrates the value of multiresolution approaches to this important problem. Results We report here a novel approach for the classification of subcellular location patterns by classifying in multiresolution subspaces. Our system is able to work with any feature set and any classifier. It consists of multiresolution (MR decomposition, followed by feature computation and classification in each MR subspace, yielding local decisions that are then combined into a global decision. With 26 texture features alone and a neural network classifier, we obtained an increase in accuracy on the 2D HeLa data set to 95.3%. Conclusion We demonstrate that the space-frequency localized information in the multiresolution subspaces adds significantly to the discriminative power of the system. Moreover, we show that a vastly reduced set of features is sufficient, consisting of our novel modified Haralick texture features. Our proposed system is general, allowing for any combinations of sets of features and any combination of classifiers.

  19. Automating the Identification of Patient Safety Incident Reports Using Multi-Label Classification.

    Science.gov (United States)

    Wang, Ying; Coiera, Enrico; Runciman, William; Magrabi, Farah

    2017-01-01

    Automated identification provides an efficient way to categorize patient safety incidents. Previous studies have focused on identifying single incident types relating to a specific patient safety problem, e.g., clinical handover. In reality, there are multiple types of incidents reflecting the breadth of patient safety problems and a single report may describe multiple problems, i.e., it can be assigned multiple type labels. This study evaluated the abilty of multi-label classification methods to identify multiple incident types in single reports. Three multi-label methods were evaluated: binary relevance, classifier chains and ensemble of classifier chains. We found that an ensemble of classifier chains was the most effective method using binary Support Vector Machines with radial basis function kernel and bag-of-words feature extraction, performing equally well on balanced and stratified datasets, (F-score: 73.7% vs. 74.7%). Classifiers were able to identify six common incident types: falls, medications, pressure injury, aggression, documentation problems and others.

  20. Automated segmentation of geographic atrophy in fundus autofluorescence images using supervised pixel classification.

    Science.gov (United States)

    Hu, Zhihong; Medioni, Gerard G; Hernandez, Matthias; Sadda, Srinivas R

    2015-01-01

    Geographic atrophy (GA) is a manifestation of the advanced or late stage of age-related macular degeneration (AMD). AMD is the leading cause of blindness in people over the age of 65 in the western world. The purpose of this study is to develop a fully automated supervised pixel classification approach for segmenting GA, including uni- and multifocal patches in fundus autofluorescene (FAF) images. The image features include region-wise intensity measures, gray-level co-occurrence matrix measures, and Gaussian filter banks. A [Formula: see text]-nearest-neighbor pixel classifier is applied to obtain a GA probability map, representing the likelihood that the image pixel belongs to GA. Sixteen randomly chosen FAF images were obtained from 16 subjects with GA. The algorithm-defined GA regions are compared with manual delineation performed by a certified image reading center grader. Eight-fold cross-validation is applied to evaluate the algorithm performance. The mean overlap ratio (OR), area correlation (Pearson's [Formula: see text]), accuracy (ACC), true positive rate (TPR), specificity (SPC), positive predictive value (PPV), and false discovery rate (FDR) between the algorithm- and manually defined GA regions are [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], respectively.

  1. Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm

    Science.gov (United States)

    Wu, Zhuoting; Thenkabail, Prasad S.; Mueller, Rick; Zakzeski, Audra; Melton, Forrest; Johnson, Lee; Rosevelt, Carolyn; Dwyer, John; Jones, Jeanine; Verdin, James P.

    2014-01-01

    Increasing drought occurrences and growing populations demand accurate, routine, and consistent cultivated and fallow cropland products to enable water and food security analysis. The overarching goal of this research was to develop and test automated cropland classification algorithm (ACCA) that provide accurate, consistent, and repeatable information on seasonal cultivated as well as seasonal fallow cropland extents and areas based on the Moderate Resolution Imaging Spectroradiometer remote sensing data. Seasonal ACCA development process involves writing series of iterative decision tree codes to separate cultivated and fallow croplands from noncroplands, aiming to accurately mirror reliable reference data sources. A pixel-by-pixel accuracy assessment when compared with the U.S. Department of Agriculture (USDA) cropland data showed, on average, a producer’s accuracy of 93% and a user’s accuracy of 85% across all months. Further, ACCA-derived cropland maps agreed well with the USDA Farm Service Agency crop acreage-reported data for both cultivated and fallow croplands with R-square values over 0.7 and field surveys with an accuracy of ≥95% for cultivated croplands and ≥76% for fallow croplands. Our results demonstrated the ability of ACCA to generate cropland products, such as cultivated and fallow cropland extents and areas, accurately, automatically, and repeatedly throughout the growing season.

  2. Deep SOMs for automated feature extraction and classification from big data streaming

    Science.gov (United States)

    Sakkari, Mohamed; Ejbali, Ridha; Zaied, Mourad

    2017-03-01

    In this paper, we proposed a deep self-organizing map model (Deep-SOMs) for automated features extracting and learning from big data streaming which we benefit from the framework Spark for real time streams and highly parallel data processing. The SOMs deep architecture is based on the notion of abstraction (patterns automatically extract from the raw data, from the less to more abstract). The proposed model consists of three hidden self-organizing layers, an input and an output layer. Each layer is made up of a multitude of SOMs, each map only focusing at local headmistress sub-region from the input image. Then, each layer trains the local information to generate more overall information in the higher layer. The proposed Deep-SOMs model is unique in terms of the layers architecture, the SOMs sampling method and learning. During the learning stage we use a set of unsupervised SOMs for feature extraction. We validate the effectiveness of our approach on large data sets such as Leukemia dataset and SRBCT. Results of comparison have shown that the Deep-SOMs model performs better than many existing algorithms for images classification.

  3. Classification of Acute Decompensated Heart Failure (ADHF): An Automated Algorithm Compared to a Physician Reviewer Panel: The ARIC Study

    Science.gov (United States)

    Loehr, Laura R.; Agarwal, Sunil K.; Baggett, Chris; Wruck, Lisa M.; Chang, Patricia P.; Solomon, Scott D.; Shahar, Eyal; Ni, Hanyu; Rosamond, Wayne D.; Heiss, Gerardo

    2013-01-01

    Background An algorithm to classify heart failure (HF) endpoints inclusive of contemporary measures of biomarkers and echocardiography was recently proposed by an international expert panel. Our objective was to assess agreement of HF classification by this contemporaneous algorithm with that by a standardized physician reviewer panel, when applied to data abstracted from community-based hospital records. Methods and Results During 2005-2007, all hospitalizations were identified from four U.S. communities under surveillance as part of the Atherosclerosis Risk in Communities (ARIC) study. Potential HF hospitalizations were sampled by ICD discharge codes and demographics from men and women aged 55 years and older. The HF classification algorithm was automated and applied to 2,729 (N=13,854 weighted hospitalizations) hospitalizations in which either BNP measures or ejection fraction were documented (mean age 75 years). There were 1,403 (54%, N=7,534 weighted) events classified as acute, decompensated HF (ADHF) by the automated algorithm, and 1,748 (68%, N=9,276 weighted) such events by the ARIC reviewer panel. The chance-corrected agreement between ADHF by physician reviewer panel and the automated algorithm was moderate (Kappa=0.39). Sensitivity and specificity of the automated algorithm with ARIC reviewer panel as the referent standard was 0.68 (95% CI, 0.67 - 0.69), and 0.75 (95% CI, 0.74 - 0.76), respectively. Conclusions Although the automated classification improved efficiency and decreased costs, its accuracy in classifying HF hospitalizations was modest compared to a standardized physician reviewer panel. PMID:23650310

  4. Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy

    OpenAIRE

    Welikala, R; Fraz, M; Dehmeshki, J; Hoppe, A; Tah, V; Mann, S; Williamson, T H; Barman, S A

    2015-01-01

    Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is the growth of abnormal new vessels. In this paper, an automated method for the detection of new vessels from retinal images is presented. This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel map which each hold vital information. Local morphology features are measured from eac...

  5. Entropy-based probabilistic fatigue damage prognosis and algorithmic performance comparison

    Data.gov (United States)

    National Aeronautics and Space Administration — In this paper, a maximum entropy-based general framework for probabilistic fatigue damage prognosis is investigated. The proposed methodology is based on an...

  6. Entropy-based Probabilistic Fatigue Damage Prognosis and Algorithmic Performance Comparison

    Data.gov (United States)

    National Aeronautics and Space Administration — In this paper, a maximum entropy-based general framework for probabilistic fatigue damage prognosis is investigated. The proposed methodology is based on an...

  7. Automated classification of Permanent Scatterers time-series based on statistical characterization tests

    Science.gov (United States)

    Berti, Matteo; Corsini, Alessandro; Franceschini, Silvia; Iannacone, Jean Pascal

    2013-04-01

    time series are typically affected by a significant noise to signal ratio. The results of the analysis show that even with such a rough-quality dataset, our automated classification procedure can greatly improve radar interpretation of mass movements. In general, uncorrelated PS (type 0) are concentrated in flat areas such as fluvial terraces and valley bottoms, and along stable watershed divides; linear PS (type 1) are mainly located on slopes (both inside or outside mapped landslides) or near the edge of scarps or steep slopes; non-linear PS (types 2 to 5) typically fall inside landslide deposits or in the surrounding areas. The spatial distribution of classified PS allows to detect deformation phenomena not visible by considering the average velocity alone, and provide important information on the temporal evolution of the phenomena such as acceleration, deceleration, seasonal fluctuations, abrupt or continuous changes of the displacement rate. Based on these encouraging results we integrated all the classification algorithms into a Graphical User Interface (called PSTime) which is freely available as a standalone application.

  8. CONSTRUCTION OF A CALIBRATED PROBABILISTIC CLASSIFICATION CATALOG: APPLICATION TO 50k VARIABLE SOURCES IN THE ALL-SKY AUTOMATED SURVEY

    International Nuclear Information System (INIS)

    Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.; Bloom, Joshua S.; Brink, Henrik; Crellin-Quick, Arien; Butler, Nathaniel R.

    2012-01-01

    With growing data volumes from synoptic surveys, astronomers necessarily must become more abstracted from the discovery and introspection processes. Given the scarcity of follow-up resources, there is a particularly sharp onus on the frameworks that replace these human roles to provide accurate and well-calibrated probabilistic classification catalogs. Such catalogs inform the subsequent follow-up, allowing consumers to optimize the selection of specific sources for further study and permitting rigorous treatment of classification purities and efficiencies for population studies. Here, we describe a process to produce a probabilistic classification catalog of variability with machine learning from a multi-epoch photometric survey. In addition to producing accurate classifications, we show how to estimate calibrated class probabilities and motivate the importance of probability calibration. We also introduce a methodology for feature-based anomaly detection, which allows discovery of objects in the survey that do not fit within the predefined class taxonomy. Finally, we apply these methods to sources observed by the All-Sky Automated Survey (ASAS), and release the Machine-learned ASAS Classification Catalog (MACC), a 28 class probabilistic classification catalog of 50,124 ASAS sources in the ASAS Catalog of Variable Stars. We estimate that MACC achieves a sub-20% classification error rate and demonstrate that the class posterior probabilities are reasonably calibrated. MACC classifications compare favorably to the classifications of several previous domain-specific ASAS papers and to the ASAS Catalog of Variable Stars, which had classified only 24% of those sources into one of 12 science classes.

  9. CONSTRUCTION OF A CALIBRATED PROBABILISTIC CLASSIFICATION CATALOG: APPLICATION TO 50k VARIABLE SOURCES IN THE ALL-SKY AUTOMATED SURVEY

    Energy Technology Data Exchange (ETDEWEB)

    Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.; Bloom, Joshua S.; Brink, Henrik; Crellin-Quick, Arien [Astronomy Department, University of California, Berkeley, CA 94720-3411 (United States); Butler, Nathaniel R., E-mail: jwrichar@stat.berkeley.edu [School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85287 (United States)

    2012-12-15

    With growing data volumes from synoptic surveys, astronomers necessarily must become more abstracted from the discovery and introspection processes. Given the scarcity of follow-up resources, there is a particularly sharp onus on the frameworks that replace these human roles to provide accurate and well-calibrated probabilistic classification catalogs. Such catalogs inform the subsequent follow-up, allowing consumers to optimize the selection of specific sources for further study and permitting rigorous treatment of classification purities and efficiencies for population studies. Here, we describe a process to produce a probabilistic classification catalog of variability with machine learning from a multi-epoch photometric survey. In addition to producing accurate classifications, we show how to estimate calibrated class probabilities and motivate the importance of probability calibration. We also introduce a methodology for feature-based anomaly detection, which allows discovery of objects in the survey that do not fit within the predefined class taxonomy. Finally, we apply these methods to sources observed by the All-Sky Automated Survey (ASAS), and release the Machine-learned ASAS Classification Catalog (MACC), a 28 class probabilistic classification catalog of 50,124 ASAS sources in the ASAS Catalog of Variable Stars. We estimate that MACC achieves a sub-20% classification error rate and demonstrate that the class posterior probabilities are reasonably calibrated. MACC classifications compare favorably to the classifications of several previous domain-specific ASAS papers and to the ASAS Catalog of Variable Stars, which had classified only 24% of those sources into one of 12 science classes.

  10. Studying the potential impact of automated document classification on scheduling a systematic review update

    Science.gov (United States)

    2012-01-01

    Background Systematic Reviews (SRs) are an essential part of evidence-based medicine, providing support for clinical practice and policy on a wide range of medical topics. However, producing SRs is resource-intensive, and progress in the research they review leads to SRs becoming outdated, requiring updates. Although the question of how and when to update SRs has been studied, the best method for determining when to update is still unclear, necessitating further research. Methods In this work we study the potential impact of a machine learning-based automated system for providing alerts when new publications become available within an SR topic. Some of these new publications are especially important, as they report findings that are more likely to initiate a review update. To this end, we have designed a classification algorithm to identify articles that are likely to be included in an SR update, along with an annotation scheme designed to identify the most important publications in a topic area. Using an SR database containing over 70,000 articles, we annotated articles from 9 topics that had received an update during the study period. The algorithm was then evaluated in terms of the overall correct and incorrect alert rate for publications meeting the topic inclusion criteria, as well as in terms of its ability to identify important, update-motivating publications in a topic area. Results Our initial approach, based on our previous work in topic-specific SR publication classification, identifies over 70% of the most important new publications, while maintaining a low overall alert rate. Conclusions We performed an initial analysis of the opportunities and challenges in aiding the SR update planning process with an informatics-based machine learning approach. Alerts could be a useful tool in the planning, scheduling, and allocation of resources for SR updates, providing an improvement in timeliness and coverage for the large number of medical topics needing SRs

  11. Automated classification of immunostaining patterns in breast tissue from the human protein Atlas

    Directory of Open Access Journals (Sweden)

    Issac Niwas Swamidoss

    2013-01-01

    Full Text Available Background: The Human Protein Atlas (HPA is an effort to map the location of all human proteins (http://www.proteinatlas.org/. It contains a large number of histological images of sections from human tissue. Tissue micro arrays (TMA are imaged by a slide scanning microscope, and each image represents a thin slice of a tissue core with a dark brown antibody specific stain and a blue counter stain. When generating antibodies for protein profiling of the human proteome, an important step in the quality control is to compare staining patterns of different antibodies directed towards the same protein. This comparison is an ultimate control that the antibody recognizes the right protein. In this paper, we propose and evaluate different approaches for classifying sub-cellular antibody staining patterns in breast tissue samples. Materials and Methods: The proposed methods include the computation of various features including gray level co-occurrence matrix (GLCM features, complex wavelet co-occurrence matrix (CWCM features, and weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHARM-inspired features. The extracted features are used into two different multivariate classifiers (support vector machine (SVM and linear discriminant analysis (LDA classifier. Before extracting features, we use color deconvolution to separate different tissue components, such as the brownly stained positive regions and the blue cellular regions, in the immuno-stained TMA images of breast tissue. Results: We present classification results based on combinations of feature measurements. The proposed complex wavelet features and the WND-CHARM features have accuracy similar to that of a human expert. Conclusions: Both human experts and the proposed automated methods have difficulties discriminating between nuclear and cytoplasmic staining patterns. This is to a large extent due to mixed staining of nucleus and cytoplasm. Methods for

  12. Automated classification of free-text pathology reports for registration of incident cases of cancer.

    Science.gov (United States)

    Jouhet, V; Defossez, G; Burgun, A; le Beux, P; Levillain, P; Ingrand, P; Claveau, V

    2012-01-01

    Our study aimed to construct and evaluate functions called "classifiers", produced by supervised machine learning techniques, in order to categorize automatically pathology reports using solely their content. Patients from the Poitou-Charentes Cancer Registry having at least one pathology report and a single non-metastatic invasive neoplasm were included. A descriptor weighting function accounting for the distribution of terms among targeted classes was developed and compared to classic methods based on inverse document frequencies. The classification was performed with support vector machine (SVM) and Naive Bayes classifiers. Two levels of granularity were tested for both the topographical and the morphological axes of the ICD-O3 code. The ability to correctly attribute a precise ICD-O3 code and the ability to attribute the broad category defined by the International Agency for Research on Cancer (IARC) for the multiple primary cancer registration rules were evaluated using F1-measures. 5121 pathology reports produced by 35 pathologists were selected. The best performance was achieved by our class-weighted descriptor, associated with a SVM classifier. Using this method, the pathology reports were properly classified in the IARC categories with F1-measures of 0.967 for both topography and morphology. The ICD-O3 code attribution had lower performance with a 0.715 F1-measure for topography and 0.854 for morphology. These results suggest that free-text pathology reports could be useful as a data source for automated systems in order to identify and notify new cases of cancer. Future work is needed to evaluate the improvement in performance obtained from the use of natural language processing, including the case of multiple tumor description and possible incorporation of other medical documents such as surgical reports.

  13. Rapid Classification of Landsat TM Imagery for Phase 1 Stratification Using the Automated NDVI Threshold Supervised Classification (ANTSC) Methodology

    Science.gov (United States)

    William H. Cooke; Dennis M. Jacobs

    2005-01-01

    FIA annual inventories require rapid updating of pixel-based Phase 1 estimates. Scientists at the Southern Research Station are developing an automated methodology that uses a Normalized Difference Vegetation Index (NDVI) for identifying and eliminating problem FIA plots from the analysis. Problem plots are those that have questionable land use/land cover information....

  14. Automated classification of mouse pup isolation syllables: from cluster analysis to an Excel based ‘mouse pup syllable classification calculator’

    Directory of Open Access Journals (Sweden)

    Jasmine eGrimsley

    2013-01-01

    Full Text Available Mouse pups vocalize at high rates when they are cold or isolated from the nest. The proportions of each syllable type produced carry information about disease state and are being used as behavioral markers for the internal state of animals. Manual classifications of these vocalizations identified ten syllable types based on their spectro-temporal features. However, manual classification of mouse syllables is time consuming and vulnerable to experimenter bias. This study uses an automated cluster analysis to identify acoustically distinct syllable types produced by CBA/CaJ mouse pups, and then compares the results to prior manual classification methods. The cluster analysis identified two syllable types, based on their frequency bands, that have continuous frequency-time structure, and two syllable types featuring abrupt frequency transitions. Although cluster analysis computed fewer syllable types than manual classification, the clusters represented well the probability distributions of the acoustic features within syllables. These probability distributions indicate that some of the manually classified syllable types are not statistically distinct. The characteristics of the four classified clusters were used to generate a Microsoft Excel-based mouse syllable classifier that rapidly categorizes syllables, with over a 90% match, into the syllable types determined by cluster analysis.

  15. Automated classification of self-grooming in mice using open-source software

    NARCIS (Netherlands)

    Van den Boom, B.; Pavlidi, Pavlina; Wolf, Casper M H; Mooij, Hanne A H; Willuhn, Ingo

    BACKGROUND: Manual analysis of behavior is labor intensive and subject to inter-rater variability. Although considerable progress in automation of analysis has been made, complex behavior such as grooming still lacks satisfactory automated quantification. NEW METHOD: We trained a freely available,

  16. Automated classification of self-grooming in mice using open-source software

    NARCIS (Netherlands)

    van den Boom, Bastijn J. G.; Pavlidi, Pavlina; Wolf, Casper M. H.; Mooij, Hanne A. H.; Willuhn, Ingo

    2017-01-01

    Background: Manual analysis of behavior is labor intensive and subject to inter-rater variability. Although considerable progress in automation of analysis has been made, complex behavior such as grooming still lacks satisfactory automated quantification. New method: We trained a freely available,

  17. Vertebral Body Compression Fractures and Bone Density: Automated Detection and Classification on CT Images.

    Science.gov (United States)

    Burns, Joseph E; Yao, Jianhua; Summers, Ronald M

    2017-09-01

    Purpose To create and validate a computer system with which to detect, localize, and classify compression fractures and measure bone density of thoracic and lumbar vertebral bodies on computed tomographic (CT) images. Materials and Methods Institutional review board approval was obtained, and informed consent was waived in this HIPAA-compliant retrospective study. A CT study set of 150 patients (mean age, 73 years; age range, 55-96 years; 92 women, 58 men) with (n = 75) and without (n = 75) compression fractures was assembled. All case patients were age and sex matched with control subjects. A total of 210 thoracic and lumbar vertebrae showed compression fractures and were electronically marked and classified by a radiologist. Prototype fully automated spinal segmentation and fracture detection software were then used to analyze the study set. System performance was evaluated with free-response receiver operating characteristic analysis. Results Sensitivity for detection or localization of compression fractures was 95.7% (201 of 210; 95% confidence interval [CI]: 87.0%, 98.9%), with a false-positive rate of 0.29 per patient. Additionally, sensitivity was 98.7% and specificity was 77.3% at case-based receiver operating characteristic curve analysis. Accuracy for classification by Genant type (anterior, middle, or posterior height loss) was 0.95 (107 of 113; 95% CI: 0.89, 0.98), with weighted κ of 0.90 (95% CI: 0.81, 0.99). Accuracy for categorization by Genant height loss grade was 0.68 (77 of 113; 95% CI: 0.59, 0.76), with a weighted κ of 0.59 (95% CI: 0.47, 0.71). The average bone attenuation for T12-L4 vertebrae was 146 HU ± 29 (standard deviation) in case patients and 173 HU ± 42 in control patients; this difference was statistically significant (P high sensitivity and with a low false-positive rate, as well as to calculate vertebral bone density, on CT images. © RSNA, 2017 Online supplemental material is available for this article.

  18. Using support vector machines with tract-based spatial statistics for automated classification of Tourette syndrome children

    Science.gov (United States)

    Wen, Hongwei; Liu, Yue; Wang, Jieqiong; Zhang, Jishui; Peng, Yun; He, Huiguang

    2016-03-01

    Tourette syndrome (TS) is a developmental neuropsychiatric disorder with the cardinal symptoms of motor and vocal tics which emerges in early childhood and fluctuates in severity in later years. To date, the neural basis of TS is not fully understood yet and TS has a long-term prognosis that is difficult to accurately estimate. Few studies have looked at the potential of using diffusion tensor imaging (DTI) in conjunction with machine learning algorithms in order to automate the classification of healthy children and TS children. Here we apply Tract-Based Spatial Statistics (TBSS) method to 44 TS children and 48 age and gender matched healthy children in order to extract the diffusion values from each voxel in the white matter (WM) skeleton, and a feature selection algorithm (ReliefF) was used to select the most salient voxels for subsequent classification with support vector machine (SVM). We use a nested cross validation to yield an unbiased assessment of the classification method and prevent overestimation. The accuracy (88.04%), sensitivity (88.64%) and specificity (87.50%) were achieved in our method as peak performance of the SVM classifier was achieved using the axial diffusion (AD) metric, demonstrating the potential of a joint TBSS and SVM pipeline for fast, objective classification of healthy and TS children. These results support that our methods may be useful for the early identification of subjects with TS, and hold promise for predicting prognosis and treatment outcome for individuals with TS.

  19. Entropy-Based Video Steganalysis of Motion Vectors

    Directory of Open Access Journals (Sweden)

    Elaheh Sadat Sadat

    2018-04-01

    Full Text Available In this paper, a new method is proposed for motion vector steganalysis using the entropy value and its combination with the features of the optimized motion vector. In this method, the entropy of blocks is calculated to determine their texture and the precision of their motion vectors. Then, by using a fuzzy cluster, the blocks are clustered into the blocks with high and low texture, while the membership function of each block to a high texture class indicates the texture of that block. These membership functions are used to weight the effective features that are extracted by reconstructing the motion estimation equations. Characteristics of the results indicate that the use of entropy and the irregularity of each block increases the precision of the final video classification into cover and stego classes.

  20. An on-line automated sleep-wake classification system for laboratory animals

    NARCIS (Netherlands)

    Witting, W; vanderWerf, D; Mirmiran, M

    A computerized sleep-wake classification program is presented that is capable of classifying sleep-wake states on-line in four animals simultaneously. Every 10 s the classification algorithm assigns sleep-wake states on the basis of the power spectrum of an EEG signal and the standard deviation of

  1. Automated classification of limb fractures from free-text radiology reports using a clinician-informed gazetteer methodology

    Directory of Open Access Journals (Sweden)

    Amol Wagholikar

    2013-05-01

    Full Text Available BackgroundTimely diagnosis and reporting of patient symptoms in hospital emergency departments (ED is a critical component of health services delivery. However, due to dispersed information resources and a vast amount of manual processing of unstructured information, accurate point-of-care diagnosis is often difficult. AimsThe aim of this research is to report initial experimental evaluation of a clinician-informed automated method for the issue of initial misdiagnoses associated with delayed receipt of unstructured radiology reports. Method A method was developed that resembles clinical reasoning for identifying limb abnormalities. The method consists of a gazetteer of keywords related to radiological findings; the method classifies an X-ray report as abnormal if it contains evidence contained in the gazetteer. A set of 99 narrative reports of radiological findings was sourced from a tertiary hospital. Reports were manually assessed by two clinicians and discrepancies were validated by a third expert ED clinician; the final manual classification generated by the expert ED clinician was used as ground truth to empirically evaluate the approach.ResultsThe automated method that attempts to individuate limb abnormalities by searching for keywords expressed by clinicians achieved an F-measure of 0.80 and an accuracy of 0.80.ConclusionWhile the automated clinician-driven method achieved promising performances, a number of avenues for improvement were identified using advanced natural language processing (NLP and machine learning techniques.

  2. Precision automation of cell type classification and sub-cellular fluorescence quantification from laser scanning confocal images

    Directory of Open Access Journals (Sweden)

    Hardy Craig Hall

    2016-02-01

    Full Text Available While novel whole-plant phenotyping technologies have been successfully implemented into functional genomics and breeding programs, the potential of automated phenotyping with cellular resolution is largely unexploited. Laser scanning confocal microscopy has the potential to close this gap by providing spatially highly resolved images containing anatomic as well as chemical information on a subcellular basis. However, in the absence of automated methods, the assessment of the spatial patterns and abundance of fluorescent markers with subcellular resolution is still largely qualitative and time-consuming. Recent advances in image acquisition and analysis, coupled with improvements in microprocessor performance, have brought such automated methods within reach, so that information from thousands of cells per image for hundreds of images may be derived in an experimentally convenient time-frame. Here, we present a MATLAB-based analytical pipeline to 1 segment radial plant organs into individual cells, 2 classify cells into cell type categories based upon random forest classification, 3 divide each cell into sub-regions, and 4 quantify fluorescence intensity to a subcellular degree of precision for a separate fluorescence channel. In this research advance, we demonstrate the precision of this analytical process for the relatively complex tissues of Arabidopsis hypocotyls at various stages of development. High speed and robustness make our approach suitable for phenotyping of large collections of stem-like material and other tissue types.

  3. Exploratory analysis of methods for automated classification of laboratory test orders into syndromic groups in veterinary medicine.

    Science.gov (United States)

    Dórea, Fernanda C; Muckle, C Anne; Kelton, David; McClure, J T; McEwen, Beverly J; McNab, W Bruce; Sanchez, Javier; Revie, Crawford W

    2013-01-01

    Recent focus on earlier detection of pathogen introduction in human and animal populations has led to the development of surveillance systems based on automated monitoring of health data. Real- or near real-time monitoring of pre-diagnostic data requires automated classification of records into syndromes--syndromic surveillance--using algorithms that incorporate medical knowledge in a reliable and efficient way, while remaining comprehensible to end users. This paper describes the application of two of machine learning (Naïve Bayes and Decision Trees) and rule-based methods to extract syndromic information from laboratory test requests submitted to a veterinary diagnostic laboratory. High performance (F1-macro = 0.9995) was achieved through the use of a rule-based syndrome classifier, based on rule induction followed by manual modification during the construction phase, which also resulted in clear interpretability of the resulting classification process. An unmodified rule induction algorithm achieved an F(1-micro) score of 0.979 though this fell to 0.677 when performance for individual classes was averaged in an unweighted manner (F(1-macro)), due to the fact that the algorithm failed to learn 3 of the 16 classes from the training set. Decision Trees showed equal interpretability to the rule-based approaches, but achieved an F(1-micro) score of 0.923 (falling to 0.311 when classes are given equal weight). A Naïve Bayes classifier learned all classes and achieved high performance (F(1-micro)= 0.994 and F(1-macro) = .955), however the classification process is not transparent to the domain experts. The use of a manually customised rule set allowed for the development of a system for classification of laboratory tests into syndromic groups with very high performance, and high interpretability by the domain experts. Further research is required to develop internal validation rules in order to establish automated methods to update model rules without user

  4. Exploratory analysis of methods for automated classification of laboratory test orders into syndromic groups in veterinary medicine.

    Directory of Open Access Journals (Sweden)

    Fernanda C Dórea

    Full Text Available BACKGROUND: Recent focus on earlier detection of pathogen introduction in human and animal populations has led to the development of surveillance systems based on automated monitoring of health data. Real- or near real-time monitoring of pre-diagnostic data requires automated classification of records into syndromes--syndromic surveillance--using algorithms that incorporate medical knowledge in a reliable and efficient way, while remaining comprehensible to end users. METHODS: This paper describes the application of two of machine learning (Naïve Bayes and Decision Trees and rule-based methods to extract syndromic information from laboratory test requests submitted to a veterinary diagnostic laboratory. RESULTS: High performance (F1-macro = 0.9995 was achieved through the use of a rule-based syndrome classifier, based on rule induction followed by manual modification during the construction phase, which also resulted in clear interpretability of the resulting classification process. An unmodified rule induction algorithm achieved an F(1-micro score of 0.979 though this fell to 0.677 when performance for individual classes was averaged in an unweighted manner (F(1-macro, due to the fact that the algorithm failed to learn 3 of the 16 classes from the training set. Decision Trees showed equal interpretability to the rule-based approaches, but achieved an F(1-micro score of 0.923 (falling to 0.311 when classes are given equal weight. A Naïve Bayes classifier learned all classes and achieved high performance (F(1-micro= 0.994 and F(1-macro = .955, however the classification process is not transparent to the domain experts. CONCLUSION: The use of a manually customised rule set allowed for the development of a system for classification of laboratory tests into syndromic groups with very high performance, and high interpretability by the domain experts. Further research is required to develop internal validation rules in order to establish

  5. Automated Processing of Imaging Data through Multi-tiered Classification of Biological Structures Illustrated Using Caenorhabditis elegans.

    Directory of Open Access Journals (Sweden)

    Mei Zhan

    2015-04-01

    Full Text Available Quantitative imaging has become a vital technique in biological discovery and clinical diagnostics; a plethora of tools have recently been developed to enable new and accelerated forms of biological investigation. Increasingly, the capacity for high-throughput experimentation provided by new imaging modalities, contrast techniques, microscopy tools, microfluidics and computer controlled systems shifts the experimental bottleneck from the level of physical manipulation and raw data collection to automated recognition and data processing. Yet, despite their broad importance, image analysis solutions to address these needs have been narrowly tailored. Here, we present a generalizable formulation for autonomous identification of specific biological structures that is applicable for many problems. The process flow architecture we present here utilizes standard image processing techniques and the multi-tiered application of classification models such as support vector machines (SVM. These low-level functions are readily available in a large array of image processing software packages and programming languages. Our framework is thus both easy to implement at the modular level and provides specific high-level architecture to guide the solution of more complicated image-processing problems. We demonstrate the utility of the classification routine by developing two specific classifiers as a toolset for automation and cell identification in the model organism Caenorhabditis elegans. To serve a common need for automated high-resolution imaging and behavior applications in the C. elegans research community, we contribute a ready-to-use classifier for the identification of the head of the animal under bright field imaging. Furthermore, we extend our framework to address the pervasive problem of cell-specific identification under fluorescent imaging, which is critical for biological investigation in multicellular organisms or tissues. Using these examples as a

  6. Entropy based classifier for cross-domain opinion mining

    Directory of Open Access Journals (Sweden)

    Jyoti S. Deshmukh

    2018-01-01

    Full Text Available In recent years, the growth of social network has increased the interest of people in analyzing reviews and opinions for products before they buy them. Consequently, this has given rise to the domain adaptation as a prominent area of research in sentiment analysis. A classifier trained from one domain often gives poor results on data from another domain. Expression of sentiment is different in every domain. The labeling cost of each domain separately is very high as well as time consuming. Therefore, this study has proposed an approach that extracts and classifies opinion words from one domain called source domain and predicts opinion words of another domain called target domain using a semi-supervised approach, which combines modified maximum entropy and bipartite graph clustering. A comparison of opinion classification on reviews on four different product domains is presented. The results demonstrate that the proposed method performs relatively well in comparison to the other methods. Comparison of SentiWordNet of domain-specific and domain-independent words reveals that on an average 72.6% and 88.4% words, respectively, are correctly classified.

  7. Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Jinde Zheng

    2014-01-01

    Full Text Available A new rolling bearing fault diagnosis approach based on multiscale permutation entropy (MPE, Laplacian score (LS, and support vector machines (SVMs is proposed in this paper. Permutation entropy (PE was recently proposed and defined to measure the randomicity and detect dynamical changes of time series. However, for the complexity of mechanical systems, the randomicity and dynamic changes of the vibration signal will exist in different scales. Thus, the definition of MPE is introduced and employed to extract the nonlinear fault characteristics from the bearing vibration signal in different scales. Besides, the SVM is utilized to accomplish the fault feature classification to fulfill diagnostic procedure automatically. Meanwhile, in order to avoid a high dimension of features, the Laplacian score (LS is used to refine the feature vector by ranking the features according to their importance and correlations with the main fault information. Finally, the rolling bearing fault diagnosis method based on MPE, LS, and SVM is proposed and applied to the experimental data. The experimental data analysis results indicate that the proposed method could identify the fault categories effectively.

  8. Classification of acute decompensated heart failure: an automated algorithm compared with a physician reviewer panel: the Atherosclerosis Risk in Communities study.

    Science.gov (United States)

    Loehr, Laura R; Agarwal, Sunil K; Baggett, Chris; Wruck, Lisa M; Chang, Patricia P; Solomon, Scott D; Shahar, Eyal; Ni, Hanyu; Rosamond, Wayne D; Heiss, Gerardo

    2013-07-01

    An algorithm to classify heart failure (HF) end points inclusive of contemporary measures of biomarkers and echocardiography was recently proposed by an international expert panel. Our objective was to assess agreement of HF classification by this contemporaneous algorithm with that by a standardized physician reviewer panel, when applied to data abstracted from community-based hospital records. During 2005-2007, all hospitalizations were identified from 4 US communities under surveillance as part of the Atherosclerosis Risk in Communities (ARIC) study. Potential HF hospitalizations were sampled by International Classification of Diseases discharge codes and demographics from men and women aged ≥ 55 years. The HF classification algorithm was automated and applied to 2729 (n=13854 weighted hospitalizations) hospitalizations in which either brain natriuretic peptide measures or ejection fraction were documented (mean age, 75 years). There were 1403 (54%; n=7534 weighted) events classified as acute decompensated HF by the automated algorithm, and 1748 (68%; n=9276 weighted) such events by the ARIC reviewer panel. The chance-corrected agreement between acute decompensated HF by physician reviewer panel and the automated algorithm was moderate (κ=0.39). Sensitivity and specificity of the automated algorithm with ARIC reviewer panel as the referent standard were 0.68 (95% confidence interval, 0.67-0.69) and 0.75 (95% confidence interval, 0.74-0.76), respectively. Although the automated classification improved efficiency and decreased costs, its accuracy in classifying HF hospitalizations was modest compared with a standardized physician reviewer panel.

  9. An Entropy-Based Measure for Assessing Fuzziness in Logistic Regression

    Science.gov (United States)

    Weiss, Brandi A.; Dardick, William

    2016-01-01

    This article introduces an entropy-based measure of data-model fit that can be used to assess the quality of logistic regression models. Entropy has previously been used in mixture-modeling to quantify how well individuals are classified into latent classes. The current study proposes the use of entropy for logistic regression models to quantify…

  10. A two-phase copula entropy-based multiobjective optimization approach to hydrometeorological gauge network design

    Science.gov (United States)

    Xu, Pengcheng; Wang, Dong; Singh, Vijay P.; Wang, Yuankun; Wu, Jichun; Wang, Lachun; Zou, Xinqing; Chen, Yuanfang; Chen, Xi; Liu, Jiufu; Zou, Ying; He, Ruimin

    2017-12-01

    Hydrometeorological data are needed for obtaining point and areal mean, quantifying the spatial variability of hydrometeorological variables, and calibration and verification of hydrometeorological models. Hydrometeorological networks are utilized to collect such data. Since data collection is expensive, it is essential to design an optimal network based on the minimal number of hydrometeorological stations in order to reduce costs. This study proposes a two-phase copula entropy- based multiobjective optimization approach that includes: (1) copula entropy-based directional information transfer (CDIT) for clustering the potential hydrometeorological gauges into several groups, and (2) multiobjective method for selecting the optimal combination of gauges for regionalized groups. Although entropy theory has been employed for network design before, the joint histogram method used for mutual information estimation has several limitations. The copula entropy-based mutual information (MI) estimation method is shown to be more effective for quantifying the uncertainty of redundant information than the joint histogram (JH) method. The effectiveness of this approach is verified by applying to one type of hydrometeorological gauge network, with the use of three model evaluation measures, including Nash-Sutcliffe Coefficient (NSC), arithmetic mean of the negative copula entropy (MNCE), and MNCE/NSC. Results indicate that the two-phase copula entropy-based multiobjective technique is capable of evaluating the performance of regional hydrometeorological networks and can enable decision makers to develop strategies for water resources management.

  11. Systems Operation Studies for Automated Guideway Transit Systems - Classification and Definition of AGT Systems

    Science.gov (United States)

    1980-02-01

    The report describes the development of an AGT classification structure. Five classes are defined based on three system characteristics: service type, minimum travelling unit capacity, and maximum operating velocity. The five classes defined are: Per...

  12. Rapid automated classification of anesthetic depth levels using GPU based parallelization of neural networks.

    Science.gov (United States)

    Peker, Musa; Şen, Baha; Gürüler, Hüseyin

    2015-02-01

    The effect of anesthesia on the patient is referred to as depth of anesthesia. Rapid classification of appropriate depth level of anesthesia is a matter of great importance in surgical operations. Similarly, accelerating classification algorithms is important for the rapid solution of problems in the field of biomedical signal processing. However numerous, time-consuming mathematical operations are required when training and testing stages of the classification algorithms, especially in neural networks. In this study, to accelerate the process, parallel programming and computing platform (Nvidia CUDA) facilitates dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU) was utilized. The system was employed to detect anesthetic depth level on related electroencephalogram (EEG) data set. This dataset is rather complex and large. Moreover, the achieving more anesthetic levels with rapid response is critical in anesthesia. The proposed parallelization method yielded high accurate classification results in a faster time.

  13. Accuracy of automated classification of major depressive disorder as a function of symptom severity

    Directory of Open Access Journals (Sweden)

    Rajamannar Ramasubbu, MD, FRCPC, MSc

    2016-01-01

    Conclusions: Binary linear SVM classifiers achieved significant classification of very severe depression with resting-state fMRI, but the contribution of brain measurements may have limited potential in differentiating patients with less severe depression from healthy controls.

  14. Support-vector-machine tree-based domain knowledge learning toward automated sports video classification

    Science.gov (United States)

    Xiao, Guoqiang; Jiang, Yang; Song, Gang; Jiang, Jianmin

    2010-12-01

    We propose a support-vector-machine (SVM) tree to hierarchically learn from domain knowledge represented by low-level features toward automatic classification of sports videos. The proposed SVM tree adopts a binary tree structure to exploit the nature of SVM's binary classification, where each internal node is a single SVM learning unit, and each external node represents the classified output type. Such a SVM tree presents a number of advantages, which include: 1. low computing cost; 2. integrated learning and classification while preserving individual SVM's learning strength; and 3. flexibility in both structure and learning modules, where different numbers of nodes and features can be added to address specific learning requirements, and various learning models can be added as individual nodes, such as neural networks, AdaBoost, hidden Markov models, dynamic Bayesian networks, etc. Experiments support that the proposed SVM tree achieves good performances in sports video classifications.

  15. Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy.

    Science.gov (United States)

    Welikala, R A; Fraz, M M; Dehmeshki, J; Hoppe, A; Tah, V; Mann, S; Williamson, T H; Barman, S A

    2015-07-01

    Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is the growth of abnormal new vessels. In this paper, an automated method for the detection of new vessels from retinal images is presented. This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel map which each hold vital information. Local morphology features are measured from each binary vessel map to produce two separate 4-D feature vectors. Independent classification is performed for each feature vector using a support vector machine (SVM) classifier. The system then combines these individual outcomes to produce a final decision. This is followed by the creation of additional features to generate 21-D feature vectors, which feed into a genetic algorithm based feature selection approach with the objective of finding feature subsets that improve the performance of the classification. Sensitivity and specificity results using a dataset of 60 images are 0.9138 and 0.9600, respectively, on a per patch basis and 1.000 and 0.975, respectively, on a per image basis. Copyright © 2015 Elsevier Ltd. All rights reserved.

  16. An Automated Method for Semantic Classification of Regions in Coastal Images

    NARCIS (Netherlands)

    Hoonhout, B.M.; Radermacher, M.; Baart, F.; Van der Maaten, L.J.P.

    2015-01-01

    Large, long-term coastal imagery datasets are nowadays a low-cost source of information for various coastal research disciplines. However, the applicability of many existing algorithms for coastal image analysis is limited for these large datasets due to a lack of automation and robustness.

  17. Automated and unbiased image analyses as tools in phenotypic classification of small-spored Alternaria species

    DEFF Research Database (Denmark)

    Andersen, Birgitte; Hansen, Michael Edberg; Smedsgaard, Jørn

    2005-01-01

    often has been broadly applied to various morphologically and chemically distinct groups of isolates from different hosts. The purpose of this study was to develop and evaluate automated and unbiased image analysis systems that will analyze different phenotypic characters and facilitate testing...

  18. An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by combining Landsat, MODIS, and secondary data

    Science.gov (United States)

    Thenkabail, Prasad S.; Wu, Zhuoting

    2012-01-01

    The overarching goal of this research was to develop and demonstrate an automated Cropland Classification Algorithm (ACCA) that will rapidly, routinely, and accurately classify agricultural cropland extent, areas, and characteristics (e.g., irrigated vs. rainfed) over large areas such as a country or a region through combination of multi-sensor remote sensing and secondary data. In this research, a rule-based ACCA was conceptualized, developed, and demonstrated for the country of Tajikistan using mega file data cubes (MFDCs) involving data from Landsat Global Land Survey (GLS), Landsat Enhanced Thematic Mapper Plus (ETM+) 30 m, Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m time-series, a suite of secondary data (e.g., elevation, slope, precipitation, temperature), and in situ data. First, the process involved producing an accurate reference (or truth) cropland layer (TCL), consisting of cropland extent, areas, and irrigated vs. rainfed cropland areas, for the entire country of Tajikistan based on MFDC of year 2005 (MFDC2005). The methods involved in producing TCL included using ISOCLASS clustering, Tasseled Cap bi-spectral plots, spectro-temporal characteristics from MODIS 250 m monthly normalized difference vegetation index (NDVI) maximum value composites (MVC) time-series, and textural characteristics of higher resolution imagery. The TCL statistics accurately matched with the national statistics of Tajikistan for irrigated and rainfed croplands, where about 70% of croplands were irrigated and the rest rainfed. Second, a rule-based ACCA was developed to replicate the TCL accurately (~80% producer’s and user’s accuracies or within 20% quantity disagreement involving about 10 million Landsat 30 m sized cropland pixels of Tajikistan). Development of ACCA was an iterative process involving series of rules that are coded, refined, tweaked, and re-coded till ACCA derived croplands (ACLs) match accurately with TCLs. Third, the ACCA derived cropland

  19. An Automated Cropland Classification Algorithm (ACCA for Tajikistan by Combining Landsat, MODIS, and Secondary Data

    Directory of Open Access Journals (Sweden)

    Prasad S. Thenkabail

    2012-09-01

    Full Text Available The overarching goal of this research was to develop and demonstrate an automated Cropland Classification Algorithm (ACCA that will rapidly, routinely, and accurately classify agricultural cropland extent, areas, and characteristics (e.g., irrigated vs. rainfed over large areas such as a country or a region through combination of multi-sensor remote sensing and secondary data. In this research, a rule-based ACCA was conceptualized, developed, and demonstrated for the country of Tajikistan using mega file data cubes (MFDCs involving data from Landsat Global Land Survey (GLS, Landsat Enhanced Thematic Mapper Plus (ETM+ 30 m, Moderate Resolution Imaging Spectroradiometer (MODIS 250 m time-series, a suite of secondary data (e.g., elevation, slope, precipitation, temperature, and in situ data. First, the process involved producing an accurate reference (or truth cropland layer (TCL, consisting of cropland extent, areas, and irrigated vs. rainfed cropland areas, for the entire country of Tajikistan based on MFDC of year 2005 (MFDC2005. The methods involved in producing TCL included using ISOCLASS clustering, Tasseled Cap bi-spectral plots, spectro-temporal characteristics from MODIS 250 m monthly normalized difference vegetation index (NDVI maximum value composites (MVC time-series, and textural characteristics of higher resolution imagery. The TCL statistics accurately matched with the national statistics of Tajikistan for irrigated and rainfed croplands, where about 70% of croplands were irrigated and the rest rainfed. Second, a rule-based ACCA was developed to replicate the TCL accurately (~80% producer’s and user’s accuracies or within 20% quantity disagreement involving about 10 million Landsat 30 m sized cropland pixels of Tajikistan. Development of ACCA was an iterative process involving series of rules that are coded, refined, tweaked, and re-coded till ACCA derived croplands (ACLs match accurately with TCLs. Third, the ACCA derived

  20. Automated classification of histopathology images of prostate cancer using a Bag-of-Words approach

    Science.gov (United States)

    Sanghavi, Foram M.; Agaian, Sos S.

    2016-05-01

    The goals of this paper are (1) test the Computer Aided Classification of the prostate cancer histopathology images based on the Bag-of-Words (BoW) approach (2) evaluate the performance of the classification grade 3 and 4 of the proposed method using the results of the approach proposed by the authors Khurd et al. in [9] and (3) classify the different grades of cancer namely, grade 0, 3, 4, and 5 using the proposed approach. The system performance is assessed using 132 prostate cancer histopathology of different grades. The system performance of the SURF features are also analyzed by comparing the results with SIFT features using different cluster sizes. The results show 90.15% accuracy in detection of prostate cancer images using SURF features with 75 clusters for k-mean clustering. The results showed higher sensitivity for SURF based BoW classification compared to SIFT based BoW.

  1. 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…

  2. Technical Note: Semi-automated classification of time-lapse RGB imagery for a remote Greenlandic river

    Science.gov (United States)

    Gleason, C. J.; Smith, L. C.; Finnegan, D. C.; LeWinter, A. L.; Pitcher, L. H.; Chu, V. W.

    2015-01-01

    River systems in remote environments are often challenging to monitor and understand where traditional gauging apparatus are difficult to install or where safety concerns prohibit field measurements. In such cases, remote sensing, especially terrestrial time lapse imaging platforms, offer a means to better understand these fluvial systems. One such environment is found at the proglacial Isortoq River in southwest Greenland, a river with a constantly shifting floodplain and remote Arctic location that make gauging and in situ measurements all but impossible. In order to derive relevant hydraulic parameters for this river, two RGB cameras were installed in July of 2011, and these cameras collected over 10 000 half hourly time-lapse images of the river by September of 2012. Existing approaches for extracting hydraulic parameters from RGB imagery require manual or supervised classification of images into water and non-water areas, a task that was impractical for the volume of data in this study. As such, automated image filters were developed that removed images with environmental obstacles (e.g. shadows, sun glint, snow) from the processing stream. Further image filtering was accomplished via a novel automated histogram similarity filtering process. This similarity filtering allowed successful (mean accuracy 79.6%) supervised classification of filtered images from training data collected from just 10% of those images. Effective width, a hydraulic parameter highly correlated with discharge in braided rivers, was extracted from these classified images, producing a hydrograph proxy for the Isortoq River between 2011 and 2012. This hydrograph proxy shows agreement with historic flooding observed in other parts of Greenland in July 2012 and offers promise that the imaging platform and processing methodology presented here will be useful for future monitoring studies of remote rivers.

  3. Automated Classification of Variable Stars in the Asteroseismology Program of the Kepler Space Mission

    DEFF Research Database (Denmark)

    Blomme, J.; Debosscher, J.; De Ridder, J.

    2010-01-01

    We present the first results of the application of supervised classification methods to the Kepler Q1 long-cadence light curves of a subsample of 2288 stars measured in the asteroseismology program of the mission. The methods, originally developed in the framework of the CoRoT and Gaia space...

  4. Enhancing social tagging with automated keywords from the Dewey Decimal Classification

    DEFF Research Database (Denmark)

    Golub, Koraljka; Lykke, Marianne; Tudhope, Duglas

    2014-01-01

    Purpose – The purpose of this paper is to explore the potential of applying the Dewey Decimal Classification (DDC) as an established knowledge organization system (KOS) for enhancing social tagging, with the ultimate purpose of improving subject indexing and information retrieval. Design...

  5. Exploring repetitive DNA landscapes using REPCLASS, a tool that automates the classification of transposable elements in eukaryotic genomes.

    Science.gov (United States)

    Feschotte, Cédric; Keswani, Umeshkumar; Ranganathan, Nirmal; Guibotsy, Marcel L; Levine, David

    2009-07-23

    Eukaryotic genomes contain large amount of repetitive DNA, most of which is derived from transposable elements (TEs). Progress has been made to develop computational tools for ab initio identification of repeat families, but there is an urgent need to develop tools to automate the annotation of TEs in genome sequences. Here we introduce REPCLASS, a tool that automates the classification of TE sequences. Using control repeat libraries, we show that the program can classify accurately virtually any known TE types. Combining REPCLASS to ab initio repeat finding in the genomes of Caenorhabditis elegans and Drosophila melanogaster allowed us to recover the contrasting TE landscape characteristic of these species. Unexpectedly, REPCLASS also uncovered several novel TE families in both genomes, augmenting the TE repertoire of these model species. When applied to the genomes of distant Caenorhabditis and Drosophila species, the approach revealed a remarkable conservation of TE composition profile within each genus, despite substantial interspecific covariations in genome size and in the number of TEs and TE families. Lastly, we applied REPCLASS to analyze 10 fungal genomes from a wide taxonomic range, most of which have not been analyzed for TE content previously. The results showed that TE diversity varies widely across the fungi "kingdom" and appears to positively correlate with genome size, in particular for DNA transposons. Together, these data validate REPCLASS as a powerful tool to explore the repetitive DNA landscapes of eukaryotes and to shed light onto the evolutionary forces shaping TE diversity and genome architecture.

  6. Automated segmentation of thyroid gland on CT images with multi-atlas label fusion and random classification forest

    Science.gov (United States)

    Liu, Jiamin; Chang, Kevin; Kim, Lauren; Turkbey, Evrim; Lu, Le; Yao, Jianhua; Summers, Ronald

    2015-03-01

    The thyroid gland plays an important role in clinical practice, especially for radiation therapy treatment planning. For patients with head and neck cancer, radiation therapy requires a precise delineation of the thyroid gland to be spared on the pre-treatment planning CT images to avoid thyroid dysfunction. In the current clinical workflow, the thyroid gland is normally manually delineated by radiologists or radiation oncologists, which is time consuming and error prone. Therefore, a system for automated segmentation of the thyroid is desirable. However, automated segmentation of the thyroid is challenging because the thyroid is inhomogeneous and surrounded by structures that have similar intensities. In this work, the thyroid gland segmentation is initially estimated by multi-atlas label fusion algorithm. The segmentation is refined by supervised statistical learning based voxel labeling with a random forest algorithm. Multiatlas label fusion (MALF) transfers expert-labeled thyroids from atlases to a target image using deformable registration. Errors produced by label transfer are reduced by label fusion that combines the results produced by all atlases into a consensus solution. Then, random forest (RF) employs an ensemble of decision trees that are trained on labeled thyroids to recognize features. The trained forest classifier is then applied to the thyroid estimated from the MALF by voxel scanning to assign the class-conditional probability. Voxels from the expert-labeled thyroids in CT volumes are treated as positive classes; background non-thyroid voxels as negatives. We applied this automated thyroid segmentation system to CT scans of 20 patients. The results showed that the MALF achieved an overall 0.75 Dice Similarity Coefficient (DSC) and the RF classification further improved the DSC to 0.81.

  7. 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.

  8. 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...

  9. Intelligent Classification of Heartbeats for Automated Real-Time ECG Monitoring

    Science.gov (United States)

    Park, Juyoung

    2014-01-01

    Abstract Background: The automatic interpretation of electrocardiography (ECG) data can provide continuous analysis of heart activity, allowing the effective use of wireless devices such as the Holter monitor. Materials and Methods: We propose an intelligent heartbeat monitoring system to detect the possibility of arrhythmia in real time. We detected heartbeats and extracted features such as the QRS complex and P wave from ECG signals using the Pan–Tompkins algorithm, and the heartbeats were then classified into 16 types using a decision tree. Results: We tested the sensitivity, specificity, and accuracy of our system against data from the MIT-BIH Arrhythmia Database. Our system achieved an average accuracy of 97% in heartbeat detection and an average heartbeat classification accuracy of above 96%, which is comparable with the best competing schemes. Conclusions: This work provides a guide to the systematic design of an intelligent classification system for decision support in Holter ECG monitoring. PMID:25010717

  10. Automated and unbiased classification of chemical profiles from fungi using high performance liquid chromatography

    DEFF Research Database (Denmark)

    Hansen, Michael Edberg; Andersen, Birgitte; Smedsgaard, Jørn

    2005-01-01

    In this paper we present a method for unbiased/unsupervised classification and identification of closely related fungi, using chemical analysis of secondary metabolite profiles created by HPLC with UV diode array detection. For two chromatographic data matrices a vector of locally aligned full...... Penicillium species. Then the algorithm was validated on fungal isolates belonging to the genus Alternaria. The results showed that the species may be segregated into taxa in full accordance with published taxonomy....

  11. Value at risk estimation with entropy-based wavelet analysis in exchange markets

    Science.gov (United States)

    He, Kaijian; Wang, Lijun; Zou, Yingchao; Lai, Kin Keung

    2014-08-01

    In recent years, exchange markets are increasingly integrated together. Fluctuations and risks across different exchange markets exhibit co-moving and complex dynamics. In this paper we propose the entropy-based multivariate wavelet based approaches to analyze the multiscale characteristic in the multidimensional domain and improve further the Value at Risk estimation reliability. Wavelet analysis has been introduced to construct the entropy-based Multiscale Portfolio Value at Risk estimation algorithm to account for the multiscale dynamic correlation. The entropy measure has been proposed as the more effective measure with the error minimization principle to select the best basis when determining the wavelet families and the decomposition level to use. The empirical studies conducted in this paper have provided positive evidence as to the superior performance of the proposed approach, using the closely related Chinese Renminbi and European Euro exchange market.

  12. Optimal Entropy-Based Cooperative Spectrum Sensing for Maritime Cognitive Radio Networks

    Directory of Open Access Journals (Sweden)

    Waleed Ejaz

    2013-11-01

    Full Text Available Maritime cognitive radio networks (MCRNs have recently been proposed for opportunistic utilization of the licensed band. Spectrum sensing is one of the key issues for the successful deployment of the MCRNs. The maritime environment is unique in terms of radio wave propagation over water, surface reflection and wave occlusions. In order to deal with the challenging maritime environment, we proposed an optimal entropy-based cooperative spectrum sensing. As the results of spectrum sensing are sensitive to the number of samples in an entropy-based local detection scheme, we first calculated the optimal number of samples. Next, a cooperative spectrum sensing scheme considering the conditions of the sea environment is proposed. Finally, the throughput optimization of the m-out-of-n rule is considered. Results revealed that although the existing schemes work well for the lower sea states, they fail to perform at higher sea states. Moreover, simulation results also indicated the robustness of the entropy-based scheme and the proposed cooperative spectrum sensing scheme at higher sea states in comparison with the traditional energy detector.

  13. The Grading Entropy-based Criteria for Structural Stability of Granular Materials and Filters

    Directory of Open Access Journals (Sweden)

    Janos Lőrincz

    2015-05-01

    Full Text Available This paper deals with three grading entropy-based rules that describe different soil structure stability phenomena: an internal stability rule, a filtering rule and a segregation rule. These rules are elaborated on the basis of a large amount of laboratory testing and from existing knowledge in the field. Use is made of the theory of grading entropy to derive parameters which incorporate all of the information of the grading curve into a pair of entropy-based parameters that allow soils with common behaviours to be grouped into domains on an entropy diagram. Applications of the derived entropy-based rules are presented by examining the reason of a dam failure, by testing against the existing filter rules from the literature, and by giving some examples for the design of non-segregating grading curves (discrete particle size distributions by dry weight. A physical basis for the internal stability rule is established, wherein the higher values of base entropy required for granular stability are shown to reflect the closeness between the mean and maximum grain diameters, which explains how there are sufficient coarser grains to achieve a stable grain skeleton.

  14. Automated Classification of Selected Data Elements from Free-text Diagnostic Reports for Clinical Research.

    Science.gov (United States)

    Löpprich, Martin; Krauss, Felix; Ganzinger, Matthias; Senghas, Karsten; Riezler, Stefan; Knaup, Petra

    2016-08-05

    In the Multiple Myeloma clinical registry at Heidelberg University Hospital, most data are extracted from discharge letters. Our aim was to analyze if it is possible to make the manual documentation process more efficient by using methods of natural language processing for multiclass classification of free-text diagnostic reports to automatically document the diagnosis and state of disease of myeloma patients. The first objective was to create a corpus consisting of free-text diagnosis paragraphs of patients with multiple myeloma from German diagnostic reports, and its manual annotation of relevant data elements by documentation specialists. The second objective was to construct and evaluate a framework using different NLP methods to enable automatic multiclass classification of relevant data elements from free-text diagnostic reports. The main diagnoses paragraph was extracted from the clinical report of one third randomly selected patients of the multiple myeloma research database from Heidelberg University Hospital (in total 737 selected patients). An EDC system was setup and two data entry specialists performed independently a manual documentation of at least nine specific data elements for multiple myeloma characterization. Both data entries were compared and assessed by a third specialist and an annotated text corpus was created. A framework was constructed, consisting of a self-developed package to split multiple diagnosis sequences into several subsequences, four different preprocessing steps to normalize the input data and two classifiers: a maximum entropy classifier (MEC) and a support vector machine (SVM). In total 15 different pipelines were examined and assessed by a ten-fold cross-validation, reiterated 100 times. For quality indication the average error rate and the average F1-score were conducted. For significance testing the approximate randomization test was used. The created annotated corpus consists of 737 different diagnoses paragraphs with a

  15. Automated Region of Interest Retrieval of Metallographic Images for Quality Classification in Industry

    Directory of Open Access Journals (Sweden)

    Petr Kotas

    2012-01-01

    Full Text Available The aim of the research is development and testing of new methods to classify the quality of metallographic samples of steels with high added value (for example grades X70 according API. In this paper, we address the development of methods to classify the quality of slab samples images with the main emphasis on the quality of the image center called as segregation area. For this reason, we introduce an alternative method for automated retrieval of region of interest. In the first step, the metallographic image is segmented using both spectral method and thresholding. Then, the extracted macrostructure of the metallographic image is automatically analyzed by statistical methods. Finally, automatically extracted region of interests are compared with results of human experts.  Practical experience with retrieval of non-homogeneous noised digital images in industrial environment is discussed as well.

  16. Automated fault extraction and classification using 3-D seismic data for the Ekofisk field development

    Energy Technology Data Exchange (ETDEWEB)

    Signer, C.; Nickel, M.; Randen, T.; Saeter, T.; Soenneland, H.H.

    1998-12-31

    Mapping of fractures is important for the prediction of fluid flow in many reservoir types. The fluid flow depends mainly on the efficiency of the reservoir seals. Improved spatial mapping of the open and closed fracture systems will allow a better prediction of the fluid flow pattern. The primary objectives of this paper is to present fracture characterization at the reservoir scale combined with seismic facies mapping. The complexity of the giant Ekofisk field on the Norwegian continental shelf provides an ideal framework for testing the validity and the applicability of an automated seismic fault and fracture detection and mapping tool. The mapping of the faults can be based on seismic attribute grids, which means that attribute-responses related to faults are extracted along key horizons which were interpreted in the reservoir interval. 3 refs., 3 figs.

  17. A controlled trial of automated classification of negation from clinical notes

    Directory of Open Access Journals (Sweden)

    Carruth William

    2005-05-01

    Full Text Available Abstract Background Identification of negation in electronic health records is essential if we are to understand the computable meaning of the records: Our objective is to compare the accuracy of an automated mechanism for assignment of Negation to clinical concepts within a compositional expression with Human Assigned Negation. Also to perform a failure analysis to identify the causes of poorly identified negation (i.e. Missed Conceptual Representation, Inaccurate Conceptual Representation, Missed Negation, Inaccurate identification of Negation. Methods 41 Clinical Documents (Medical Evaluations; sometimes outside of Mayo these are referred to as History and Physical Examinations were parsed using the Mayo Vocabulary Server Parsing Engine. SNOMED-CT™ was used to provide concept coverage for the clinical concepts in the record. These records resulted in identification of Concepts and textual clues to Negation. These records were reviewed by an independent medical terminologist, and the results were tallied in a spreadsheet. Where questions on the review arose Internal Medicine Faculty were employed to make a final determination. Results SNOMED-CT was used to provide concept coverage of the 14,792 Concepts in 41 Health Records from John's Hopkins University. Of these, 1,823 Concepts were identified as negative by Human review. The sensitivity (Recall of the assignment of negation was 97.2% (p Conclusion Automated assignment of negation to concepts identified in health records based on review of the text is feasible and practical. Lexical assignment of negation is a good test of true Negativity as judged by the high sensitivity, specificity and positive likelihood ratio of the test. SNOMED-CT had overall coverage of 88.7% of the concepts being negated.

  18. Automated tissue classification of intracardiac optical coherence tomography images (Conference Presentation)

    Science.gov (United States)

    Gan, Yu; Tsay, David; Amir, Syed B.; Marboe, Charles C.; Hendon, Christine P.

    2016-03-01

    Remodeling of the myocardium is associated with increased risk of arrhythmia and heart failure. Our objective is to automatically identify regions of fibrotic myocardium, dense collagen, and adipose tissue, which can serve as a way to guide radiofrequency ablation therapy or endomyocardial biopsies. Using computer vision and machine learning, we present an automated algorithm to classify tissue compositions from cardiac optical coherence tomography (OCT) images. Three dimensional OCT volumes were obtained from 15 human hearts ex vivo within 48 hours of donor death (source, NDRI). We first segmented B-scans using a graph searching method. We estimated the boundary of each region by minimizing a cost function, which consisted of intensity, gradient, and contour smoothness. Then, features, including texture analysis, optical properties, and statistics of high moments, were extracted. We used a statistical model, relevance vector machine, and trained this model with abovementioned features to classify tissue compositions. To validate our method, we applied our algorithm to 77 volumes. The datasets for validation were manually segmented and classified by two investigators who were blind to our algorithm results and identified the tissues based on trichrome histology and pathology. The difference between automated and manual segmentation was 51.78 +/- 50.96 μm. Experiments showed that the attenuation coefficients of dense collagen were significantly different from other tissue types (P < 0.05, ANOVA). Importantly, myocardial fibrosis tissues were different from normal myocardium in entropy and kurtosis. The tissue types were classified with an accuracy of 84%. The results show good agreements with histology.

  19. A detailed comparison of analysis processes for MCC-IMS data in disease classification-Automated methods can replace manual peak annotations.

    Directory of Open Access Journals (Sweden)

    Salome Horsch

    Full Text Available Disease classification from molecular measurements typically requires an analysis pipeline from raw noisy measurements to final classification results. Multi capillary column-ion mobility spectrometry (MCC-IMS is a promising technology for the detection of volatile organic compounds in the air of exhaled breath. From raw measurements, the peak regions representing the compounds have to be identified, quantified, and clustered across different experiments. Currently, several steps of this analysis process require manual intervention of human experts. Our goal is to identify a fully automatic pipeline that yields competitive disease classification results compared to an established but subjective and tedious semi-manual process.We combine a large number of modern methods for peak detection, peak clustering, and multivariate classification into analysis pipelines for raw MCC-IMS data. We evaluate all combinations on three different real datasets in an unbiased cross-validation setting. We determine which specific algorithmic combinations lead to high AUC values in disease classifications across the different medical application scenarios.The best fully automated analysis process achieves even better classification results than the established manual process. The best algorithms for the three analysis steps are (i SGLTR (Savitzky-Golay Laplace-operator filter thresholding regions and LM (Local Maxima for automated peak identification, (ii EM clustering (Expectation Maximization and DBSCAN (Density-Based Spatial Clustering of Applications with Noise for the clustering step and (iii RF (Random Forest for multivariate classification. Thus, automated methods can replace the manual steps in the analysis process to enable an unbiased high throughput use of the technology.

  20. Towards automated human gait disease classification using phase space representation of intrinsic mode functions

    Science.gov (United States)

    Pratiher, Sawon; Patra, Sayantani; Pratiher, Souvik

    2017-06-01

    A novel analytical methodology for segregating healthy and neurological disorders from gait patterns is proposed by employing a set of oscillating components called intrinsic mode functions (IMF's). These IMF's are generated by the Empirical Mode Decomposition of the gait time series and the Hilbert transformed analytic signal representation forms the complex plane trace of the elliptical shaped analytic IMFs. The area measure and the relative change in the centroid position of the polygon formed by the Convex Hull of these analytic IMF's are taken as the discriminative features. Classification accuracy of 79.31% with Ensemble learning based Adaboost classifier validates the adequacy of the proposed methodology for a computer aided diagnostic (CAD) system for gait pattern identification. Also, the efficacy of several potential biomarkers like Bandwidth of Amplitude Modulation and Frequency Modulation IMF's and it's Mean Frequency from the Fourier-Bessel expansion from each of these analytic IMF's has been discussed for its potency in diagnosis of gait pattern identification and classification.

  1. Automated classification of maxillofacial cysts in cone beam CT images using contourlet transformation and Spherical Harmonics.

    Science.gov (United States)

    Abdolali, Fatemeh; Zoroofi, Reza Aghaeizadeh; Otake, Yoshito; Sato, Yoshinobu

    2017-02-01

    Accurate detection of maxillofacial cysts is an essential step for diagnosis, monitoring and planning therapeutic intervention. Cysts can be of various sizes and shapes and existing detection methods lead to poor results. Customizing automatic detection systems to gain sufficient accuracy in clinical practice is highly challenging. For this purpose, integrating the engineering knowledge in efficient feature extraction is essential. This paper presents a novel framework for maxillofacial cysts detection. A hybrid methodology based on surface and texture information is introduced. The proposed approach consists of three main steps as follows: At first, each cystic lesion is segmented with high accuracy. Then, in the second and third steps, feature extraction and classification are performed. Contourlet and SPHARM coefficients are utilized as texture and shape features which are fed into the classifier. Two different classifiers are used in this study, i.e. support vector machine and sparse discriminant analysis. Generally SPHARM coefficients are estimated by the iterative residual fitting (IRF) algorithm which is based on stepwise regression method. In order to improve the accuracy of IRF estimation, a method based on extra orthogonalization is employed to reduce linear dependency. We have utilized a ground-truth dataset consisting of cone beam CT images of 96 patients, belonging to three maxillofacial cyst categories: radicular cyst, dentigerous cyst and keratocystic odontogenic tumor. Using orthogonalized SPHARM, residual sum of squares is decreased which leads to a more accurate estimation. Analysis of the results based on statistical measures such as specificity, sensitivity, positive predictive value and negative predictive value is reported. The classification rate of 96.48% is achieved using sparse discriminant analysis and orthogonalized SPHARM features. Classification accuracy at least improved by 8.94% with respect to conventional features. This study

  2. Challenges in the automated classification of variable stars in large databases

    Directory of Open Access Journals (Sweden)

    Graham Matthew

    2017-01-01

    Full Text Available With ever-increasing numbers of astrophysical transient surveys, new facilities and archives of astronomical time series, time domain astronomy is emerging as a mainstream discipline. However, the sheer volume of data alone - hundreds of observations for hundreds of millions of sources – necessitates advanced statistical and machine learning methodologies for scientific discovery: characterization, categorization, and classification. Whilst these techniques are slowly entering the astronomer’s toolkit, their application to astronomical problems is not without its issues. In this paper, we will review some of the challenges posed by trying to identify variable stars in large data collections, including appropriate feature representations, dealing with uncertainties, establishing ground truths, and simple discrete classes.

  3. Automated fault detection and classification of etch systems using modular neural networks

    Science.gov (United States)

    Hong, Sang J.; May, Gary S.; Yamartino, John; Skumanich, Andrew

    2004-04-01

    Modular neural networks (MNNs) are investigated as a tool for modeling process behavior and fault detection and classification (FDC) using tool data in plasma etching. Principal component analysis (PCA) is initially employed to reduce the dimensionality of the voluminous multivariate tool data and to establish relationships between the acquired data and the process state. MNNs are subsequently used to identify anomalous process behavior. A gradient-based fuzzy C-means clustering algorithm is implemented to enhance MNN performance. MNNs for eleven individual steps of etch runs are trained with data acquired from baseline, control (acceptable), and perturbed (unacceptable) runs, and then tested with data not used for training. In the fault identification phase, a 0% of false alarm rate for the control runs is achieved.

  4. An automated satellite cloud classification scheme using self-organizing maps: Alternative ISCCP weather states

    Science.gov (United States)

    McDonald, Adrian J.; Cassano, John J.; Jolly, Ben; Parsons, Simon; Schuddeboom, Alex

    2016-11-01

    This study explores the application of the self-organizing map (SOM) methodology to cloud classification. In particular, the SOM is applied to the joint frequency distribution of the cloud top pressure and optical depth from the International Satellite Cloud Climatology Project (ISCCP) D1 data set. We demonstrate that this scheme produces clusters which have geographical and seasonal patterns similar to those produced in previous studies using the k-means clustering technique but potentially provides complementary information. For example, this study identifies a wider range of clusters representative of low cloud cover states with distinct geographic patterns. We also demonstrate that two rather similar clusters, which might be considered the same cloud regime in other classifications, are distinct based on the seasonal variation of their geographic distributions and their cloud radiative effect in the shortwave. Examination of the transitions between regimes at particular geographic positions between one day and the next also shows that the SOM produces an objective organization of the various cloud regimes that can aid in their interpretation. This is also supported by examination of the SOM's Sammon map and correlations between neighboring nodes geographic distributions. Ancillary ERA-Interim reanalysis output also allows us to demonstrate that the clusters, identified based on the joint histograms, are related to an ordered continuum of vertical velocity profiles and two-dimensional vertical velocity versus lower tropospheric stability histograms which have a clear structure within the SOM. The different nodes can also be separated by their longwave and shortwave cloud radiative effect at the top of the atmosphere.

  5. Assessing Rotation-Invariant Feature Classification for Automated Wildebeest Population Counts.

    Directory of Open Access Journals (Sweden)

    Colin J Torney

    Full Text Available Accurate and on-demand animal population counts are the holy grail for wildlife conservation organizations throughout the world because they enable fast and responsive adaptive management policies. While the collection of image data from camera traps, satellites, and manned or unmanned aircraft has advanced significantly, the detection and identification of animals within images remains a major bottleneck since counting is primarily conducted by dedicated enumerators or citizen scientists. Recent developments in the field of computer vision suggest a potential resolution to this issue through the use of rotation-invariant object descriptors combined with machine learning algorithms. Here we implement an algorithm to detect and count wildebeest from aerial images collected in the Serengeti National Park in 2009 as part of the biennial wildebeest count. We find that the per image error rates are greater than, but comparable to, two separate human counts. For the total count, the algorithm is more accurate than both manual counts, suggesting that human counters have a tendency to systematically over or under count images. While the accuracy of the algorithm is not yet at an acceptable level for fully automatic counts, our results show this method is a promising avenue for further research and we highlight specific areas where future research should focus in order to develop fast and accurate enumeration of aerial count data. If combined with a bespoke image collection protocol, this approach may yield a fully automated wildebeest count in the near future.

  6. Automated Arabidopsis plant root cell segmentation based on SVM classification and region merging.

    Science.gov (United States)

    Marcuzzo, Monica; Quelhas, Pedro; Campilho, Ana; Mendonça, Ana Maria; Campilho, Aurélio

    2009-09-01

    To obtain development information of individual plant cells, it is necessary to perform in vivo imaging of the specimen under study, through time-lapse confocal microscopy. Automation of cell detection/marking process is important to provide research tools in order to ease the search for special events, such as cell division. In this paper we discuss an automatic cell detection approach for Arabidopsis thaliana based on segmentation, which selects the best cell candidates from a starting watershed-based image segmentation and improves the result by merging adjacent regions. The selection of individual cells is obtained using a support vector machine (SVM) classifier, based on a cell descriptor constructed from the shape and edge strength of the cells' contour. In addition we proposed a novel cell merging criterion based on edge strength along the line that connects adjacent cells' centroids, which is a valuable tool in the reduction of cell over-segmentation. The result is largely pruned of badly segmented and over-segmented cells, thus facilitating the study of cells. When comparing the results after merging with the basic watershed segmentation, we obtain 1.5% better coverage (increase in F-measure) and up to 27% better precision in correct cell segmentation.

  7. An Automated Strategy for Unbiased Morphometric Analyses and Classifications of Growth Cones In Vitro.

    Directory of Open Access Journals (Sweden)

    Daryan Chitsaz

    Full Text Available During neural circuit development, attractive or repulsive guidance cue molecules direct growth cones (GCs to their targets by eliciting cytoskeletal remodeling, which is reflected in their morphology. The experimental power of in vitro neuronal cultures to assay this process and its molecular mechanisms is well established, however, a method to rapidly find and quantify multiple morphological aspects of GCs is lacking. To this end, we have developed a free, easy to use, and fully automated Fiji macro, Conographer, which accurately identifies and measures many morphological parameters of GCs in 2D explant culture images. These measurements are then subjected to principle component analysis and k-means clustering to mathematically classify the GCs as "collapsed" or "extended". The morphological parameters measured for each GC are found to be significantly different between collapsed and extended GCs, and are sufficient to classify GCs as such with the same level of accuracy as human observers. Application of a known collapse-inducing ligand results in significant changes in all parameters, resulting in an increase in 'collapsed' GCs determined by k-means clustering, as expected. Our strategy provides a powerful tool for exploring the relationship between GC morphology and guidance cue signaling, which in particular will greatly facilitate high-throughput studies of the effects of drugs, gene silencing or overexpression, or any other experimental manipulation in the context of an in vitro axon guidance assay.

  8. Increasing reticle inspection efficiency and reducing wafer printchecks at 14nm using automated defect classification and simulation

    Science.gov (United States)

    Paracha, Shazad; Goodman, Eliot; Eynon, Benjamin G.; Noyes, Ben F.; Ha, Steven; Kim, Jong-Min; Lee, Dong-Seok; Lee, Dong-Heok; Cho, Sang-Soo; Ham, Young M.; Vacca, Anthony D.; Fiekowsky, Peter J.; Fiekowsky, Daniel I.

    2014-10-01

    IC fabs inspect critical masks on a regular basis to ensure high wafer yields. These requalification inspections are costly for many reasons including the capital equipment, system maintenance, and labor costs. In addition, masks typically remain in the "requal" phase for extended, non-productive periods of time. The overall "requal" cycle time in which reticles remain non-productive is challenging to control. Shipping schedules can slip when wafer lots are put on hold until the master critical layer reticle is returned to production. Unfortunately, substituting backup critical layer reticles can significantly reduce an otherwise tightly controlled process window adversely affecting wafer yields. One major requal cycle time component is the disposition process of mask inspections containing hundreds of defects. Not only is precious non-productive time extended by reviewing hundreds of potentially yield-limiting detections, each additional classification increases the risk of manual review techniques accidentally passing real yield limiting defects. Even assuming all defects of interest are flagged by operators, how can any person's judgment be confident regarding lithographic impact of such defects? The time reticles spend away from scanners combined with potential yield loss due to lithographic uncertainty presents significant cycle time loss and increased production costs An automatic defect analysis system (ADAS), which has been in fab production for numerous years, has been improved to handle the new challenges of 14nm node automate reticle defect classification by simulating each defect's printability under the intended illumination conditions. In this study, we have created programmed defects on a production 14nm node critical-layer reticle. These defects have been analyzed with lithographic simulation software and compared to the results of both AIMS optical simulation and to actual wafer prints.

  9. Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles.

    Science.gov (United States)

    Barker, Jocelyn; Hoogi, Assaf; Depeursinge, Adrien; Rubin, Daniel L

    2016-05-01

    Computerized analysis of digital pathology images offers the potential of improving clinical care (e.g. automated diagnosis) and catalyzing research (e.g. discovering disease subtypes). There are two key challenges thwarting computerized analysis of digital pathology images: first, whole slide pathology images are massive, making computerized analysis inefficient, and second, diverse tissue regions in whole slide images that are not directly relevant to the disease may mislead computerized diagnosis algorithms. We propose a method to overcome both of these challenges that utilizes a coarse-to-fine analysis of the localized characteristics in pathology images. An initial surveying stage analyzes the diversity of coarse regions in the whole slide image. This includes extraction of spatially localized features of shape, color and texture from tiled regions covering the slide. Dimensionality reduction of the features assesses the image diversity in the tiled regions and clustering creates representative groups. A second stage provides a detailed analysis of a single representative tile from each group. An Elastic Net classifier produces a diagnostic decision value for each representative tile. A weighted voting scheme aggregates the decision values from these tiles to obtain a diagnosis at the whole slide level. We evaluated our method by automatically classifying 302 brain cancer cases into two possible diagnoses (glioblastoma multiforme (N = 182) versus lower grade glioma (N = 120)) with an accuracy of 93.1% (p < 0.001). We also evaluated our method in the dataset provided for the 2014 MICCAI Pathology Classification Challenge, in which our method, trained and tested using 5-fold cross validation, produced a classification accuracy of 100% (p < 0.001). Our method showed high stability and robustness to parameter variation, with accuracy varying between 95.5% and 100% when evaluated for a wide range of parameters. Our approach may be useful to automatically

  10. Automated segmentation by pixel classification of retinal layers in ophthalmic OCT images.

    Science.gov (United States)

    Vermeer, K A; van der Schoot, J; Lemij, H G; de Boer, J F

    2011-06-01

    Current OCT devices provide three-dimensional (3D) in-vivo images of the human retina. The resulting very large data sets are difficult to manually assess. Automated segmentation is required to automatically process the data and produce images that are clinically useful and easy to interpret. In this paper, we present a method to segment the retinal layers in these images. Instead of using complex heuristics to define each layer, simple features are defined and machine learning classifiers are trained based on manually labeled examples. When applied to new data, these classifiers produce labels for every pixel. After regularization of the 3D labeled volume to produce a surface, this results in consistent, three-dimensionally segmented layers that match known retinal morphology. Six labels were defined, corresponding to the following layers: Vitreous, retinal nerve fiber layer (RNFL), ganglion cell layer & inner plexiform layer, inner nuclear layer & outer plexiform layer, photoreceptors & retinal pigment epithelium and choroid. For both normal and glaucomatous eyes that were imaged with a Spectralis (Heidelberg Engineering) OCT system, the five resulting interfaces were compared between automatic and manual segmentation. RMS errors for the top and bottom of the retina were between 4 and 6 μm, while the errors for intra-retinal interfaces were between 6 and 15 μm. The resulting total retinal thickness maps corresponded with known retinal morphology. RNFL thickness maps were compared to GDx (Carl Zeiss Meditec) thickness maps. Both maps were mostly consistent but local defects were better visualized in OCT-derived thickness maps.

  11. Automated Thermal Image Processing for Detection and Classification of Birds and Bats - FY2012 Annual Report

    Energy Technology Data Exchange (ETDEWEB)

    Duberstein, Corey A.; Matzner, Shari; Cullinan, Valerie I.; Virden, Daniel J.; Myers, Joshua R.; Maxwell, Adam R.

    2012-09-01

    Surveying wildlife at risk from offshore wind energy development is difficult and expensive. Infrared video can be used to record birds and bats that pass through the camera view, but it is also time consuming and expensive to review video and determine what was recorded. We proposed to conduct algorithm and software development to identify and to differentiate thermally detected targets of interest that would allow automated processing of thermal image data to enumerate birds, bats, and insects. During FY2012 we developed computer code within MATLAB to identify objects recorded in video and extract attribute information that describes the objects recorded. We tested the efficiency of track identification using observer-based counts of tracks within segments of sample video. We examined object attributes, modeled the effects of random variability on attributes, and produced data smoothing techniques to limit random variation within attribute data. We also began drafting and testing methodology to identify objects recorded on video. We also recorded approximately 10 hours of infrared video of various marine birds, passerine birds, and bats near the Pacific Northwest National Laboratory (PNNL) Marine Sciences Laboratory (MSL) at Sequim, Washington. A total of 6 hours of bird video was captured overlooking Sequim Bay over a series of weeks. An additional 2 hours of video of birds was also captured during two weeks overlooking Dungeness Bay within the Strait of Juan de Fuca. Bats and passerine birds (swallows) were also recorded at dusk on the MSL campus during nine evenings. An observer noted the identity of objects viewed through the camera concurrently with recording. These video files will provide the information necessary to produce and test software developed during FY2013. The annotation will also form the basis for creation of a method to reliably identify recorded objects.

  12. Automated Image Sampling and Classification Can Be Used to Explore Perceived Naturalness of Urban Spaces.

    Directory of Open Access Journals (Sweden)

    Roger Hyam

    Full Text Available The psychological restorative effects of exposure to nature are well established and extend to just viewing of images of nature. A previous study has shown that Perceived Naturalness (PN of images correlates with their restorative value. This study tests whether it is possible to detect degree of PN of images using an image classifier. It takes images that have been scored by humans for PN (including a subset that have been assessed for restorative value and passes them through the Google Vision API image classification service. The resulting labels are assigned to broad semantic classes to create a Calculated Semantic Naturalness (CSN metric for each image. It was found that CSN correlates with PN. CSN was then calculated for a geospatial sampling of Google Street View images across the city of Edinburgh. CSN was found to correlate with PN in this sample also indicating the technique may be useful in large scale studies. Because CSN correlates with PN which correlates with restorativeness it is suggested that CSN or a similar measure may be useful in automatically detecting restorative images and locations. In an exploratory aside CSN was not found to correlate with an indicator of socioeconomic deprivation.

  13. ViCTree: An automated framework for taxonomic classification from protein sequences.

    Science.gov (United States)

    Modha, Sejal; Thanki, Anil; Cotmore, Susan F; Davison, Andrew J; Hughes, Joseph

    2018-02-20

    The increasing rate of submission of genetic sequences into public databases is providing a growing resource for classifying the organisms that these sequences represent. To aid viral classification, we have developed ViCTree, which automatically integrates the relevant sets of sequences in NCBI GenBank and transforms them into an interactive maximum likelihood phylogenetic tree that can be updated automatically. ViCTree incorporates ViCTreeView, which is a JavaScript-based visualisation tool that enables the tree to be explored interactively in the context of pairwise distance data. To demonstrate utility, ViCTree was applied to subfamily Densovirinae of family Parvoviridae. This led to the identification of six new species of insect virus. ViCTree is open-source and can be run on any Linux- or Unix-based computer or cluster. A tutorial, the documentation and the source code are available under a GPL3 license, and can be accessed at http://bioinformatics.cvr.ac.uk/victree_web/. sejal.modha@glasgow.ac.uk.

  14. VizieR Online Data Catalog: GALAH semi-automated classification scheme (Traven+, 2017)

    Science.gov (United States)

    Traven, G.; Matijevic, G.; Zwitter, T.; Zerjal, M.; Kos, J.; Asplund, M.; Bland-Hawthorn, J.; Casey, A. R.; de Silva, G.; Freeman, K.; Lin, J.; Martell, S. L.; Schlesinger, K. J.; Sharma, S.; Simpson, J. D.; Zucker, D. B.; Anguiano, B.; da Costa, G.; Duong, L.; Horner, J.; Hyde, E. A.; Kafle, P. R.; Munari, U.; Nataf, D.; Navin, C. A.; Reid, W.; Ting, Y.-S.

    2017-04-01

    The GALactic Archaeology with HERMES (GALAH) survey was the main driver for the construction of Hermes (High Efficiency and Resolution Multi-Element Spectrograph), a fiber-fed multi-object spectrograph on the 3.9m Anglo-Australian Telescope. Its spectral resolving power (R) is about 28000, and there is also an R=45000 mode using a slit mask. Hermes has four simultaneous non-contiguous spectral arms centered at 4800, 5761, 6610, and 7740Å, covering about 1000Å in total, including Hα and Hβ lines. About 300000 spectra have been taken to date, including various calibration exposures. However, we concentrate on ~210000 spectra recorded before 2016 January 30. We devise a custom classification procedure which is based on two independently developed methods, the novel dimensionality reduction technique t-SNE (t-distributed stochastic neighbor embedding; van der Maaten & Hinton 2008, Journal of Machine Learning Research 9, 2579) and the renowned clustering algorithm DBSCAN (Ester+ 1996, Proc. 2nd Int. Conf. on KDD, 226 ed. E. Simoudis, J. Han, and U. Fayyad). (4 data files).

  15. Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN classification method

    Directory of Open Access Journals (Sweden)

    D.A. Adeniyi

    2016-01-01

    Full Text Available The major problem of many on-line web sites is the presentation of many choices to the client at a time; this usually results to strenuous and time consuming task in finding the right product or information on the site. In this work, we present a study of automatic web usage data mining and recommendation system based on current user behavior through his/her click stream data on the newly developed Really Simple Syndication (RSS reader website, in order to provide relevant information to the individual without explicitly asking for it. The K-Nearest-Neighbor (KNN classification method has been trained to be used on-line and in Real-Time to identify clients/visitors click stream data, matching it to a particular user group and recommend a tailored browsing option that meet the need of the specific user at a particular time. To achieve this, web users RSS address file was extracted, cleansed, formatted and grouped into meaningful session and data mart was developed. Our result shows that the K-Nearest Neighbor classifier is transparent, consistent, straightforward, simple to understand, high tendency to possess desirable qualities and easy to implement than most other machine learning techniques specifically when there is little or no prior knowledge about data distribution.

  16. Automated age-related macular degeneration classification in OCT using unsupervised feature learning

    Science.gov (United States)

    Venhuizen, Freerk G.; van Ginneken, Bram; Bloemen, Bart; van Grinsven, Mark J. J. P.; Philipsen, Rick; Hoyng, Carel; Theelen, Thomas; Sánchez, Clara I.

    2015-03-01

    Age-related Macular Degeneration (AMD) is a common eye disorder with high prevalence in elderly people. The disease mainly affects the central part of the retina, and could ultimately lead to permanent vision loss. Optical Coherence Tomography (OCT) is becoming the standard imaging modality in diagnosis of AMD and the assessment of its progression. However, the evaluation of the obtained volumetric scan is time consuming, expensive and the signs of early AMD are easy to miss. In this paper we propose a classification method to automatically distinguish AMD patients from healthy subjects with high accuracy. The method is based on an unsupervised feature learning approach, and processes the complete image without the need for an accurate pre-segmentation of the retina. The method can be divided in two steps: an unsupervised clustering stage that extracts a set of small descriptive image patches from the training data, and a supervised training stage that uses these patches to create a patch occurrence histogram for every image on which a random forest classifier is trained. Experiments using 384 volume scans show that the proposed method is capable of identifying AMD patients with high accuracy, obtaining an area under the Receiver Operating Curve of 0:984. Our method allows for a quick and reliable assessment of the presence of AMD pathology in OCT volume scans without the need for accurate layer segmentation algorithms.

  17. Video and accelerometer-based motion analysis for automated surgical skills assessment.

    Science.gov (United States)

    Zia, Aneeq; Sharma, Yachna; Bettadapura, Vinay; Sarin, Eric L; Essa, Irfan

    2018-03-01

    Basic surgical skills of suturing and knot tying are an essential part of medical training. Having an automated system for surgical skills assessment could help save experts time and improve training efficiency. There have been some recent attempts at automated surgical skills assessment using either video analysis or acceleration data. In this paper, we present a novel approach for automated assessment of OSATS-like surgical skills and provide an analysis of different features on multi-modal data (video and accelerometer data). We conduct a large study for basic surgical skill assessment on a dataset that contained video and accelerometer data for suturing and knot-tying tasks. We introduce "entropy-based" features-approximate entropy and cross-approximate entropy, which quantify the amount of predictability and regularity of fluctuations in time series data. The proposed features are compared to existing methods of Sequential Motion Texture, Discrete Cosine Transform and Discrete Fourier Transform, for surgical skills assessment. We report average performance of different features across all applicable OSATS-like criteria for suturing and knot-tying tasks. Our analysis shows that the proposed entropy-based features outperform previous state-of-the-art methods using video data, achieving average classification accuracies of 95.1 and 92.2% for suturing and knot tying, respectively. For accelerometer data, our method performs better for suturing achieving 86.8% average accuracy. We also show that fusion of video and acceleration features can improve overall performance for skill assessment. Automated surgical skills assessment can be achieved with high accuracy using the proposed entropy features. Such a system can significantly improve the efficiency of surgical training in medical schools and teaching hospitals.

  18. Automated time activity classification based on global positioning system (GPS) tracking data.

    Science.gov (United States)

    Wu, Jun; Jiang, Chengsheng; Houston, Douglas; Baker, Dean; Delfino, Ralph

    2011-11-14

    Air pollution epidemiological studies are increasingly using global positioning system (GPS) to collect time-location data because they offer continuous tracking, high temporal resolution, and minimum reporting burden for participants. However, substantial uncertainties in the processing and classifying of raw GPS data create challenges for reliably characterizing time activity patterns. We developed and evaluated models to classify people's major time activity patterns from continuous GPS tracking data. We developed and evaluated two automated models to classify major time activity patterns (i.e., indoor, outdoor static, outdoor walking, and in-vehicle travel) based on GPS time activity data collected under free living conditions for 47 participants (N = 131 person-days) from the Harbor Communities Time Location Study (HCTLS) in 2008 and supplemental GPS data collected from three UC-Irvine research staff (N = 21 person-days) in 2010. Time activity patterns used for model development were manually classified by research staff using information from participant GPS recordings, activity logs, and follow-up interviews. We evaluated two models: (a) a rule-based model that developed user-defined rules based on time, speed, and spatial location, and (b) a random forest decision tree model. Indoor, outdoor static, outdoor walking and in-vehicle travel activities accounted for 82.7%, 6.1%, 3.2% and 7.2% of manually-classified time activities in the HCTLS dataset, respectively. The rule-based model classified indoor and in-vehicle travel periods reasonably well (Indoor: sensitivity > 91%, specificity > 80%, and precision > 96%; in-vehicle travel: sensitivity > 71%, specificity > 99%, and precision > 88%), but the performance was moderate for outdoor static and outdoor walking predictions. No striking differences in performance were observed between the rule-based and the random forest models. The random forest model was fast and easy to execute, but was likely less robust

  19. Automated Multiclass Classification of Spontaneous EEG Activity in Alzheimer’s Disease and Mild Cognitive Impairment

    Directory of Open Access Journals (Sweden)

    Saúl J. Ruiz-Gómez

    2018-01-01

    Full Text Available The discrimination of early Alzheimer’s disease (AD and its prodromal form (i.e., mild cognitive impairment, MCI from cognitively healthy control (HC subjects is crucial since the treatment is more effective in the first stages of the dementia. The aim of our study is to evaluate the usefulness of a methodology based on electroencephalography (EEG to detect AD and MCI. EEG rhythms were recorded from 37 AD patients, 37 MCI subjects and 37 HC subjects. Artifact-free trials were analyzed by means of several spectral and nonlinear features: relative power in the conventional frequency bands, median frequency, individual alpha frequency, spectral entropy, Lempel–Ziv complexity, central tendency measure, sample entropy, fuzzy entropy, and auto-mutual information. Relevance and redundancy analyses were also conducted through the fast correlation-based filter (FCBF to derive an optimal set of them. The selected features were used to train three different models aimed at classifying the trials: linear discriminant analysis (LDA, quadratic discriminant analysis (QDA and multi-layer perceptron artificial neural network (MLP. Afterwards, each subject was automatically allocated in a particular group by applying a trial-based majority vote procedure. After feature extraction, the FCBF method selected the optimal set of features: individual alpha frequency, relative power at delta frequency band, and sample entropy. Using the aforementioned set of features, MLP showed the highest diagnostic performance in determining whether a subject is not healthy (sensitivity of 82.35% and positive predictive value of 84.85% for HC vs. all classification task and whether a subject does not suffer from AD (specificity of 79.41% and negative predictive value of 84.38% for AD vs. all comparison. Our findings suggest that our methodology can help physicians to discriminate AD, MCI and HC.

  20. Automated cloud classification using a ground based infra-red camera and texture analysis techniques

    Science.gov (United States)

    Rumi, Emal; Kerr, David; Coupland, Jeremy M.; Sandford, Andrew P.; Brettle, Mike J.

    2013-10-01

    Clouds play an important role in influencing the dynamics of local and global weather and climate conditions. Continuous monitoring of clouds is vital for weather forecasting and for air-traffic control. Convective clouds such as Towering Cumulus (TCU) and Cumulonimbus clouds (CB) are associated with thunderstorms, turbulence and atmospheric instability. Human observers periodically report the presence of CB and TCU clouds during operational hours at airports and observatories; however such observations are expensive and time limited. Robust, automatic classification of cloud type using infrared ground-based instrumentation offers the advantage of continuous, real-time (24/7) data capture and the representation of cloud structure in the form of a thermal map, which can greatly help to characterise certain cloud formations. The work presented here utilised a ground based infrared (8-14 μm) imaging device mounted on a pan/tilt unit for capturing high spatial resolution sky images. These images were processed to extract 45 separate textural features using statistical and spatial frequency based analytical techniques. These features were used to train a weighted k-nearest neighbour (KNN) classifier in order to determine cloud type. Ground truth data were obtained by inspection of images captured simultaneously from a visible wavelength colour camera at the same installation, with approximately the same field of view as the infrared device. These images were classified by a trained cloud observer. Results from the KNN classifier gave an encouraging success rate. A Probability of Detection (POD) of up to 90% with a Probability of False Alarm (POFA) as low as 16% was achieved.

  1. Entropy-based Measures of EEG Arousals as Biomarkers for Sleep Dynamics: Applications to Hypertension

    Science.gov (United States)

    Jamasebi, Reza; Redline, Susan; Patel, Sanjay R; Loparo, Kenneth A.

    2008-01-01

    Study Objectives: We propose a generation of PSG-derived measures that using entropy can quantify temporal patterns of sleep, and investigate the role of these measures as predictors of hypertension. We also investigate the influence of age on these entropy-based measures as compared to traditional indices. Design and Setting: Cross-sectional analyses of the association between hypertension status with traditional PSG and novel measures using adjusted and unadjusted logistic regression models. The novel measures were developed to quantify variability of the arousal event process. Patients or Participants: Analyses were based on a subsample of subjects from the Cleveland Family Study with clearly disparate hypertension status. Measurements and Results: Among traditional PSG indices, the apnea hypopnea index (AHI) has the highest Odds Ratio (unadjusted and adjusted for age, gender, race, BMI: OR = 2.36 (95% CI: 1.48, 3.75, P = 0.0003) and 1.18, (95% CI: 0.76, 1.84, P = 0.46), respectively). The best predictor among the entropy-based measures is derived from analysis of the temporal patterns of arousal duration with unadjusted and adjusted ORs of 1.36 (95% CI: 1.08, 1.71, P = 0.0085) and 2.08 (95% CI: 1.19, 3.64, P = 0.01), respectively. Conclusions: Our findings suggest that when adjusted for common confounders such as age, gender, race, and BMI, the entropy-based features that quantify the variability of the arousal event process are more strongly associated with hypertension as compared to traditional PSG indices; they are not as strongly influenced by age as are the traditional indices. The result implies that the regularity of arousals may be an important feature associated with hypertension. These measures may provide a powerful tool for discriminating individuals at risk for comorbidities, such as hypertension, associated with sleep disturbances. Citation: Jamasebi R; Redline S; Patel SR; Loparo KA. Entropy-based measures of EEG arousals as biomarkers for sleep

  2. Wavelet based automated postural event detection and activity classification with single imu - biomed 2013.

    Science.gov (United States)

    Lockhart, Thurmon E; Soangra, Rahul; Zhang, Jian; Wu, Xuefan

    2013-01-01

    and classification algorithm using denoised signals from single wireless IMU placed at sternum. The algorithm was further validated and verified with motion capture system in laboratory environment. Wavelet denoising highlighted postural events and transition durations that further provided clinical information on postural control and motor coordination. The presented method can be applied in real life ambulatory monitoring approaches for assessing condition of elderly.

  3. Classification

    Science.gov (United States)

    Oza, Nikunj C.

    2011-01-01

    A supervised learning task involves constructing a mapping from input data (normally described by several features) to the appropriate outputs. Within supervised learning, one type of task is a classification learning task, in which each output is one or more classes to which the input belongs. In supervised learning, a set of training examples---examples with known output values---is used by a learning algorithm to generate a model. This model is intended to approximate the mapping between the inputs and outputs. This model can be used to generate predicted outputs for inputs that have not been seen before. For example, we may have data consisting of observations of sunspots. In a classification learning task, our goal may be to learn to classify sunspots into one of several types. Each example may correspond to one candidate sunspot with various measurements or just an image. A learning algorithm would use the supplied examples to generate a model that approximates the mapping between each supplied set of measurements and the type of sunspot. This model can then be used to classify previously unseen sunspots based on the candidate's measurements. This chapter discusses methods to perform machine learning, with examples involving astronomy.

  4. On the Entropy Based Associative Memory Model with Higher-Order Correlations

    Directory of Open Access Journals (Sweden)

    Masahiro Nakagawa

    2010-01-01

    Full Text Available In this paper, an entropy based associative memory model will be proposed and applied to memory retrievals with an orthogonal learning model so as to compare with the conventional model based on the quadratic Lyapunov functional to be minimized during the retrieval process. In the present approach, the updating dynamics will be constructed on the basis of the entropy minimization strategy which may be reduced asymptotically to the above-mentioned conventional dynamics as a special case ignoring the higher-order correlations. According to the introduction of the entropy functional, one may involve higer-order correlation effects between neurons in a self-contained manner without any heuristic coupling coefficients as in the conventional manner. In fact we shall show such higher order coupling tensors are to be uniquely determined in the framework of the entropy based approach. From numerical results, it will be found that the presently proposed novel approach realizes much larger memory capacity than that of the quadratic Lyapunov functional approach, e.g., associatron.

  5. A methodology for the automated creation of fuzzy expert systems for ischaemic and arrhythmic beat classification based on a set of rules obtained by a decision tree.

    Science.gov (United States)

    Exarchos, Themis P; Tsipouras, Markos G; Exarchos, Costas P; Papaloukas, Costas; Fotiadis, Dimitrios I; Michalis, Lampros K

    2007-07-01

    In the current work we propose a methodology for the automated creation of fuzzy expert systems, applied in ischaemic and arrhythmic beat classification. The proposed methodology automatically creates a fuzzy expert system from an initial training dataset. The approach consists of three stages: (a) extraction of a crisp set of rules from a decision tree induced from the training dataset, (b) transformation of the crisp set of rules into a fuzzy model and (c) optimization of the fuzzy model's parameters using global optimization. The above methodology is employed in order to create fuzzy expert systems for ischaemic and arrhythmic beat classification in ECG recordings. The fuzzy expert system for ischaemic beat detection is evaluated in a cardiac beat dataset that was constructed using recordings from the European Society of Cardiology ST-T database. The arrhythmic beat classification fuzzy expert system is evaluated using the MIT-BIH arrhythmia database. The fuzzy expert system for ischaemic beat classification reported 91% sensitivity and 92% specificity. The arrhythmic beat classification fuzzy expert system reported 96% average sensitivity and 99% average specificity for all categories. The proposed methodology provides high accuracy and the ability to interpret the decisions made. The fuzzy expert systems for ischaemic and arrhythmic beat classification compare well with previously reported results, indicating that they could be part of an overall clinical system for ECG analysis and diagnosis.

  6. Web-Enabled Distributed Health-Care Framework for Automated Malaria Parasite Classification: an E-Health Approach.

    Science.gov (United States)

    Maity, Maitreya; Dhane, Dhiraj; Mungle, Tushar; Maiti, A K; Chakraborty, Chandan

    2017-10-26

    Web-enabled e-healthcare system or computer assisted disease diagnosis has a potential to improve the quality and service of conventional healthcare delivery approach. The article describes the design and development of a web-based distributed healthcare management system for medical information and quantitative evaluation of microscopic images using machine learning approach for malaria. In the proposed study, all the health-care centres are connected in a distributed computer network. Each peripheral centre manages its' own health-care service independently and communicates with the central server for remote assistance. The proposed methodology for automated evaluation of parasites includes pre-processing of blood smear microscopic images followed by erythrocytes segmentation. To differentiate between different parasites; a total of 138 quantitative features characterising colour, morphology, and texture are extracted from segmented erythrocytes. An integrated pattern classification framework is designed where four feature selection methods viz. Correlation-based Feature Selection (CFS), Chi-square, Information Gain, and RELIEF are employed with three different classifiers i.e. Naive Bayes', C4.5, and Instance-Based Learning (IB1) individually. Optimal features subset with the best classifier is selected for achieving maximum diagnostic precision. It is seen that the proposed method achieved with 99.2% sensitivity and 99.6% specificity by combining CFS and C4.5 in comparison with other methods. Moreover, the web-based tool is entirely designed using open standards like Java for a web application, ImageJ for image processing, and WEKA for data mining considering its feasibility in rural places with minimal health care facilities.

  7. Automated morphological analysis of bone marrow cells in microscopic images for diagnosis of leukemia: nucleus-plasma separation and cell classification using a hierarchical tree model of hematopoesis

    Science.gov (United States)

    Krappe, Sebastian; Wittenberg, Thomas; Haferlach, Torsten; Münzenmayer, Christian

    2016-03-01

    The morphological differentiation of bone marrow is fundamental for the diagnosis of leukemia. Currently, the counting and classification of the different types of bone marrow cells is done manually under the use of bright field microscopy. This is a time-consuming, subjective, tedious and error-prone process. Furthermore, repeated examinations of a slide may yield intra- and inter-observer variances. For that reason a computer assisted diagnosis system for bone marrow differentiation is pursued. In this work we focus (a) on a new method for the separation of nucleus and plasma parts and (b) on a knowledge-based hierarchical tree classifier for the differentiation of bone marrow cells in 16 different classes. Classification trees are easily interpretable and understandable and provide a classification together with an explanation. Using classification trees, expert knowledge (i.e. knowledge about similar classes and cell lines in the tree model of hematopoiesis) is integrated in the structure of the tree. The proposed segmentation method is evaluated with more than 10,000 manually segmented cells. For the evaluation of the proposed hierarchical classifier more than 140,000 automatically segmented bone marrow cells are used. Future automated solutions for the morphological analysis of bone marrow smears could potentially apply such an approach for the pre-classification of bone marrow cells and thereby shortening the examination time.

  8. Risk Contagion in Chinese Banking Industry: A Transfer Entropy-Based Analysis

    Directory of Open Access Journals (Sweden)

    Jianping Li

    2013-12-01

    Full Text Available What is the impact of a bank failure on the whole banking industry? To resolve this issue, the paper develops a transfer entropy-based method to determine the interbank exposure matrix between banks. This method constructs the interbank market structure by calculating the transfer entropy matrix using bank stock price sequences. This paper also evaluates the stability of Chinese banking system by simulating the risk contagion process. This paper contributes to the literature on interbank contagion mainly in two ways: it establishes a convincing connection between interbank market and transfer entropy, and exploits the market information (stock price rather than presumptions to determine the interbank exposure matrix. Second, the empirical analysis provides an in depth understanding of the stability of the current Chinese banking system.

  9. Special Issue on Entropy-Based Applied Cryptography and Enhanced Security for Ubiquitous Computing

    Directory of Open Access Journals (Sweden)

    James (Jong Hyuk Park

    2016-09-01

    Full Text Available Entropy is a basic and important concept in information theory. It is also often used as a measure of the unpredictability of a cryptographic key in cryptography research areas. Ubiquitous computing (Ubi-comp has emerged rapidly as an exciting new paradigm. In this special issue, we mainly selected and discussed papers related with ore theories based on the graph theory to solve computational problems on cryptography and security, practical technologies; applications and services for Ubi-comp including secure encryption techniques, identity and authentication; credential cloning attacks and countermeasures; switching generator with resistance against the algebraic and side channel attacks; entropy-based network anomaly detection; applied cryptography using chaos function, information hiding and watermark, secret sharing, message authentication, detection and modeling of cyber attacks with Petri Nets, and quantum flows for secret key distribution, etc.

  10. Entropy-based critical reaction time for mixing-controlled reactive transport

    DEFF Research Database (Denmark)

    Chiogna, Gabriele; Rolle, Massimo

    2017-01-01

    Entropy-based metrics, such as the dilution index, have been proposed to quantify dilution and reactive mixing in solute transport problems. In this work, we derive the transient advection dispersion equation for the entropy density of a reactive plume. We restrict our analysis to the case where...... the concentration distribution of the transported species is Gaussian and we observe that, even in case of an instantaneous complete bimolecular reaction, dilution caused by dispersive processes dominates the entropy balance at early times and results in the net increase of the entropy density of a reactive species....... Our results show that, differently from the critical dilution index, the critical reaction time depends on solute transport processes such as advection and hydrodynamic dispersion....

  11. Feature extraction and learning using context cue and Rényi entropy based mutual information

    DEFF Research Database (Denmark)

    Pan, Hong; Olsen, Søren Ingvor; Zhu, Yaping

    2015-01-01

    Feature extraction and learning play a critical role for visual perception tasks. We focus on improving the robustness of the kernel descriptors (KDES) by embedding context cues and further learning a compact and discriminative feature codebook for feature reduction using Rényi entropy based mutu....... Experimental results show that our method has promising potential for visual object recognition and detection applications....... as the information about the underlying labels of the CKD using CSQMI. Thus the resulting codebook and reduced CKD are discriminative. We verify the effectiveness of our method on several public image benchmark datasets such as YaleB, Caltech-101 and CIFAR-10, as well as a challenging chicken feet dataset of our own...

  12. An Entropy-Based Adaptive Hybrid Particle Swarm Optimization for Disassembly Line Balancing Problems

    Directory of Open Access Journals (Sweden)

    Shanli Xiao

    2017-11-01

    Full Text Available In order to improve the product disassembly efficiency, the disassembly line balancing problem (DLBP is transformed into a problem of searching for the optimum path in the directed and weighted graph by constructing the disassembly hierarchy information graph (DHIG. Then, combining the characteristic of the disassembly sequence, an entropy-based adaptive hybrid particle swarm optimization algorithm (AHPSO is presented. In this algorithm, entropy is introduced to measure the changing tendency of population diversity, and the dimension learning, crossover and mutation operator are used to increase the probability of producing feasible disassembly solutions (FDS. Performance of the proposed methodology is tested on the primary problem instances available in the literature, and the results are compared with other evolutionary algorithms. The results show that the proposed algorithm is efficient to solve the complex DLBP.

  13. Entropy Based Analysis of DNS Query Traffic in the Campus Network

    Directory of Open Access Journals (Sweden)

    Dennis Arturo Ludeña Romaña

    2008-10-01

    Full Text Available We carried out the entropy based study on the DNS query traffic from the campus network in a university through January 1st, 2006 to March 31st, 2007. The results are summarized, as follows: (1 The source IP addresses- and query keyword-based entropies change symmetrically in the DNS query traffic from the outside of the campus network when detecting the spam bot activity on the campus network. On the other hand (2, the source IP addresses- and query keywordbased entropies change similarly each other when detecting big DNS query traffic caused by prescanning or distributed denial of service (DDoS attack from the campus network. Therefore, we can detect the spam bot and/or DDoS attack bot by only watching DNS query access traffic.

  14. Multilocus genotypic association with vascular dementia by multifactor dimensionality reduction and entropy-based estimation.

    Science.gov (United States)

    Kim, Younyoung; Park, Jungdae; Lee, Chaeyoung

    2009-10-01

    We conducted a simultaneous analysis of candidate genetic loci for their genotypic association with the susceptibility to vascular dementia (VaD) to put forth the best model for predicting genetic susceptibility to VaD. Individual-locus effects and their epistatic effects on susceptibility to VaD were simultaneously assessed by multifactor dimensionality reduction and entropy-based method. The 23 loci in 12 genes were studied in 207 VaD patients and age-matched and sex-matched 207 controls. The multifactor dimensionality reduction analysis revealed that the best single-locus candidate model included angiotensinogen (AGT) Thr235Met with testing accuracy (TA) of 58.31%, the best two-locus candidate model included AGT Thr235Met and transforming growth factor-beta1 Pro10Leu with TA of 58.06%, the best three-locus candidate model was not significant (P>0.05), and the best four-locus candidate model included transforming growth factor-beta1 Pro10Leu, AGT Thr235Met, sterol regulatory element binding protein 2 G34995T, and leukemia inhibitory factor T4524G with TA of 57.13% (P<0.05). The best four-locus model was, however, still in question because of the inconsistent best model selection by cross-validation. Synergistic epistatic effect of the best two-locus model was proven by entropy-based estimation. The best predictor for genetic susceptibility to VaD was the single-locus model of AGT. The best two-locus model reflecting epistasis would be also employed for predicting its susceptibility. Further studies on the epistasis are to elucidate their underlying mechanisms.

  15. Toward automated classification of acetabular shape in ultrasound for diagnosis of DDH: Contour alpha angle and the rounding index.

    Science.gov (United States)

    Hareendranathan, Abhilash Rakkunedeth; Mabee, Myles; Punithakumar, Kumaradevan; Noga, Michelle; Jaremko, Jacob L

    2016-06-01

    The diagnosis of Developmental Dysplasia of the Hip (DDH) in infants is currently made primarily by ultrasound. However, two-dimensional ultrasound (2DUS) images capture only an incomplete portion of the acetabular shape, and the alpha and beta angles measured on 2DUS for the Graf classification technique show high inter-scan and inter-observer variability. This variability relates partly to the manual determination of the apex point separating the acetabular roof from the ilium during index measurement. This study proposes a new 2DUS image processing technique for semi-automated tracing of the bony surface followed by automatic calculation of two indices: a contour-based alpha angle (αA), and a new modality-independent quantitative rounding index (M). The new index M is independent of the apex point, and can be directly extended to 3D surface models. We tested the proposed indices on a dataset of 114 2DUS scans of infant hips aged between 4 and 183 days scanned using a 12MHz linear transducer. We calculated the manual alpha angle (αM), coverage, contour-based alpha angle and rounding index for each of the recordings and statistically evaluated these indices based on regression analysis, area under the receiver operating characteristic curve (AUC) and analysis of variance (ANOVA). Processing time for calculating αA and M was similar to manual alpha angle measurement, ∼30s per image. Reliability of the new indices was high, with inter-observer intraclass correlation coefficients (ICC) 0.90 for αA and 0.89 for M. For a diagnostic test classifying hips as normal or dysplastic, AUC was 93.0% for αA vs. 92.7% for αM, 91.6% for M alone, and up to 95.7% for combination of M with αM, αA or coverage. The rounding index provides complimentary information to conventional indices such as alpha angle and coverage. Calculation of the contour-based alpha angle and rounding index is rapid, shows potential to improve the reliability and accuracy of DDH diagnosis from 2DUS

  16. AUTOMATED CLASSIFICATION OF LAND COVER USING LANDSAT 8 OLI SURFACE REFLECTANCE PRODUCT AND SPECTRAL PATTERN ANALYSIS CONCEPT - CASE STUDY IN HANOI, VIETNAM

    Directory of Open Access Journals (Sweden)

    D. Nguyen Dinh

    2016-06-01

    Full Text Available Recently USGS released provisional Landsat 8 Surface Reflectance product, which allows conducting land cover mapping over large composed of number of image scenes without necessity of atmospheric correction. In this study, the authors present a new concept for automated classification of land cover. This concept is based on spectral patterns analysis of reflected bands and can be automated using predefined classification rule set constituted of spectral pattern shape, total reflected radiance index (TRRI and ratios of spectral bands. Given a pixel vector B6 = {b1,b2,b3,b4,b5,b6} where b1, b2,...,b6 denote bands 2, 3, ...,7 of OLI sensor respectively. By using the pixel vector B6 we can construct spectral reflectance curve. Each spectral curve is featured by a shape, which can be described in simplified form of an analogue pattern, which is consisted of 15 digits of 0, 1 and 2 showing mutual relative position of spectral vertices. Value of comparison between band i and j is 2 if bj > bi, 1 if bj = bi and 0 if bj i. Simplified spectral pattern is defined by 15 digits as m1,2m1,3m1,4m1,5m1,6m2,3m2,4m2,5m2,6m3,4m3,5m3,6m4,5m4,6m5,6 where mi,j is result of comparison of reflectance between bi and bj and has values of 0, 1 and 2. After construction of SSP for each pixel in the input image, the original image will be decomposed to component images, which contain pixels with the same SRCS pattern. The decomposition can be written analytically by equation A = Σnk=1Ck where A stands for original image with 6 spectral bands, n is number of component images decomposed from A and Ck is component image. For this study, we use Landsat 8 OLI reflectance image LC81270452013352LGN00 and LC81270452015182LGN00. For the decomposition, we use only six reflective bands. Each land cover class is defined by SSP code, threshold values for TRRI and band ratios. Automated classification of land cover was realized with 8 classes: forest, shrub, grass, water, wetland

  17. A Weighted Belief Entropy-Based Uncertainty Measure for Multi-Sensor Data Fusion.

    Science.gov (United States)

    Tang, Yongchuan; Zhou, Deyun; Xu, Shuai; He, Zichang

    2017-04-22

    In real applications, how to measure the uncertain degree of sensor reports before applying sensor data fusion is a big challenge. In this paper, in the frame of Dempster-Shafer evidence theory, a weighted belief entropy based on Deng entropy is proposed to quantify the uncertainty of uncertain information. The weight of the proposed belief entropy is based on the relative scale of a proposition with regard to the frame of discernment (FOD). Compared with some other uncertainty measures in Dempster-Shafer framework, the new measure focuses on the uncertain information represented by not only the mass function, but also the scale of the FOD, which means less information loss in information processing. After that, a new multi-sensor data fusion approach based on the weighted belief entropy is proposed. The rationality and superiority of the new multi-sensor data fusion method is verified according to an experiment on artificial data and an application on fault diagnosis of a motor rotor.

  18. An Entropy-Based Multiobjective Evolutionary Algorithm with an Enhanced Elite Mechanism

    Directory of Open Access Journals (Sweden)

    Yufang Qin

    2012-01-01

    Full Text Available Multiobjective optimization problem (MOP is an important and challenging topic in the fields of industrial design and scientific research. Multi-objective evolutionary algorithm (MOEA has proved to be one of the most efficient algorithms solving the multi-objective optimization. In this paper, we propose an entropy-based multi-objective evolutionary algorithm with an enhanced elite mechanism (E-MOEA, which improves the convergence and diversity of solution set in MOPs effectively. In this algorithm, an enhanced elite mechanism is applied to guide the direction of the evolution of the population. Specifically, it accelerates the population to approach the true Pareto front at the early stage of the evolution process. A strategy based on entropy is used to maintain the diversity of population when the population is near to the Pareto front. The proposed algorithm is executed on widely used test problems, and the simulated results show that the algorithm has better or comparative performances in convergence and diversity of solutions compared with two state-of-the-art evolutionary algorithms: NSGA-II, SPEA2 and the MOSADE.

  19. Entropy-based derivation of generalized distributions for hydrometeorological frequency analysis

    Science.gov (United States)

    Chen, Lu; Singh, Vijay P.

    2018-02-01

    Frequency analysis of hydrometeorological and hydrological extremes is needed for the design of hydraulic and civil infrastructure facilities as well as water resources management. A multitude of distributions have been employed for frequency analysis of these extremes. However, no single distribution has been accepted as a global standard. Employing the entropy theory, this study derived five generalized distributions for frequency analysis that used different kinds of information encoded as constraints. These distributions were the generalized gamma (GG), the generalized beta distribution of the second kind (GB2), and the Halphen type A distribution (Hal-A), Halphen type B distribution (Hal-B) and Halphen type inverse B distribution (Hal-IB), among which the GG and GB2 distribution were previously derived by Papalexiou and Koutsoyiannis (2012) and the Halphen family was first derived using entropy theory in this paper. The entropy theory allowed to estimate parameters of the distributions in terms of the constraints used for their derivation. The distributions were tested using extreme daily and hourly rainfall data. Results show that the root mean square error (RMSE) values were very small, which indicated that the five generalized distributions fitted the extreme rainfall data well. Among them, according to the Akaike information criterion (AIC) values, generally the GB2 and Halphen family gave a better fit. Therefore, those general distributions are one of the best choices for frequency analysis. The entropy-based derivation led to a new way for frequency analysis of hydrometeorological extremes.

  20. MSCT follow-up in malignant lymphoma: comparison of manual linear measurements with semi-automated lymph node analysis for therapy response classification.

    Science.gov (United States)

    Weßling, J; Puesken, M; Koch, R; Kohlhase, N; Persigehl, T; Mesters, R; Heindel, W; Buerke, B

    2012-09-01

    Assignment of semi-automated lymph node analysis compared to manual measurements for therapy response classification of malignant lymphoma in MSCT. MSCT scans of 63 malignant lymphoma patients before and after 2 cycles of chemotherapy (307 target lymph nodes) were evaluated. The long axis diameter (LAD), short axis diameter (SAD) and bi-dimensional WHO were determined manually and semi-automatically. The time for manual and semi-automatic segmentation was evaluated. The ref. standard response was defined as the mean relative change across all manual and semi-automatic measurements (mean manual/semi-automatic LAD, SAD, semi-automatic volume). Statistical analysis encompassed t-test and McNemar's test for clustered data. Response classification per lymph node revealed semi-automated volumetry and bi-dimensional WHO to be significantly more accurate than manual linear metric measurements. Response classification per patient based on RECIST revealed more patients to be correctly classified by semi-automatic measurements, e. g. 96.0 %/92.9 % (WHO bi-dimensional/volume) compared to 85.7/84.1 % for manual LAD and SAD, respectively (mean reduction in misclassified patients of 9.95 %). Considering the use of correction tools, the time expenditure for lymph node segmentation (29.7 ± 17.4 sec) was the same as with the manual approach (29.1 ± 14.5 sec). Semi-automatically derived "lymph node volume" and "bi-dimensional WHO" significantly reduce the number of misclassified patients in the CT follow-up of malignant lymphoma by at least 10 %. However, lymph node volumetry does not outperform bi-dimensional WHO. © Georg Thieme Verlag KG Stuttgart · New York.

  1. An image analysis pipeline for automated classification of imaging light conditions and for quantification of wheat canopy cover time series in field phenotyping.

    Science.gov (United States)

    Yu, Kang; Kirchgessner, Norbert; Grieder, Christoph; Walter, Achim; Hund, Andreas

    2017-01-01

    Robust segmentation of canopy cover (CC) from large amounts of images taken under different illumination/light conditions in the field is essential for high throughput field phenotyping (HTFP). We attempted to address this challenge by evaluating different vegetation indices and segmentation methods for analyzing images taken at varying illuminations throughout the early growth phase of wheat in the field. 40,000 images taken on 350 wheat genotypes in two consecutive years were assessed for this purpose. We proposed an image analysis pipeline that allowed for image segmentation using automated thresholding and machine learning based classification methods and for global quality control of the resulting CC time series. This pipeline enabled accurate classification of imaging light conditions into two illumination scenarios, i.e. high light-contrast (HLC) and low light-contrast (LLC), in a series of continuously collected images by employing a support vector machine (SVM) model. Accordingly, the scenario-specific pixel-based classification models employing decision tree and SVM algorithms were able to outperform the automated thresholding methods, as well as improved the segmentation accuracy compared to general models that did not discriminate illumination differences. The three-band vegetation difference index (NDI3) was enhanced for segmentation by incorporating the HSV-V and the CIE Lab-a color components, i.e. the product images NDI3*V and NDI3*a. Field illumination scenarios can be successfully identified by the proposed image analysis pipeline, and the illumination-specific image segmentation can improve the quantification of CC development. The integrated image analysis pipeline proposed in this study provides great potential for automatically delivering robust data in HTFP.

  2. MSCT follow-up in malignant lymphoma. Comparison of manual linear measurements with semi-automated lymph node analysis for therapy response classification

    International Nuclear Information System (INIS)

    Wessling, J.; Puesken, M.; Kohlhase, N.; Persigehl, T.; Mesters, R.; Heindel, W.; Buerke, B.; Koch, R.

    2012-01-01

    Purpose: Assignment of semi-automated lymph node analysis compared to manual measurements for therapy response classification of malignant lymphoma in MSCT. Materials and Methods: MSCT scans of 63 malignant lymphoma patients before and after 2 cycles of chemotherapy (307 target lymph nodes) were evaluated. The long axis diameter (LAD), short axis diameter (SAD) and bi-dimensional WHO were determined manually and semi-automatically. The time for manual and semi-automatic segmentation was evaluated. The ref. standard response was defined as the mean relative change across all manual and semi-automatic measurements (mean manual/semi-automatic LAD, SAD, semi-automatic volume). Statistical analysis encompassed t-test and McNemar's test for clustered data. Results: Response classification per lymph node revealed semi-automated volumetry and bi-dimensional WHO to be significantly more accurate than manual linear metric measurements. Response classification per patient based on RECIST revealed more patients to be correctly classified by semi-automatic measurements, e.g. 96.0 %/92.9 % (WHO bi-dimensional/volume) compared to 85.7/84.1 % for manual LAD and SAD, respectively (mean reduction in misclassified patients of 9.95 %). Considering the use of correction tools, the time expenditure for lymph node segmentation (29.7 ± 17.4 sec) was the same as with the manual approach (29.1 ± 14.5 sec). Conclusion: Semi-automatically derived 'lymph node volume' and 'bi-dimensional WHO' significantly reduce the number of misclassified patients in the CT follow-up of malignant lymphoma by at least 10 %. However, lymph node volumetry does not outperform bi-dimensional WHO. (orig.)

  3. Sensitivity and specificity of manual and automated measurements of reticulocyte parameters for classification of anemia in dogs: 174 cases (1993-2013).

    Science.gov (United States)

    Paltrinieri, Saverio; Rossi, Gabriele; Manca, Michela; Scarpa, Paola; Vitiello, Tiziana; Giordano, Alessia

    2016-10-01

    OBJECTIVE To assess sensitivity and specificity of manual and automated measurements of reticulocyte percentage, number, and production index for classification of anemia in dogs. DESIGN Retrospective case series SAMPLE 174 blood smears from client-owned dogs with anemia collected between 1993 and 2013 for which reticulocyte parameters were determined manually (nonregenerative anemia, 22; preregenerative anemia, 23; regenerative anemia, 28) or with an automated laser-based counter (nonregenerative anemia, 66; preregenerative anemia, 17; regenerative anemia, 18). PROCEDURES Diagnostic performance was evaluated with receiver operating characteristic (ROC) curves by considering preregenerative anemia as nonregenerative or regenerative. Sensitivity, specificity, and positive likelihood ratio were calculated by use of cutoffs determined from ROC curves or published reference limits. RESULTS Considering preregenerative anemia as non regenerative, areas under the curve (AUCs) for reticulocyte percentage, number, and production index were 97%, 93%, and 91% for manual counting and 93%, 90%, and 93% for automated counting. Sensitivity, specificity, and positive likelihood ratio were 82% to 86%, 82% to 87%, and 4.6 to 6.4, respectively. Considering preregenerative anemia as regenerative, AUCs were 77%, 82%, and 80% for manual counting and 81%, 82%, and 92% for automated counting. Sensitivity, specificity, and positive likelihood ratio were 72% to 74%, 76 to 87%, and 2.7 to 6.2, respectively. CONCLUSIONS AND CLINICAL RELEVANCE Whereas all reticulocyte parameters identified regeneration in anemic dogs, the performance of specific parameters was dependent on the method used. Findings suggested that lower cutoffs than published reference limits are preferred for reticulocyte number and production index and higher cutoffs are preferred for reticulocyte percentage. Reticulocyte production index may be useful when the pretest probability of regeneration is moderate.

  4. Spider Neurotoxins, Short Linear Cationic Peptides and Venom Protein Classification Improved by an Automated Competition between Exhaustive Profile HMM Classifiers.

    Science.gov (United States)

    Koua, Dominique; Kuhn-Nentwig, Lucia

    2017-08-08

    Spider venoms are rich cocktails of bioactive peptides, proteins, and enzymes that are being intensively investigated over the years. In order to provide a better comprehension of that richness, we propose a three-level family classification system for spider venom components. This classification is supported by an exhaustive set of 219 new profile hidden Markov models (HMMs) able to attribute a given peptide to its precise peptide type, family, and group. The proposed classification has the advantages of being totally independent from variable spider taxonomic names and can easily evolve. In addition to the new classifiers, we introduce and demonstrate the efficiency of hmmcompete , a new standalone tool that monitors HMM-based family classification and, after post-processing the result, reports the best classifier when multiple models produce significant scores towards given peptide queries. The combined used of hmmcompete and the new spider venom component-specific classifiers demonstrated 96% sensitivity to properly classify all known spider toxins from the UniProtKB database. These tools are timely regarding the important classification needs caused by the increasing number of peptides and proteins generated by transcriptomic projects.

  5. An entropy-based improved k-top scoring pairs (TSP) method for ...

    African Journals Online (AJOL)

    DR. NJ TONUKARI

    2012-06-05

    Jun 5, 2012 ... machine learning approach without any parameter. The classification rules of TSP contain only a pair of genes. Moreover, because in some datasets the classification rules of TSP classifier will change with the addition or deletion of training samples, Tan et al. (2005) proposed an improved method named ...

  6. Using Global Positioning Systems (GPS) and temperature data to generate time-activity classifications for estimating personal exposure in air monitoring studies: an automated method.

    Science.gov (United States)

    Nethery, Elizabeth; Mallach, Gary; Rainham, Daniel; Goldberg, Mark S; Wheeler, Amanda J

    2014-05-08

    Personal exposure studies of air pollution generally use self-reported diaries to capture individuals' time-activity data. Enhancements in the accuracy, size, memory and battery life of personal Global Positioning Systems (GPS) units have allowed for higher resolution tracking of study participants' locations. Improved time-activity classifications combined with personal continuous air pollution sampling can improve assessments of location-related air pollution exposures for health studies. Data was collected using a GPS and personal temperature from 54 children with asthma living in Montreal, Canada, who participated in a 10-day personal air pollution exposure study. A method was developed that incorporated personal temperature data and then matched a participant's position against available spatial data (i.e., road networks) to generate time-activity categories. The diary-based and GPS-generated time-activity categories were compared and combined with continuous personal PM2.5 data to assess the impact of exposure misclassification when using diary-based methods. There was good agreement between the automated method and the diary method; however, the automated method (means: outdoors = 5.1%, indoors other =9.8%) estimated less time spent in some locations compared to the diary method (outdoors = 6.7%, indoors other = 14.4%). Agreement statistics (AC1 = 0.778) suggest 'good' agreement between methods over all location categories. However, location categories (Outdoors and Transit) where less time is spent show greater disagreement: e.g., mean time "Indoors Other" using the time-activity diary was 14.4% compared to 9.8% using the automated method. While mean daily time "In Transit" was relatively consistent between the methods, the mean daily exposure to PM2.5 while "In Transit" was 15.9 μg/m3 using the automated method compared to 6.8 μg/m3 using the daily diary. Mean times spent in different locations as categorized by a GPS-based method were

  7. Radiological assessment of breast density by visual classification (BI-RADS) compared to automated volumetric digital software (Quantra): implications for clinical practice.

    Science.gov (United States)

    Regini, Elisa; Mariscotti, Giovanna; Durando, Manuela; Ghione, Gianluca; Luparia, Andrea; Campanino, Pier Paolo; Bianchi, Caterina Chiara; Bergamasco, Laura; Fonio, Paolo; Gandini, Giovanni

    2014-10-01

    This study was done to assess breast density on digital mammography and digital breast tomosynthesis according to the visual Breast Imaging Reporting and Data System (BI-RADS) classification, to compare visual assessment with Quantra software for automated density measurement, and to establish the role of the software in clinical practice. We analysed 200 digital mammograms performed in 2D and 3D modality, 100 of which positive for breast cancer and 100 negative. Radiological density was assessed with the BI-RADS classification; a Quantra density cut-off value was sought on the 2D images only to discriminate between BI-RADS categories 1-2 and BI-RADS 3-4. Breast density was correlated with age, use of hormone therapy, and increased risk of disease. The agreement between the 2D and 3D assessments of BI-RADS density was high (K 0.96). A cut-off value of 21% is that which allows us to best discriminate between BI-RADS categories 1-2 and 3-4. Breast density was negatively correlated to age (r = -0.44) and positively to use of hormone therapy (p = 0.0004). Quantra density was higher in breasts with cancer than in healthy breasts. There is no clear difference between the visual assessments of density on 2D and 3D images. Use of the automated system requires the adoption of a cut-off value (set at 21%) to effectively discriminate BI-RADS 1-2 and 3-4, and could be useful in clinical practice.

  8. Low-Pass Filtering Approach via Empirical Mode Decomposition Improves Short-Scale Entropy-Based Complexity Estimation of QT Interval Variability in Long QT Syndrome Type 1 Patients

    Directory of Open Access Journals (Sweden)

    Vlasta Bari

    2014-09-01

    Full Text Available Entropy-based complexity of cardiovascular variability at short time scales is largely dependent on the noise and/or action of neural circuits operating at high frequencies. This study proposes a technique for canceling fast variations from cardiovascular variability, thus limiting the effect of these overwhelming influences on entropy-based complexity. The low-pass filtering approach is based on the computation of the fastest intrinsic mode function via empirical mode decomposition (EMD and its subtraction from the original variability. Sample entropy was exploited to estimate complexity. The procedure was applied to heart period (HP and QT (interval from Q-wave onset to T-wave end variability derived from 24-hour Holter recordings in 14 non-mutation carriers (NMCs and 34 mutation carriers (MCs subdivided into 11 asymptomatic MCs (AMCs and 23 symptomatic MCs (SMCs. All individuals belonged to the same family developing long QT syndrome type 1 (LQT1 via KCNQ1-A341V mutation. We found that complexity indexes computed over EMD-filtered QT variability differentiated AMCs from NMCs and detected the effect of beta-blocker therapy, while complexity indexes calculated over EMD-filtered HP variability separated AMCs from SMCs. The EMD-based filtering method enhanced features of the cardiovascular control that otherwise would have remained hidden by the dominant presence of noise and/or fast physiological variations, thus improving classification in LQT1.

  9. Application of a new genetic classification and semi-automated geomorphic mapping approach in the Perth submarine canyon, Australia

    Science.gov (United States)

    Picard, K.; Nanson, R.; Huang, Z.; Nichol, S.; McCulloch, M.

    2017-12-01

    The acquisition of high resolution marine geophysical data has intensified in recent years (e.g. multibeam echo-sounding, sub-bottom profiling). This progress provides the opportunity to classify and map the seafloor in greater detail, using new methods that preserve the links between processes and morphology. Geoscience Australia has developed a new genetic classification approach, nested within the Harris et al (2014) global seafloor mapping framework. The approach divides parent units into sub-features based on established classification schemes and feature descriptors defined by Bradwell et al. (2016: http://nora.nerc.ac.uk/), the International Hydrographic Organization (https://www.iho.int) and the Coastal Marine and Ecological Classification Standard (https://www.cmecscatalog.org). Owing to the ecological significance of submarine canyon systems in particular, much recent attention has focused on defining their variation in form and process, whereby they can be classified using a range of topographic metrics, fluvial dis/connection and shelf-incising status. The Perth Canyon is incised into the continental slope and shelf of southwest Australia, covering an area of >1500 km2 and extending from 4700 m water depth to the shelf break in 170 m. The canyon sits within a Marine Protected Area, incorporating a Marine National Park and Habitat Protection Zone in recognition of its benthic and pelagic biodiversity values. However, detailed information of the spatial patterns of the seabed habitats that influence this biodiversity is lacking. Here we use 20 m resolution bathymetry and acoustic backscatter data acquired in 2015 by the Schmidt Ocean Institute plus sub-bottom datasets and sediment samples collected Geoscience Australia in 2005 to apply the new geomorphic classification system to the Perth Canyon. This presentation will show the results of the geomorphic feature mapping of the canyon and its application to better defining potential benthic habitats.

  10. Food intake monitoring: an acoustical approach to automated food intake activity detection and classification of consumed food

    International Nuclear Information System (INIS)

    Päßler, Sebastian; Fischer, Wolf-Joachim; Wolff, Matthias

    2012-01-01

    Obesity and nutrition-related diseases are currently growing challenges for medicine. A precise and timesaving method for food intake monitoring is needed. For this purpose, an approach based on the classification of sounds produced during food intake is presented. Sounds are recorded non-invasively by miniature microphones in the outer ear canal. A database of 51 participants eating seven types of food and consuming one drink has been developed for algorithm development and model training. The database is labeled manually using a protocol with introductions for annotation. The annotation procedure is evaluated using Cohen's kappa coefficient. The food intake activity is detected by the comparison of the signal energy of in-ear sounds to environmental sounds recorded by a reference microphone. Hidden Markov models are used for the recognition of single chew or swallowing events. Intake cycles are modeled as event sequences in finite-state grammars. Classification of consumed food is realized by a finite-state grammar decoder based on the Viterbi algorithm. We achieved a detection accuracy of 83% and a food classification accuracy of 79% on a test set of 10% of all records. Our approach faces the need of monitoring the time and occurrence of eating. With differentiation of consumed food, a first step toward the goal of meal weight estimation is taken. (paper)

  11. Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning.

    Science.gov (United States)

    Wang, Xinggang; Yang, Wei; Weinreb, Jeffrey; Han, Juan; Li, Qiubai; Kong, Xiangchuang; Yan, Yongluan; Ke, Zan; Luo, Bo; Liu, Tao; Wang, Liang

    2017-11-13

    Prostate cancer (PCa) is a major cause of death since ancient time documented in Egyptian Ptolemaic mummy imaging. PCa detection is critical to personalized medicine and varies considerably under an MRI scan. 172 patients with 2,602 morphologic images (axial 2D T2-weighted imaging) of the prostate were obtained. A deep learning with deep convolutional neural network (DCNN) and a non-deep learning with SIFT image feature and bag-of-word (BoW), a representative method for image recognition and analysis, were used to distinguish pathologically confirmed PCa patients from prostate benign conditions (BCs) patients with prostatitis or prostate benign hyperplasia (BPH). In fully automated detection of PCa patients, deep learning had a statistically higher area under the receiver operating characteristics curve (AUC) than non-deep learning (P = 0.0007 deep learning method and 0.70 (95% CI 0.63-0.77) for non-deep learning method, respectively. Our results suggest that deep learning with DCNN is superior to non-deep learning with SIFT image feature and BoW model for fully automated PCa patients differentiation from prostate BCs patients. Our deep learning method is extensible to image modalities such as MR imaging, CT and PET of other organs.

  12. Semi-Automated Classification of Gray Scale Aerial Photographs using Geographic Object Based Image Analysis (GEOBIA) Technique

    Science.gov (United States)

    Harb Rabia, Ahmed; Terribile, Fabio

    2013-04-01

    Aerial photography is an important source of high resolution remotely sensed data. Before 1970, aerial photographs were the only remote sensing data source for land use and land cover classification. Using these old aerial photographs improve the final output of land use and land cover change detection. However, classic techniques of aerial photographs classification like manual interpretation or screen digitization require great experience, long processing time and vast effort. A new technique needs to be developed in order to reduce processing time and effort and to give better results. Geographic object based image analysis (GEOBIA) is a newly developed area of Geographic Information Science and remote sensing in which automatic segmentation of images into objects of similar spectral, temporal and spatial characteristics is undertaken. Unlike pixel-based technique, GEOBIA deals with the object properties such as texture, square fit, roundness and many other properties that can improve classification results. GEOBIA technique can be divided into two main steps; segmentation and classification. Segmentation process is grouping adjacent pixels into objects of similar spectral and spatial characteristics. Classification process is assigning classes to the generated objects based on the characteristics of the individual objects. This study aimed to use GEOBIA technique to develop a novel approach for land use and land cover classification of aerial photographs that saves time and effort and gives improved results. Aerial photographs from 1954 of Valle Telesina in Italy were used in this study. Images were rectified and georeferenced in Arcmap using topographic maps. Images were then processed in eCognition software to generate land use and land cover map of 1954. A decision tree rule set was developed in eCognition to classify images and finally nine classes of general land use and land cover in the study area were recognized (forest, trees stripes, agricultural

  13. Automated Detection, Localization, and Classification of Traumatic Vertebral Body Fractures in the Thoracic and Lumbar Spine at CT.

    Science.gov (United States)

    Burns, Joseph E; Yao, Jianhua; Muñoz, Hector; Summers, Ronald M

    2016-01-01

    To design and validate a fully automated computer system for the detection and anatomic localization of traumatic thoracic and lumbar vertebral body fractures at computed tomography (CT). This retrospective study was HIPAA compliant. Institutional review board approval was obtained, and informed consent was waived. CT examinations in 104 patients (mean age, 34.4 years; range, 14-88 years; 32 women, 72 men), consisting of 94 examinations with positive findings for fractures (59 with vertebral body fractures) and 10 control examinations (without vertebral fractures), were performed. There were 141 thoracic and lumbar vertebral body fractures in the case set. The locations of fractures were marked and classified by a radiologist according to Denis column involvement. The CT data set was divided into training and testing subsets (37 and 67 subsets, respectively) for analysis by means of prototype software for fully automated spinal segmentation and fracture detection. Free-response receiver operating characteristic analysis was performed. Training set sensitivity for detection and localization of fractures within each vertebra was 0.82 (28 of 34 findings; 95% confidence interval [CI]: 0.68, 0.90), with a false-positive rate of 2.5 findings per patient. The sensitivity for fracture localization to the correct vertebra was 0.88 (23 of 26 findings; 95% CI: 0.72, 0.96), with a false-positive rate of 1.3. Testing set sensitivity for the detection and localization of fractures within each vertebra was 0.81 (87 of 107 findings; 95% CI: 0.75, 0.87), with a false-positive rate of 2.7. The sensitivity for fracture localization to the correct vertebra was 0.92 (55 of 60 findings; 95% CI: 0.79, 0.94), with a false-positive rate of 1.6. The most common cause of false-positive findings was nutrient foramina (106 of 272 findings [39%]). The fully automated computer system detects and anatomically localizes vertebral body fractures in the thoracic and lumbar spine on CT images with a

  14. Automated classification and visualization of healthy and pathological dental tissues based on near-infrared hyper-spectral imaging

    Science.gov (United States)

    Usenik, Peter; Bürmen, Miran; Vrtovec, Tomaž; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan

    2011-03-01

    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. If detected early enough, such demineralization can be arrested and reversed by non-surgical means through well established dental treatments (fluoride therapy, anti-bacterial therapy, low intensity laser irradiation). Near-infrared (NIR) hyper-spectral imaging is a new promising technique for early detection of demineralization based on distinct spectral features of healthy and pathological dental tissues. In this study, we apply NIR hyper-spectral imaging to classify and visualize healthy and pathological dental tissues including enamel, dentin, calculus, dentin caries, enamel caries and demineralized areas. For this purpose, a standardized teeth database was constructed consisting of 12 extracted human teeth with different degrees of natural dental lesions imaged by NIR hyper-spectral system, X-ray and digital color camera. The color and X-ray images of teeth were presented to a clinical expert for localization and classification of the dental tissues, thereby obtaining the gold standard. Principal component analysis was used for multivariate local modeling of healthy and pathological dental tissues. Finally, the dental tissues were classified by employing multiple discriminant analysis. High agreement was observed between the resulting classification and the gold standard with the classification sensitivity and specificity exceeding 85 % and 97 %, respectively. This study demonstrates that NIR hyper-spectral imaging has considerable diagnostic potential for imaging hard dental tissues.

  15. Present perspectives on the automated classification of the G-protein coupled receptors (GPCRs) at the protein sequence level

    DEFF Research Database (Denmark)

    Davies, Matthew N; Gloriam, David E; Secker, Andrew

    2011-01-01

    to machine learning as well as a variety of alignment-free techniques based on the physiochemical properties of sequences. We review here the available methodologies for the classification of GPCRs. Part of this work focuses on how we have tried to build the intrinsically hierarchical nature of sequence......The G-protein coupled receptors--or GPCRs--comprise simultaneously one of the largest and one of the most multi-functional protein families known to modern-day molecular bioscience. From a drug discovery and pharmaceutical industry perspective, the GPCRs constitute one of the most commercially...

  16. Security Classification Using Automated Learning (SCALE): Optimizing Statistical Natural Language Processing Techniques to Assign Security Labels to Unstructured Text

    Science.gov (United States)

    2010-12-01

    2010 © Sa Majesté la Reine (en droit du Canada), telle que représentée par le ministre de la Défense nationale, 2010 Abstract Automating the... fonction de son expérience et des politiques de sécurité. Pour étiqueter de manière efficace toutes les données disponibles dans les réseaux du...bien que l’on ait étudié en profondeur la catégorisation automatique de données en fonction du sujet, peu de recherches axées sur l’évaluation

  17. A machine vision system for automated non-invasive assessment of cell viability via dark field microscopy, wavelet feature selection and classification.

    Science.gov (United States)

    Wei, Ning; Flaschel, Erwin; Friehs, Karl; Nattkemper, Tim Wilhelm

    2008-10-21

    Cell viability is one of the basic properties indicating the physiological state of the cell, thus, it has long been one of the major considerations in biotechnological applications. Conventional methods for extracting information about cell viability usually need reagents to be applied on the targeted cells. These reagent-based techniques are reliable and versatile, however, some of them might be invasive and even toxic to the target cells. In support of automated noninvasive assessment of cell viability, a machine vision system has been developed. This system is based on supervised learning technique. It learns from images of certain kinds of cell populations and trains some classifiers. These trained classifiers are then employed to evaluate the images of given cell populations obtained via dark field microscopy. Wavelet decomposition is performed on the cell images. Energy and entropy are computed for each wavelet subimage as features. A feature selection algorithm is implemented to achieve better performance. Correlation between the results from the machine vision system and commonly accepted gold standards becomes stronger if wavelet features are utilized. The best performance is achieved with a selected subset of wavelet features. The machine vision system based on dark field microscopy in conjugation with supervised machine learning and wavelet feature selection automates the cell viability assessment, and yields comparable results to commonly accepted methods. Wavelet features are found to be suitable to describe the discriminative properties of the live and dead cells in viability classification. According to the analysis, live cells exhibit morphologically more details and are intracellularly more organized than dead ones, which display more homogeneous and diffuse gray values throughout the cells. Feature selection increases the system's performance. The reason lies in the fact that feature selection plays a role of excluding redundant or misleading

  18. A machine vision system for automated non-invasive assessment of cell viability via dark field microscopy, wavelet feature selection and classification

    Directory of Open Access Journals (Sweden)

    Friehs Karl

    2008-10-01

    Full Text Available Abstract Background Cell viability is one of the basic properties indicating the physiological state of the cell, thus, it has long been one of the major considerations in biotechnological applications. Conventional methods for extracting information about cell viability usually need reagents to be applied on the targeted cells. These reagent-based techniques are reliable and versatile, however, some of them might be invasive and even toxic to the target cells. In support of automated noninvasive assessment of cell viability, a machine vision system has been developed. Results This system is based on supervised learning technique. It learns from images of certain kinds of cell populations and trains some classifiers. These trained classifiers are then employed to evaluate the images of given cell populations obtained via dark field microscopy. Wavelet decomposition is performed on the cell images. Energy and entropy are computed for each wavelet subimage as features. A feature selection algorithm is implemented to achieve better performance. Correlation between the results from the machine vision system and commonly accepted gold standards becomes stronger if wavelet features are utilized. The best performance is achieved with a selected subset of wavelet features. Conclusion The machine vision system based on dark field microscopy in conjugation with supervised machine learning and wavelet feature selection automates the cell viability assessment, and yields comparable results to commonly accepted methods. Wavelet features are found to be suitable to describe the discriminative properties of the live and dead cells in viability classification. According to the analysis, live cells exhibit morphologically more details and are intracellularly more organized than dead ones, which display more homogeneous and diffuse gray values throughout the cells. Feature selection increases the system's performance. The reason lies in the fact that feature

  19. Present perspectives on the automated classification of the G-protein coupled receptors (GPCRs) at the protein sequence level

    DEFF Research Database (Denmark)

    Davies, Matthew N; Gloriam, David E; Secker, Andrew

    2011-01-01

    The G-protein coupled receptors--or GPCRs--comprise simultaneously one of the largest and one of the most multi-functional protein families known to modern-day molecular bioscience. From a drug discovery and pharmaceutical industry perspective, the GPCRs constitute one of the most commercially...... and economically important groups of proteins known. The GPCRs undertake numerous vital metabolic functions and interact with a hugely diverse range of small and large ligands. Many different methodologies have been developed to efficiently and accurately classify the GPCRs. These range from motif-based techniques...... to machine learning as well as a variety of alignment-free techniques based on the physiochemical properties of sequences. We review here the available methodologies for the classification of GPCRs. Part of this work focuses on how we have tried to build the intrinsically hierarchical nature of sequence...

  20. A cellular neural network based method for classification of magnetic resonance images: towards an automated detection of hippocampal sclerosis.

    Science.gov (United States)

    Döhler, Florian; Mormann, Florian; Weber, Bernd; Elger, Christian E; Lehnertz, Klaus

    2008-05-30

    We present a cellular neuronal network (CNN) based approach to classify magnetic resonance images with and without hippocampal or Ammon's horn sclerosis (AHS) in the medial temporal lobe. A CNN combines the architecture of cellular automata and artificial neural networks and is an array of locally coupled nonlinear electrical circuits or cells, which is capable of processing a large amount of information in parallel and in real time. Using an exemplary database that consists of a large number of volumes of interest extracted from T1-weighted magnetic resonance images from 144 subjects we here demonstrate that the network allows to classify brain tissue with respect to the presence or absence of mesial temporal sclerosis. Results indicate the general feasibility of CNN-based computer-aided systems for diagnosis and classification of images generated by medical imaging systems.

  1. Automated classification of seismic sources in a large database: a comparison of Random Forests and Deep Neural Networks.

    Science.gov (United States)

    Hibert, Clement; Stumpf, André; Provost, Floriane; Malet, Jean-Philippe

    2017-04-01

    In the past decades, the increasing quality of seismic sensors and capability to transfer remotely large quantity of data led to a fast densification of local, regional and global seismic networks for near real-time monitoring of crustal and surface processes. This technological advance permits the use of seismology to document geological and natural/anthropogenic processes (volcanoes, ice-calving, landslides, snow and rock avalanches, geothermal fields), but also led to an ever-growing quantity of seismic data. This wealth of seismic data makes the construction of complete seismicity catalogs, which include earthquakes but also other sources of seismic waves, more challenging and very time-consuming as this critical pre-processing stage is classically done by human operators and because hundreds of thousands of seismic signals have to be processed. To overcome this issue, the development of automatic methods for the processing of continuous seismic data appears to be a necessity. The classification algorithm should satisfy the need of a method that is robust, precise and versatile enough to be deployed to monitor the seismicity in very different contexts. In this study, we evaluate the ability of machine learning algorithms for the analysis of seismic sources at the Piton de la Fournaise volcano being Random Forest and Deep Neural Network classifiers. We gather a catalog of more than 20,000 events, belonging to 8 classes of seismic sources. We define 60 attributes, based on the waveform, the frequency content and the polarization of the seismic waves, to parameterize the seismic signals recorded. We show that both algorithms provide similar positive classification rates, with values exceeding 90% of the events. When trained with a sufficient number of events, the rate of positive identification can reach 99%. These very high rates of positive identification open the perspective of an operational implementation of these algorithms for near-real time monitoring of

  2. Automated Surface Classification of SRF Cavities for the Investigation of the Influence of Surface Properties onto the Operational Performance

    International Nuclear Information System (INIS)

    Wenskat, Marc

    2015-07-01

    Superconducting niobium radio-frequency cavities are fundamental for the European XFEL and the International Linear Collider. To use the operational advantages of superconducting cavities, the inner surface has to fulfill quite demanding requirements. The surface roughness and cleanliness improved over the last decades and with them, the achieved maximal accelerating field. Still, limitations of the maximal achieved accelerating field are observed, which are not explained by localized geometrical defects or impurities. The scope of this thesis is a better understanding of these limitations in defect free cavities based on global, rather than local, surface properties. For this goal, more than 30 cavities underwent subsequent surface treatments, cold RF tests and optical inspections within the ILC-HiGrade research program and the XFEL cavity production. An algorithm was developed which allows an automated surface characterization based on an optical inspection robot. This algorithm delivers a set of optical surface properties, which describes the inner cavity surface. These optical surface properties deliver a framework for a quality assurance of the fabrication procedures. Furthermore, they shows promising results for a better understanding of the observed limitations in defect free cavities.

  3. A Cross-Entropy-Based Admission Control Optimization Approach for Heterogeneous Virtual Machine Placement in Public Clouds

    Directory of Open Access Journals (Sweden)

    Li Pan

    2016-03-01

    Full Text Available Virtualization technologies make it possible for cloud providers to consolidate multiple IaaS provisions into a single server in the form of virtual machines (VMs. Additionally, in order to fulfill the divergent service requirements from multiple users, a cloud provider needs to offer several types of VM instances, which are associated with varying configurations and performance, as well as different prices. In such a heterogeneous virtual machine placement process, one significant problem faced by a cloud provider is how to optimally accept and place multiple VM service requests into its cloud data centers to achieve revenue maximization. To address this issue, in this paper, we first formulate such a revenue maximization problem during VM admission control as a multiple-dimensional knapsack problem, which is known to be NP-hard to solve. Then, we propose to use a cross-entropy-based optimization approach to address this revenue maximization problem, by obtaining a near-optimal eligible set for the provider to accept into its data centers, from the waiting VM service requests in the system. Finally, through extensive experiments and measurements in a simulated environment with the settings of VM instance classes derived from real-world cloud systems, we show that our proposed cross-entropy-based admission control optimization algorithm is efficient and effective in maximizing cloud providers’ revenue in a public cloud computing environment.

  4. Evaluation of a rule-based method for epidemiological document classification towards the automation of systematic reviews.

    Science.gov (United States)

    Karystianis, George; Thayer, Kristina; Wolfe, Mary; Tsafnat, Guy

    2017-06-01

    Most data extraction efforts in epidemiology are focused on obtaining targeted information from clinical trials. In contrast, limited research has been conducted on the identification of information from observational studies, a major source for human evidence in many fields, including environmental health. The recognition of key epidemiological information (e.g., exposures) through text mining techniques can assist in the automation of systematic reviews and other evidence summaries. We designed and applied a knowledge-driven, rule-based approach to identify targeted information (study design, participant population, exposure, outcome, confounding factors, and the country where the study was conducted) from abstracts of epidemiological studies included in several systematic reviews of environmental health exposures. The rules were based on common syntactical patterns observed in text and are thus not specific to any systematic review. To validate the general applicability of our approach, we compared the data extracted using our approach versus hand curation for 35 epidemiological study abstracts manually selected for inclusion in two systematic reviews. The returned F-score, precision, and recall ranged from 70% to 98%, 81% to 100%, and 54% to 97%, respectively. The highest precision was observed for exposure, outcome and population (100%) while recall was best for exposure and study design with 97% and 89%, respectively. The lowest recall was observed for the population (54%), which also had the lowest F-score (70%). The generated performance of our text-mining approach demonstrated encouraging results for the identification of targeted information from observational epidemiological study abstracts related to environmental exposures. We have demonstrated that rules based on generic syntactic patterns in one corpus can be applied to other observational study design by simple interchanging the dictionaries aiming to identify certain characteristics (i.e., outcomes

  5. Automated detection and classification of major retinal vessels for determination of diameter ratio of arteries and veins

    Science.gov (United States)

    Muramatsu, Chisako; Hatanaka, Yuji; Iwase, Tatsuhiko; Hara, Takeshi; Fujita, Hiroshi

    2010-03-01

    Abnormalities of retinal vasculatures can indicate health conditions in the body, such as the high blood pressure and diabetes. Providing automatically determined width ratio of arteries and veins (A/V ratio) on retinal fundus images may help physicians in the diagnosis of hypertensive retinopathy, which may cause blindness. The purpose of this study was to detect major retinal vessels and classify them into arteries and veins for the determination of A/V ratio. Images used in this study were obtained from DRIVE database, which consists of 20 cases each for training and testing vessel detection algorithms. Starting with the reference standard of vasculature segmentation provided in the database, major arteries and veins each in the upper and lower temporal regions were manually selected for establishing the gold standard. We applied the black top-hat transformation and double-ring filter to detect retinal blood vessels. From the extracted vessels, large vessels extending from the optic disc to temporal regions were selected as target vessels for calculation of A/V ratio. Image features were extracted from the vessel segments from quarter-disc to one disc diameter from the edge of optic discs. The target segments in the training cases were classified into arteries and veins by using the linear discriminant analysis, and the selected parameters were applied to those in the test cases. Out of 40 pairs, 30 pairs (75%) of arteries and veins in the 20 test cases were correctly classified. The result can be used for the automated calculation of A/V ratio.

  6. Automated correlation and classification of secondary ion mass spectrometry images using a k-means cluster method.

    Science.gov (United States)

    Konicek, Andrew R; Lefman, Jonathan; Szakal, Christopher

    2012-08-07

    We present a novel method for correlating and classifying ion-specific time-of-flight secondary ion mass spectrometry (ToF-SIMS) images within a multispectral dataset by grouping images with similar pixel intensity distributions. Binary centroid images are created by employing a k-means-based custom algorithm. Centroid images are compared to grayscale SIMS images using a newly developed correlation method that assigns the SIMS images to classes that have similar spatial (rather than spectral) patterns. Image features of both large and small spatial extent are identified without the need for image pre-processing, such as normalization or fixed-range mass-binning. A subsequent classification step tracks the class assignment of SIMS images over multiple iterations of increasing n classes per iteration, providing information about groups of images that have similar chemistry. Details are discussed while presenting data acquired with ToF-SIMS on a model sample of laser-printed inks. This approach can lead to the identification of distinct ion-specific chemistries for mass spectral imaging by ToF-SIMS, as well as matrix-assisted laser desorption ionization (MALDI), and desorption electrospray ionization (DESI).

  7. Automated and simultaneous fovea center localization and macula segmentation using the new dynamic identification and classification of edges model

    Science.gov (United States)

    Onal, Sinan; Chen, Xin; Satamraju, Veeresh; Balasooriya, Maduka; Dabil-Karacal, Humeyra

    2016-01-01

    Abstract. Detecting the position of retinal structures, including the fovea center and macula, in retinal images plays a key role in diagnosing eye diseases such as optic nerve hypoplasia, amblyopia, diabetic retinopathy, and macular edema. However, current detection methods are unreliable for infants or certain ethnic populations. Thus, a methodology is proposed here that may be useful for infants and across ethnicities that automatically localizes the fovea center and segments the macula on digital fundus images. First, dark structures and bright artifacts are removed from the input image using preprocessing operations, and the resulting image is transformed to polar space. Second, the fovea center is identified, and the macula region is segmented using the proposed dynamic identification and classification of edges (DICE) model. The performance of the method was evaluated using 1200 fundus images obtained from the relatively large, diverse, and publicly available Messidor database. In 96.1% of these 1200 cases, the distance between the fovea center identified manually by ophthalmologists and automatically using the proposed method remained within 0 to 8 pixels. The dice similarity index comparing the manually obtained results with those of the model for macula segmentation was 96.12% for these 1200 cases. Thus, the proposed method displayed a high degree of accuracy. The methodology using the DICE model is unique and advantageous over previously reported methods because it simultaneously determines the fovea center and segments the macula region without using any structural information, such as optic disc or blood vessel location, and it may prove useful for all populations, including infants. PMID:27660803

  8. Analysis of entropies based on empirical mode decomposition in amnesic mild cognitive impairment of diabetes mellitus

    Directory of Open Access Journals (Sweden)

    Dong Cui

    2015-09-01

    Full Text Available EEG characteristics that correlate with the cognitive functions are important in detecting mild cognitive impairment (MCI in T2DM. To investigate the complexity between aMCI group and age-matched non-aMCI control group in T2DM, six entropies combining empirical mode decomposition (EMD, including Approximate entropy (ApEn, Sample entropy (SaEn, Fuzzy entropy (FEn, Permutation entropy (PEn, Power spectrum entropy (PsEn and Wavelet entropy (WEn were used in the study. A feature extraction technique based on maximization of the area under the curve (AUC and a support vector machine (SVM were subsequently used to for features selection and classification. Finally, Pearson's linear correlation was employed to study associations between these entropies and cognitive functions. Compared to other entropies, FEn had a higher classification accuracy, sensitivity and specificity of 68%, 67.1% and 71.9%, respectively. Top 43 salient features achieved classification accuracy, sensitivity and specificity of 73.8%, 72.3% and 77.9%, respectively. P4, T4 and C4 were the highest ranking salient electrodes. Correlation analysis showed that FEn based on EMD was positively correlated to memory at electrodes F7, F8 and P4, and PsEn based on EMD was positively correlated to Montreal cognitive assessment (MoCA and memory at electrode T4. In sum, FEn based on EMD in right-temporal and occipital regions may be more suitable for early diagnosis of the MCI with T2DM.

  9. Entropy-Based Application Layer DDoS Attack Detection Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Khundrakpam Johnson Singh

    2016-10-01

    Full Text Available Distributed denial-of-service (DDoS attack is one of the major threats to the web server. The rapid increase of DDoS attacks on the Internet has clearly pointed out the limitations in current intrusion detection systems or intrusion prevention systems (IDS/IPS, mostly caused by application-layer DDoS attacks. Within this context, the objective of the paper is to detect a DDoS attack using a multilayer perceptron (MLP classification algorithm with genetic algorithm (GA as learning algorithm. In this work, we analyzed the standard EPA-HTTP (environmental protection agency-hypertext transfer protocol dataset and selected the parameters that will be used as input to the classifier model for differentiating the attack from normal profile. The parameters selected are the HTTP GET request count, entropy, and variance for every connection. The proposed model can provide a better accuracy of 98.31%, sensitivity of 0.9962, and specificity of 0.0561 when compared to other traditional classification models.

  10. Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data

    Science.gov (United States)

    Teluguntla, Pardhasaradhi G.; Thenkabail, Prasad S.; Xiong, Jun N.; Gumma, Murali Krishna; Congalton, Russell G.; Oliphant, Adam; Poehnelt, Justin; Yadav, Kamini; Rao, Mahesh N.; Massey, Richard

    2017-01-01

    Mapping croplands, including fallow areas, are an important measure to determine the quantity of food that is produced, where they are produced, and when they are produced (e.g. seasonality). Furthermore, croplands are known as water guzzlers by consuming anywhere between 70% and 90% of all human water use globally. Given these facts and the increase in global population to nearly 10 billion by the year 2050, the need for routine, rapid, and automated cropland mapping year-after-year and/or season-after-season is of great importance. The overarching goal of this study was to generate standard and routine cropland products, year-after-year, over very large areas through the use of two novel methods: (a) quantitative spectral matching techniques (QSMTs) applied at continental level and (b) rule-based Automated Cropland Classification Algorithm (ACCA) with the ability to hind-cast, now-cast, and future-cast. Australia was chosen for the study given its extensive croplands, rich history of agriculture, and yet nonexistent routine yearly generated cropland products using multi-temporal remote sensing. This research produced three distinct cropland products using Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m normalized difference vegetation index 16-day composite time-series data for 16 years: 2000 through 2015. The products consisted of: (1) cropland extent/areas versus cropland fallow areas, (2) irrigated versus rainfed croplands, and (3) cropping intensities: single, double, and continuous cropping. An accurate reference cropland product (RCP) for the year 2014 (RCP2014) produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000–2015. A comparison between the ACCA-derived cropland products (ACPs) for the year 2014 (ACP2014) versus RCP2014 provided an overall agreement of 89.4% (kappa = 0.814) with six classes: (a) producer’s accuracies varying

  11. Automated Water Extraction Index

    DEFF Research Database (Denmark)

    Feyisa, Gudina Legese; Meilby, Henrik; Fensholt, Rasmus

    2014-01-01

    of various sorts of environmental noise and at the same time offers a stable threshold value. Thus we introduced a new Automated Water Extraction Index (AWEI) improving classification accuracy in areas that include shadow and dark surfaces that other classification methods often fail to classify correctly...

  12. Entropy based unsupervised Feature Selection in digital mammogram image using rough set theory.

    Science.gov (United States)

    Velayutham, C; Thangavel, K

    2012-01-01

    Feature Selection (FS) is a process, which attempts to select features, which are more informative. In the supervised FS methods various feature subsets are evaluated using an evaluation function or metric to select only those features, which are related to the decision classes of the data under consideration. However, for many data mining applications, decision class labels are often unknown or incomplete, thus indicating the significance of unsupervised FS. However, in unsupervised learning, decision class labels are not provided. The problem is that not all features are important. Some of the features may be redundant, and others may be irrelevant and noisy. In this paper, a novel unsupervised FS in mammogram image, using rough set-based entropy measures, is proposed. A typical mammogram image processing system generally consists of mammogram image acquisition, pre-processing of image, segmentation, features extracted from the segmented mammogram image. The proposed method is used to select features from data set, the method is compared with the existing rough set-based supervised FS methods and classification performance of both methods are recorded and demonstrates the efficiency of the method.

  13. Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation.

    Science.gov (United States)

    Qin, Wenjian; Wu, Jia; Han, Fei; Yuan, Yixuan; Zhao, Wei; Ibragimov, Bulat; Gu, Jia; Xing, Lei

    2018-04-10

    Segmentation of liver in abdominal computed tomography (CT) is an important step for radiation therapy planning of hepatocellular carcinoma. Practically, a fully automatic segmentation of liver remains challenging because of low soft tissue contrast between liver and its surrounding organs, and its highly deformable shape. The purpose of this work is to develop a novel superpixel-based and boundary sensitive convolutional neural network (SBBS-CNN) pipeline for automated liver segmentation. Method: The entire CT images were first partitioned into superpixel regions, where nearby pixels with similar CT number were aggregated. Secondly, we converted the conventional binary segmentation into a multinomial classification by labeling the superpixels into three classes: interior liver, liver boundary, and non-liver background. By doing this, the boundary region of the liver was explicitly identified and highlighted for the subsequent classification. Thirdly, we computed an entropy-based saliency map for each CT volume, and leveraged this map to guide the sampling of image patches over the superpixels. In this way, more patches were extracted from informative regions (e.g., the liver boundary with irregular changes) and fewer patches were extracted from homogeneous regions. Finally, deep CNN pipeline was built and trained to predict the probability map of the liver boundary. Results: We tested the proposed algorithm in a cohort of 100 patients. With 10-fold cross validation, the SBBS-CNN achieved mean Dice similarity coefficients of 97.31±0.36% and average symmetric surface distance of 1.77±0.49mm. Moreover, it showed superior performance in comparison with state-of-art methods, including U-Net, pixel-based CNN, active contour, level-sets and graph-cut algorithms. Conclusion: SBBS-CNN provides an accurate and effective tool for automated liver segmentation. It is also envisioned that the proposed framework is directly applicable in other medical image segmentation

  14. Automated Classification of Power Signals

    Science.gov (United States)

    2008-06-01

    high purity product stream (i.e. purified water) and the concentrated reject stream (i.e. brine ). The brine is piped directly overboard while the...RC7000 Plus Reverse Osmosis Desalination Plant Operations and Maintenance Manual. Gardena, CA : Village Marine Tec, 2004. 108

  15. The Effect of Automation on Job Duties, Classifications, Staffing Patterns, and Labor Costs in the UBC Library's Cataloguing Divisions: A Comparison of 1973 and 1986.

    Science.gov (United States)

    de Bruijn, Erik

    This report discusses an ex post facto study that was done to examine the effect that the implementation of automated systems has had on libraries and support staff, labor costs, and productivity in the cataloging divisions of the library of the University of British Columbia. A comparison was made between two years: 1973, a pre-automated period…

  16. Shannon Entropy-Based Wavelet Transform Method for Autonomous Coherent Structure Identification in Fluid Flow Field Data

    Directory of Open Access Journals (Sweden)

    Kartik V. Bulusu

    2015-09-01

    Full Text Available The coherent secondary flow structures (i.e., swirling motions in a curved artery model possess a variety of spatio-temporal morphologies and can be encoded over an infinitely-wide range of wavelet scales. Wavelet analysis was applied to the following vorticity fields: (i a numerically-generated system of Oseen-type vortices for which the theoretical solution is known, used for bench marking and evaluation of the technique; and (ii experimental two-dimensional, particle image velocimetry data. The mother wavelet, a two-dimensional Ricker wavelet, can be dilated to infinitely large or infinitesimally small scales. We approached the problem of coherent structure detection by means of continuous wavelet transform (CWT and decomposition (or Shannon entropy. The main conclusion of this study is that the encoding of coherent secondary flow structures can be achieved by an optimal number of binary digits (or bits corresponding to an optimal wavelet scale. The optimal wavelet-scale search was driven by a decomposition entropy-based algorithmic approach and led to a threshold-free coherent structure detection method. The method presented in this paper was successfully utilized in the detection of secondary flow structures in three clinically-relevant blood flow scenarios involving the curved artery model under a carotid artery-inspired, pulsatile inflow condition. These scenarios were: (i a clean curved artery; (ii stent-implanted curved artery; and (iii an idealized Type IV stent fracture within the curved artery.

  17. An Entropy-Based Kernel Learning Scheme toward Efficient Data Prediction in Cloud-Assisted Network Environments

    Directory of Open Access Journals (Sweden)

    Xiong Luo

    2016-07-01

    Full Text Available With the recent emergence of wireless sensor networks (WSNs in the cloud computing environment, it is now possible to monitor and gather physical information via lots of sensor nodes to meet the requirements of cloud services. Generally, those sensor nodes collect data and send data to sink node where end-users can query all the information and achieve cloud applications. Currently, one of the main disadvantages in the sensor nodes is that they are with limited physical performance relating to less memory for storage and less source of power. Therefore, in order to avoid such limitation, it is necessary to develop an efficient data prediction method in WSN. To serve this purpose, by reducing the redundant data transmission between sensor nodes and sink node while maintaining the required acceptable errors, this article proposes an entropy-based learning scheme for data prediction through the use of kernel least mean square (KLMS algorithm. The proposed scheme called E-KLMS develops a mechanism to maintain the predicted data synchronous at both sides. Specifically, the kernel-based method is able to adjust the coefficients adaptively in accordance with every input, which will achieve a better performance with smaller prediction errors, while employing information entropy to remove these data which may cause relatively large errors. E-KLMS can effectively solve the tradeoff problem between prediction accuracy and computational efforts while greatly simplifying the training structure compared with some other data prediction approaches. What’s more, the kernel-based method and entropy technique could ensure the prediction effect by both improving the accuracy and reducing errors. Experiments with some real data sets have been carried out to validate the efficiency and effectiveness of E-KLMS learning scheme, and the experiment results show advantages of the our method in prediction accuracy and computational time.

  18. 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....... Although other methods exist, we concentrate on Bayesian modeling approaches, in which generative image models are constructed and subsequently ‘inverted’ to obtain automated segmentations. This general framework encompasses a large number of segmentation methods, including those implemented in widely used...

  19. Integrating dimension reduction and out-of-sample extension in automated classification of ex vivo human patellar cartilage on phase contrast X-ray computed tomography.

    Directory of Open Access Journals (Sweden)

    Mahesh B Nagarajan

    Full Text Available Phase contrast X-ray computed tomography (PCI-CT has been demonstrated as a novel imaging technique that can visualize human cartilage with high spatial resolution and soft tissue contrast. Different textural approaches have been previously investigated for characterizing chondrocyte organization on PCI-CT to enable classification of healthy and osteoarthritic cartilage. However, the large size of feature sets extracted in such studies motivates an investigation into algorithmic feature reduction for computing efficient feature representations without compromising their discriminatory power. For this purpose, geometrical feature sets derived from the scaling index method (SIM were extracted from 1392 volumes of interest (VOI annotated on PCI-CT images of ex vivo human patellar cartilage specimens. The extracted feature sets were subject to linear and non-linear dimension reduction techniques as well as feature selection based on evaluation of mutual information criteria. The reduced feature set was subsequently used in a machine learning task with support vector regression to classify VOIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver-operating characteristic (ROC curve (AUC. Our results show that the classification performance achieved by 9-D SIM-derived geometric feature sets (AUC: 0.96 ± 0.02 can be maintained with 2-D representations computed from both dimension reduction and feature selection (AUC values as high as 0.97 ± 0.02. Thus, such feature reduction techniques can offer a high degree of compaction to large feature sets extracted from PCI-CT images while maintaining their ability to characterize the underlying chondrocyte patterns.

  20. Automated classification of seismic sources in large database using random forest algorithm: First results at Piton de la Fournaise volcano (La Réunion).

    Science.gov (United States)

    Hibert, Clément; Provost, Floriane; Malet, Jean-Philippe; Stumpf, André; Maggi, Alessia; Ferrazzini, Valérie

    2016-04-01

    In the past decades the increasing quality of seismic sensors and capability to transfer remotely large quantity of data led to a fast densification of local, regional and global seismic networks for near real-time monitoring. This technological advance permits the use of seismology to document geological and natural/anthropogenic processes (volcanoes, ice-calving, landslides, snow and rock avalanches, geothermal fields), but also led to an ever-growing quantity of seismic data. This wealth of seismic data makes the construction of complete seismicity catalogs, that include earthquakes but also other sources of seismic waves, more challenging and very time-consuming as this critical pre-processing stage is classically done by human operators. To overcome this issue, the development of automatic methods for the processing of continuous seismic data appears to be a necessity. The classification algorithm should satisfy the need of a method that is robust, precise and versatile enough to be deployed to monitor the seismicity in very different contexts. We propose a multi-class detection method based on the random forests algorithm to automatically classify the source of seismic signals. Random forests is a supervised machine learning technique that is based on the computation of a large number of decision trees. The multiple decision trees are constructed from training sets including each of the target classes. In the case of seismic signals, these attributes may encompass spectral features but also waveform characteristics, multi-stations observations and other relevant information. The Random Forests classifier is used because it provides state-of-the-art performance when compared with other machine learning techniques (e.g. SVM, Neural Networks) and requires no fine tuning. Furthermore it is relatively fast, robust, easy to parallelize, and inherently suitable for multi-class problems. In this work, we present the first results of the classification method applied

  1. Automated Instrumentation System Verification.

    Science.gov (United States)

    1983-04-01

    fUig JDma Entered) i. _-_J I ___________ UNCLASSI FI ED SECURITY CLASSIFICATION OF TIHIS PAGE(II7,m Daca Entod) 20. ABSTRACT (Continued). ) contain...automatic measurement should arise. 15 I "_......_______.....____,_.........____ _ ’ " AFWL-TR-82-137 11. TRADITIONAL PROCEDURES The necessity to measure data...measurement (Ref. 8). Finally, when the necessity for automation was recognized and funds were provided, the effort described in this report was started

  2. Fully automated classification of bone marrow infiltration in low-dose CT of patients with multiple myeloma based on probabilistic density model and supervised learning.

    Science.gov (United States)

    Martínez-Martínez, Francisco; Kybic, Jan; Lambert, Lukáš; Mecková, Zuzana

    2016-04-01

    This paper presents a fully automated method for the identification of bone marrow infiltration in femurs in low-dose CT of patients with multiple myeloma. We automatically find the femurs and the bone marrow within them. In the next step, we create a probabilistic, spatially dependent density model of normal tissue. At test time, we detect unexpectedly high density voxels which may be related to bone marrow infiltration, as outliers to this model. Based on a set of global, aggregated features representing all detections from one femur, we classify the subjects as being either healthy or not. This method was validated on a dataset of 127 subjects with ground truth created from a consensus of two expert radiologists, obtaining an AUC of 0.996 for the task of distinguishing healthy controls and patients with bone marrow infiltration. To the best of our knowledge, no other automatic image-based method for this task has been published before. Copyright © 2016 Elsevier Ltd. All rights reserved.

  3. Detection of high-silica lava flows and lava morphology at the Alarcon Rise, Gulf of California, Mexico using automated classification of the morphological-compositional relationship in AUV multibeam bathymetry and sonar backscatter

    Science.gov (United States)

    Maschmeyer, C.; White, S. M.; Dreyer, B. M.; Clague, D. A.

    2015-12-01

    An automated compositional classification by adaptive neuro-fuzzy inference system (ANFIS) was developed to study volcanic processes that create high-silica lava at oceanic ridges. The objective of this research is to determine the existence of a relationship between lava morphology and composition. Researchers from the Monterey Bay Aquarium Research Institute (MBARI) recorded morphologic observations and collected samples for geochemical analysis during ROV dives at the Alarcon Rise in 2012 and 2015. The Alarcon Rise is a unique spreading ridge environment where composition ranges from basaltic to rhyolitic, making it an ideal location to examine the compositional-morphologic relationship of lava flows. Preliminary interpretation of field data indicates that high-silica lavas are typically associated with 3-5 m, blocky pillows at the heavily faulted north end of the Alarcon. Visual analysis of multibeam bathymetry and side-scan sonar backscatter from MBARI AUV D. Allen B. and gridded at 1 m suggests that lava flow morphology (pillow, lobate, sheet) can be distinguished by seafloor roughness. Bathymetric products used by ANFIS to quantify the morphologic-compositional relationship were slope, aspect, and bathymetric position index (BPI, a measure of local height relative to the adjacent terrain). Sonar backscatter intensity is influenced by surface roughness and previously used to distinguish lava morphology. Gray-level co-occurrence matrices (GLCM) were applied to backscatter to create edge-detection filters that recognized faults and fissures. Input data are slope, aspect, bathymetric value, BPI at 100 m scale, BPI at 500 m scale, backscatter intensity, and the first principle component of backscatter GLCM. After lava morphology was classified on the Alarcon Rise map, another classification was completed to detect locations of high-silica lava. Application of an expert classifier like ANFIS to distinguish lava composition may become an important tool in oceanic

  4. Automation of Nuclear Fuel Pellet Quality Control

    International Nuclear Information System (INIS)

    Keyvan, Shahla; Song, Xiaolong

    2001-01-01

    It would be highly desirable to automate the pellet inspection process, which at the present time is done by humans using the naked eye for inspection. A prototype of an automated inspection system was developed. The system examines photographic images of pellets using various artificial intelligence techniques for image analysis and defect classification. The steps in the process are described

  5. AUTOMATED PEDAGOGICAL DIAGNOSTICS IN MODERN UNIVERSITY

    Directory of Open Access Journals (Sweden)

    Oleksandr H. Kolhatin

    2010-08-01

    Full Text Available Realisation of the pedagogical assessment functions at using of automated pedagogical diagnostics systems in university instruction process is considered. Pedagogical diagnostics software requirements are determined on the base of automated pedagogical diagnostics systems classifications analysis according to didactical aim of the diagnostics.

  6. Automated Detection of Optic Disc in Fundus Images

    Science.gov (United States)

    Burman, R.; Almazroa, A.; Raahemifar, K.; Lakshminarayanan, V.

    Optic disc (OD) localization is an important preprocessing step in the automated image detection of fundus image infected with glaucoma. An Interval Type-II fuzzy entropy based thresholding scheme along with Differential Evolution (DE) is applied to determine the location of the OD in the right of left eye retinal fundus image. The algorithm, when applied to 460 fundus images from the MESSIDOR dataset, shows a success rate of 99.07 % for 217 normal images and 95.47 % for 243 pathological images. The mean computational time is 1.709 s for normal images and 1.753 s for pathological images. These results are important for automated detection of glaucoma and for telemedicine purposes.

  7. Impact of Office Automation: An Empirical Assessment

    Science.gov (United States)

    1988-12-01

    imp F rq(I NAVAL POSTGRADUATE SCHOOL Monterey, California N I < DTIC S ELECTEI THESIS -’° "I I MPACT OF OFFICE AUTOMATION : AN EMPIRICAL ASSESSMENT by...FLNDiNG NUMBERS PROGRAM PROCT TASK IWORK UNIT ELEMNT O NONO ACCESSION NO 11 TITLE (Include Security Classification) IMPACT OF OFFICE AUTOMATION : AN...identity by block number) FIELD GROUP I SB-GROLP Productivity Assessment; SACONS; Office Automation I I 19 ABSTRACT (Continue on reverse if necessary

  8. Classifying Classifications

    DEFF Research Database (Denmark)

    Debus, Michael S.

    2017-01-01

    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......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...... into the topic of game classifications....

  9. 21 CFR 864.5700 - Automated platelet aggregation system.

    Science.gov (United States)

    2010-04-01

    ... addition of an aggregating reagent to a platelet-rich plasma. (b) Classification. Class II (performance... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Automated platelet aggregation system. 864.5700... § 864.5700 Automated platelet aggregation system. (a) Identification. An automated platelet aggregation...

  10. 21 CFR 864.5620 - Automated hemoglobin system.

    Science.gov (United States)

    2010-04-01

    ... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Automated hemoglobin system. 864.5620 Section 864....5620 Automated hemoglobin system. (a) Identification. An automated hemoglobin system is a fully... hemoglobin content of human blood. (b) Classification. Class II (performance standards). [45 FR 60601, Sept...

  11. Quantitative Estimation for the Effectiveness of Automation

    International Nuclear Information System (INIS)

    Lee, Seung Min; Seong, Poong Hyun

    2012-01-01

    In advanced MCR, various automation systems are applied to enhance the human performance and reduce the human errors in industrial fields. It is expected that automation provides greater efficiency, lower workload, and fewer human errors. However, these promises are not always fulfilled. As the new types of events related to application of the imperfect and complex automation are occurred, it is required to analyze the effects of automation system for the performance of human operators. Therefore, we suggest the quantitative estimation method to analyze the effectiveness of the automation systems according to Level of Automation (LOA) classification, which has been developed over 30 years. The estimation of the effectiveness of automation will be achieved by calculating the failure probability of human performance related to the cognitive activities

  12. Library Automation

    OpenAIRE

    Dhakne, B. N.; Giri, V. V; Waghmode, S. S.

    2010-01-01

    New technologies library provides several new materials, media and mode of storing and communicating the information. Library Automation reduces the drudgery of repeated manual efforts in library routine. By use of library automation collection, Storage, Administration, Processing, Preservation and communication etc.

  13. Navigation and Self-Semantic Location of Drones in Indoor Environments by Combining the Visual Bug Algorithm and Entropy-Based Vision

    Science.gov (United States)

    Maravall, Darío; de Lope, Javier; Fuentes, Juan P.

    2017-01-01

    We introduce a hybrid algorithm for the self-semantic location and autonomous navigation of robots using entropy-based vision and visual topological maps. In visual topological maps the visual landmarks are considered as leave points for guiding the robot to reach a target point (robot homing) in indoor environments. These visual landmarks are defined from images of relevant objects or characteristic scenes in the environment. The entropy of an image is directly related to the presence of a unique object or the presence of several different objects inside it: the lower the entropy the higher the probability of containing a single object inside it and, conversely, the higher the entropy the higher the probability of containing several objects inside it. Consequently, we propose the use of the entropy of images captured by the robot not only for the landmark searching and detection but also for obstacle avoidance. If the detected object corresponds to a landmark, the robot uses the suggestions stored in the visual topological map to reach the next landmark or to finish the mission. Otherwise, the robot considers the object as an obstacle and starts a collision avoidance maneuver. In order to validate the proposal we have defined an experimental framework in which the visual bug algorithm is used by an Unmanned Aerial Vehicle (UAV) in typical indoor navigation tasks. PMID:28900394

  14. Entropy-Based and Weighted Selective SIFT Clustering as an Energy Aware Framework for Supervised Visual Recognition of Man-Made Structures

    Directory of Open Access Journals (Sweden)

    Ayman El Mobacher

    2013-01-01

    Full Text Available Using local invariant features has been proven by published literature to be powerful for image processing and pattern recognition tasks. However, in energy aware environments, these invariant features would not scale easily because of their computational requirements. Motivated to find an efficient building recognition algorithm based on scale invariant feature transform (SIFT keypoints, we present in this paper uSee, a supervised learning framework which exploits the symmetrical and repetitive structural patterns in buildings to identify subsets of relevant clusters formed by these keypoints. Once an image is captured by a smart phone, uSee preprocesses it using variations in gradient angle- and entropy-based measures before extracting the building signature and comparing its representative SIFT keypoints against a repository of building images. Experimental results on 2 different databases confirm the effectiveness of uSee in delivering, at a greatly reduced computational cost, the high matching scores for building recognition that local descriptors can achieve. With only 14.3% of image SIFT keypoints, uSee exceeded prior literature results by achieving an accuracy of 99.1% on the Zurich Building Database with no manual rotation; thus saving significantly on the computational requirements of the task at hand.

  15. Application of an entropy-based Bayesian optimization technique to the redesign of an existing monitoring network for single air pollutants.

    Science.gov (United States)

    Ainslie, B; Reuten, C; Steyn, D G; Le, Nhu D; Zidek, James V

    2009-06-01

    We apply the entropy-based Bayesian optimizing approach of Le and Zidek to the spatial redesign of the extensive air pollution monitoring network operated by Metro Vancouver, in the Lower Fraser Valley, British Columbia. This method is chosen because of its statistical sophistication, relative to other possible approaches, and because of the very rich, two-decade long data record available from this network. The redesign analysis is applied to ozone, carbon monoxide and PM(2.5) pollutants. The analysis provides guidance with regard to stations monitoring the three pollutants. For both ozone and PM(2.5), the analysis indicates a need for more stations in the eastern part of the monitoring domain. A parallel analysis indicates that stations may be removed from the more central parts of the domain. An analysis of the carbon monoxide network produces results that are not nearly as clearly defined as those for the other two pollutants, presumably because carbon monoxide is a primary pollutant with many locally important sources. The work demonstrates the great utility of the analysis technique, and also provides statistically defensible guidance on the spatial redesign of this important monitoring network.

  16. Navigation and Self-Semantic Location of Drones in Indoor Environments by Combining the Visual Bug Algorithm and Entropy-Based Vision.

    Science.gov (United States)

    Maravall, Darío; de Lope, Javier; Fuentes, Juan P

    2017-01-01

    We introduce a hybrid algorithm for the self-semantic location and autonomous navigation of robots using entropy-based vision and visual topological maps. In visual topological maps the visual landmarks are considered as leave points for guiding the robot to reach a target point (robot homing) in indoor environments. These visual landmarks are defined from images of relevant objects or characteristic scenes in the environment. The entropy of an image is directly related to the presence of a unique object or the presence of several different objects inside it: the lower the entropy the higher the probability of containing a single object inside it and, conversely, the higher the entropy the higher the probability of containing several objects inside it. Consequently, we propose the use of the entropy of images captured by the robot not only for the landmark searching and detection but also for obstacle avoidance. If the detected object corresponds to a landmark, the robot uses the suggestions stored in the visual topological map to reach the next landmark or to finish the mission. Otherwise, the robot considers the object as an obstacle and starts a collision avoidance maneuver. In order to validate the proposal we have defined an experimental framework in which the visual bug algorithm is used by an Unmanned Aerial Vehicle (UAV) in typical indoor navigation tasks.

  17. Navigation and Self-Semantic Location of Drones in Indoor Environments by Combining the Visual Bug Algorithm and Entropy-Based Vision

    Directory of Open Access Journals (Sweden)

    Darío Maravall

    2017-08-01

    Full Text Available We introduce a hybrid algorithm for the self-semantic location and autonomous navigation of robots using entropy-based vision and visual topological maps. In visual topological maps the visual landmarks are considered as leave points for guiding the robot to reach a target point (robot homing in indoor environments. These visual landmarks are defined from images of relevant objects or characteristic scenes in the environment. The entropy of an image is directly related to the presence of a unique object or the presence of several different objects inside it: the lower the entropy the higher the probability of containing a single object inside it and, conversely, the higher the entropy the higher the probability of containing several objects inside it. Consequently, we propose the use of the entropy of images captured by the robot not only for the landmark searching and detection but also for obstacle avoidance. If the detected object corresponds to a landmark, the robot uses the suggestions stored in the visual topological map to reach the next landmark or to finish the mission. Otherwise, the robot considers the object as an obstacle and starts a collision avoidance maneuver. In order to validate the proposal we have defined an experimental framework in which the visual bug algorithm is used by an Unmanned Aerial Vehicle (UAV in typical indoor navigation tasks.

  18. Process automation

    International Nuclear Information System (INIS)

    Moser, D.R.

    1986-01-01

    Process automation technology has been pursued in the chemical processing industries and to a very limited extent in nuclear fuel reprocessing. Its effective use has been restricted in the past by the lack of diverse and reliable process instrumentation and the unavailability of sophisticated software designed for process control. The Integrated Equipment Test (IET) facility was developed by the Consolidated Fuel Reprocessing Program (CFRP) in part to demonstrate new concepts for control of advanced nuclear fuel reprocessing plants. A demonstration of fuel reprocessing equipment automation using advanced instrumentation and a modern, microprocessor-based control system is nearing completion in the facility. This facility provides for the synergistic testing of all chemical process features of a prototypical fuel reprocessing plant that can be attained with unirradiated uranium-bearing feed materials. The unique equipment and mission of the IET facility make it an ideal test bed for automation studies. This effort will provide for the demonstration of the plant automation concept and for the development of techniques for similar applications in a full-scale plant. A set of preliminary recommendations for implementing process automation has been compiled. Some of these concepts are not generally recognized or accepted. The automation work now under way in the IET facility should be useful to others in helping avoid costly mistakes because of the underutilization or misapplication of process automation. 6 figs

  19. Automated spectroscopic tissue classification in colorectal surgery

    NARCIS (Netherlands)

    Schols, R.M.; Alic, L.; Beets, G.L.; Breukink, S.O.; Wieringa, F.P.; Stassen, L.P.S.

    2015-01-01

    In colorectal surgery, detecting ureters and mesenteric arteries is of utmost importance to prevent iatrogenic injury and to facilitate intraoperative decision making. A tool enabling ureter- and artery-specific image enhancement within (and possibly through) surrounding adipose tissue would

  20. Empirical evaluation of three machine learning method for automatic classification of neoplastic diagnoses Evaluación empírica de tres métodos de aprendizaje automático para clasificar automáticamente diagnósticos de neoplasias

    Directory of Open Access Journals (Sweden)

    José Luis Jara

    2011-12-01

    Full Text Available Diagnoses are a valuable source of information for evaluating a health system. However, they are not used extensively by information systems because diagnoses are normally written in natural language. This work empirically evaluates three machine learning methods to automatically assign codes from the International Classification of Diseases (10th Revision to 3,335 distinct diagnoses of neoplasms obtained from UMLS®. This evaluation is conducted on three different types of preprocessing. The results are encouraging: a well-known rule induction method and maximum entropy models achieve 90% accuracy in a balanced cross-validation experiment.Los diagnósticos médicos son una fuente valiosa de información para evaluar el funcionamiento de un sistema de salud. Sin embargo, su utilización en sistemas de información se ve dificultada porque éstos se encuentran normalmente escritos en lenguaje natural. Este trabajo evalúa empíricamente tres métodos de Aprendizaje Automático para asignar códigos de acuerdo a la Clasificación Internacional de Enfermedades (décima versión a 3.335 diferentes diagnósticos de neoplasias extraídos desde UMLS®. Esta evaluación se realiza con tres tipos distintos de preprocesamiento. Los resultados son alentadores: un conocido método de inducción de reglas de decisión y modelos de entropía máxima obtienen alrededor de 90% accuracy en una validación cruzada balanceada.

  1. Text classification

    OpenAIRE

    Deveikis, Karolis

    2016-01-01

    This paper investigates the problem of text classification. The task of text classification is to assign a piece of text to one of several categories based on its content. Text classification is one of the tasks of natural language processing. Like the others, it is often solved using machine learning algorithms. There are many algorithms suitable for text classification. As a result, a problem of choice arises. In an effort to solve this problem, this paper analyzes various feature extractio...

  2. Automated External Defibrillator

    Science.gov (United States)

    ... To Health Topics / Automated External Defibrillator Automated External Defibrillator Also known as What Is An automated external ... in survival. Training To Use an Automated External Defibrillator Learning how to use an AED and taking ...

  3. Library Automation.

    Science.gov (United States)

    Husby, Ole

    1990-01-01

    The challenges and potential benefits of automating university libraries are reviewed, with special attention given to cooperative systems. Aspects discussed include database size, the role of the university computer center, storage modes, multi-institutional systems, resource sharing, cooperative system management, networking, and intelligent…

  4. Principles and methods for automated palynology.

    Science.gov (United States)

    Holt, K A; Bennett, K D

    2014-08-01

    Pollen grains are microscopic so their identification and quantification has, for decades, depended upon human observers using light microscopes: a labour-intensive approach. Modern improvements in computing and imaging hardware and software now bring automation of pollen analyses within reach. In this paper, we provide the first review in over 15 yr of progress towards automation of the part of palynology concerned with counting and classifying pollen, bringing together literature published from a wide spectrum of sources. We consider which attempts offer the most potential for an automated palynology system for universal application across all fields of research concerned with pollen classification and counting. We discuss what is required to make the datasets of these automated systems as acceptable as those produced by human palynologists, and present suggestions for how automation will generate novel approaches to counting and classifying pollen that have hitherto been unthinkable.

  5. Automated image analysis of the pathological lung in CT

    NARCIS (Netherlands)

    Sluimer, Ingrid Christine

    2005-01-01

    The general objective of the thesis is automation of the analysis of the pathological lung from CT images. Specifically, we aim for automated detection and classification of abnormalities in the lung parenchyma. We first provide a review of computer analysis techniques applied to CT of the

  6. Influence of automated cataloguing system on manual cataloguing ...

    African Journals Online (AJOL)

    This study examied the automation of cataloguing and classification practices in academic libraries in South-West Nigerian and what effect the automated cataloguing systme has on manual cataloguing in the the libraries. The study population comprised 110 library professional and paraprofessional personnel working in ...

  7. Automated Change Detection for Synthetic Aperture Sonar

    Science.gov (United States)

    2014-01-01

    2014 2. REPORT TYPE 3. DATES COVERED 00-00-2014 to 00-00-2014 4. TITLE AND SUBTITLE Automated Change Detection for Synthetic Aperture Sonar...R. Azimi-Sadjadi and S. Srinivasan, “Coherent Change Detection and Classification in Synthetic Aper - ture Radar Imagery Using Canonical Correlation

  8. Pattern recognition and classification an introduction

    CERN Document Server

    Dougherty, Geoff

    2012-01-01

    The use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. However, despite the existence of a number of notable books in the field, the subject remains very challenging, especially for the beginner. Pattern Recognition and Classification presents a comprehensive introduction to the core concepts involved in automated pattern recognition. It is designed to be accessible to newcomers from varied backgrounds, but it will also be useful to researchers and professionals in image and signal processing and analysis, and in computer visi

  9. Autonomous Systems: Habitat Automation

    Data.gov (United States)

    National Aeronautics and Space Administration — The Habitat Automation Project Element within the Autonomous Systems Project is developing software to automate the automation of habitats and other spacecraft. This...

  10. An Automation Planning Primer.

    Science.gov (United States)

    Paynter, Marion

    1988-01-01

    This brief planning guide for library automation incorporates needs assessment and evaluation of options to meet those needs. A bibliography of materials on automation planning and software reviews, library software directories, and library automation journals is included. (CLB)

  11. Future Control and Automation : Proceedings of the 2nd International Conference on Future Control and Automation

    CERN Document Server

    2012-01-01

    This volume Future Control and Automation- Volume 1 includes best papers selected from 2012 2nd International Conference on Future Control and Automation (ICFCA 2012) held on July 1-2, 2012, Changsha, China. Future control and automation is the use of control systems and information technologies to reduce the need for human work in the production of goods and services. This volume can be divided into five sessions on the basis of the classification of manuscripts considered, which is listed as follows: Identification and Control, Navigation, Guidance and Sensor, Simulation Technology, Future Telecommunications and Control

  12. Deep neural network and noise classification-based speech enhancement

    Science.gov (United States)

    Shi, Wenhua; Zhang, Xiongwei; Zou, Xia; Han, Wei

    2017-07-01

    In this paper, a speech enhancement method using noise classification and Deep Neural Network (DNN) was proposed. Gaussian mixture model (GMM) was employed to determine the noise type in speech-absent frames. DNN was used to model the relationship between noisy observation and clean speech. Once the noise type was determined, the corresponding DNN model was applied to enhance the noisy speech. GMM was trained with mel-frequency cepstrum coefficients (MFCC) and the parameters were estimated with an iterative expectation-maximization (EM) algorithm. Noise type was updated by spectrum entropy-based voice activity detection (VAD). Experimental results demonstrate that the proposed method could achieve better objective speech quality and smaller distortion under stationary and non-stationary conditions.

  13. Automated Budget System -

    Data.gov (United States)

    Department of Transportation — The Automated Budget System (ABS) automates management and planning of the Mike Monroney Aeronautical Center (MMAC) budget by providing enhanced capability to plan,...

  14. Learning features for tissue classification with the classification restricted Boltzmann machine

    DEFF Research Database (Denmark)

    van Tulder, Gijs; de Bruijne, Marleen

    2014-01-01

    Performance of automated tissue classification in medical imaging depends on the choice of descriptive features. In this paper, we show how restricted Boltzmann machines (RBMs) can be used to learn features that are especially suited for texture-based tissue classification. We introduce...... the convolutional classification RBM, a combination of the existing convolutional RBM and classification RBM, and use it for discriminative feature learning. We evaluate the classification accuracy of convolutional and non-convolutional classification RBMs on two lung CT problems. We find that RBM-learned features...... outperform conventional RBM-based feature learning, which is unsupervised and uses only a generative learning objective, as well as often-used filter banks. We show that a mixture of generative and discriminative learning can produce filters that give a higher classification accuracy....

  15. Automation 2017

    CERN Document Server

    Zieliński, Cezary; Kaliczyńska, Małgorzata

    2017-01-01

    This book consists of papers presented at Automation 2017, an international conference held in Warsaw from March 15 to 17, 2017. It discusses research findings associated with the concepts behind INDUSTRY 4.0, with a focus on offering a better understanding of and promoting participation in the Fourth Industrial Revolution. Each chapter presents a detailed analysis of a specific technical problem, in most cases followed by a numerical analysis, simulation and description of the results of implementing the solution in a real-world context. The theoretical results, practical solutions and guidelines presented are valuable for both researchers working in the area of engineering sciences and practitioners looking for solutions to industrial problems. .

  16. Marketing automation

    Directory of Open Access Journals (Sweden)

    TODOR Raluca Dania

    2017-01-01

    Full Text Available The automation of the marketing process seems to be nowadays, the only solution to face the major changes brought by the fast evolution of technology and the continuous increase in supply and demand. In order to achieve the desired marketing results, businessis have to employ digital marketing and communication services. These services are efficient and measurable thanks to the marketing technology used to track, score and implement each campaign. Due to the technical progress, the marketing fragmentation, demand for customized products and services on one side and the need to achieve constructive dialogue with the customers, immediate and flexible response and the necessity to measure the investments and the results on the other side, the classical marketing approached had changed continue to improve substantially.

  17. AUTOMATING THE DATA SECURITY PROCESS

    Directory of Open Access Journals (Sweden)

    Florin Ogigau-Neamtiu

    2017-11-01

    Full Text Available Contemporary organizations face big data security challenges in the cyber environment due to modern threats and actual business working model which relies heavily on collaboration, data sharing, tool integration, increased mobility, etc. The nowadays data classification and data obfuscation selection processes (encryption, masking or tokenization suffer because of the human implication in the process. Organizations need to shirk data security domain by classifying information based on its importance, conduct risk assessment plans and use the most cost effective data obfuscation technique. The paper proposes a new model for data protection by using automated machine decision making procedures to classify data and to select the appropriate data obfuscation technique. The proposed system uses natural language processing capabilities to analyze input data and to select the best course of action. The system has capabilities to learn from previous experiences thus improving itself and reducing the risk of wrong data classification.

  18. Both Automation and Paper.

    Science.gov (United States)

    Purcell, Royal

    1988-01-01

    Discusses the concept of a paperless society and the current situation in library automation. Various applications of automation and telecommunications are addressed, and future library automation is considered. Automation at the Monroe County Public Library in Bloomington, Indiana, is described as an example. (MES)

  19. Using Machine Learning for Land Suitability Classification ...

    African Journals Online (AJOL)

    Artificial intelligence and machine learning methods can be used to automate the land suitability classification. Multiple Classifier System (MCS) or ensemble methods are rapidly growing and receiving a lot of attention and proved to be more accurate and robust than an excellent single classifier in many fields. In this study ...

  20. Improving settlement type classification of aerial images

    CSIR Research Space (South Africa)

    Mdakane, L

    2014-10-01

    Full Text Available , an automated method can be used to help identify human settlements in a fixed, repeatable and timely manner. The main contribution of this work is to improve generalisation on settlement type classification of aerial imagery. Images acquired at different dates...

  1. Using Machine Learning for Land Suitability Classification

    African Journals Online (AJOL)

    User

    Abstract. Artificial intelligence and machine learning methods can be used to automate the land suitability classification. Multiple Classifier System (MCS) or ensemble methods are rapidly growing and receiving a lot of attention and proved to be more accurate and robust than an excellent single classifier in many fields.

  2. Xenolog classification.

    Science.gov (United States)

    Darby, Charlotte A; Stolzer, Maureen; Ropp, Patrick J; Barker, Daniel; Durand, Dannie

    2017-03-01

    Orthology analysis is a fundamental tool in comparative genomics. Sophisticated methods have been developed to distinguish between orthologs and paralogs and to classify paralogs into subtypes depending on the duplication mechanism and timing, relative to speciation. However, no comparable framework exists for xenologs: gene pairs whose history, since their divergence, includes a horizontal transfer. Further, the diversity of gene pairs that meet this broad definition calls for classification of xenologs with similar properties into subtypes. We present a xenolog classification that uses phylogenetic reconciliation to assign each pair of genes to a class based on the event responsible for their divergence and the historical association between genes and species. Our classes distinguish between genes related through transfer alone and genes related through duplication and transfer. Further, they separate closely-related genes in distantly-related species from distantly-related genes in closely-related species. We present formal rules that assign gene pairs to specific xenolog classes, given a reconciled gene tree with an arbitrary number of duplications and transfers. These xenology classification rules have been implemented in software and tested on a collection of ∼13 000 prokaryotic gene families. In addition, we present a case study demonstrating the connection between xenolog classification and gene function prediction. The xenolog classification rules have been implemented in N otung 2.9, a freely available phylogenetic reconciliation software package. http://www.cs.cmu.edu/~durand/Notung . Gene trees are available at http://dx.doi.org/10.7488/ds/1503 . durand@cmu.edu. Supplementary data are available at Bioinformatics online.

  3. 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)...

  4. Acne image analysis: lesion localization and classification

    Science.gov (United States)

    Abas, Fazly Salleh; Kaffenberger, Benjamin; Bikowski, Joseph; Gurcan, Metin N.

    2016-03-01

    Acne is a common skin condition present predominantly in the adolescent population, but may continue into adulthood. Scarring occurs commonly as a sequel to severe inflammatory acne. The presence of acne and resultant scars are more than cosmetic, with a significant potential to alter quality of life and even job prospects. The psychosocial effects of acne and scars can be disturbing and may be a risk factor for serious psychological concerns. Treatment efficacy is generally determined based on an invalidated gestalt by the physician and patient. However, the validated assessment of acne can be challenging and time consuming. Acne can be classified into several morphologies including closed comedones (whiteheads), open comedones (blackheads), papules, pustules, cysts (nodules) and scars. For a validated assessment, the different morphologies need to be counted independently, a method that is far too time consuming considering the limited time available for a consultation. However, it is practical to record and analyze images since dermatologists can validate the severity of acne within seconds after uploading an image. This paper covers the processes of region-ofinterest determination using entropy-based filtering and thresholding as well acne lesion feature extraction. Feature extraction methods using discrete wavelet frames and gray-level co-occurence matrix were presented and their effectiveness in separating the six major acne lesion classes were discussed. Several classifiers were used to test the extracted features. Correct classification accuracy as high as 85.5% was achieved using the binary classification tree with fourteen principle components used as descriptors. Further studies are underway to further improve the algorithm performance and validate it on a larger database.

  5. Autonomy and Automation

    Science.gov (United States)

    Shively, Jay

    2017-01-01

    A significant level of debate and confusion has surrounded the meaning of the terms autonomy and automation. Automation is a multi-dimensional concept, and we propose that Remotely Piloted Aircraft Systems (RPAS) automation should be described with reference to the specific system and task that has been automated, the context in which the automation functions, and other relevant dimensions. In this paper, we present definitions of automation, pilot in the loop, pilot on the loop and pilot out of the loop. We further propose that in future, the International Civil Aviation Organization (ICAO) RPAS Panel avoids the use of the terms autonomy and autonomous when referring to automated systems on board RPA. Work Group 7 proposes to develop, in consultation with other workgroups, a taxonomy of Levels of Automation for RPAS.

  6. An automated swimming respirometer

    DEFF Research Database (Denmark)

    STEFFENSEN, JF; JOHANSEN, K; BUSHNELL, PG

    1984-01-01

    An automated respirometer is described that can be used for computerized respirometry of trout and sharks.......An automated respirometer is described that can be used for computerized respirometry of trout and sharks....

  7. Configuration Management Automation (CMA) -

    Data.gov (United States)

    Department of Transportation — Configuration Management Automation (CMA) will provide an automated, integrated enterprise solution to support CM of FAA NAS and Non-NAS assets and investments. CMA...

  8. Automation in College Libraries.

    Science.gov (United States)

    Werking, Richard Hume

    1991-01-01

    Reports the results of a survey of the "Bowdoin List" group of liberal arts colleges. The survey obtained information about (1) automation modules in place and when they had been installed; (2) financing of automation and its impacts on the library budgets; and (3) library director's views on library automation and the nature of the…

  9. CLASSIFICATION OF LEARNING MANAGEMENT SYSTEMS

    Directory of Open Access Journals (Sweden)

    Yu. B. Popova

    2016-01-01

    Full Text Available Using of information technologies and, in particular, learning management systems, increases opportunities of teachers and students in reaching their goals in education. Such systems provide learning content, help organize and monitor training, collect progress statistics and take into account the individual characteristics of each user. Currently, there is a huge inventory of both paid and free systems are physically located both on college servers and in the cloud, offering different features sets of different licensing scheme and the cost. This creates the problem of choosing the best system. This problem is partly due to the lack of comprehensive classification of such systems. Analysis of more than 30 of the most common now automated learning management systems has shown that a classification of such systems should be carried out according to certain criteria, under which the same type of system can be considered. As classification features offered by the author are: cost, functionality, modularity, keeping the customer’s requirements, the integration of content, the physical location of a system, adaptability training. Considering the learning management system within these classifications and taking into account the current trends of their development, it is possible to identify the main requirements to them: functionality, reliability, ease of use, low cost, support for SCORM standard or Tin Can API, modularity and adaptability. According to the requirements at the Software Department of FITR BNTU under the guidance of the author since 2009 take place the development, the use and continuous improvement of their own learning management system.

  10. Automation in Clinical Microbiology

    Science.gov (United States)

    Ledeboer, Nathan A.

    2013-01-01

    Historically, the trend toward automation in clinical pathology laboratories has largely bypassed the clinical microbiology laboratory. In this article, we review the historical impediments to automation in the microbiology laboratory and offer insight into the reasons why we believe that we are on the cusp of a dramatic change that will sweep a wave of automation into clinical microbiology laboratories. We review the currently available specimen-processing instruments as well as the total laboratory automation solutions. Lastly, we outline the types of studies that will need to be performed to fully assess the benefits of automation in microbiology laboratories. PMID:23515547

  11. Automation of industrial bioprocesses.

    Science.gov (United States)

    Beyeler, W; DaPra, E; Schneider, K

    2000-01-01

    The dramatic development of new electronic devices within the last 25 years has had a substantial influence on the control and automation of industrial bioprocesses. Within this short period of time the method of controlling industrial bioprocesses has changed completely. In this paper, the authors will use a practical approach focusing on the industrial applications of automation systems. From the early attempts to use computers for the automation of biotechnological processes up to the modern process automation systems some milestones are highlighted. Special attention is given to the influence of Standards and Guidelines on the development of automation systems.

  12. Serum PTH reference values established by an automated third-generation assay in vitamin D-replete subjects with normal renal function: consequences of diagnosing primary hyperparathyroidism and the classification of dialysis patients.

    Science.gov (United States)

    Souberbielle, Jean-Claude; Massart, Catherine; Brailly-Tabard, Sylvie; Cormier, Catherine; Cavalier, Etienne; Delanaye, Pierre; Chanson, Philippe

    2016-03-01

    To determine parathyroid hormone (PTH) reference values in French healthy adults, taking into account serum 25-hydroxyvitamin D (25OHD), renal function, age, gender, and BMI. We studied 898 healthy subjects (432 women) aged 18-89 years with a normal BMI and estimated glomerular filtration rate (eGFR), 81 patients with surgically proven primary hyperparathyroidism (PHPT), and 264 dialysis patients. 25OHD and third-generation PTH assays were implemented on the LIAISON XL platform. Median PTH and 25OHD values in the 898 healthy subjects were 18.8  ng/l and 23.6  ng/ml respectively. PTH was lower in subjects with 25OHD ≥30  ng/ml than in those with lower values. Among the 183 subjects with 25OHD ≥30  ng/ml, those aged ≥60 years (n=31) had higher PTH values than younger subjects, independent of 25OHD, BMI, and eGFR (PPTH values for the entire group of 183 vitamin D-replete subjects (9.4-28.9  ng/l) as our reference values. With 28.9  ng/l as the upper limit of normal (ULN) rather than the manufacturer's ULN of 38.4  ng/l, the percentage of PHPT patients with 'high' PTH values rose to 90.1% from 66.6% (PPTH ULN fell by 22.4%, diagnostic sensitivity for PHPT improved, and the classification of dialysis patients was modified. © 2016 European Society of Endocrinology.

  13. 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....

  14. 21 CFR 864.9175 - Automated blood grouping and antibody test system.

    Science.gov (United States)

    2010-04-01

    ...) Identification. An automated blood grouping and antibody test system is a device used to group erythrocytes (red blood cells) and to detect antibodies to blood group antigens. (b) Classification. Class II (performance... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Automated blood grouping and antibody test system...

  15. Research on Scientific Data Sharing and Distribution Policy in Advanced Manufacturing and Automation Fields

    Directory of Open Access Journals (Sweden)

    Liya Li

    2007-12-01

    Full Text Available Scientific data sharing is a long-term and complicated task. The related data sharing and distribution policies are prime concerns. By using both domestic and international experiences in scientific data sharing, the sources, distribution, and classification of scientific data in advanced manufacturing and automation are discussed. A primary data sharing and distribution policy in advanced manufacture and automation is introduced.

  16. A Local Area Network to Facilitate Office Automation in the Administrative Sciences Department.

    Science.gov (United States)

    1986-03-27

    NO (nclude Securtry Classification) A L.OCAL AREA NET.CO IRK TO FACILITATE OFFICE AUTOMATION IN THE ADMINISTR.ATIVE SCIE-NCES DL P.RTMENT ullmHoward...edltorls are ob~solete Approved for public release; distribution is unlimited. A Local Area Network to Facilitate Office Automation in the... office automation . Accesion For NTIS CRA&I ii DTIC TAB 0 Unannounced 0 Justification ................. BY .......... . ........ .. ... D . ibution

  17. 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

  18. Support Vector Machine and Parametric Wavelet-Based Texture Classification of Stem Cell Images

    National Research Council Canada - National Science Library

    Jeffreys, Christopher

    2004-01-01

    .... Since colony texture is a major discriminating feature in determining quality, we introduce a non-invasive, semi-automated texture-based stem cell colony classification methodology to aid researchers...

  19. New York State Thruway Authority automatic vehicle classification (AVC) : research report.

    Science.gov (United States)

    2008-03-31

    In December 2007, the N.Y.S. Thruway Authority (Thruway) concluded a Federal : funded research effort to study technology and develop a design for retrofitting : devices required in implementing a fully automated vehicle classification system i...

  20. Automation systems for radioimmunoassay

    International Nuclear Information System (INIS)

    Yamasaki, Paul

    1974-01-01

    The application of automation systems for radioimmunoassay (RIA) was discussed. Automated systems could be useful in the second step, of the four basic processes in the course of RIA, i.e., preparation of sample for reaction. There were two types of instrumentation, a semi-automatic pipete, and a fully automated pipete station, both providing for fast and accurate dispensing of the reagent or for the diluting of sample with reagent. Illustrations of the instruments were shown. (Mukohata, S.)

  1. Automated stopcock actuator

    OpenAIRE

    Vandehey, N. T.; O\\'Neil, J. P.

    2015-01-01

    Introduction We have developed a low-cost stopcock valve actuator for radiochemistry automation built using a stepper motor and an Arduino, an open-source single-board microcontroller. The con-troller hardware can be programmed to run by serial communication or via two 5–24 V digital lines for simple integration into any automation control system. This valve actuator allows for automated use of a single, disposable stopcock, providing a number of advantages over stopcock manifold systems ...

  2. Automated Analysis of Accountability

    DEFF Research Database (Denmark)

    Bruni, Alessandro; Giustolisi, Rosario; Schürmann, Carsten

    2017-01-01

    that are amenable to automated verification. Our definitions are general enough to be applied to different classes of protocols and different automated security verification tools. Furthermore, we point out formally the relation between verifiability and accountability. We validate our definitions...... with the automatic verification of three protocols: a secure exam protocol, Google’s Certificate Transparency, and an improved version of Bingo Voting. We find through automated verification that all three protocols satisfy verifiability while only the first two protocols meet accountability....

  3. Management Planning for Workplace Automation.

    Science.gov (United States)

    McDole, Thomas L.

    Several factors must be considered when implementing office automation. Included among these are whether or not to automate at all, the effects of automation on employees, requirements imposed by automation on the physical environment, effects of automation on the total organization, and effects on clientele. The reasons behind the success or…

  4. Laboratory Automation and Middleware.

    Science.gov (United States)

    Riben, Michael

    2015-06-01

    The practice of surgical pathology is under constant pressure to deliver the highest quality of service, reduce errors, increase throughput, and decrease turnaround time while at the same time dealing with an aging workforce, increasing financial constraints, and economic uncertainty. Although not able to implement total laboratory automation, great progress continues to be made in workstation automation in all areas of the pathology laboratory. This report highlights the benefits and challenges of pathology automation, reviews middleware and its use to facilitate automation, and reviews the progress so far in the anatomic pathology laboratory. Copyright © 2015 Elsevier Inc. All rights reserved.

  5. Automated cloning methods.; TOPICAL

    International Nuclear Information System (INIS)

    Collart, F.

    2001-01-01

    Argonne has developed a series of automated protocols to generate bacterial expression clones by using a robotic system designed to be used in procedures associated with molecular biology. The system provides plate storage, temperature control from 4 to 37 C at various locations, and Biomek and Multimek pipetting stations. The automated system consists of a robot that transports sources from the active station on the automation system. Protocols for the automated generation of bacterial expression clones can be grouped into three categories (Figure 1). Fragment generation protocols are initiated on day one of the expression cloning procedure and encompass those protocols involved in generating purified coding region (PCR)

  6. Complacency and Automation Bias in the Use of Imperfect Automation.

    Science.gov (United States)

    Wickens, Christopher D; Clegg, Benjamin A; Vieane, Alex Z; Sebok, Angelia L

    2015-08-01

    We examine the effects of two different kinds of decision-aiding automation errors on human-automation interaction (HAI), occurring at the first failure following repeated exposure to correctly functioning automation. The two errors are incorrect advice, triggering the automation bias, and missing advice, reflecting complacency. Contrasts between analogous automation errors in alerting systems, rather than decision aiding, have revealed that alerting false alarms are more problematic to HAI than alerting misses are. Prior research in decision aiding, although contrasting the two aiding errors (incorrect vs. missing), has confounded error expectancy. Participants performed an environmental process control simulation with and without decision aiding. For those with the aid, automation dependence was created through several trials of perfect aiding performance, and an unexpected automation error was then imposed in which automation was either gone (one group) or wrong (a second group). A control group received no automation support. The correct aid supported faster and more accurate diagnosis and lower workload. The aid failure degraded all three variables, but "automation wrong" had a much greater effect on accuracy, reflecting the automation bias, than did "automation gone," reflecting the impact of complacency. Some complacency was manifested for automation gone, by a longer latency and more modest reduction in accuracy. Automation wrong, creating the automation bias, appears to be a more problematic form of automation error than automation gone, reflecting complacency. Decision-aiding automation should indicate its lower degree of confidence in uncertain environments to avoid the automation bias. © 2015, Human Factors and Ergonomics Society.

  7. Automated detection and categorization of genital injuries using digital colposcopy

    DEFF Research Database (Denmark)

    Fernandes, Kelwin; Cardoso, Jaime S.; Astrup, Birgitte Schmidt

    2017-01-01

    handcrafted features and deep learning techniques in the automated processing of colposcopic images for genital injury detection. Positive results where achieved by both paradigms in segmentation and classification subtasks, being traditional and deep models the best strategy for each subtask type...

  8. Automated mapping of building facades by machine learning

    DEFF Research Database (Denmark)

    Höhle, Joachim

    2014-01-01

    Facades of buildings contain various types of objects which have to be recorded for information systems. The article describes a solution for this task focussing on automated classification by means of machine learning techniques. Stereo pairs of oblique images are used to derive 3D point clouds...

  9. Automated Discovery of Speech Act Categories in Educational Games

    Science.gov (United States)

    Rus, Vasile; Moldovan, Cristian; Niraula, Nobal; Graesser, Arthur C.

    2012-01-01

    In this paper we address the important task of automated discovery of speech act categories in dialogue-based, multi-party educational games. Speech acts are important in dialogue-based educational systems because they help infer the student speaker's intentions (the task of speech act classification) which in turn is crucial to providing adequate…

  10. Automated tone grading of granite

    International Nuclear Information System (INIS)

    Catalina Hernández, J.C.; Fernández Ramón, G.

    2017-01-01

    The production of a natural stone processing plant is subject to the intrinsic variability of the stone blocks that constitute its raw material, which may cause problems of lack of uniformity in the visual appearance of the produced material that often triggers complaints from customers. The best way to tackle this problem is to classify the product according to its visual features, which is traditionally done by hand: an operator observes each and every piece that comes out of the production line and assigns it to the closest match among a number of predefined classes, taking into account visual features of the material such as colour, texture, grain, veins, etc. However, this manual procedure presents significant consistency problems, due to the inherent subjectivity of the classification performed by each operator, and the errors caused by their progressive fatigue. Attempts to employ automated sorting systems like the ones used in the ceramic tile industry have not been successful, as natural stone presents much higher variability than ceramic tiles. Therefore, it has been necessary to develop classification systems specifically designed for the treatment of the visual parameters that distinguish the different types of natural stone. This paper describes the details of a computer vision system developed by AITEMIN for the automatic classification of granite pieces according to their tone, which provides an integral solution to tone grading problems in the granite processing and marketing industry. The system has been designed to be easily trained by the end user, through the learning of the samples established as tone patterns by the user. [es

  11. Automated System Marketplace 1994.

    Science.gov (United States)

    Griffiths, Jose-Marie; Kertis, Kimberly

    1994-01-01

    Reports results of the 1994 Automated System Marketplace survey based on responses from 60 vendors. Highlights include changes in the library automation marketplace; estimated library systems revenues; minicomputer and microcomputer-based systems; marketplace trends; global markets and mergers; research needs; new purchase processes; and profiles…

  12. Automation benefits BWR customers

    International Nuclear Information System (INIS)

    Anon.

    1982-01-01

    A description is given of the increasing use of automation at General Electric's Wilmington fuel fabrication plant. Computerised systems and automated equipment perform a large number of inspections, inventory and process operations, and new advanced systems are being continuously introduced to reduce operator errors and expand product reliability margins. (U.K.)

  13. Automate functional testing

    Directory of Open Access Journals (Sweden)

    Ramesh Kalindri

    2014-06-01

    Full Text Available Currently, software engineers are increasingly turning to the option of automating functional tests, but not always have successful in this endeavor. Reasons range from low planning until over cost in the process. Some principles that can guide teams in automating these tests are described in this article.

  14. Automation in Warehouse Development

    NARCIS (Netherlands)

    Hamberg, R.; Verriet, J.

    2012-01-01

    The warehouses of the future will come in a variety of forms, but with a few common ingredients. Firstly, human operational handling of items in warehouses is increasingly being replaced by automated item handling. Extended warehouse automation counteracts the scarcity of human operators and

  15. Identity Management Processes Automation

    Directory of Open Access Journals (Sweden)

    A. Y. Lavrukhin

    2010-03-01

    Full Text Available Implementation of identity management systems consists of two main parts, consulting and automation. The consulting part includes development of a role model and identity management processes description. The automation part is based on the results of consulting part. This article describes the most important aspects of IdM implementation.

  16. Work and Programmable Automation.

    Science.gov (United States)

    DeVore, Paul W.

    A new industrial era based on electronics and the microprocessor has arrived, an era that is being called intelligent automation. Intelligent automation, in the form of robots, replaces workers, and the new products, using microelectronic devices, require significantly less labor to produce than the goods they replace. The microprocessor thus…

  17. Library Automation in Pakistan.

    Science.gov (United States)

    Haider, Syed Jalaluddin

    1998-01-01

    Examines the state of library automation in Pakistan. Discusses early developments; financial support by the Netherlands Library Development Project (Pakistan); lack of automated systems in college/university and public libraries; usage by specialist libraries; efforts by private-sector libraries and the National Library in Pakistan; commonly used…

  18. Library Automation Style Guide.

    Science.gov (United States)

    Gaylord Bros., Liverpool, NY.

    This library automation style guide lists specific terms and names often used in the library automation industry. The terms and/or acronyms are listed alphabetically and each is followed by a brief definition. The guide refers to the "Chicago Manual of Style" for general rules, and a notes section is included for the convenience of individual…

  19. Planning for Office Automation.

    Science.gov (United States)

    Sherron, Gene T.

    1982-01-01

    The steps taken toward office automation by the University of Maryland are described. Office automation is defined and some types of word processing systems are described. Policies developed in the writing of a campus plan are listed, followed by a section on procedures adopted to implement the plan. (Author/MLW)

  20. The Automated Office.

    Science.gov (United States)

    Naclerio, Nick

    1979-01-01

    Clerical personnel may be able to climb career ladders as a result of office automation and expanded job opportunities in the word processing area. Suggests opportunities in an automated office system and lists books and periodicals on word processing for counselors and teachers. (MF)

  1. Automating the Small Library.

    Science.gov (United States)

    Skapura, Robert

    1987-01-01

    Discusses the use of microcomputers for automating school libraries, both for entire systems and for specific library tasks. Highlights include available library management software, newsletters that evaluate software, constructing an evaluation matrix, steps to consider in library automation, and a brief discussion of computerized card catalogs.…

  2. Entropy-Based Dark Frame Subtraction

    OpenAIRE

    Goesele, Michael; Heidrich, Wolfgang; Seidel, Hans-Peter

    2001-01-01

    Noise due to dark current is a serious limitation for taking long exposure time images with a CCD digital camera. Current solutions have serious drawbacks: interpolation of pixels with high dark current leads to smoothing effects or other artifacts -- especially if a large number of pixels are corrupted. Due to the exponential temperature dependence of the dark current, dark frame subtraction works best for temperature controlled high end CCD imaging systems. On the ...

  3. Entropy-based financial asset pricing.

    Science.gov (United States)

    Ormos, Mihály; Zibriczky, Dávid

    2014-01-01

    We investigate entropy as a financial risk measure. Entropy explains the equity premium of securities and portfolios in a simpler way and, at the same time, with higher explanatory power than the beta parameter of the capital asset pricing model. For asset pricing we define the continuous entropy as an alternative measure of risk. Our results show that entropy decreases in the function of the number of securities involved in a portfolio in a similar way to the standard deviation, and that efficient portfolios are situated on a hyperbola in the expected return-entropy system. For empirical investigation we use daily returns of 150 randomly selected securities for a period of 27 years. Our regression results show that entropy has a higher explanatory power for the expected return than the capital asset pricing model beta. Furthermore we show the time varying behavior of the beta along with entropy.

  4. The Entropy-Based Quantum Metric

    Directory of Open Access Journals (Sweden)

    Roger Balian

    2014-07-01

    Full Text Available The von Neumann entropy S( D ^ generates in the space of quantum density matrices  D ^ the Riemannian metric ds2 = −d2S( D ^ , which is physically founded and which characterises the amount of quantum information lost by mixing  D ^ and  D ^ + d D ^ . A rich geometric structure is thereby implemented in quantum mechanics. It includes a canonical mapping between the spaces of states and of observables, which involves the Legendre transform of S( D ^ . The Kubo scalar product is recovered within the space of observables. Applications are given to equilibrium and non equilibrium quantum statistical mechanics. There the formalism is specialised to the relevant space of observables and to the associated reduced states issued from the maximum entropy criterion, which result from the exact states through an orthogonal projection. Von Neumann’s entropy specialises into a relevant entropy. Comparison is made with other metrics. The Riemannian properties of the metric ds2 = −d2S( D ^ are derived. The curvature arises from the non-Abelian nature of quantum mechanics; its general expression and its explicit form for q-bits are given, as well as geodesics.

  5. Entropy based fingerprint for local crystalline order

    Science.gov (United States)

    Piaggi, Pablo M.; Parrinello, Michele

    2017-09-01

    We introduce a new fingerprint that allows distinguishing between liquid-like and solid-like atomic environments. This fingerprint is based on an approximate expression for the entropy projected on individual atoms. When combined with local enthalpy, this fingerprint acquires an even finer resolution and it is capable of discriminating between different crystal structures.

  6. Entropy-based adaptive attitude estimation

    Science.gov (United States)

    Kiani, Maryam; Barzegar, Aylin; Pourtakdoust, Seid H.

    2018-03-01

    Gaussian approximation filters have increasingly been developed to enhance the accuracy of attitude estimation in space missions. The effective employment of these algorithms demands accurate knowledge of system dynamics and measurement models, as well as their noise characteristics, which are usually unavailable or unreliable. An innovation-based adaptive filtering approach has been adopted as a solution to this problem; however, it exhibits two major challenges, namely appropriate window size selection and guaranteed assurance of positive definiteness for the estimated noise covariance matrices. The current work presents two novel techniques based on relative entropy and confidence level concepts in order to address the abovementioned drawbacks. The proposed adaptation techniques are applied to two nonlinear state estimation algorithms of the extended Kalman filter and cubature Kalman filter for attitude estimation of a low earth orbit satellite equipped with three-axis magnetometers and Sun sensors. The effectiveness of the proposed adaptation scheme is demonstrated by means of comprehensive sensitivity analysis on the system and environmental parameters by using extensive independent Monte Carlo simulations.

  7. Entropy based file type identification and partitioning

    Science.gov (United States)

    2017-06-01

    87 ix LIST OF FIGURES Figure 1. Process Adopted for File Type Identification...the identification of file types and file partitioning. This approach has applications in cybersecurity as it allows for a quick determination of...0.75p X p= = which yields 2 2( ) [(0.25 log 0.25) (0.75 log 0.75)] 0.8113.H X = − × + × = Entropy analysis offers a convenient and quick method

  8. Advances in inspection automation

    Science.gov (United States)

    Weber, Walter H.; Mair, H. Douglas; Jansen, Dion; Lombardi, Luciano

    2013-01-01

    This new session at QNDE reflects the growing interest in inspection automation. Our paper describes a newly developed platform that makes the complex NDE automation possible without the need for software programmers. Inspection tasks that are tedious, error-prone or impossible for humans to perform can now be automated using a form of drag and drop visual scripting. Our work attempts to rectify the problem that NDE is not keeping pace with the rest of factory automation. Outside of NDE, robots routinely and autonomously machine parts, assemble components, weld structures and report progress to corporate databases. By contrast, components arriving in the NDT department typically require manual part handling, calibrations and analysis. The automation examples in this paper cover the development of robotic thickness gauging and the use of adaptive contour following on the NRU reactor inspection at Chalk River.

  9. Automated model building

    CERN Document Server

    Caferra, Ricardo; Peltier, Nicholas

    2004-01-01

    This is the first book on automated model building, a discipline of automated deduction that is of growing importance Although models and their construction are important per se, automated model building has appeared as a natural enrichment of automated deduction, especially in the attempt to capture the human way of reasoning The book provides an historical overview of the field of automated deduction, and presents the foundations of different existing approaches to model construction, in particular those developed by the authors Finite and infinite model building techniques are presented The main emphasis is on calculi-based methods, and relevant practical results are provided The book is of interest to researchers and graduate students in computer science, computational logic and artificial intelligence It can also be used as a textbook in advanced undergraduate courses

  10. Automation in Warehouse Development

    CERN Document Server

    Verriet, Jacques

    2012-01-01

    The warehouses of the future will come in a variety of forms, but with a few common ingredients. Firstly, human operational handling of items in warehouses is increasingly being replaced by automated item handling. Extended warehouse automation counteracts the scarcity of human operators and supports the quality of picking processes. Secondly, the development of models to simulate and analyse warehouse designs and their components facilitates the challenging task of developing warehouses that take into account each customer’s individual requirements and logistic processes. Automation in Warehouse Development addresses both types of automation from the innovative perspective of applied science. In particular, it describes the outcomes of the Falcon project, a joint endeavour by a consortium of industrial and academic partners. The results include a model-based approach to automate warehouse control design, analysis models for warehouse design, concepts for robotic item handling and computer vision, and auton...

  11. Automation in Immunohematology

    Directory of Open Access Journals (Sweden)

    Meenu Bajpai

    2012-01-01

    Full Text Available There have been rapid technological advances in blood banking in South Asian region over the past decade with an increasing emphasis on quality and safety of blood products. The conventional test tube technique has given way to newer techniques such as column agglutination technique, solid phase red cell adherence assay, and erythrocyte-magnetized technique. These new technologies are adaptable to automation and major manufacturers in this field have come up with semi and fully automated equipments for immunohematology tests in the blood bank. Automation improves the objectivity and reproducibility of tests. It reduces human errors in patient identification and transcription errors. Documentation and traceability of tests, reagents and processes and archiving of results is another major advantage of automation. Shifting from manual methods to automation is a major undertaking for any transfusion service to provide quality patient care with lesser turnaround time for their ever increasing workload. This article discusses the various issues involved in the process.

  12. 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...

  13. Using Genetic Algorithms for Texts Classification Problems

    Directory of Open Access Journals (Sweden)

    A. A. Shumeyko

    2009-01-01

    Full Text Available The avalanche quantity of the information developed by mankind has led to concept of automation of knowledge extraction – Data Mining ([1]. This direction is connected with a wide spectrum of problems - from recognition of the fuzzy set to creation of search machines. Important component of Data Mining is processing of the text information. Such problems lean on concept of classification and clustering ([2]. Classification consists in definition of an accessory of some element (text to one of in advance created classes. Clustering means splitting a set of elements (texts on clusters which quantity are defined by localization of elements of the given set in vicinities of these some natural centers of these clusters. Realization of a problem of classification initially should lean on the given postulates, basic of which – the aprioristic information on primary set of texts and a measure of affinity of elements and classes.

  14. Towards Automatic Classification of Wikipedia Content

    Science.gov (United States)

    Szymański, Julian

    Wikipedia - the Free Encyclopedia encounters the problem of proper classification of new articles everyday. The process of assignment of articles to categories is performed manually and it is a time consuming task. It requires knowledge about Wikipedia structure, which is beyond typical editor competence, which leads to human-caused mistakes - omitting or wrong assignments of articles to categories. The article presents application of SVM classifier for automatic classification of documents from The Free Encyclopedia. The classifier application has been tested while using two text representations: inter-documents connections (hyperlinks) and word content. The results of the performed experiments evaluated on hand crafted data show that the Wikipedia classification process can be partially automated. The proposed approach can be used for building a decision support system which suggests editors the best categories that fit new content entered to Wikipedia.

  15. An ordinal classification approach for CTG categorization.

    Science.gov (United States)

    Georgoulas, George; Karvelis, Petros; Gavrilis, Dimitris; Stylios, Chrysostomos D; Nikolakopoulos, George

    2017-07-01

    Evaluation of cardiotocogram (CTG) is a standard approach employed during pregnancy and delivery. But, its interpretation requires high level expertise to decide whether the recording is Normal, Suspicious or Pathological. Therefore, a number of attempts have been carried out over the past three decades for development automated sophisticated systems. These systems are usually (multiclass) classification systems that assign a category to the respective CTG. However most of these systems usually do not take into consideration the natural ordering of the categories associated with CTG recordings. In this work, an algorithm that explicitly takes into consideration the ordering of CTG categories, based on binary decomposition method, is investigated. Achieved results, using as a base classifier the C4.5 decision tree classifier, prove that the ordinal classification approach is marginally better than the traditional multiclass classification approach, which utilizes the standard C4.5 algorithm for several performance criteria.

  16. A neural network for noise correlation classification

    Science.gov (United States)

    Paitz, Patrick; Gokhberg, Alexey; Fichtner, Andreas

    2018-02-01

    We present an artificial neural network (ANN) for the classification of ambient seismic noise correlations into two categories, suitable and unsuitable for noise tomography. By using only a small manually classified data subset for network training, the ANN allows us to classify large data volumes with low human effort and to encode the valuable subjective experience of data analysts that cannot be captured by a deterministic algorithm. Based on a new feature extraction procedure that exploits the wavelet-like nature of seismic time-series, we efficiently reduce the dimensionality of noise correlation data, still keeping relevant features needed for automated classification. Using global- and regional-scale data sets, we show that classification errors of 20 per cent or less can be achieved when the network training is performed with as little as 3.5 per cent and 16 per cent of the data sets, respectively. Furthermore, the ANN trained on the regional data can be applied to the global data, and vice versa, without a significant increase of the classification error. An experiment where four students manually classified the data, revealed that the classification error they would assign to each other is substantially larger than the classification error of the ANN (>35 per cent). This indicates that reproducibility would be hampered more by human subjectivity than by imperfections of the ANN.

  17. Systematic review automation technologies

    Science.gov (United States)

    2014-01-01

    Systematic reviews, a cornerstone of evidence-based medicine, are not produced quickly enough to support clinical practice. The cost of production, availability of the requisite expertise and timeliness are often quoted as major contributors for the delay. This detailed survey of the state of the art of information systems designed to support or automate individual tasks in the systematic review, and in particular systematic reviews of randomized controlled clinical trials, reveals trends that see the convergence of several parallel research projects. We surveyed literature describing informatics systems that support or automate the processes of systematic review or each of the tasks of the systematic review. Several projects focus on automating, simplifying and/or streamlining specific tasks of the systematic review. Some tasks are already fully automated while others are still largely manual. In this review, we describe each task and the effect that its automation would have on the entire systematic review process, summarize the existing information system support for each task, and highlight where further research is needed for realizing automation for the task. Integration of the systems that automate systematic review tasks may lead to a revised systematic review workflow. We envisage the optimized workflow will lead to system in which each systematic review is described as a computer program that automatically retrieves relevant trials, appraises them, extracts and synthesizes data, evaluates the risk of bias, performs meta-analysis calculations, and produces a report in real time. PMID:25005128

  18. On-Site School Library Automation: Automation Anywhere with Laptops.

    Science.gov (United States)

    Gunn, Holly; Oxner, June

    2000-01-01

    Four years after the Halifax Regional School Board was formed through amalgamation, over 75% of its school libraries were automated. On-site automation with laptops was a quicker, more efficient way of automating than sending a shelf list to the Technical Services Department. The Eastern Shore School Library Automation Project was a successful…

  19. 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...

  20. Automated Single Cell Data Decontamination Pipeline

    Energy Technology Data Exchange (ETDEWEB)

    Tennessen, Kristin [Lawrence Berkeley National Lab. (LBNL), Walnut Creek, CA (United States). Dept. of Energy Joint Genome Inst.; Pati, Amrita [Lawrence Berkeley National Lab. (LBNL), Walnut Creek, CA (United States). Dept. of Energy Joint Genome Inst.

    2014-03-21

    Recent technological advancements in single-cell genomics have encouraged the classification and functional assessment of microorganisms from a wide span of the biospheres phylogeny.1,2 Environmental processes of interest to the DOE, such as bioremediation and carbon cycling, can be elucidated through the genomic lens of these unculturable microbes. However, contamination can occur at various stages of the single-cell sequencing process. Contaminated data can lead to wasted time and effort on meaningless analyses, inaccurate or erroneous conclusions, and pollution of public databases. A fully automated decontamination tool is necessary to prevent these instances and increase the throughput of the single-cell sequencing process

  1. Automated reliability assessment for spectroscopic redshift measurements

    Science.gov (United States)

    Jamal, S.; Le Brun, V.; Le Fèvre, O.; Vibert, D.; Schmitt, A.; Surace, C.; Copin, Y.; Garilli, B.; Moresco, M.; Pozzetti, L.

    2018-03-01

    Context. Future large-scale surveys, such as the ESA Euclid mission, will produce a large set of galaxy redshifts (≥106) that will require fully automated data-processing pipelines to analyze the data, extract crucial information and ensure that all requirements are met. A fundamental element in these pipelines is to associate to each galaxy redshift measurement a quality, or reliability, estimate. Aim. In this work, we introduce a new approach to automate the spectroscopic redshift reliability assessment based on machine learning (ML) and characteristics of the redshift probability density function. Methods: We propose to rephrase the spectroscopic redshift estimation into a Bayesian framework, in order to incorporate all sources of information and uncertainties related to the redshift estimation process and produce a redshift posterior probability density function (PDF). To automate the assessment of a reliability flag, we exploit key features in the redshift posterior PDF and machine learning algorithms. Results: As a working example, public data from the VIMOS VLT Deep Survey is exploited to present and test this new methodology. We first tried to reproduce the existing reliability flags using supervised classification in order to describe different types of redshift PDFs, but due to the subjective definition of these flags (classification accuracy 58%), we soon opted for a new homogeneous partitioning of the data into distinct clusters via unsupervised classification. After assessing the accuracy of the new clusters via resubstitution and test predictions (classification accuracy 98%), we projected unlabeled data from preliminary mock simulations for the Euclid space mission into this mapping to predict their redshift reliability labels. Conclusions: Through the development of a methodology in which a system can build its own experience to assess the quality of a parameter, we are able to set a preliminary basis of an automated reliability assessment for

  2. Ontology Building Using Classification Rules and Discovered Concepts

    Directory of Open Access Journals (Sweden)

    Gorskis Henrihs

    2015-12-01

    Full Text Available Building an ontology is a difficult and time-consuming task. In order to make this task easier and faster, some automatic methods can be employed. This paper examines the feasibility of using rules and concepts discovered during the classification tree building process in the C4.5 algorithm, in a completely automated way, for the purposes of building an ontology from data. By building the ontology directly from continuous data, concepts and relations can be discovered without specific knowledge about the domain. This paper also examines how this method reproduces the classification capabilities of the classification three within an ontology using concepts and class expression axioms.

  3. Automated electron microprobe

    International Nuclear Information System (INIS)

    Thompson, K.A.; Walker, L.R.

    1986-01-01

    The Plant Laboratory at the Oak Ridge Y-12 Plant has recently obtained a Cameca MBX electron microprobe with a Tracor Northern TN5500 automation system. This allows full stage and spectrometer automation and digital beam control. The capabilities of the system include qualitative and quantitative elemental microanalysis for all elements above and including boron in atomic number, high- and low-magnification imaging and processing, elemental mapping and enhancement, and particle size, shape, and composition analyses. Very low magnification, quantitative elemental mapping using stage control (which is of particular interest) has been accomplished along with automated size, shape, and composition analysis over a large relative area

  4. Operational proof of automation

    International Nuclear Information System (INIS)

    Jaerschky, R.; Reifenhaeuser, R.; Schlicht, K.

    1976-01-01

    Automation of the power plant process may imply quite a number of problems. The automation of dynamic operations requires complicated programmes often interfering in several branched areas. This reduces clarity for the operating and maintenance staff, whilst increasing the possibilities of errors. The synthesis and the organization of standardized equipment have proved very successful. The possibilities offered by this kind of automation for improving the operation of power plants will only sufficiently and correctly be turned to profit, however, if the application of these technics of equipment is further improved and if its volume is tallied with a definite etc. (orig.) [de

  5. Chef infrastructure automation cookbook

    CERN Document Server

    Marschall, Matthias

    2013-01-01

    Chef Infrastructure Automation Cookbook contains practical recipes on everything you will need to automate your infrastructure using Chef. The book is packed with illustrated code examples to automate your server and cloud infrastructure.The book first shows you the simplest way to achieve a certain task. Then it explains every step in detail, so that you can build your knowledge about how things work. Eventually, the book shows you additional things to consider for each approach. That way, you can learn step-by-step and build profound knowledge on how to go about your configuration management

  6. Classification of ASASSN-18dl as a type Ia supernova

    Science.gov (United States)

    Pessi, P.; Quirola, J.; Navarro, G.; Dennefeld, M.; Ferrero, L.; Sani, E.; Schmidtobreick, L.

    2018-02-01

    We report the classification of the supernova candidate ASASSN-18dl which was discovered as a V 17.6mag transient by the All Sky Automated Survey for SuperNovae (ASAS-SN, Shappee et al. 2014) on 2018-02-21.25 UT. The discovery is reported in ATel #11343 (Stone et al. 2018).

  7. Automated Diagnosis and Classification of Steam Generator Tube Defects

    International Nuclear Information System (INIS)

    Garcia, Gabe V.

    2004-01-01

    A major cause of failure in nuclear steam generators is tube degradation. Tube defects are divided into seven categories, one of which is intergranular attack/stress corrosion cracking (IGA/SCC). Defects of this type usually begin on the outer surface of the tubes and propagate both inward and laterally. In many cases these defects occur at or near the tube support plates. Several different methods exist for the nondestructive evaluation of nuclear steam generator tubes for defect characterization

  8. A Framework for Automated Marmoset Vocalization Detection And Classification

    Science.gov (United States)

    2016-09-08

    7] G. Epple, “Comparative Studies on Vocalization in Marmoset Monkeys,” Folia Primatol. ( Basel ), vol. 8, no. 1, pp. 1–40, 1968. [8] C.-J. Chang...odontocetes in the Southern California Bight,” J. Acoust. Soc. Am., vol. 129, no. 1, pp. 467–475, 2011. [25] V. Berisha, A. Wisler, A. O. Hero III

  9. Automated Classification of Martian Morphology Using a Terrain Fingerprinting Method

    NARCIS (Netherlands)

    Koenders, R.; Lindenbergh, R.C.; Zegers, T.E.

    2009-01-01

    The planet Mars has a relatively short human exploration history, while the size of the scientific community studying Mars is also smaller than its Earth equivalent. On the other hand the interest in Mars is large, basically because it is the planet in the solar system most similar to Earth. Several

  10. Clever Toolbox - the Art of Automated Genre Classification

    DEFF Research Database (Denmark)

    2005-01-01

    -regressive coefficients (ARs) are extracted along with the mean and gain to get a single (30 dimensional) feature vector on the time-scale of 1 second. These features have been used because they have performed well in a previous study (Meng, Ahrendt, Larsen (2005)). Linear regression (or single-layer linear NN...

  11. Advanced Automated Detection Analysis and Classification of Cracks in Pavement

    OpenAIRE

    Scott, Dennis

    2014-01-01

    Functional Session 5: Pavement Management Moderated by Akyiaa Hosten This presentation was held at the Pavement Evaluation 2014 Conference, which took place from September 15-18, 2014 in Blacksburg, Virginia. Presentation only

  12. Automated Diagnosis and Classification of Steam Generator Tube Defects

    Energy Technology Data Exchange (ETDEWEB)

    Dr. Gabe V. Garcia

    2004-10-01

    A major cause of failure in nuclear steam generators is tube degradation. Tube defects are divided into seven categories, one of which is intergranular attack/stress corrosion cracking (IGA/SCC). Defects of this type usually begin on the outer surface of the tubes and propagate both inward and laterally. In many cases these defects occur at or near the tube support plates. Several different methods exist for the nondestructive evaluation of nuclear steam generator tubes for defect characterization.

  13. utomated real-time classification of functional states: the significance of individual tuning stage

    Directory of Open Access Journals (Sweden)

    Galatenko, Vladimir V.

    2013-09-01

    Full Text Available Automated classification of a human functional state is an important problem, with applications including stress resistance evaluation, supervision over operators of critical infrastructure, teaching and phobia therapy. Such classification is particularly efficient in systems for teaching and phobia therapy that include a virtual reality module, and provide the capability for dynamic adjustment of task complexity. In this paper, a method for automated real-time binary classification of human functional states (calm wakefulness vs. stress based on discrete wavelet transform of EEG data is considered. It is shown that an individual tuning stage of the classification algorithm — a stage that allows the involvement of certain information on individual peculiarities in the classification, using very short individual learning samples, significantly increases classification reliability. The experimental study that proved this assertion was based on a specialized scenario in which individuals solved the task of detecting objects with given properties in a dynamic set of flying objects.

  14. 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.

  15. Automation Interface Design Development

    Data.gov (United States)

    National Aeronautics and Space Administration — Our research makes its contributions at two levels. At one level, we addressed the problems of interaction between humans and computers/automation in a particular...

  16. Automated Vehicles Symposium 2014

    CERN Document Server

    Beiker, Sven; Road Vehicle Automation 2

    2015-01-01

    This paper collection is the second volume of the LNMOB series on Road Vehicle Automation. The book contains a comprehensive review of current technical, socio-economic, and legal perspectives written by experts coming from public authorities, companies and universities in the U.S., Europe and Japan. It originates from the Automated Vehicle Symposium 2014, which was jointly organized by the Association for Unmanned Vehicle Systems International (AUVSI) and the Transportation Research Board (TRB) in Burlingame, CA, in July 2014. The contributions discuss the challenges arising from the integration of highly automated and self-driving vehicles into the transportation system, with a focus on human factors and different deployment scenarios. This book is an indispensable source of information for academic researchers, industrial engineers, and policy makers interested in the topic of road vehicle automation.

  17. Fixed automated spray technology.

    Science.gov (United States)

    2011-04-19

    This research project evaluated the construction and performance of Boschungs Fixed Automated : Spray Technology (FAST) system. The FAST system automatically sprays de-icing material on : the bridge when icing conditions are about to occur. The FA...

  18. Automated Vehicles Symposium 2015

    CERN Document Server

    Beiker, Sven

    2016-01-01

    This edited book comprises papers about the impacts, benefits and challenges of connected and automated cars. It is the third volume of the LNMOB series dealing with Road Vehicle Automation. The book comprises contributions from researchers, industry practitioners and policy makers, covering perspectives from the U.S., Europe and Japan. It is based on the Automated Vehicles Symposium 2015 which was jointly organized by the Association of Unmanned Vehicle Systems International (AUVSI) and the Transportation Research Board (TRB) in Ann Arbor, Michigan, in July 2015. The topical spectrum includes, but is not limited to, public sector activities, human factors, ethical and business aspects, energy and technological perspectives, vehicle systems and transportation infrastructure. This book is an indispensable source of information for academic researchers, industrial engineers and policy makers interested in the topic of road vehicle automation.

  19. Automation synthesis modules review

    International Nuclear Information System (INIS)

    Boschi, S.; Lodi, F.; Malizia, C.; Cicoria, G.; Marengo, M.

    2013-01-01

    The introduction of 68 Ga labelled tracers has changed the diagnostic approach to neuroendocrine tumours and the availability of a reliable, long-lived 68 Ge/ 68 Ga generator has been at the bases of the development of 68 Ga radiopharmacy. The huge increase in clinical demand, the impact of regulatory issues and a careful radioprotection of the operators have boosted for extensive automation of the production process. The development of automated systems for 68 Ga radiochemistry, different engineering and software strategies and post-processing of the eluate were discussed along with impact of automation with regulations. - Highlights: ► Generators availability and robust chemistry boosted for the huge diffusion of 68Ga radiopharmaceuticals. ► Different technological approaches for 68Ga radiopharmaceuticals will be discussed. ► Generator eluate post processing and evolution to cassette based systems were the major issues in automation. ► Impact of regulations on the technological development will be also considered

  20. Disassembly automation automated systems with cognitive abilities

    CERN Document Server

    Vongbunyong, Supachai

    2015-01-01

    This book presents a number of aspects to be considered in the development of disassembly automation, including the mechanical system, vision system and intelligent planner. The implementation of cognitive robotics increases the flexibility and degree of autonomy of the disassembly system. Disassembly, as a step in the treatment of end-of-life products, can allow the recovery of embodied value left within disposed products, as well as the appropriate separation of potentially-hazardous components. In the end-of-life treatment industry, disassembly has largely been limited to manual labor, which is expensive in developed countries. Automation is one possible solution for economic feasibility. The target audience primarily comprises researchers and experts in the field, but the book may also be beneficial for graduate students.

  1. Automated Lattice Perturbation Theory

    Energy Technology Data Exchange (ETDEWEB)

    Monahan, Christopher

    2014-11-01

    I review recent developments in automated lattice perturbation theory. Starting with an overview of lattice perturbation theory, I focus on the three automation packages currently "on the market": HiPPy/HPsrc, Pastor and PhySyCAl. I highlight some recent applications of these methods, particularly in B physics. In the final section I briefly discuss the related, but distinct, approach of numerical stochastic perturbation theory.

  2. Automated ISMS control auditability

    OpenAIRE

    Suomu, Mikko

    2015-01-01

    This thesis focuses on researching a possible reference model for automated ISMS’s (Information Security Management System) technical control auditability. The main objective was to develop a generic framework for automated compliance status monitoring of the ISO27001:2013 standard which could be re‐used in any ISMS system. The framework was tested with Proof of Concept (PoC) empirical research in a test infrastructure which simulates the framework target deployment environment. To fulfi...

  3. Marketing automation supporting sales

    OpenAIRE

    Sandell, Niko

    2016-01-01

    The past couple of decades has been a time of major changes in marketing. Digitalization has become a permanent part of marketing and at the same time enabled efficient collection of data. Personalization and customization of content are playing a crucial role in marketing when new customers are acquired. This has also created a need for automation to facilitate the distribution of targeted content. As a result of successful marketing automation more information of the customers is gathered ...

  4. Automated security management

    CERN Document Server

    Al-Shaer, Ehab; Xie, Geoffrey

    2013-01-01

    In this contributed volume, leading international researchers explore configuration modeling and checking, vulnerability and risk assessment, configuration analysis, and diagnostics and discovery. The authors equip readers to understand automated security management systems and techniques that increase overall network assurability and usability. These constantly changing networks defend against cyber attacks by integrating hundreds of security devices such as firewalls, IPSec gateways, IDS/IPS, authentication servers, authorization/RBAC servers, and crypto systems. Automated Security Managemen

  5. Automated lattice data generation

    Directory of Open Access Journals (Sweden)

    Ayyar Venkitesh

    2018-01-01

    Full Text Available The process of generating ensembles of gauge configurations (and measuring various observables over them can be tedious and error-prone when done “by hand”. In practice, most of this procedure can be automated with the use of a workflow manager. We discuss how this automation can be accomplished using Taxi, a minimal Python-based workflow manager built for generating lattice data. We present a case study demonstrating this technology.

  6. Automated carotid artery intima layer regional segmentation

    Energy Technology Data Exchange (ETDEWEB)

    Meiburger, Kristen M; Molinari, Filippo [Biolab, Department of Electronics, Politecnico di Torino, Torino (Italy); Acharya, U Rajendra [Department of ECE, Ngee Ann Polytechnic (Singapore); Saba, Luca [Department of Radiology, A.O.U. di Cagliari, Cagliari (Italy); Rodrigues, Paulo [Department of Computer Science, Centro Universitario da FEI, Sao Paulo (Brazil); Liboni, William [Neurology Division, Gradenigo Hospital, Torino (Italy); Nicolaides, Andrew [Vascular Screening and Diagnostic Centre, London (United Kingdom); Suri, Jasjit S, E-mail: filippo.molinari@polito.it [Fellow AIMBE, CTO, Global Biomedical Technologies Inc., CA (United States)

    2011-07-07

    Evaluation of the carotid artery wall is essential for the assessment of a patient's cardiovascular risk or for the diagnosis of cardiovascular pathologies. This paper presents a new, completely user-independent algorithm called carotid artery intima layer regional segmentation (CAILRS, a class of AtheroEdge(TM) systems), which automatically segments the intima layer of the far wall of the carotid ultrasound artery based on mean shift classification applied to the far wall. Further, the system extracts the lumen-intima and media-adventitia borders in the far wall of the carotid artery. Our new system is characterized and validated by comparing CAILRS borders with the manual tracings carried out by experts. The new technique is also benchmarked with a semi-automatic technique based on a first-order absolute moment edge operator (FOAM) and compared to our previous edge-based automated methods such as CALEX (Molinari et al 2010 J. Ultrasound Med. 29 399-418, 2010 IEEE Trans. Ultrason. Ferroelectr. Freq. Control 57 1112-24), CULEX (Delsanto et al 2007 IEEE Trans. Instrum. Meas. 56 1265-74, Molinari et al 2010 IEEE Trans. Ultrason. Ferroelectr. Freq. Control 57 1112-24), CALSFOAM (Molinari et al Int. Angiol. (at press)), and CAUDLES-EF (Molinari et al J. Digit. Imaging (at press)). Our multi-institutional database consisted of 300 longitudinal B-mode carotid images. In comparison to semi-automated FOAM, CAILRS showed the IMT bias of -0.035 {+-} 0.186 mm while FOAM showed -0.016 {+-} 0.258 mm. Our IMT was slightly underestimated with respect to the ground truth IMT, but showed uniform behavior over the entire database. CAILRS outperformed all the four previous automated methods. The system's figure of merit was 95.6%, which was lower than that of the semi-automated method (98%), but higher than that of the other automated techniques.

  7. Automated carotid artery intima layer regional segmentation

    Science.gov (United States)

    Meiburger, Kristen M.; Molinari, Filippo; Rajendra Acharya, U.; Saba, Luca; Rodrigues, Paulo; Liboni, William; Nicolaides, Andrew; Suri, Jasjit S.

    2011-07-01

    Evaluation of the carotid artery wall is essential for the assessment of a patient's cardiovascular risk or for the diagnosis of cardiovascular pathologies. This paper presents a new, completely user-independent algorithm called carotid artery intima layer regional segmentation (CAILRS, a class of AtheroEdge™ systems), which automatically segments the intima layer of the far wall of the carotid ultrasound artery based on mean shift classification applied to the far wall. Further, the system extracts the lumen-intima and media-adventitia borders in the far wall of the carotid artery. Our new system is characterized and validated by comparing CAILRS borders with the manual tracings carried out by experts. The new technique is also benchmarked with a semi-automatic technique based on a first-order absolute moment edge operator (FOAM) and compared to our previous edge-based automated methods such as CALEX (Molinari et al 2010 J. Ultrasound Med. 29 399-418, 2010 IEEE Trans. Ultrason. Ferroelectr. Freq. Control 57 1112-24), CULEX (Delsanto et al 2007 IEEE Trans. Instrum. Meas. 56 1265-74, Molinari et al 2010 IEEE Trans. Ultrason. Ferroelectr. Freq. Control 57 1112-24), CALSFOAM (Molinari et al Int. Angiol. (at press)), and CAUDLES-EF (Molinari et al J. Digit. Imaging (at press)). Our multi-institutional database consisted of 300 longitudinal B-mode carotid images. In comparison to semi-automated FOAM, CAILRS showed the IMT bias of -0.035 ± 0.186 mm while FOAM showed -0.016 ± 0.258 mm. Our IMT was slightly underestimated with respect to the ground truth IMT, but showed uniform behavior over the entire database. CAILRS outperformed all the four previous automated methods. The system's figure of merit was 95.6%, which was lower than that of the semi-automated method (98%), but higher than that of the other automated techniques.

  8. Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

    Directory of Open Access Journals (Sweden)

    Eduardo Ribeiro

    2016-01-01

    Full Text Available Recently, Deep Learning, especially through Convolutional Neural Networks (CNNs has been widely used to enable the extraction of highly representative features. This is done among the network layers by filtering, selecting, and using these features in the last fully connected layers for pattern classification. However, CNN training for automated endoscopic image classification still provides a challenge due to the lack of large and publicly available annotated databases. In this work we explore Deep Learning for the automated classification of colonic polyps using different configurations for training CNNs from scratch (or full training and distinct architectures of pretrained CNNs tested on 8-HD-endoscopic image databases acquired using different modalities. We compare our results with some commonly used features for colonic polyp classification and the good results suggest that features learned by CNNs trained from scratch and the “off-the-shelf” CNNs features can be highly relevant for automated classification of colonic polyps. Moreover, we also show that the combination of classical features and “off-the-shelf” CNNs features can be a good approach to further improve the results.

  9. PASTEC: an automatic transposable element classification tool.

    Science.gov (United States)

    Hoede, Claire; Arnoux, Sandie; Moisset, Mark; Chaumier, Timothée; Inizan, Olivier; Jamilloux, Véronique; Quesneville, Hadi

    2014-01-01

    The classification of transposable elements (TEs) is key step towards deciphering their potential impact on the genome. However, this process is often based on manual sequence inspection by TE experts. With the wealth of genomic sequences now available, this task requires automation, making it accessible to most scientists. We propose a new tool, PASTEC, which classifies TEs by searching for structural features and similarities. This tool outperforms currently available software for TE classification. The main innovation of PASTEC is the search for HMM profiles, which is useful for inferring the classification of unknown TE on the basis of conserved functional domains of the proteins. In addition, PASTEC is the only tool providing an exhaustive spectrum of possible classifications to the order level of the Wicker hierarchical TE classification system. It can also automatically classify other repeated elements, such as SSR (Simple Sequence Repeats), rDNA or potential repeated host genes. Finally, the output of this new tool is designed to facilitate manual curation by providing to biologists with all the evidence accumulated for each TE consensus. PASTEC is available as a REPET module or standalone software (http://urgi.versailles.inra.fr/download/repet/REPET_linux-x64-2.2.tar.gz). It requires a Unix-like system. There are two standalone versions: one of which is parallelized (requiring Sun grid Engine or Torque), and the other of which is not.

  10. PASTEC: an automatic transposable element classification tool.

    Directory of Open Access Journals (Sweden)

    Claire Hoede

    Full Text Available SUMMARY: The classification of transposable elements (TEs is key step towards deciphering their potential impact on the genome. However, this process is often based on manual sequence inspection by TE experts. With the wealth of genomic sequences now available, this task requires automation, making it accessible to most scientists. We propose a new tool, PASTEC, which classifies TEs by searching for structural features and similarities. This tool outperforms currently available software for TE classification. The main innovation of PASTEC is the search for HMM profiles, which is useful for inferring the classification of unknown TE on the basis of conserved functional domains of the proteins. In addition, PASTEC is the only tool providing an exhaustive spectrum of possible classifications to the order level of the Wicker hierarchical TE classification system. It can also automatically classify other repeated elements, such as SSR (Simple Sequence Repeats, rDNA or potential repeated host genes. Finally, the output of this new tool is designed to facilitate manual curation by providing to biologists with all the evidence accumulated for each TE consensus. AVAILABILITY: PASTEC is available as a REPET module or standalone software (http://urgi.versailles.inra.fr/download/repet/REPET_linux-x64-2.2.tar.gz. It requires a Unix-like system. There are two standalone versions: one of which is parallelized (requiring Sun grid Engine or Torque, and the other of which is not.

  11. Demonstration of automated robotic workcell for hazardous waste characterization

    International Nuclear Information System (INIS)

    Holliday, M.; Dougan, A.; Gavel, D.; Gustaveson, D.; Johnson, R.; Kettering, B.; Wilhelmsen, K.

    1993-02-01

    An automated robotic workcell to classify hazardous waste stream items with previously unknown characteristics has been designed, tested and demonstrated The object attributes being quantified are radiation signature, metal content, and object orientation and volume. The multi sensor information is used to make segregation decisions plus do automatic grasping of objects. The work-cell control program uses an off-line programming system by Cimetrix Inc. as a server to do both simulation control as well as actual hardware control of the workcell. This paper will discuss the overall workcell layout, sensor specifications, workcell supervisory control, 2D vision based automated grasp planning and object classification algorithms

  12. Classification of cultivated plants.

    NARCIS (Netherlands)

    Brandenburg, W.A.

    1986-01-01

    Agricultural practice demands principles for classification, starting from the basal entity in cultivated plants: the cultivar. In establishing biosystematic relationships between wild, weedy and cultivated plants, the species concept needs re-examination. Combining of botanic classification, based

  13. Classification of Pemphigus

    Directory of Open Access Journals (Sweden)

    Ayşe Akman

    2008-08-01

    Full Text Available Clinical classification of pemphigus is not yet complete. The classic classification based on clinical and histologic features. Because of the progress in the pathogenesis of pemphigus, the current classifications based on accumulating analyses of antigen molecules and subclasses of immunoglobulins and etiologic aspects of pemphigus as weel as the clinical, histologic features. The aim of this paper is to review classification of pemphigus.

  14. Dynamic time warping and sparse representation classification for birdsong phrase classification using limited training data.

    Science.gov (United States)

    Tan, Lee N; Alwan, Abeer; Kossan, George; Cody, Martin L; Taylor, Charles E

    2015-03-01

    Annotation of phrases in birdsongs can be helpful to behavioral and population studies. To reduce the need for manual annotation, an automated birdsong phrase classification algorithm for limited data is developed. Limited data occur because of limited recordings or the existence of rare phrases. In this paper, classification of up to 81 phrase classes of Cassin's Vireo is performed using one to five training samples per class. The algorithm involves dynamic time warping (DTW) and two passes of sparse representation (SR) classification. DTW improves the similarity between training and test phrases from the same class in the presence of individual bird differences and phrase segmentation inconsistencies. The SR classifier works by finding a sparse linear combination of training feature vectors from all classes that best approximates the test feature vector. When the class decisions from DTW and the first pass SR classification are different, SR classification is repeated using training samples from these two conflicting classes. Compared to DTW, support vector machines, and an SR classifier without DTW, the proposed classifier achieves the highest classification accuracies of 94% and 89% on manually segmented and automatically segmented phrases, respectively, from unseen Cassin's Vireo individuals, using five training samples per class.

  15. 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.

  16. Automation model of sewerage rehabilitation planning.

    Science.gov (United States)

    Yang, M D; Su, T C

    2006-01-01

    The major steps of sewerage rehabilitation include inspection of sewerage, assessment of structural conditions, computation of structural condition grades, and determination of rehabilitation methods and materials. Conventionally, sewerage rehabilitation planning relies on experts with professional background that is tedious and time-consuming. This paper proposes an automation model of planning optimal sewerage rehabilitation strategies for the sewer system by integrating image process, clustering technology, optimization, and visualization display. Firstly, image processing techniques, such as wavelet transformation and co-occurrence features extraction, were employed to extract various characteristics of structural failures from CCTV inspection images. Secondly, a classification neural network was established to automatically interpret the structural conditions by comparing the extracted features with the typical failures in a databank. Then, to achieve optimal rehabilitation efficiency, a genetic algorithm was used to determine appropriate rehabilitation methods and substitution materials for the pipe sections with a risk of mal-function and even collapse. Finally, the result from the automation model can be visualized in a geographic information system in which essential information of the sewer system and sewerage rehabilitation plans are graphically displayed. For demonstration, the automation model of optimal sewerage rehabilitation planning was applied to a sewer system in east Taichung, Chinese Taiwan.

  17. An automated approach to mapping corn from Landsat imagery

    Science.gov (United States)

    Maxwell, S.K.; Nuckols, J.R.; Ward, M.H.; Hoffer, R.M.

    2004-01-01

    Most land cover maps generated from Landsat imagery involve classification of a wide variety of land cover types, whereas some studies may only need spatial information on a single cover type. For example, we required a map of corn in order to estimate exposure to agricultural chemicals for an environmental epidemiology study. Traditional classification techniques, which require the collection and processing of costly ground reference data, were not feasible for our application because of the large number of images to be analyzed. We present a new method that has the potential to automate the classification of corn from Landsat satellite imagery, resulting in a more timely product for applications covering large geographical regions. Our approach uses readily available agricultural areal estimates to enable automation of the classification process resulting in a map identifying land cover as ‘highly likely corn,’ ‘likely corn’ or ‘unlikely corn.’ To demonstrate the feasibility of this approach, we produced a map consisting of the three corn likelihood classes using a Landsat image in south central Nebraska. Overall classification accuracy of the map was 92.2% when compared to ground reference data.

  18. 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.

  19. Automating the CMS DAQ

    Energy Technology Data Exchange (ETDEWEB)

    Bauer, G.; et al.

    2014-01-01

    We present the automation mechanisms that have been added to the Data Acquisition and Run Control systems of the Compact Muon Solenoid (CMS) experiment during Run 1 of the LHC, ranging from the automation of routine tasks to automatic error recovery and context-sensitive guidance to the operator. These mechanisms helped CMS to maintain a data taking efficiency above 90% and to even improve it to 95% towards the end of Run 1, despite an increase in the occurrence of single-event upsets in sub-detector electronics at high LHC luminosity.

  20. Control and automation systems

    International Nuclear Information System (INIS)

    Schmidt, R.; Zillich, H.

    1986-01-01

    A survey is given of the development of control and automation systems for energy uses. General remarks about control and automation schemes are followed by a description of modern process control systems along with process control processes as such. After discussing the particular process control requirements of nuclear power plants the paper deals with the reliability and availability of process control systems and refers to computerized simulation processes. The subsequent paragraphs are dedicated to descriptions of the operating floor, ergonomic conditions, existing systems, flue gas desulfurization systems, the electromagnetic influences on digital circuits as well as of light wave uses. (HAG) [de

  1. Automated nuclear materials accounting

    International Nuclear Information System (INIS)

    Pacak, P.; Moravec, J.

    1982-01-01

    An automated state system of accounting for nuclear materials data was established in Czechoslovakia in 1979. A file was compiled of 12 programs in the PL/1 language. The file is divided into four groups according to logical associations, namely programs for data input and checking, programs for handling the basic data file, programs for report outputs in the form of worksheets and magnetic tape records, and programs for book inventory listing, document inventory handling and materials balance listing. A similar automated system of nuclear fuel inventory for a light water reactor was introduced for internal purposes in the Institute of Nuclear Research (UJV). (H.S.)

  2. An automated approach to the design of decision tree classifiers

    Science.gov (United States)

    Argentiero, P.; Chin, R.; Beaudet, P.

    1982-01-01

    An automated technique is presented for designing effective decision tree classifiers predicated only on a priori class statistics. The procedure relies on linear feature extractions and Bayes table look-up decision rules. Associated error matrices are computed and utilized to provide an optimal design of the decision tree at each so-called 'node'. A by-product of this procedure is a simple algorithm for computing the global probability of correct classification assuming the statistical independence of the decision rules. Attention is given to a more precise definition of decision tree classification, the mathematical details on the technique for automated decision tree design, and an example of a simple application of the procedure using class statistics acquired from an actual Landsat scene.

  3. Altering user' acceptance of automation through prior automation exposure.

    Science.gov (United States)

    Bekier, Marek; Molesworth, Brett R C

    2017-06-01

    Air navigation service providers worldwide see increased use of automation as one solution to overcome the capacity constraints imbedded in the present air traffic management (ATM) system. However, increased use of automation within any system is dependent on user acceptance. The present research sought to determine if the point at which an individual is no longer willing to accept or cooperate with automation can be manipulated. Forty participants underwent training on a computer-based air traffic control programme, followed by two ATM exercises (order counterbalanced), one with and one without the aid of automation. Results revealed after exposure to a task with automation assistance, user acceptance of high(er) levels of automation ('tipping point') decreased; suggesting it is indeed possible to alter automation acceptance. Practitioner Summary: This paper investigates whether the point at which a user of automation rejects automation (i.e. 'tipping point') is constant or can be manipulated. The results revealed after exposure to a task with automation assistance, user acceptance of high(er) levels of automation decreased; suggesting it is possible to alter automation acceptance.

  4. Automatic mineral classification in the macroscopic scale

    Science.gov (United States)

    Marschallinger, R.

    1997-02-01

    A method is introduced which enables reliable, automated mineral classification in the macroscopic scale. Polished rock samples are scanned by a color image scanner. Scanner output is split into red, green and blue component images to be evaluated by multispectral image processing methods. Different unsupervised and supervised image classification algorithms have been tested with medium-to-coarse grained crystalline rocks. Statistical evaluation of the results showed that the supervised maximum likelihood algorithm was the most robust approach: provided the minerals of interest show different hues, average mineral-phase recognition levels are approximately 90%. The result of the method is a classified raster image of mineral distribution which, besides giving rock modes, can be postprocessed by shape and fabric analysis programs or passed to 3D serial section reconstruction algorithms.

  5. Idaho: Library Automation and Connectivity.

    Science.gov (United States)

    Bolles, Charles

    1996-01-01

    Provides an overview of the development of cooperative library automation and connectivity in Idaho, including telecommunications capacity, library networks, the Internet, and the role of the state library. Information on six shared automation systems in Idaho is included. (LRW)

  6. Recursive heuristic classification

    Science.gov (United States)

    Wilkins, David C.

    1994-01-01

    The author will describe a new problem-solving approach called recursive heuristic classification, whereby a subproblem of heuristic classification is itself formulated and solved by heuristic classification. This allows the construction of more knowledge-intensive classification programs in a way that yields a clean organization. Further, standard knowledge acquisition and learning techniques for heuristic classification can be used to create, refine, and maintain the knowledge base associated with the recursively called classification expert system. The method of recursive heuristic classification was used in the Minerva blackboard shell for heuristic classification. Minerva recursively calls itself every problem-solving cycle to solve the important blackboard scheduler task, which involves assigning a desirability rating to alternative problem-solving actions. Knowing these ratings is critical to the use of an expert system as a component of a critiquing or apprenticeship tutoring system. One innovation of this research is a method called dynamic heuristic classification, which allows selection among dynamically generated classification categories instead of requiring them to be prenumerated.

  7. 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.

  8. Automated HAZOP revisited

    DEFF Research Database (Denmark)

    Taylor, J. R.

    2017-01-01

    Hazard and operability analysis (HAZOP) has developed from a tentative approach to hazard identification for process plants in the early 1970s to an almost universally accepted approach today, and a central technique of safety engineering. Techniques for automated HAZOP analysis were developed...

  9. Automated data model evaluation

    International Nuclear Information System (INIS)

    Kazi, Zoltan; Kazi, Ljubica; Radulovic, Biljana

    2012-01-01

    Modeling process is essential phase within information systems development and implementation. This paper presents methods and techniques for analysis and evaluation of data model correctness. Recent methodologies and development results regarding automation of the process of model correctness analysis and relations with ontology tools has been presented. Key words: Database modeling, Data model correctness, Evaluation

  10. Automated Vehicle Monitoring System

    OpenAIRE

    Wibowo, Agustinus Deddy Arief; Heriansyah, Rudi

    2014-01-01

    An automated vehicle monitoring system is proposed in this paper. The surveillance system is based on image processing techniques such as background subtraction, colour balancing, chain code based shape detection, and blob. The proposed system will detect any human's head as appeared at the side mirrors. The detected head will be tracked and recorded for further action.

  11. Automated Accounting. Instructor Guide.

    Science.gov (United States)

    Moses, Duane R.

    This curriculum guide was developed to assist business instructors using Dac Easy Accounting College Edition Version 2.0 software in their accounting programs. The module consists of four units containing assignment sheets and job sheets designed to enable students to master competencies identified in the area of automated accounting. The first…

  12. Mechatronic Design Automation

    DEFF Research Database (Denmark)

    Fan, Zhun

    successfully design analogue filters, vibration absorbers, micro-electro-mechanical systems, and vehicle suspension systems, all in an automatic or semi-automatic way. It also investigates the very important issue of co-designing plant-structures and dynamic controllers in automated design of Mechatronic...

  13. Protokoller til Home Automation

    DEFF Research Database (Denmark)

    Kjær, Kristian Ellebæk

    2008-01-01

    computer, der kan skifte mellem foruddefinerede indstillinger. Nogle gange kan computeren fjernstyres over internettet, så man kan se hjemmets status fra en computer eller måske endda fra en mobiltelefon. Mens nævnte anvendelser er klassiske indenfor home automation, er yderligere funktionalitet dukket op...

  14. Myths in test automation

    Directory of Open Access Journals (Sweden)

    Jazmine Francis

    2015-01-01

    Full Text Available Myths in automation of software testing is an issue of discussion that echoes about the areas of service in validation of software industry. Probably, the first though that appears in knowledgeable reader would be Why this old topic again? What's New to discuss the matter? But, for the first time everyone agrees that undoubtedly automation testing today is not today what it used to be ten or fifteen years ago, because it has evolved in scope and magnitude. What began as a simple linear scripts for web applications today has a complex architecture and a hybrid framework to facilitate the implementation of testing applications developed with various platforms and technologies. Undoubtedly automation has advanced, but so did the myths associated with it. The change in perspective and knowledge of people on automation has altered the terrain. This article reflects the points of views and experience of the author in what has to do with the transformation of the original myths in new versions, and how they are derived; also provides his thoughts on the new generation of myths.

  15. Driver Psychology during Automated Platooning

    NARCIS (Netherlands)

    Heikoop, D.D.

    2017-01-01

    With the rapid increase in vehicle automation technology, the call for understanding how humans behave while driving in an automated vehicle becomes more urgent. Vehicles that have automated systems such as Lane Keeping Assist (LKA) or Adaptive Cruise Control (ACC) not only support drivers in their

  16. Classification of Flotation Frothers

    Directory of Open Access Journals (Sweden)

    Jan Drzymala

    2018-02-01

    Full Text Available In this paper, a scheme of flotation frothers classification is presented. The scheme first indicates the physical system in which a frother is present and four of them i.e., pure state, aqueous solution, aqueous solution/gas system and aqueous solution/gas/solid system are distinguished. As a result, there are numerous classifications of flotation frothers. The classifications can be organized into a scheme described in detail in this paper. The frother can be present in one of four physical systems, that is pure state, aqueous solution, aqueous solution/gas and aqueous solution/gas/solid system. It results from the paper that a meaningful classification of frothers relies on choosing the physical system and next feature, trend, parameter or parameters according to which the classification is performed. The proposed classification can play a useful role in characterizing and evaluation of flotation frothers.

  17. Phenotype classification of zebrafish embryos by supervised learning.

    Directory of Open Access Journals (Sweden)

    Nathalie Jeanray

    Full Text Available Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100% agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification.

  18. Phenotype classification of zebrafish embryos by supervised learning.

    Science.gov (United States)

    Jeanray, Nathalie; Marée, Raphaël; Pruvot, Benoist; Stern, Olivier; Geurts, Pierre; Wehenkel, Louis; Muller, Marc

    2015-01-01

    Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100% agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification.

  19. Rapid Classification of Ordinary Chondrites Using Raman Spectroscopy

    Science.gov (United States)

    Fries, M.; Welzenbach, L.

    2014-01-01

    Classification of ordinary chondrites is typically done through measurements of the composition of olivine and pyroxenes. Historically, this measurement has usually been performed via electron microprobe, oil immersion or other methods which can be costly through lost sample material during thin section preparation. Raman microscopy can perform the same measurements but considerably faster and with much less sample preparation allowing for faster classification. Raman spectroscopy can facilitate more rapid classification of large amounts of chondrites such as those retrieved from North Africa and potentially Antarctica, are present in large collections, or are submitted to a curation facility by the public. With development, this approach may provide a completely automated classification method of all chondrite types.

  20. Ontologies vs. Classification Systems

    DEFF Research Database (Denmark)

    Madsen, Bodil Nistrup; Erdman Thomsen, Hanne

    2009-01-01

    What is an ontology compared to a classification system? Is a taxonomy a kind of classification system or a kind of ontology? These are questions that we meet when working with people from industry and public authorities, who need methods and tools for concept clarification, for developing meta...... data sets or for obtaining advanced search facilities. In this paper we will present an attempt at answering these questions. We will give a presentation of various types of ontologies and briefly introduce terminological ontologies. Furthermore we will argue that classification systems, e.g. product...... classification systems and meta data taxonomies, should be based on ontologies....

  1. EEG BASED COGNITIVE WORKLOAD CLASSIFICATION DURING NASA MATB-II MULTITASKING

    Directory of Open Access Journals (Sweden)

    Sushil Chandra

    2015-06-01

    Full Text Available The objective of this experiment was to determine the best possible input EEG feature for classification of the workload while designing load balancing logic for an automated operator. The input features compared in this study consisted of spectral features of Electroencephalography, objective scoring and subjective scoring. Method utilizes to identify best EEG feature as an input in Neural Network Classifiers for workload classification, to identify channels which could provide classification with the highest accuracy and for identification of EEG feature which could give discrimination among workload level without adding any classifiers. The result had shown Engagement Index is the best feature for neural network classification.

  2. Integrating human and machine intelligence in galaxy morphology classification tasks

    Science.gov (United States)

    Beck, Melanie R.; Scarlata, Claudia; Fortson, Lucy F.; Lintott, Chris J.; Simmons, B. D.; Galloway, Melanie A.; Willett, Kyle W.; Dickinson, Hugh; Masters, Karen L.; Marshall, Philip J.; Wright, Darryl

    2018-03-01

    Quantifying galaxy morphology is a challenging yet scientifically rewarding task. As the scale of data continues to increase with upcoming surveys, traditional classification methods will struggle to handle the load. We present a solution through an integration of visual and automated classifications, preserving the best features of both human and machine. We demonstrate the effectiveness of such a system through a re-analysis of visual galaxy morphology classifications collected during the Galaxy Zoo 2 (GZ2) project. We reprocess the top-level question of the GZ2 decision tree with a Bayesian classification aggregation algorithm dubbed SWAP, originally developed for the Space Warps gravitational lens project. Through a simple binary classification scheme we increase the classification rate nearly 5-fold classifying 226,124 galaxies in 92 days of GZ2 project time while reproducing labels derived from GZ2 classification data with 95.7% accuracy. We next combine this with a Random Forest machine learning algorithm that learns on a suite of non-parametric morphology indicators widely used for automated morphologies. We develop a decision engine that delegates tasks between human and machine and demonstrate that the combined system provides at least a factor of 8 increase in the classification rate, classifying 210,803 galaxies in just 32 days of GZ2 project time with 93.1% accuracy. As the Random Forest algorithm requires a minimal amount of computational cost, this result has important implications for galaxy morphology identification tasks in the era of Euclid and other large-scale surveys.

  3. Behavioral state classification in epileptic brain using intracranial electrophysiology

    Science.gov (United States)

    Kremen, Vaclav; Duque, Juliano J.; Brinkmann, Benjamin H.; Berry, Brent M.; Kucewicz, Michal T.; Khadjevand, Fatemeh; Van Gompel, Jamie; Stead, Matt; St. Louis, Erik K.; Worrell, Gregory A.

    2017-04-01

    Objective. Automated behavioral state classification can benefit next generation implantable epilepsy devices. In this study we explored the feasibility of automated awake (AW) and slow wave sleep (SWS) classification using wide bandwidth intracranial EEG (iEEG) in patients undergoing evaluation for epilepsy surgery. Approach. Data from seven patients (age 34+/- 12 , 4 women) who underwent intracranial depth electrode implantation for iEEG monitoring were included. Spectral power features (0.1-600 Hz) spanning several frequency bands from a single electrode were used to train and test a support vector machine classifier. Main results. Classification accuracy of 97.8  ±  0.3% (normal tissue) and 89.4  ±  0.8% (epileptic tissue) across seven subjects using multiple spectral power features from a single electrode was achieved. Spectral power features from electrodes placed in normal temporal neocortex were found to be more useful (accuracy 90.8  ±  0.8%) for sleep-wake state classification than electrodes located in normal hippocampus (87.1  ±  1.6%). Spectral power in high frequency band features (Ripple (80-250 Hz), Fast Ripple (250-600 Hz)) showed comparable performance for AW and SWS classification as the best performing Berger bands (Alpha, Beta, low Gamma) with accuracy  ⩾90% using a single electrode contact and single spectral feature. Significance. Automated classification of wake and SWS should prove useful for future implantable epilepsy devices with limited computational power, memory, and number of electrodes. Applications include quantifying patient sleep patterns and behavioral state dependent detection, prediction, and electrical stimulation therapies.

  4. Dimensional Representation and Gradient Boosting for Seismic Event Classification

    Science.gov (United States)

    Semmelmayer, F. C.; Kappedal, R. D.; Magana-Zook, S. A.

    2017-12-01

    In this research, we conducted experiments of representational structures on 5009 seismic signals with the intent of finding a method to classify signals as either an explosion or an earthquake in an automated fashion. We also applied a gradient boosted classifier. While perfect classification was not attained (approximately 88% was our best model), some cases demonstrate that many events can be filtered out as very high probability being explosions or earthquakes, diminishing subject-matter experts'(SME) workload for first stage analysis. It is our hope that these methods can be refined, further increasing the classification probability.

  5. Linear Classification Functions.

    Science.gov (United States)

    Huberty, Carl J.; Smith, Jerry D.

    Linear classification functions (LCFs) arise in a predictive discriminant analysis for the purpose of classifying experimental units into criterion groups. The relative contribution of the response variables to classification accuracy may be based on LCF-variable correlations for each group. It is proved that, if the raw response measures are…

  6. Classification, confusion and misclassification

    African Journals Online (AJOL)

    Classifications change and in that process, we can see that someone or some group has recognise that a previous classification hindered understanding or moulded ... to a pathologist's ability to distinguish, had led to confusion and mismanagement by gynaecologists. What is worrying if not the word. 'complex'? But this is ...

  7. Spectroscopic classification of transients

    DEFF Research Database (Denmark)

    Stritzinger, M. D.; Fraser, M.; Hummelmose, N. N.

    2017-01-01

    We report the spectroscopic classification of several transients based on observations taken with the Nordic Optical Telescope (NOT) equipped with ALFOSC, over the nights 23-25 August 2017.......We report the spectroscopic classification of several transients based on observations taken with the Nordic Optical Telescope (NOT) equipped with ALFOSC, over the nights 23-25 August 2017....

  8. Library Classification 2020

    Science.gov (United States)

    Harris, Christopher

    2013-01-01

    In this article the author explores how a new library classification system might be designed using some aspects of the Dewey Decimal Classification (DDC) and ideas from other systems to create something that works for school libraries in the year 2020. By examining what works well with the Dewey Decimal System, what features should be carried…

  9. Colombia: Territorial classification

    International Nuclear Information System (INIS)

    Mendoza Morales, Alberto

    1998-01-01

    The article is about the approaches of territorial classification, thematic axes, handling principles and territorial occupation, politician and administrative units and administration regions among other topics. Understanding as Territorial Classification the space distribution on the territory of the country, of the geographical configurations, the human communities, the political-administrative units and the uses of the soil, urban and rural, existent and proposed

  10. Automated campaign system

    Science.gov (United States)

    Vondran, Gary; Chao, Hui; Lin, Xiaofan; Beyer, Dirk; Joshi, Parag; Atkins, Brian; Obrador, Pere

    2006-02-01

    To run a targeted campaign involves coordination and management across numerous organizations and complex process flows. Everything from market analytics on customer databases, acquiring content and images, composing the materials, meeting the sponsoring enterprise brand standards, driving through production and fulfillment, and evaluating results; all processes are currently performed by experienced highly trained staff. Presented is a developed solution that not only brings together technologies that automate each process, but also automates the entire flow so that a novice user could easily run a successful campaign from their desktop. This paper presents the technologies, structure, and process flows used to bring this system together. Highlighted will be how the complexity of running a targeted campaign is hidden from the user through technologies, all while providing the benefits of a professionally managed campaign.

  11. Rapid automated nuclear chemistry

    International Nuclear Information System (INIS)

    Meyer, R.A.

    1979-01-01

    Rapid Automated Nuclear Chemistry (RANC) can be thought of as the Z-separation of Neutron-rich Isotopes by Automated Methods. The range of RANC studies of fission and its products is large. In a sense, the studies can be categorized into various energy ranges from the highest where the fission process and particle emission are considered, to low energies where nuclear dynamics are being explored. This paper presents a table which gives examples of current research using RANC on fission and fission products. The remainder of this text is divided into three parts. The first contains a discussion of the chemical methods available for the fission product elements, the second describes the major techniques, and in the last section, examples of recent results are discussed as illustrations of the use of RANC

  12. ATLAS Distributed Computing Automation

    CERN Document Server

    Schovancova, J; The ATLAS collaboration; Borrego, C; Campana, S; Di Girolamo, A; Elmsheuser, J; Hejbal, J; Kouba, T; Legger, F; Magradze, E; Medrano Llamas, R; Negri, G; Rinaldi, L; Sciacca, G; Serfon, C; Van Der Ster, D C

    2012-01-01

    The ATLAS Experiment benefits from computing resources distributed worldwide at more than 100 WLCG sites. The ATLAS Grid sites provide over 100k CPU job slots, over 100 PB of storage space on disk or tape. Monitoring of status of such a complex infrastructure is essential. The ATLAS Grid infrastructure is monitored 24/7 by two teams of shifters distributed world-wide, by the ATLAS Distributed Computing experts, and by site administrators. In this paper we summarize automation efforts performed within the ATLAS Distributed Computing team in order to reduce manpower costs and improve the reliability of the system. Different aspects of the automation process are described: from the ATLAS Grid site topology provided by the ATLAS Grid Information System, via automatic site testing by the HammerCloud, to automatic exclusion from production or analysis activities.

  13. Automated Assembly Center (AAC)

    Science.gov (United States)

    Stauffer, Robert J.

    1993-01-01

    The objectives of this project are as follows: to integrate advanced assembly and assembly support technology under a comprehensive architecture; to implement automated assembly technologies in the production of high-visibility DOD weapon systems; and to document the improved cost, quality, and lead time. This will enhance the production of DOD weapon systems by utilizing the latest commercially available technologies combined into a flexible system that will be able to readily incorporate new technologies as they emerge. Automated assembly encompasses the following areas: product data, process planning, information management policies and framework, three schema architecture, open systems communications, intelligent robots, flexible multi-ability end effectors, knowledge-based/expert systems, intelligent workstations, intelligent sensor systems, and PDES/PDDI data standards.

  14. Automated drawing generation system

    International Nuclear Information System (INIS)

    Yoshinaga, Toshiaki; Kawahata, Junichi; Yoshida, Naoto; Ono, Satoru

    1991-01-01

    Since automated CAD drawing generation systems still require human intervention, improvements were focussed on an interactive processing section (data input and correcting operation) which necessitates a vast amount of work. As a result, human intervention was eliminated, the original objective of a computerized system. This is the first step taken towards complete automation. The effects of development and commercialization of the system are as described below. (1) The interactive processing time required for generating drawings was improved. It was determined that introduction of the CAD system has reduced the time required for generating drawings. (2) The difference in skills between workers preparing drawings has been eliminated and the quality of drawings has been made uniform. (3) The extent of knowledge and experience demanded of workers has been reduced. (author)

  15. Automated fingerprint identification system

    International Nuclear Information System (INIS)

    Bukhari, U.A.; Sheikh, N.M.; Khan, U.I.; Mahmood, N.; Aslam, M.

    2002-01-01

    In this paper we present selected stages of an automated fingerprint identification system. The software for the system is developed employing algorithm for two-tone conversion, thinning, feature extraction and matching. Keeping FBI standards into account, it has been assured that no details of the image are lost in the comparison process. We have deployed a general parallel thinning algorithm for specialized images like fingerprints and modified the original algorithm after a series of experimentation selecting the one giving the best results. We also proposed an application-based approach for designing automated fingerprint identification systems keeping in view systems requirements. We will show that by using our system, the precision and efficiency of current fingerprint matching techniques are increased. (author)

  16. Automated breeder fuel fabrication

    International Nuclear Information System (INIS)

    Goldmann, L.H.; Frederickson, J.R.

    1983-01-01

    The objective of the Secure Automated Fabrication (SAF) Project is to develop remotely operated equipment for the processing and manufacturing of breeder reactor fuel pins. The SAF line will be installed in the Fuels and Materials Examination Facility (FMEF). The FMEF is presently under construction at the Department of Energy's (DOE) Hanford site near Richland, Washington, and is operated by the Westinghouse Hanford Company (WHC). The fabrication and support systems of the SAF line are designed for computer-controlled operation from a centralized control room. Remote and automated fuel fabriction operations will result in: reduced radiation exposure to workers; enhanced safeguards; improved product quality; near real-time accountability, and increased productivity. The present schedule calls for installation of SAF line equipment in the FMEF beginning in 1984, with qualifying runs starting in 1986 and production commencing in 1987. 5 figures

  17. Cavendish Balance Automation

    Science.gov (United States)

    Thompson, Bryan

    2000-01-01

    This is the final report for a project carried out to modify a manual commercial Cavendish Balance for automated use in cryostat. The scope of this project was to modify an off-the-shelf manually operated Cavendish Balance to allow for automated operation for periods of hours or days in cryostat. The purpose of this modification was to allow the balance to be used in the study of effects of superconducting materials on the local gravitational field strength to determine if the strength of gravitational fields can be reduced. A Cavendish Balance was chosen because it is a fairly simple piece of equipment for measuring gravity, one the least accurately known and least understood physical constants. The principle activities that occurred under this purchase order were: (1) All the components necessary to hold and automate the Cavendish Balance in a cryostat were designed. Engineering drawings were made of custom parts to be fabricated, other off-the-shelf parts were procured; (2) Software was written in LabView to control the automation process via a stepper motor controller and stepper motor, and to collect data from the balance during testing; (3)Software was written to take the data collected from the Cavendish Balance and reduce it to give a value for the gravitational constant; (4) The components of the system were assembled and fitted to a cryostat. Also the LabView hardware including the control computer, stepper motor driver, data collection boards, and necessary cabling were assembled; and (5) The system was operated for a number of periods, data collected, and reduced to give an average value for the gravitational constant.

  18. DOE LLW classification rationale

    International Nuclear Information System (INIS)

    Flores, A.Y.

    1991-01-01

    This report was about the rationale which the US Department of Energy had with low-level radioactive waste (LLW) classification. It is based on the Nuclear Regulatory Commission's classification system. DOE site operators met to review the qualifications and characteristics of the classification systems. They evaluated performance objectives, developed waste classification tables, and compiled dose limits on the waste. A goal of the LLW classification system was to allow each disposal site the freedom to develop limits to radionuclide inventories and concentrations according to its own site-specific characteristics. This goal was achieved with the adoption of a performance objectives system based on a performance assessment, with site-specific environmental conditions and engineered disposal systems

  19. Autonomy, Automation, and Systems

    Science.gov (United States)

    Turner, Philip R.

    1987-02-01

    Aerospace industry interest in autonomy and automation, given fresh impetus by the national goal of establishing a Space Station, is becoming a major item of research and technology development. The promise of new technology arising from research in Artificial Intelligence (AI) has focused much attention on its potential in autonomy and automation. These technologies can improve performance in autonomous control functions that involve planning, scheduling, and fault diagnosis of complex systems. There are, however, many aspects of system and subsystem design in an autonomous system that impact AI applications, but do not directly involve AI technology. Development of a system control architecture, establishment of an operating system within the design, providing command and sensory data collection features appropriate to automated operation, and the use of design analysis tools to support system engineering are specific examples of major design issues. Aspects such as these must also receive attention and technology development support if we are to implement complex autonomous systems within the realistic limitations of mass, power, cost, and available flight-qualified technology that are all-important to a flight project.

  20. Automation in biological crystallization.

    Science.gov (United States)

    Stewart, Patrick Shaw; Mueller-Dieckmann, Jochen

    2014-06-01

    Crystallization remains the bottleneck in the crystallographic process leading from a gene to a three-dimensional model of the encoded protein or RNA. Automation of the individual steps of a crystallization experiment, from the preparation of crystallization cocktails for initial or optimization screens to the imaging of the experiments, has been the response to address this issue. Today, large high-throughput crystallization facilities, many of them open to the general user community, are capable of setting up thousands of crystallization trials per day. It is thus possible to test multiple constructs of each target for their ability to form crystals on a production-line basis. This has improved success rates and made crystallization much more convenient. High-throughput crystallization, however, cannot relieve users of the task of producing samples of high quality. Moreover, the time gained from eliminating manual preparations must now be invested in the careful evaluation of the increased number of experiments. The latter requires a sophisticated data and laboratory information-management system. A review of the current state of automation at the individual steps of crystallization with specific attention to the automation of optimization is given.

  1. A comparative study in ultrasound breast imaging classification

    Science.gov (United States)

    Yap, Moi Hoon; Edirisinghe, Eran A.; Bez, Helmut E.

    2009-02-01

    American College of Radiology introduces a standard in classification, the breast imaging reporting and data system (BIRADS), standardize the reporting of ultrasound findings, clarify its interpretation, and facilitate communication between clinicians. The effective use of new technologies to support healthcare initiatives is important and current research is moving towards implementing computer tools in the diagnostics process. Initially a detailed study was carried out to evaluate the performance of two commonly used appearance based classification algorithms, based on the use of Principal Component Analysis (PCA), and two dimensional linear discriminant analysis (2D-LDA). The study showed that these two appearance based classification approaches are not capable of handling the classification of ultrasound breast image lesions. Therefore further investigations in the use of a popular feature based classifier - Support Vector Machine (SVM) was conducted. A pre-processing step before feature based classification is feature extraction, which involve shape, texture and edge descriptors for the Region of Interest (ROI). The input dataset to SVM classification is from a fully automated ROI detection. We achieve the success rate of 0.550 in PCA, 0.500 in LDA, and 0.931 in SVM. The best combination of features in SVM classification is to combine the shape, texture and edge descriptors, with sensitivity 0.840 and specificity 0.968. This paper briefly reviews the background to the project and then details the ongoing research. In conclusion, we discuss the contributions, limitations, and future plans of our work.

  2. 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.

  3. Watershed Merge Tree Classification for Electron Microscopy Image Segmentation

    OpenAIRE

    Liu, Ting; Jurrus, Elizabeth; Seyedhosseini, Mojtaba; Ellisman, Mark; Tasdizen, Tolga

    2012-01-01

    Automated segmentation of electron microscopy (EM) images is a challenging problem. In this paper, we present a novel method that utilizes a hierarchical structure and boundary classification for 2D neuron segmentation. With a membrane detection probability map, a watershed merge tree is built for the representation of hierarchical region merging from the watershed algorithm. A boundary classifier is learned with non-local image features to predict each potential merge in the tree, upon which...

  4. Binary Classification Method of Social Network Users

    Directory of Open Access Journals (Sweden)

    I. A. Poryadin

    2017-01-01

    Full Text Available The subject of research is a binary classification method of social network users based on the data analysis they have placed. Relevance of the task to gain information about a person by examining the content of his/her pages in social networks is exemplified. The most common approach to its solution is a visual browsing. The order of the regional authority in our country illustrates that its using in school education is needed. The article shows restrictions on the visual browsing of pupil’s pages in social networks as a tool for the teacher and the school psychologist and justifies that a process of social network users’ data analysis should be automated. Explores publications, which describe such data acquisition, processing, and analysis methods and considers their advantages and disadvantages. The article also gives arguments to support a proposal to study the classification method of social network users. One such method is credit scoring, which is used in banks and credit institutions to assess the solvency of clients. Based on the high efficiency of the method there is a proposal for significant expansion of its using in other areas of society. The possibility to use logistic regression as the mathematical apparatus of the proposed method of binary classification has been justified. Such an approach enables taking into account the different types of data extracted from social networks. Among them: the personal user data, information about hobbies, friends, graphic and text information, behaviour characteristics. The article describes a number of existing methods of data transformation that can be applied to solve the problem. An experiment of binary gender-based classification of social network users is described. A logistic model obtained for this example includes multiple logical variables obtained by transforming the user surnames. This experiment confirms the feasibility of the proposed method. Further work is to define a system

  5. Kappa Coefficients for Circular Classifications

    NARCIS (Netherlands)

    Warrens, Matthijs J.; Pratiwi, Bunga C.

    2016-01-01

    Circular classifications are classification scales with categories that exhibit a certain periodicity. Since linear scales have endpoints, the standard weighted kappas used for linear scales are not appropriate for analyzing agreement between two circular classifications. A family of kappa

  6. Oral epithelial dysplasia classification systems

    DEFF Research Database (Denmark)

    Warnakulasuriya, S; Reibel, J; Bouquot, J

    2008-01-01

    . In this report, we review the oral epithelial dysplasia classification systems. The three classification schemes [oral epithelial dysplasia scoring system, squamous intraepithelial neoplasia and Ljubljana classification] were presented and the Working Group recommended epithelial dysplasia grading for routine...

  7. Classification of Mobile Laser Scanning Point Clouds from Height Features

    Science.gov (United States)

    Zheng, M.; Lemmens, M.; van Oosterom, P.

    2017-09-01

    The demand for 3D maps of cities and road networks is steadily growing and mobile laser scanning (MLS) systems are often the preferred geo-data acquisition method for capturing such scenes. Because MLS systems are mounted on cars or vans they can acquire billions of points of road scenes within a few hours of survey. Manual processing of point clouds is labour intensive and thus time consuming and expensive. Hence, the need for rapid and automated methods for 3D mapping of dense point clouds is growing exponentially. The last five years the research on automated 3D mapping of MLS data has tremendously intensified. In this paper, we present our work on automated classification of MLS point clouds. In the present stage of the research we exploited three features - two height components and one reflectance value, and achieved an overall accuracy of 73 %, which is really encouraging for further refining our approach.

  8. CP-CHARM: segmentation-free image classification made accessible.

    Science.gov (United States)

    Uhlmann, Virginie; Singh, Shantanu; Carpenter, Anne E

    2016-01-27

    Automated classification using machine learning often relies on features derived from segmenting individual objects, which can be difficult to automate. WND-CHARM is a previously developed classification algorithm in which features are computed on the whole image, thereby avoiding the need for segmentation. The algorithm obtained encouraging results but requires considerable computational expertise to execute. Furthermore, some benchmark sets have been shown to be subject to confounding artifacts that overestimate classification accuracy. We developed CP-CHARM, a user-friendly image-based classification algorithm inspired by WND-CHARM in (i) its ability to capture a wide variety of morphological aspects of the image, and (ii) the absence of requirement for segmentation. In order to make such an image-based classification method easily accessible to the biological research community, CP-CHARM relies on the widely-used open-source image analysis software CellProfiler for feature extraction. To validate our method, we reproduced WND-CHARM's results and ensured that CP-CHARM obtained comparable performance. We then successfully applied our approach on cell-based assay data and on tissue images. We designed these new training and test sets to reduce the effect of batch-related artifacts. The proposed method preserves the strengths of WND-CHARM - it extracts a wide variety of morphological features directly on whole images thereby avoiding the need for cell segmentation, but additionally, it makes the methods easily accessible for researchers without computational expertise by implementing them as a CellProfiler pipeline. It has been demonstrated to perform well on a wide range of bioimage classification problems, including on new datasets that have been carefully selected and annotated to minimize batch effects. This provides for the first time a realistic and reliable assessment of the whole image classification strategy.

  9. Automation, Performance and International Competition

    DEFF Research Database (Denmark)

    Kromann, Lene; Sørensen, Anders

    This paper presents new evidence on trade‐induced automation in manufacturing firms using unique data combining a retrospective survey that we have assembled with register data for 2005‐2010. In particular, we establish a causal effect where firms that have specialized in product types for which...... the Chinese exports to the world market has risen sharply invest more in automated capital compared to firms that have specialized in other product types. We also study the relationship between automation and firm performance and find that firms with high increases in scale and scope of automation have faster...... productivity growth than other firms. Moreover, automation improves the efficiency of all stages of the production process by reducing setup time, run time, and inspection time and increasing uptime and quantity produced per worker. The efficiency improvement varies by type of automation....

  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 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...... datasets. Our model also outperforms A Decision Cluster Classification (ADCC) and the Decision Cluster Forest Classification (DCFC) models on the Reuters-21578 dataset....

  11. Astrophysical Information from Objective Prism Digitized Images: Classification with an Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Bratsolis Emmanuel

    2005-01-01

    Full Text Available Stellar spectral classification is not only a tool for labeling individual stars but is also useful in studies of stellar population synthesis. Extracting the physical quantities from the digitized spectral plates involves three main stages: detection, extraction, and classification of spectra. Low-dispersion objective prism images have been used and automated methods have been developed. The detection and extraction problems have been presented in previous works. In this paper, we present a classification method based on an artificial neural network (ANN. We make a brief presentation of the entire automated system and we compare the new classification method with the previously used method of maximum correlation coefficient (MCC. Digitized photographic material has been used here. The method can also be used on CCD spectral images.

  12. Classification of hand eczema

    DEFF Research Database (Denmark)

    Agner, T; Aalto-Korte, K; Andersen, K E

    2015-01-01

    BACKGROUND: Classification of hand eczema (HE) is mandatory in epidemiological and clinical studies, and also important in clinical work. OBJECTIVES: The aim was to test a recently proposed classification system of HE in clinical practice in a prospective multicentre study. METHODS: Patients were......%) could not be classified. 38% had one additional diagnosis and 26% had two or more additional diagnoses. Eczema on feet was found in 30% of the patients, statistically significantly more frequently associated with hyperkeratotic and vesicular endogenous eczema. CONCLUSION: We find that the classification...

  13. 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...

  14. An Approach to Office Automation

    OpenAIRE

    Ischenko, A.N.; Tumeo, M.A.

    1983-01-01

    In recent years, the increasing scale of production and degree of specialization within firms has led to a significant growth in the amount of information needed for their successful management. As a result, the use of computer systems (office automation) has become increasingly common. However, no manuals or set automation procedures exist to help organizations design and implement an efficient and effective office automation system. The goals of this paper are to outline some important...

  15. Embedded system for building automation

    OpenAIRE

    Rolih, Andrej

    2014-01-01

    Home automation is a fast developing field of computer science and electronics. Companies are offering many different products for home automation. Ranging anywhere from complete systems for building management and control, to simple smart lights that can be connected to the internet. These products offer the user greater living comfort and lower their expenses by reducing the energy usage. This thesis shows the development of a simple home automation system that focuses mainly on the enhance...

  16. World-wide distribution automation systems

    International Nuclear Information System (INIS)

    Devaney, T.M.

    1994-01-01

    A worldwide power distribution automation system is outlined. Distribution automation is defined and the status of utility automation is discussed. Other topics discussed include a distribution management system, substation feeder, and customer functions, potential benefits, automation costs, planning and engineering considerations, automation trends, databases, system operation, computer modeling of system, and distribution management systems

  17. Contaminant analysis automation, an overview

    International Nuclear Information System (INIS)

    Hollen, R.; Ramos, O. Jr.

    1996-01-01

    To meet the environmental restoration and waste minimization goals of government and industry, several government laboratories, universities, and private companies have formed the Contaminant Analysis Automation (CAA) team. The goal of this consortium is to design and fabricate robotics systems that standardize and automate the hardware and software of the most common environmental chemical methods. In essence, the CAA team takes conventional, regulatory- approved (EPA Methods) chemical analysis processes and automates them. The automation consists of standard laboratory modules (SLMs) that perform the work in a much more efficient, accurate, and cost- effective manner

  18. Applying Machine Learning to Star Cluster Classification

    Science.gov (United States)

    Fedorenko, Kristina; Grasha, Kathryn; Calzetti, Daniela; Mahadevan, Sridhar

    2016-01-01

    Catalogs describing populations of star clusters are essential in investigating a range of important issues, from star formation to galaxy evolution. Star cluster catalogs are typically created in a two-step process: in the first step, a catalog of sources is automatically produced; in the second step, each of the extracted sources is visually inspected by 3-to-5 human classifiers and assigned a category. Classification by humans is labor-intensive and time consuming, thus it creates a bottleneck, and substantially slows down progress in star cluster research.We seek to automate the process of labeling star clusters (the second step) through applying supervised machine learning techniques. This will provide a fast, objective, and reproducible classification. Our data is HST (WFC3 and ACS) images of galaxies in the distance range of 3.5-12 Mpc, with a few thousand star clusters already classified by humans as a part of the LEGUS (Legacy ExtraGalactic UV Survey) project. The classification is based on 4 labels (Class 1 - symmetric, compact cluster; Class 2 - concentrated object with some degree of asymmetry; Class 3 - multiple peak system, diffuse; and Class 4 - spurious detection). We start by looking at basic machine learning methods such as decision trees. We then proceed to evaluate performance of more advanced techniques, focusing on convolutional neural networks and other Deep Learning methods. We analyze the results, and suggest several directions for further improvement.

  19. Neural network classification of sweet potato embryos

    Science.gov (United States)

    Molto, Enrique; Harrell, Roy C.

    1993-05-01

    Somatic embryogenesis is a process that allows for the in vitro propagation of thousands of plants in sub-liter size vessels and has been successfully applied to many significant species. The heterogeneity of maturity and quality of embryos produced with this technique requires sorting to obtain a uniform product. An automated harvester is being developed at the University of Florida to sort embryos in vitro at different stages of maturation in a suspension culture. The system utilizes machine vision to characterize embryo morphology and a fluidic based separation device to isolate embryos associated with a pre-defined, targeted morphology. Two different backpropagation neural networks (BNN) were used to classify embryos based on information extracted from the vision system. One network utilized geometric features such as embryo area, length, and symmetry as inputs. The alternative network utilized polar coordinates of an embryo's perimeter with respect to its centroid as inputs. The performances of both techniques were compared with each other and with an embryo classification method based on linear discriminant analysis (LDA). Similar results were obtained with all three techniques. Classification efficiency was improved by reducing the dimension of the feature vector trough a forward stepwise analysis by LDA. In order to enhance the purity of the sample selected as harvestable, a reject to classify option was introduced in the model and analyzed. The best classifier performances (76% overall correct classifications, 75% harvestable objects properly classified, homogeneity improvement ratio 1.5) were obtained using 8 features in a BNN.

  20. Quantization of polyphenolic compounds in histological sections of grape berries by automated color image analysis

    Science.gov (United States)

    Clement, Alain; Vigouroux, Bertnand

    2003-04-01

    We present new results in applied color image analysis that put in evidence the significant influence of soil on localization and appearance of polyphenols in grapes. These results have been obtained with a new unsupervised classification algorithm founded on hierarchical analysis of color histograms. The process is automated thanks to a software platform we developed specifically for color image analysis and it's applications.

  1. Preliminary results for a semi-automated quantification of site effects ...

    Indian Academy of Sciences (India)

    We apply a semi-automated, object-oriented, multi-resolution feature segmentation method to identify and extract local terrain features. Then we classify the terrain types into mountain, piedmont and basin units using geomorphometry (topographic slope) as our parameter. Next, on the basis of the site classification schemes ...

  2. A Comparison of a Manual Library Reclassification Project with a Computer Automated Library Reclassification Project.

    Science.gov (United States)

    Wheelbarger, Johnny J.; Clouse, R. Wilburn

    An automated project for converting a library collection from Dewey Decimal to Library of Congress classification was compared with a manual reclassification project. The Joint University Libraries (JUL) of Vanderbilt University, George Peabody College for Teachers, and Scarrit College served as an example of manual reclassification. The JUL…

  3. An Automated Approach to Extracting River Bank Locations from Aerial Imagery Using Image Texture

    Science.gov (United States)

    2015-11-04

    consuming and labour intensive, and the quality is dependent on the individual doing the task. This paper describes a quick and fully automated method for...generally considered to be supervised classification techniques in that they require the active input of a trained analyst to define the characteristics of

  4. Fossil power plant automation

    International Nuclear Information System (INIS)

    Divakaruni, S.M.; Touchton, G.

    1991-01-01

    This paper elaborates on issues facing the utilities industry and seeks to address how new computer-based control and automation technologies resulting from recent microprocessor evolution, can improve fossil plant operations and maintenance. This in turn can assist utilities to emerge stronger from the challenges ahead. Many presentations at the first ISA/EPRI co-sponsored conference are targeted towards improving the use of computer and control systems in the fossil and nuclear power plants and we believe this to be the right forum to share our ideas

  5. CLASSIFICATION OF VIRUSES

    Indian Academy of Sciences (India)

    First page Back Continue Last page Overview Graphics. CLASSIFICATION OF VIRUSES. On basis of morphology. On basis of chemical composition. On basis of structure of genome. On basis of mode of replication. Notes:

  6. Update on diabetes classification.

    Science.gov (United States)

    Thomas, Celeste C; Philipson, Louis H

    2015-01-01

    This article highlights the difficulties in creating a definitive classification of diabetes mellitus in the absence of a complete understanding of the pathogenesis of the major forms. This brief review shows the evolving nature of the classification of diabetes mellitus. No classification scheme is ideal, and all have some overlap and inconsistencies. The only diabetes in which it is possible to accurately diagnose by DNA sequencing, monogenic diabetes, remains undiagnosed in more than 90% of the individuals who have diabetes caused by one of the known gene mutations. The point of classification, or taxonomy, of disease, should be to give insight into both pathogenesis and treatment. It remains a source of frustration that all schemes of diabetes mellitus continue to fall short of this goal. Copyright © 2015 Elsevier Inc. All rights reserved.

  7. Learning Apache Mahout classification

    CERN Document Server

    Gupta, Ashish

    2015-01-01

    If you are a data scientist who has some experience with the Hadoop ecosystem and machine learning methods and want to try out classification on large datasets using Mahout, this book is ideal for you. Knowledge of Java is essential.

  8. 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....

  9. Multilingual News Article Classification

    OpenAIRE

    Skjennum, Patrick L

    2016-01-01

    News is an ever-growing and global resource, reliant on robust distribution networks to spread information. This thesis investigates how exploiting semantic, contextual and ontological information may form a basis for a language independent news article classification system. In light of the above, a scalable multi-label news article classification system, based exclusively on extracted DBpedia entities, and a predetermined standardized set of fixed-size IPTC Media Topic categories, is p...

  10. Heart sound classification from unsegmented phonocardiograms.

    Science.gov (United States)

    Langley, Philip; Murray, Alan

    2017-07-31

    Most algorithms for automated analysis of phonocardiograms (PCG) require segmentation of the signal into the characteristic heart sounds. The aim was to assess the feasibility for accurate classification of heart sounds on short, unsegmented recordings. PCG segments of 5 s duration from the PhysioNet/Computing in Cardiology Challenge database were analysed. Initially the 5 s segment at the start of each recording (seg 1) was analysed. Segments were zero-mean but otherwise had no pre-processing or segmentation. Normalised spectral amplitude was determined by fast Fourier transform and wavelet entropy by wavelet analysis. For each of these a simple single feature threshold-based classifier was implemented and the frequency/scale and thresholds for optimum classification accuracy determined. The analysis was then repeated using relatively noise free 5 s segments (seg 2) of each recording. Spectral amplitude and wavelet entropy features were then combined in a classification tree. There were significant differences between normal and abnormal recordings for both wavelet entropy and spectral amplitude across scales and frequency. In the wavelet domain the differences between groups were greatest at highest frequencies (wavelet scale 1, pseudo frequency 1 kHz) whereas in the frequency domain the differences were greatest at low frequencies (12 Hz). Abnormal recordings had significantly reduced high frequency wavelet entropy: (Median (interquartile range)) 6.63 (2.42) versus 8.36 (1.91), p  <  0.0001, suggesting the presence of discrete high frequency components in these recordings. Abnormal recordings exhibited significantly greater low frequency (12 Hz) spectral amplitude: 0.24 (0.22) versus 0.09 (0.15), p  <  0.0001. Classification accuracy (mean of specificity and sensitivity) was greatest for wavelet entropy: 76% (specificity 54%, sensitivity 98%) versus 70% (specificity 65%, sensitivity 75%) and was further improved by selecting the lowest

  11. Vision-Based Perception and Classification of Mosquitoes Using Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Masataka Fuchida

    2017-01-01

    Full Text Available The need for a novel automated mosquito perception and classification method is becoming increasingly essential in recent years, with steeply increasing number of mosquito-borne diseases and associated casualties. There exist remote sensing and GIS-based methods for mapping potential mosquito inhabitants and locations that are prone to mosquito-borne diseases, but these methods generally do not account for species-wise identification of mosquitoes in closed-perimeter regions. Traditional methods for mosquito classification involve highly manual processes requiring tedious sample collection and supervised laboratory analysis. In this research work, we present the design and experimental validation of an automated vision-based mosquito classification module that can deploy in closed-perimeter mosquito inhabitants. The module is capable of identifying mosquitoes from other bugs such as bees and flies by extracting the morphological features, followed by support vector machine-based classification. In addition, this paper presents the results of three variants of support vector machine classifier in the context of mosquito classification problem. This vision-based approach to the mosquito classification problem presents an efficient alternative to the conventional methods for mosquito surveillance, mapping and sample image collection. Experimental results involving classification between mosquitoes and a predefined set of other bugs using multiple classification strategies demonstrate the efficacy and validity of the proposed approach with a maximum recall of 98%.

  12. Automated Test Case Generation

    CERN Multimedia

    CERN. Geneva

    2015-01-01

    I would like to present the concept of automated test case generation. I work on it as part of my PhD and I think it would be interesting also for other people. It is also the topic of a workshop paper that I am introducing in Paris. (abstract below) Please note that the talk itself would be more general and not about the specifics of my PhD, but about the broad field of Automated Test Case Generation. I would introduce the main approaches (combinatorial testing, symbolic execution, adaptive random testing) and their advantages and problems. (oracle problem, combinatorial explosion, ...) Abstract of the paper: Over the last decade code-based test case generation techniques such as combinatorial testing or dynamic symbolic execution have seen growing research popularity. Most algorithms and tool implementations are based on finding assignments for input parameter values in order to maximise the execution branch coverage. Only few of them consider dependencies from outside the Code Under Test’s scope such...

  13. Automation from pictures

    International Nuclear Information System (INIS)

    Kozubal, A.J.

    1992-01-01

    The state transition diagram (STD) model has been helpful in the design of real time software, especially with the emergence of graphical computer aided software engineering (CASE) tools. Nevertheless, the translation of the STD to real time code has in the past been primarily a manual task. At Los Alamos we have automated this process. The designer constructs the STD using a CASE tool (Cadre Teamwork) using a special notation for events and actions. A translator converts the STD into an intermediate state notation language (SNL), and this SNL is compiled directly into C code (a state program). Execution of the state program is driven by external events, allowing multiple state programs to effectively share the resources of the host processor. Since the design and the code are tightly integrated through the CASE tool, the design and code never diverge, and we avoid design obsolescence. Furthermore, the CASE tool automates the production of formal technical documents from the graphic description encapsulated by the CASE tool. (author)

  14. AUTOMATED GEOSPATIAL WATERSHED ASSESSMENT ...

    Science.gov (United States)

    The Automated Geospatial Watershed Assessment tool (AGWA) is a GIS interface jointly developed by the USDA Agricultural Research Service, the U.S. Environmental Protection Agency, the University of Arizona, and the University of Wyoming to automate the parameterization and execution of the Soil Water Assessment Tool (SWAT) and KINEmatic Runoff and EROSion (KINEROS2) hydrologic models. The application of these two models allows AGWA to conduct hydrologic modeling and watershed assessments at multiple temporal and spatial scales. AGWA’s current outputs are runoff (volumes and peaks) and sediment yield, plus nitrogen and phosphorus with the SWAT model. AGWA uses commonly available GIS data layers to fully parameterize, execute, and visualize results from both models. Through an intuitive interface the user selects an outlet from which AGWA delineates and discretizes the watershed using a Digital Elevation Model (DEM) based on the individual model requirements. The watershed model elements are then intersected with soils and land cover data layers to derive the requisite model input parameters. The chosen model is then executed, and the results are imported back into AGWA for visualization. This allows managers to identify potential problem areas where additional monitoring can be undertaken or mitigation activities can be focused. AGWA also has tools to apply an array of best management practices. There are currently two versions of AGWA available; AGWA 1.5 for

  15. Maneuver Automation Software

    Science.gov (United States)

    Uffelman, Hal; Goodson, Troy; Pellegrin, Michael; Stavert, Lynn; Burk, Thomas; Beach, David; Signorelli, Joel; Jones, Jeremy; Hahn, Yungsun; Attiyah, Ahlam; hide

    2009-01-01

    The Maneuver Automation Software (MAS) automates the process of generating commands for maneuvers to keep the spacecraft of the Cassini-Huygens mission on a predetermined prime mission trajectory. Before MAS became available, a team of approximately 10 members had to work about two weeks to design, test, and implement each maneuver in a process that involved running many maneuver-related application programs and then serially handing off data products to other parts of the team. MAS enables a three-member team to design, test, and implement a maneuver in about one-half hour after Navigation has process-tracking data. MAS accepts more than 60 parameters and 22 files as input directly from users. MAS consists of Practical Extraction and Reporting Language (PERL) scripts that link, sequence, and execute the maneuver- related application programs: "Pushing a single button" on a graphical user interface causes MAS to run navigation programs that design a maneuver; programs that create sequences of commands to execute the maneuver on the spacecraft; and a program that generates predictions about maneuver performance and generates reports and other files that enable users to quickly review and verify the maneuver design. MAS can also generate presentation materials, initiate electronic command request forms, and archive all data products for future reference.

  16. 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...

  17. A Demonstration of Automated DNA Sequencing.

    Science.gov (United States)

    Latourelle, Sandra; Seidel-Rogol, Bonnie

    1998-01-01

    Details a simulation that employs a paper-and-pencil model to demonstrate the principles behind automated DNA sequencing. Discusses the advantages of automated sequencing as well as the chemistry of automated DNA sequencing. (DDR)

  18. Classification of Aggregates Using Basic Shape Parameters Through Neural Networks

    Directory of Open Access Journals (Sweden)

    Mahmut SİNECEN

    2010-02-01

    Full Text Available In this paper, the aim is to classify natural or crushed aggregates by using concrete and asphalt mixes through Artificial Neural Networks. For classification, it was a used the feature vector which was calculated by using digital image processing techniques. Of the five different type coarse aggregates images were taken with 45o and 90o by a 10 Mp (Sony DSC-R1 and 7.1 Mp (Canon EOS 350D camera. Aggregates images were processed and analyzed by using MATLAB Image Processing and Neural Network Toolbox. Classification process was made with totally 18 feature vectors, which is 9 vectors each angles, by neural network. Results showed image processing and neural networks which are important methods for founding shape parameters and classification of aggregates, and performance, cost and time consuming factors of automation systems in aggregate sources will be effective with these methods.

  19. Opening up Library Automation Software

    Science.gov (United States)

    Breeding, Marshall

    2009-01-01

    Throughout the history of library automation, the author has seen a steady advancement toward more open systems. In the early days of library automation, when proprietary systems dominated, the need for standards was paramount since other means of inter-operability and data exchange weren't possible. Today's focus on Application Programming…

  20. Automated Power-Distribution System

    Science.gov (United States)

    Ashworth, Barry; Riedesel, Joel; Myers, Chris; Miller, William; Jones, Ellen F.; Freeman, Kenneth; Walsh, Richard; Walls, Bryan K.; Weeks, David J.; Bechtel, Robert T.

    1992-01-01

    Autonomous power-distribution system includes power-control equipment and automation equipment. System automatically schedules connection of power to loads and reconfigures itself when it detects fault. Potential terrestrial applications include optimization of consumption of power in homes, power supplies for autonomous land vehicles and vessels, and power supplies for automated industrial processes.

  1. Automation in Catholic College Libraries.

    Science.gov (United States)

    Stussy, Susan A.

    1981-01-01

    Reports on a 1980 survey of library automation in 105 Catholic colleges with collections containing less than 300,000 bibliographic items. The report indicates that network membership and grant funding were a vital part of library automation in the schools surveyed. (Author/LLS)

  2. Library Automation: A Year on.

    Science.gov (United States)

    Electronic Library, 1997

    1997-01-01

    A follow-up interview with librarians from Hong Kong, Mexico, Australia, Canada, and New Zealand about library automation systems in their libraries and their plans for the future. Discusses system performance, upgrades, services, resources, intranets, trends in automation, Web interfaces, full-text image/document systems, document delivery, OPACs…

  3. Library Automation: A Balanced View

    Science.gov (United States)

    Avram, Henriette

    1972-01-01

    Ellsworth Mason's two recently published papers, severely criticizing library automation, are refuted. While admitting to the failures and problems, this paper also presents the positive accomplishments in a brief evaluation of the status of library automation in 1971. (16 references) (Author/SJ)

  4. Library Automation: A Critical Review.

    Science.gov (United States)

    Overmyer, LaVahn

    This report has two main purposes: (1) To give an account of the use of automation in selected libraries throughout the country and in the development of networks; and (2) To discuss some of the fundamental considerations relevant to automation and the implications for library education, library research and the library profession. The first part…

  5. Automated methods of corrosion measurement

    DEFF Research Database (Denmark)

    Andersen, Jens Enevold Thaulov; Bech-Nielsen, Gregers; Reeve, John Ch

    1997-01-01

    to revise assumptions regarding the basis of the method, which sometimes leads to the discovery of as-yet unnoticed phenomena. The present selection of automated methods for corrosion measurements is not motivated simply by the fact that a certain measurement can be performed automatically. Automation...

  6. Automated Methods Of Corrosion Measurements

    DEFF Research Database (Denmark)

    Bech-Nielsen, Gregers; Andersen, Jens Enevold Thaulov; Reeve, John Ch

    1997-01-01

    The chapter describes the following automated measurements: Corrosion Measurements by Titration, Imaging Corrosion by Scanning Probe Microscopy, Critical Pitting Temperature and Application of the Electrochemical Hydrogen Permeation Cell.......The chapter describes the following automated measurements: Corrosion Measurements by Titration, Imaging Corrosion by Scanning Probe Microscopy, Critical Pitting Temperature and Application of the Electrochemical Hydrogen Permeation Cell....

  7. Automated Test-Form Generation

    Science.gov (United States)

    van der Linden, Wim J.; Diao, Qi

    2011-01-01

    In automated test assembly (ATA), the methodology of mixed-integer programming is used to select test items from an item bank to meet the specifications for a desired test form and optimize its measurement accuracy. The same methodology can be used to automate the formatting of the set of selected items into the actual test form. Three different…

  8. A Survey of Automated Deduction

    OpenAIRE

    Bundy, Alan

    1999-01-01

    We survey research in the automation of deductive inference, from its beginnings in the early history of computing to the present day. We identify and describe the major areas of research interest and their applications. The area is characterised by its wide variety of proof methods, forms of automated deduction and applications.

  9. The Science of Home Automation

    Science.gov (United States)

    Thomas, Brian Louis

    Smart home technologies and the concept of home automation have become more popular in recent years. This popularity has been accompanied by social acceptance of passive sensors installed throughout the home. The subsequent increase in smart homes facilitates the creation of home automation strategies. We believe that home automation strategies can be generated intelligently by utilizing smart home sensors and activity learning. In this dissertation, we hypothesize that home automation can benefit from activity awareness. To test this, we develop our activity-aware smart automation system, CARL (CASAS Activity-aware Resource Learning). CARL learns the associations between activities and device usage from historical data and utilizes the activity-aware capabilities to control the devices. To help validate CARL we deploy and test three different versions of the automation system in a real-world smart environment. To provide a foundation of activity learning, we integrate existing activity recognition and activity forecasting into CARL home automation. We also explore two alternatives to using human-labeled data to train the activity learning models. The first unsupervised method is Activity Detection, and the second is a modified DBSCAN algorithm that utilizes Dynamic Time Warping (DTW) as a distance metric. We compare the performance of activity learning with human-defined labels and with automatically-discovered activity categories. To provide evidence in support of our hypothesis, we evaluate CARL automation in a smart home testbed. Our results indicate that home automation can be boosted through activity awareness. We also find that the resulting automation has a high degree of usability and comfort for the smart home resident.

  10. Clinical classification of syncope.

    Science.gov (United States)

    Sutton, Richard

    2013-01-01

    Syncope is a presenting symptom, and in itself is not a diagnosis. An etiology or a mechanism must be sought in all cases. Currently, most clinicians classify syncope on clinical grounds by attempting to ascertain its etiology. They then use this classification to guide further management. Using this approach, reflex syncope is the most common form of syncope, occurring in approximately 60% of syncope presentations. Orthostatic hypotension presents in around 15% with arrhythmic syncope in 10% and structural heart disease as the cause of syncope in 5%; in 10% of patients no diagnosis is made. An alternative classification system uses the mechanism of syncope derived from an implanted ECG loop recorder (ILR). While this approach may be of value for optimizing therapy, it cannot be considered as the primary classification since ILRs are not typically implanted early in the evaluation process of most patients. ILRs are usually placed after "risk stratification" in those deemed not to be at high risk but remain in the uncertain etiology category. Furthermore, there exists, in current ILR technology, lack of ambulatory blood pressure monitoring capability. Thus, vasodilation leading to hypotension, the main trigger of cerebral hypoperfusion other than bradycardia, cannot be detected and is currently unavailable for use in a mechanistic-based classification. Thus, the etiological classification remains the basis for both risk stratification and subsequent clinical management. Copyright © 2013 Elsevier Inc. All rights reserved.

  11. Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images

    Directory of Open Access Journals (Sweden)

    Eva Husson

    2016-09-01

    Full Text Available Aquatic vegetation has important ecological and regulatory functions and should be monitored in order to detect ecosystem changes. Field data collection is often costly and time-consuming; remote sensing with unmanned aircraft systems (UASs provides aerial images with sub-decimetre resolution and offers a potential data source for vegetation mapping. In a manual mapping approach, UAS true-colour images with 5-cm-resolution pixels allowed for the identification of non-submerged aquatic vegetation at the species level. However, manual mapping is labour-intensive, and while automated classification methods are available, they have rarely been evaluated for aquatic vegetation, particularly at the scale of individual vegetation stands. We evaluated classification accuracy and time-efficiency for mapping non-submerged aquatic vegetation at three levels of detail at five test sites (100 m × 100 m differing in vegetation complexity. We used object-based image analysis and tested two classification methods (threshold classification and Random Forest using eCognition®. The automated classification results were compared to results from manual mapping. Using threshold classification, overall accuracy at the five test sites ranged from 93% to 99% for the water-versus-vegetation level and from 62% to 90% for the growth-form level. Using Random Forest classification, overall accuracy ranged from 56% to 94% for the growth-form level and from 52% to 75% for the dominant-taxon level. Overall classification accuracy decreased with increasing vegetation complexity. In test sites with more complex vegetation, automated classification was more time-efficient than manual mapping. This study demonstrated that automated classification of non-submerged aquatic vegetation from true-colour UAS images was feasible, indicating good potential for operative mapping of aquatic vegetation. When choosing the preferred mapping method (manual versus automated the desired level of

  12. Berkeley automated supernova search

    Energy Technology Data Exchange (ETDEWEB)

    Kare, J.T.; Pennypacker, C.R.; Muller, R.A.; Mast, T.S.; Crawford, F.S.; Burns, M.S.

    1981-01-01

    The Berkeley automated supernova search employs a computer controlled 36-inch telescope and charge coupled device (CCD) detector to image 2500 galaxies per night. A dedicated minicomputer compares each galaxy image with stored reference data to identify supernovae in real time. The threshold for detection is m/sub v/ = 18.8. We plan to monitor roughly 500 galaxies in Virgo and closer every night, and an additional 6000 galaxies out to 70 Mpc on a three night cycle. This should yield very early detection of several supernovae per year for detailed study, and reliable premaximum detection of roughly 100 supernovae per year for statistical studies. The search should be operational in mid-1982.

  13. Berkeley automated supernova search

    International Nuclear Information System (INIS)

    Kare, J.T.; Pennypacker, C.R.; Muller, R.A.; Mast, T.S.

    1981-01-01

    The Berkeley automated supernova search employs a computer controlled 36-inch telescope and charge coupled device (CCD) detector to image 2500 galaxies per night. A dedicated minicomputer compares each galaxy image with stored reference data to identify supernovae in real time. The threshold for detection is m/sub v/ = 18.8. We plan to monitor roughly 500 galaxies in Virgo and closer every night, and an additional 6000 galaxies out to 70 Mpc on a three night cycle. This should yield very early detection of several supernovae per year for detailed study, and reliable premaximum detection of roughly 100 supernovae per year for statistical studies. The search should be operational in mid-1982

  14. Automated attendance accounting system

    Science.gov (United States)

    Chapman, C. P. (Inventor)

    1973-01-01

    An automated accounting system useful for applying data to a computer from any or all of a multiplicity of data terminals is disclosed. The system essentially includes a preselected number of data terminals which are each adapted to convert data words of decimal form to another form, i.e., binary, usable with the computer. Each data terminal may take the form of a keyboard unit having a number of depressable buttons or switches corresponding to selected data digits and/or function digits. A bank of data buffers, one of which is associated with each data terminal, is provided as a temporary storage. Data from the terminals is applied to the data buffers on a digit by digit basis for transfer via a multiplexer to the computer.

  15. Robust automated knowledge capture.

    Energy Technology Data Exchange (ETDEWEB)

    Stevens-Adams, Susan Marie; Abbott, Robert G.; Forsythe, James Chris; Trumbo, Michael Christopher Stefan; Haass, Michael Joseph; Hendrickson, Stacey M. Langfitt

    2011-10-01

    This report summarizes research conducted through the Sandia National Laboratories Robust Automated Knowledge Capture Laboratory Directed Research and Development project. The objective of this project was to advance scientific understanding of the influence of individual cognitive attributes on decision making. The project has developed a quantitative model known as RumRunner that has proven effective in predicting the propensity of an individual to shift strategies on the basis of task and experience related parameters. Three separate studies are described which have validated the basic RumRunner model. This work provides a basis for better understanding human decision making in high consequent national security applications, and in particular, the individual characteristics that underlie adaptive thinking.

  16. (No) Security in Automation!?

    CERN Document Server

    Lüders, S

    2008-01-01

    Modern Information Technologies like Ethernet, TCP/IP, web server or FTP are nowadays increas-ingly used in distributed control and automation systems. Thus, information from the factory floor is now directly available at the management level (From Shop-Floor to Top-Floor) and can be ma-nipulated from there. Despite the benefits coming with this (r)evolution, new vulnerabilities are in-herited, too: worms and viruses spread within seconds via Ethernet and attackers are becoming interested in control systems. Unfortunately, control systems lack the standard security features that usual office PCs have. This contribution will elaborate on these problems, discuss the vulnerabilities of modern control systems and present international initiatives for mitigation.

  17. Automated Motivic Analysis

    DEFF Research Database (Denmark)

    Lartillot, Olivier

    2016-01-01

    Motivic analysis provides very detailed understanding of musical composi- tions, but is also particularly difficult to formalize and systematize. A computational automation of the discovery of motivic patterns cannot be reduced to a mere extraction of all possible sequences of descriptions....... The systematic approach inexorably leads to a proliferation of redundant structures that needs to be addressed properly. Global filtering techniques cause a drastic elimination of interesting structures that damages the quality of the analysis. On the other hand, a selection of closed patterns allows...... for lossless compression. The structural complexity resulting from successive repetitions of patterns can be controlled through a simple modelling of cycles. Generally, motivic patterns cannot always be defined solely as sequences of descriptions in a fixed set of dimensions: throughout the descriptions...

  18. Printing quality control automation

    Science.gov (United States)

    Trapeznikova, O. V.

    2018-04-01

    One of the most important problems in the concept of standardizing the process of offset printing is the control the quality rating of printing and its automation. To solve the problem, a software has been developed taking into account the specifics of printing system components and the behavior in printing process. In order to characterize the distribution of ink layer on the printed substrate the so-called deviation of the ink layer thickness on the sheet from nominal surface is suggested. The geometric data construction the surface projections of the color gamut bodies allows to visualize the color reproduction gamut of printing systems in brightness ranges and specific color sectors, that provides a qualitative comparison of the system by the reproduction of individual colors in a varying ranges of brightness.

  19. Automated electronic filter design

    CERN Document Server

    Banerjee, Amal

    2017-01-01

    This book describes a novel, efficient and powerful scheme for designing and evaluating the performance characteristics of any electronic filter designed with predefined specifications. The author explains techniques that enable readers to eliminate complicated manual, and thus error-prone and time-consuming, steps of traditional design techniques. The presentation includes demonstration of efficient automation, using an ANSI C language program, which accepts any filter design specification (e.g. Chebyschev low-pass filter, cut-off frequency, pass-band ripple etc.) as input and generates as output a SPICE(Simulation Program with Integrated Circuit Emphasis) format netlist. Readers then can use this netlist to run simulations with any version of the popular SPICE simulator, increasing accuracy of the final results, without violating any of the key principles of the traditional design scheme.

  20. A catalog of automated analysis methods for enterprise models.

    Science.gov (United States)

    Florez, Hector; Sánchez, Mario; Villalobos, Jorge

    2016-01-01

    Enterprise models are created for documenting and communicating the structure and state of Business and Information Technologies elements of an enterprise. After models are completed, they are mainly used to support analysis. Model analysis is an activity typically based on human skills and due to the size and complexity of the models, this process can be complicated and omissions or miscalculations are very likely. This situation has fostered the research of automated analysis methods, for supporting analysts in enterprise analysis processes. By reviewing the literature, we found several analysis methods; nevertheless, they are based on specific situations and different metamodels; then, some analysis methods might not be applicable to all enterprise models. This paper presents the work of compilation (literature review), classification, structuring, and characterization of automated analysis methods for enterprise models, expressing them in a standardized modeling language. In addition, we have implemented the analysis methods in our modeling tool.