WorldWideScience

Sample records for multi-class classification tool

  1. Multi-view Multi-sparsity Kernel Reconstruction for Multi-class Image Classification

    KAUST Repository

    Zhu, Xiaofeng; Xie, Qing; Zhu, Yonghua; Liu, Xingyi; Zhang, Shichao

    2015-01-01

    This paper addresses the problem of multi-class image classification by proposing a novel multi-view multi-sparsity kernel reconstruction (MMKR for short) model. Given images (including test images and training images) representing with multiple

  2. SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition.

    Science.gov (United States)

    Melvin, Iain; Ie, Eugene; Kuang, Rui; Weston, Jason; Stafford, William Noble; Leslie, Christina

    2007-05-22

    Predicting a protein's structural class from its amino acid sequence is a fundamental problem in computational biology. Much recent work has focused on developing new representations for protein sequences, called string kernels, for use with support vector machine (SVM) classifiers. However, while some of these approaches exhibit state-of-the-art performance at the binary protein classification problem, i.e. discriminating between a particular protein class and all other classes, few of these studies have addressed the real problem of multi-class superfamily or fold recognition. Moreover, there are only limited software tools and systems for SVM-based protein classification available to the bioinformatics community. We present a new multi-class SVM-based protein fold and superfamily recognition system and web server called SVM-Fold, which can be found at http://svm-fold.c2b2.columbia.edu. Our system uses an efficient implementation of a state-of-the-art string kernel for sequence profiles, called the profile kernel, where the underlying feature representation is a histogram of inexact matching k-mer frequencies. We also employ a novel machine learning approach to solve the difficult multi-class problem of classifying a sequence of amino acids into one of many known protein structural classes. Binary one-vs-the-rest SVM classifiers that are trained to recognize individual structural classes yield prediction scores that are not comparable, so that standard "one-vs-all" classification fails to perform well. Moreover, SVMs for classes at different levels of the protein structural hierarchy may make useful predictions, but one-vs-all does not try to combine these multiple predictions. To deal with these problems, our method learns relative weights between one-vs-the-rest classifiers and encodes information about the protein structural hierarchy for multi-class prediction. In large-scale benchmark results based on the SCOP database, our code weighting approach

  3. Binary Stochastic Representations for Large Multi-class Classification

    KAUST Repository

    Gerald, Thomas; Baskiotis, Nicolas; Denoyer, Ludovic

    2017-01-01

    Classification with a large number of classes is a key problem in machine learning and corresponds to many real-world applications like tagging of images or textual documents in social networks. If one-vs-all methods usually reach top performance

  4. Multi-class Mode of Action Classification of Toxic Compounds Using Logic Based Kernel Methods.

    Science.gov (United States)

    Lodhi, Huma; Muggleton, Stephen; Sternberg, Mike J E

    2010-09-17

    Toxicity prediction is essential for drug design and development of effective therapeutics. In this paper we present an in silico strategy, to identify the mode of action of toxic compounds, that is based on the use of a novel logic based kernel method. The technique uses support vector machines in conjunction with the kernels constructed from first order rules induced by an Inductive Logic Programming system. It constructs multi-class models by using a divide and conquer reduction strategy that splits multi-classes into binary groups and solves each individual problem recursively hence generating an underlying decision list structure. In order to evaluate the effectiveness of the approach for chemoinformatics problems like predictive toxicology, we apply it to toxicity classification in aquatic systems. The method is used to identify and classify 442 compounds with respect to the mode of action. The experimental results show that the technique successfully classifies toxic compounds and can be useful in assessing environmental risks. Experimental comparison of the performance of the proposed multi-class scheme with the standard multi-class Inductive Logic Programming algorithm and multi-class Support Vector Machine yields statistically significant results and demonstrates the potential power and benefits of the approach in identifying compounds of various toxic mechanisms. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  5. Binary Stochastic Representations for Large Multi-class Classification

    KAUST Repository

    Gerald, Thomas

    2017-10-23

    Classification with a large number of classes is a key problem in machine learning and corresponds to many real-world applications like tagging of images or textual documents in social networks. If one-vs-all methods usually reach top performance in this context, these approaches suffer of a high inference complexity, linear w.r.t. the number of categories. Different models based on the notion of binary codes have been proposed to overcome this limitation, achieving in a sublinear inference complexity. But they a priori need to decide which binary code to associate to which category before learning using more or less complex heuristics. We propose a new end-to-end model which aims at simultaneously learning to associate binary codes with categories, but also learning to map inputs to binary codes. This approach called Deep Stochastic Neural Codes (DSNC) keeps the sublinear inference complexity but do not need any a priori tuning. Experimental results on different datasets show the effectiveness of the approach w.r.t. baseline methods.

  6. Multi-view Multi-sparsity Kernel Reconstruction for Multi-class Image Classification

    KAUST Repository

    Zhu, Xiaofeng

    2015-05-28

    This paper addresses the problem of multi-class image classification by proposing a novel multi-view multi-sparsity kernel reconstruction (MMKR for short) model. Given images (including test images and training images) representing with multiple visual features, the MMKR first maps them into a high-dimensional space, e.g., a reproducing kernel Hilbert space (RKHS), where test images are then linearly reconstructed by some representative training images, rather than all of them. Furthermore a classification rule is proposed to classify test images. Experimental results on real datasets show the effectiveness of the proposed MMKR while comparing to state-of-the-art algorithms.

  7. Vision based nutrient deficiency classification in maize plants using multi class support vector machines

    Science.gov (United States)

    Leena, N.; Saju, K. K.

    2018-04-01

    Nutritional deficiencies in plants are a major concern for farmers as it affects productivity and thus profit. The work aims to classify nutritional deficiencies in maize plant in a non-destructive mannerusing image processing and machine learning techniques. The colored images of the leaves are analyzed and classified with multi-class support vector machine (SVM) method. Several images of maize leaves with known deficiencies like nitrogen, phosphorous and potassium (NPK) are used to train the SVM classifier prior to the classification of test images. The results show that the method was able to classify and identify nutritional deficiencies.

  8. Multi-class parkinsonian disorders classification with quantitative MR markers and graph-based features using support vector machines.

    Science.gov (United States)

    Morisi, Rita; Manners, David Neil; Gnecco, Giorgio; Lanconelli, Nico; Testa, Claudia; Evangelisti, Stefania; Talozzi, Lia; Gramegna, Laura Ludovica; Bianchini, Claudio; Calandra-Buonaura, Giovanna; Sambati, Luisa; Giannini, Giulia; Cortelli, Pietro; Tonon, Caterina; Lodi, Raffaele

    2018-02-01

    In this study we attempt to automatically classify individual patients with different parkinsonian disorders, making use of pattern recognition techniques to distinguish among several forms of parkinsonisms (multi-class classification), based on a set of binary classifiers that discriminate each disorder from all others. We combine diffusion tensor imaging, proton spectroscopy and morphometric-volumetric data to obtain MR quantitative markers, which are provided to support vector machines with the aim of recognizing the different parkinsonian disorders. Feature selection is used to find the most important features for classification. We also exploit a graph-based technique on the set of quantitative markers to extract additional features from the dataset, and increase classification accuracy. When graph-based features are not used, the MR markers that are most frequently automatically extracted by the feature selection procedure reflect alterations in brain regions that are also usually considered to discriminate parkinsonisms in routine clinical practice. Graph-derived features typically increase the diagnostic accuracy, and reduce the number of features required. The results obtained in the work demonstrate that support vector machines applied to multimodal brain MR imaging and using graph-based features represent a novel and highly accurate approach to discriminate parkinsonisms, and a useful tool to assist the diagnosis. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. EEG classification for motor imagery and resting state in BCI applications using multi-class Adaboost extreme learning machine

    Science.gov (United States)

    Gao, Lin; Cheng, Wei; Zhang, Jinhua; Wang, Jue

    2016-08-01

    Brain-computer interface (BCI) systems provide an alternative communication and control approach for people with limited motor function. Therefore, the feature extraction and classification approach should differentiate the relative unusual state of motion intention from a common resting state. In this paper, we sought a novel approach for multi-class classification in BCI applications. We collected electroencephalographic (EEG) signals registered by electrodes placed over the scalp during left hand motor imagery, right hand motor imagery, and resting state for ten healthy human subjects. We proposed using the Kolmogorov complexity (Kc) for feature extraction and a multi-class Adaboost classifier with extreme learning machine as base classifier for classification, in order to classify the three-class EEG samples. An average classification accuracy of 79.5% was obtained for ten subjects, which greatly outperformed commonly used approaches. Thus, it is concluded that the proposed method could improve the performance for classification of motor imagery tasks for multi-class samples. It could be applied in further studies to generate the control commands to initiate the movement of a robotic exoskeleton or orthosis, which finally facilitates the rehabilitation of disabled people.

  10. Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines.

    Science.gov (United States)

    Lajnef, Tarek; Chaibi, Sahbi; Ruby, Perrine; Aguera, Pierre-Emmanuel; Eichenlaub, Jean-Baptiste; Samet, Mounir; Kachouri, Abdennaceur; Jerbi, Karim

    2015-07-30

    Sleep staging is a critical step in a range of electrophysiological signal processing pipelines used in clinical routine as well as in sleep research. Although the results currently achievable with automatic sleep staging methods are promising, there is need for improvement, especially given the time-consuming and tedious nature of visual sleep scoring. Here we propose a sleep staging framework that consists of a multi-class support vector machine (SVM) classification based on a decision tree approach. The performance of the method was evaluated using polysomnographic data from 15 subjects (electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) recordings). The decision tree, or dendrogram, was obtained using a hierarchical clustering technique and a wide range of time and frequency-domain features were extracted. Feature selection was carried out using forward sequential selection and classification was evaluated using k-fold cross-validation. The dendrogram-based SVM (DSVM) achieved mean specificity, sensitivity and overall accuracy of 0.92, 0.74 and 0.88 respectively, compared to expert visual scoring. Restricting DSVM classification to data where both experts' scoring was consistent (76.73% of the data) led to a mean specificity, sensitivity and overall accuracy of 0.94, 0.82 and 0.92 respectively. The DSVM framework outperforms classification with more standard multi-class "one-against-all" SVM and linear-discriminant analysis. The promising results of the proposed methodology suggest that it may be a valuable alternative to existing automatic methods and that it could accelerate visual scoring by providing a robust starting hypnogram that can be further fine-tuned by expert inspection. Copyright © 2015 Elsevier B.V. All rights reserved.

  11. Multi-class machine classification of suicide-related communication on Twitter.

    Science.gov (United States)

    Burnap, Pete; Colombo, Gualtiero; Amery, Rosie; Hodorog, Andrei; Scourfield, Jonathan

    2017-08-01

    The World Wide Web, and online social networks in particular, have increased connectivity between people such that information can spread to millions of people in a matter of minutes. This form of online collective contagion has provided many benefits to society, such as providing reassurance and emergency management in the immediate aftermath of natural disasters. However, it also poses a potential risk to vulnerable Web users who receive this information and could subsequently come to harm. One example of this would be the spread of suicidal ideation in online social networks, about which concerns have been raised. In this paper we report the results of a number of machine classifiers built with the aim of classifying text relating to suicide on Twitter. The classifier distinguishes between the more worrying content, such as suicidal ideation, and other suicide-related topics such as reporting of a suicide, memorial, campaigning and support. It also aims to identify flippant references to suicide. We built a set of baseline classifiers using lexical, structural, emotive and psychological features extracted from Twitter posts. We then improved on the baseline classifiers by building an ensemble classifier using the Rotation Forest algorithm and a Maximum Probability voting classification decision method, based on the outcome of base classifiers. This achieved an F-measure of 0.728 overall (for 7 classes, including suicidal ideation) and 0.69 for the suicidal ideation class. We summarise the results by reflecting on the most significant predictive principle components of the suicidal ideation class to provide insight into the language used on Twitter to express suicidal ideation. Finally, we perform a 12-month case study of suicide-related posts where we further evaluate the classification approach - showing a sustained classification performance and providing anonymous insights into the trends and demographic profile of Twitter users posting content of this type.

  12. A Multiagent-based Intrusion Detection System with the Support of Multi-Class Supervised Classification

    Science.gov (United States)

    Shyu, Mei-Ling; Sainani, Varsha

    The increasing number of network security related incidents have made it necessary for the organizations to actively protect their sensitive data with network intrusion detection systems (IDSs). IDSs are expected to analyze a large volume of data while not placing a significantly added load on the monitoring systems and networks. This requires good data mining strategies which take less time and give accurate results. In this study, a novel data mining assisted multiagent-based intrusion detection system (DMAS-IDS) is proposed, particularly with the support of multiclass supervised classification. These agents can detect and take predefined actions against malicious activities, and data mining techniques can help detect them. Our proposed DMAS-IDS shows superior performance compared to central sniffing IDS techniques, and saves network resources compared to other distributed IDS with mobile agents that activate too many sniffers causing bottlenecks in the network. This is one of the major motivations to use a distributed model based on multiagent platform along with a supervised classification technique.

  13. FACET CLASSIFICATIONS OF E-LEARNING TOOLS

    Directory of Open Access Journals (Sweden)

    Olena Yu. Balalaieva

    2013-12-01

    Full Text Available The article deals with the classification of e-learning tools based on the facet method, which suggests the separation of the parallel set of objects into independent classification groups; at the same time it is not assumed rigid classification structure and pre-built finite groups classification groups are formed by a combination of values taken from the relevant facets. An attempt to systematize the existing classification of e-learning tools from the standpoint of classification theory is made for the first time. Modern Ukrainian and foreign facet classifications of e-learning tools are described; their positive and negative features compared to classifications based on a hierarchical method are analyzed. The original author's facet classification of e-learning tools is proposed.

  14. 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.

  15. E-LEARNING TOOLS: STRUCTURE, CONTENT, CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    Yuliya H. Loboda

    2012-05-01

    Full Text Available The article analyses the problems of organization of educational process with use of electronic means of education. Specifies the definition of "electronic learning", their structure and content. Didactic principles are considered, which are the basis of their creation and use. Given the detailed characteristics of e-learning tools for methodological purposes. On the basis of the allocated pedagogical problems of the use of electronic means of education presented and complemented by their classification, namely the means of theoretical and technological training, means of practical training, support tools, and comprehensive facilities.

  16. Assessment of multi class kinematic wave models

    NARCIS (Netherlands)

    Van Wageningen-Kessels, F.L.M.; Van Lint, J.W.C.; Vuik, C.; Hoogendoorn, S.P.

    2012-01-01

    In the last decade many multi class kinematic wave (MCKW) traffic ow models have been proposed. MCKW models introduce heterogeneity among vehicles and drivers. For example, they take into account differences in (maximum) velocities and driving style. Nevertheless, the models are macroscopic and the

  17. Multi-Class Classification for Identifying JPEG Steganography Embedding Methods

    Science.gov (United States)

    2008-09-01

    digital pictures on Web sites or sending them through email (Astrowsky, 2000). Steganography may also be used to allow communication between affiliates...B.H. (2000). STEGANOGRAPHY: Hidden Images, A New Challenge in the Fight Against Child Porn . UPDATE, Volume 13, Number 2, pp. 1-4, Retrieved June 3

  18. A multi-class classification MCLP model with particle swarm ...

    Indian Academy of Sciences (India)

    A M Viswa Bharathy

    clearly show that the proposed model performs better in terms of detection rate, false .... ease the process of target recognition and detection in ... They performed packet-level simulation analysis in ns-2 ... validated using CPLEX and MATLAB.

  19. Acute pesticide poisoning: a proposed classification tool.

    Science.gov (United States)

    Thundiyil, Josef G; Stober, Judy; Besbelli, Nida; Pronczuk, Jenny

    2008-03-01

    Cases of acute pesticide poisoning (APP) account for significant morbidity and mortality worldwide. Developing countries are particularly susceptible due to poorer regulation, lack of surveillance systems, less enforcement, lack of training and inadequate access to information systems. Previous research has demonstrated wide variability in incidence rates for APP. This is possibly due to inconsistent reporting methodology and exclusion of occupational and non-intentional poisonings. The purpose of this document is to create a standard case definition to facilitate the identification and diagnosis of all causes of APP, especially at the field level, rural clinics and primary health-care systems. This document is a synthesis of existing literature and case definitions that have been previously proposed by other authors around the world. It provides a standardized case definition and classification scheme for APP into categories of probable, possible and unlikely/unknown cases. Its use is intended to be applicable worldwide to contribute to identification of the scope of existing problems and thus promote action for improved management and prevention. By enabling a field diagnosis for APP, this standardized case definition may facilitate immediate medical management of pesticide poisoning and aid in estimating its incidence.

  20. Ichthyoplankton Classification Tool using Generative Adversarial Networks and Transfer Learning

    KAUST Repository

    Aljaafari, Nura

    2018-01-01

    . This method is time-consuming and requires a high level of experience. The recent advances in AI have helped to solve and automate several difficult tasks which motivated us to develop a classification tool for ichthyoplankton. We show that using machine

  1. Ichthyoplankton Classification Tool using Generative Adversarial Networks and Transfer Learning

    KAUST Repository

    Aljaafari, Nura

    2018-04-15

    The study and the analysis of marine ecosystems is a significant part of the marine science research. These systems are valuable resources for fisheries, improving water quality and can even be used in drugs production. The investigation of ichthyoplankton inhabiting these ecosystems is also an important research field. Ichthyoplankton are fish in their early stages of life. In this stage, the fish have relatively similar shape and are small in size. The currently used way of identifying them is not optimal. Marine scientists typically study such organisms by sending a team that collects samples from the sea which is then taken to the lab for further investigation. These samples need to be studied by an expert and usually end needing a DNA sequencing. This method is time-consuming and requires a high level of experience. The recent advances in AI have helped to solve and automate several difficult tasks which motivated us to develop a classification tool for ichthyoplankton. We show that using machine learning techniques, such as generative adversarial networks combined with transfer learning solves such a problem with high accuracy. We show that using traditional machine learning algorithms fails to solve it. We also give a general framework for creating a classification tool when the dataset used for training is a limited dataset. We aim to build a user-friendly tool that can be used by any user for the classification task and we aim to give a guide to the researchers so that they can follow in creating a classification tool.

  2. Distributed optimization of multi-class SVMs.

    Directory of Open Access Journals (Sweden)

    Maximilian Alber

    Full Text Available Training of one-vs.-rest SVMs can be parallelized over the number of classes in a straight forward way. Given enough computational resources, one-vs.-rest SVMs can thus be trained on data involving a large number of classes. The same cannot be stated, however, for the so-called all-in-one SVMs, which require solving a quadratic program of size quadratically in the number of classes. We develop distributed algorithms for two all-in-one SVM formulations (Lee et al. and Weston and Watkins that parallelize the computation evenly over the number of classes. This allows us to compare these models to one-vs.-rest SVMs on unprecedented scale. The results indicate superior accuracy on text classification data.

  3. Moving research tools into practice: the successes and challenges in promoting uptake of classification tools.

    Science.gov (United States)

    Cunningham, Barbara Jane; Hidecker, Mary Jo Cooley; Thomas-Stonell, Nancy; Rosenbaum, Peter

    2018-05-01

    In this paper, we present our experiences - both successes and challenges - in implementing evidence-based classification tools into clinical practice. We also make recommendations for others wanting to promote the uptake and application of new research-based assessment tools. We first describe classification systems and the benefits of using them in both research and practice. We then present a theoretical framework from Implementation Science to report strategies we have used to implement two research-based classification tools into practice. We also illustrate some of the challenges we have encountered by reporting results from an online survey investigating 58 Speech-language Pathologists' knowledge and use of the Communication Function Classification System (CFCS), a new tool to classify children's functional communication skills. We offer recommendations for researchers wanting to promote the uptake of new tools in clinical practice. Specifically, we identify structural, organizational, innovation, practitioner, and patient-related factors that we recommend researchers address in the design of implementation interventions. Roles and responsibilities of both researchers and clinicians in making implementations science a success are presented. Implications for rehabilitation Promoting uptake of new and evidence-based tools into clinical practice is challenging. Implementation science can help researchers to close the knowledge-to-practice gap. Using concrete examples, we discuss our experiences in implementing evidence-based classification tools into practice within a theoretical framework. Recommendations are provided for researchers wanting to implement new tools in clinical practice. Implications for researchers and clinicians are presented.

  4. Multi-class oscillating systems of interacting neurons

    DEFF Research Database (Denmark)

    Ditlevsen, Susanne; Löcherbach, Eva

    2017-01-01

    We consider multi-class systems of interacting nonlinear Hawkes processes modeling several large families of neurons and study their mean field limits. As the total number of neurons goes to infinity we prove that the evolution within each class can be described by a nonlinear limit differential...

  5. A Multi-Class, Interdisciplinary Project Using Elementary Statistics

    Science.gov (United States)

    Reese, Margaret

    2012-01-01

    This article describes a multi-class project that employs statistical computing and writing in a statistics class. Three courses, General Ecology, Meteorology, and Introductory Statistics, cooperated on a project for the EPA's Student Design Competition. The continuing investigation has also spawned several undergraduate research projects in…

  6. Approximations for Markovian multi-class queues with preemptive priorities

    NARCIS (Netherlands)

    van der Heijden, Matthijs C.; van Harten, Aart; Sleptchenko, Andrei

    2004-01-01

    We discuss the approximation of performance measures in multi-class M/M/k queues with preemptive priorities for large problem instances (many classes and servers) using class aggregation and server reduction. We compared our approximations to exact and simulation results and found that our approach

  7. A fast learning method for large scale and multi-class samples of SVM

    Science.gov (United States)

    Fan, Yu; Guo, Huiming

    2017-06-01

    A multi-class classification SVM(Support Vector Machine) fast learning method based on binary tree is presented to solve its low learning efficiency when SVM processing large scale multi-class samples. This paper adopts bottom-up method to set up binary tree hierarchy structure, according to achieved hierarchy structure, sub-classifier learns from corresponding samples of each node. During the learning, several class clusters are generated after the first clustering of the training samples. Firstly, central points are extracted from those class clusters which just have one type of samples. For those which have two types of samples, cluster numbers of their positive and negative samples are set respectively according to their mixture degree, secondary clustering undertaken afterwards, after which, central points are extracted from achieved sub-class clusters. By learning from the reduced samples formed by the integration of extracted central points above, sub-classifiers are obtained. Simulation experiment shows that, this fast learning method, which is based on multi-level clustering, can guarantee higher classification accuracy, greatly reduce sample numbers and effectively improve learning efficiency.

  8. Automatic Parallelization Tool: Classification of Program Code for Parallel Computing

    Directory of Open Access Journals (Sweden)

    Mustafa Basthikodi

    2016-04-01

    Full Text Available Performance growth of single-core processors has come to a halt in the past decade, but was re-enabled by the introduction of parallelism in processors. Multicore frameworks along with Graphical Processing Units empowered to enhance parallelism broadly. Couples of compilers are updated to developing challenges forsynchronization and threading issues. Appropriate program and algorithm classifications will have advantage to a great extent to the group of software engineers to get opportunities for effective parallelization. In present work we investigated current species for classification of algorithms, in that related work on classification is discussed along with the comparison of issues that challenges the classification. The set of algorithms are chosen which matches the structure with different issues and perform given task. We have tested these algorithms utilizing existing automatic species extraction toolsalong with Bones compiler. We have added functionalities to existing tool, providing a more detailed characterization. The contributions of our work include support for pointer arithmetic, conditional and incremental statements, user defined types, constants and mathematical functions. With this, we can retain significant data which is not captured by original speciesof algorithms. We executed new theories into the device, empowering automatic characterization of program code.

  9. New decision support tool for acute lymphoblastic leukemia classification

    Science.gov (United States)

    Madhukar, Monica; Agaian, Sos; Chronopoulos, Anthony T.

    2012-03-01

    In this paper, we build up a new decision support tool to improve treatment intensity choice in childhood ALL. The developed system includes different methods to accurately measure furthermore cell properties in microscope blood film images. The blood images are exposed to series of pre-processing steps which include color correlation, and contrast enhancement. By performing K-means clustering on the resultant images, the nuclei of the cells under consideration are obtained. Shape features and texture features are then extracted for classification. The system is further tested on the classification of spectra measured from the cell nuclei in blood samples in order to distinguish normal cells from those affected by Acute Lymphoblastic Leukemia. The results show that the proposed system robustly segments and classifies acute lymphoblastic leukemia based on complete microscopic blood images.

  10. Automatic SLEEP staging: From young aduslts to elderly patients using multi-class support vector machine

    DEFF Research Database (Denmark)

    Kempfner, Jacob; Jennum, Poul; Sorensen, Helge B. D.

    2013-01-01

    an automatic sleep stage detector, which can separate wakefulness, rapid-eye-movement (REM) sleep and non-REM (NREM) sleep using only EEG and EOG. Most sleep events, which define the sleep stages, are reduced with age. This is addressed by focusing on the amplitude of the clinical EEG bands......Aging is a process that is inevitable, and makes our body vulnerable to age-related diseases. Age is the most consistent factor affecting the sleep structure. Therefore, new automatic sleep staging methods, to be used in both of young and elderly patients, are needed. This study proposes......, and not the affected sleep events. The age-related influences are then reduced by robust subject-specific scaling. The classification of the three sleep stages are achieved by a multi-class support vector machine using the one-versus-rest scheme. It was possible to obtain a high classification accuracy of 0...

  11. Intrusion detection model using fusion of chi-square feature selection and multi class SVM

    Directory of Open Access Journals (Sweden)

    Ikram Sumaiya Thaseen

    2017-10-01

    Full Text Available Intrusion detection is a promising area of research in the domain of security with the rapid development of internet in everyday life. Many intrusion detection systems (IDS employ a sole classifier algorithm for classifying network traffic as normal or abnormal. Due to the large amount of data, these sole classifier models fail to achieve a high attack detection rate with reduced false alarm rate. However by applying dimensionality reduction, data can be efficiently reduced to an optimal set of attributes without loss of information and then classified accurately using a multi class modeling technique for identifying the different network attacks. In this paper, we propose an intrusion detection model using chi-square feature selection and multi class support vector machine (SVM. A parameter tuning technique is adopted for optimization of Radial Basis Function kernel parameter namely gamma represented by ‘ϒ’ and over fitting constant ‘C’. These are the two important parameters required for the SVM model. The main idea behind this model is to construct a multi class SVM which has not been adopted for IDS so far to decrease the training and testing time and increase the individual classification accuracy of the network attacks. The investigational results on NSL-KDD dataset which is an enhanced version of KDDCup 1999 dataset shows that our proposed approach results in a better detection rate and reduced false alarm rate. An experimentation on the computational time required for training and testing is also carried out for usage in time critical applications.

  12. Action Recognition Using 3D Histograms of Texture and A Multi-Class Boosting Classifier.

    Science.gov (United States)

    Zhang, Baochang; Yang, Yun; Chen, Chen; Yang, Linlin; Han, Jungong; Shao, Ling

    2017-10-01

    Human action recognition is an important yet challenging task. This paper presents a low-cost descriptor called 3D histograms of texture (3DHoTs) to extract discriminant features from a sequence of depth maps. 3DHoTs are derived from projecting depth frames onto three orthogonal Cartesian planes, i.e., the frontal, side, and top planes, and thus compactly characterize the salient information of a specific action, on which texture features are calculated to represent the action. Besides this fast feature descriptor, a new multi-class boosting classifier (MBC) is also proposed to efficiently exploit different kinds of features in a unified framework for action classification. Compared with the existing boosting frameworks, we add a new multi-class constraint into the objective function, which helps to maintain a better margin distribution by maximizing the mean of margin, whereas still minimizing the variance of margin. Experiments on the MSRAction3D, MSRGesture3D, MSRActivity3D, and UTD-MHAD data sets demonstrate that the proposed system combining 3DHoTs and MBC is superior to the state of the art.

  13. Classification and optimization of training tools for NPP simulator

    International Nuclear Information System (INIS)

    Billoen, G. van

    1994-01-01

    The training cycle of nuclear power plant (NPP) operators has evolved during the last decade in parallel with the evolution of the training tools. The phases of the training cycle can be summarized as follows: (1) basic principle learning, (2) specific functional training, (3) full operating range training, and (4) detailed accident analyses. The progress in simulation technology and man/machine interface (MMI) gives the training centers new opportunities to improve their training methods and effectiveness in the transfer of knowledge. To take advantage of these new opportunities a significant investment in simulation tools may be required. It is therefore important to propose an optimized approach when dealing with the overall equipment program for these training centers. An overall look of tools proposed on the international simulation market shows that there is a need for systematic approach in this field. Classification of the different training tools needed for each training cycle is the basis for an optimized approach in terms of hardware configuration and software specifications of the equipment to install in training centers. The 'Multi-Function Simulator' is one of the approaches. (orig.) (3 tabs.)

  14. Computationally efficient SVM multi-class image recognition with confidence measures

    International Nuclear Information System (INIS)

    Makili, Lazaro; Vega, Jesus; Dormido-Canto, Sebastian; Pastor, Ignacio; Murari, Andrea

    2011-01-01

    Typically, machine learning methods produce non-qualified estimates, i.e. the accuracy and reliability of the predictions are not provided. Transductive predictors are very recent classifiers able to provide, simultaneously with the prediction, a couple of values (confidence and credibility) to reflect the quality of the prediction. Usually, a drawback of the transductive techniques for huge datasets and large dimensionality is the high computational time. To overcome this issue, a more efficient classifier has been used in a multi-class image classification problem in the TJ-II stellarator database. It is based on the creation of a hash function to generate several 'one versus the rest' classifiers for every class. By using Support Vector Machines as the underlying classifier, a comparison between the pure transductive approach and the new method has been performed. In both cases, the success rates are high and the computation time with the new method is up to 0.4 times the old one.

  15. Ingenious Snake: An Adaptive Multi-Class Contours Extraction

    Science.gov (United States)

    Li, Baolin; Zhou, Shoujun

    2018-04-01

    Active contour model (ACM) plays an important role in computer vision and medical image application. The traditional ACMs were used to extract single-class of object contours. While, simultaneous extraction of multi-class of interesting contours (i.e., various contours with closed- or open-ended) have not been solved so far. Therefore, a novel ACM model named “Ingenious Snake” is proposed to adaptively extract these interesting contours. In the first place, the ridge-points are extracted based on the local phase measurement of gradient vector flow field; the consequential ridgelines initialization are automated with high speed. Secondly, the contours’ deformation and evolvement are implemented with the ingenious snake. In the experiments, the result from initialization, deformation and evolvement are compared with the existing methods. The quantitative evaluation of the structure extraction is satisfying with respect of effectiveness and accuracy.

  16. Multi-Class Motor Imagery EEG Decoding for Brain-Computer Interfaces

    Science.gov (United States)

    Wang, Deng; Miao, Duoqian; Blohm, Gunnar

    2012-01-01

    Recent studies show that scalp electroencephalography (EEG) as a non-invasive interface has great potential for brain-computer interfaces (BCIs). However, one factor that has limited practical applications for EEG-based BCI so far is the difficulty to decode brain signals in a reliable and efficient way. This paper proposes a new robust processing framework for decoding of multi-class motor imagery (MI) that is based on five main processing steps. (i) Raw EEG segmentation without the need of visual artifact inspection. (ii) Considering that EEG recordings are often contaminated not just by electrooculography (EOG) but also other types of artifacts, we propose to first implement an automatic artifact correction method that combines regression analysis with independent component analysis for recovering the original source signals. (iii) The significant difference between frequency components based on event-related (de-) synchronization and sample entropy is then used to find non-contiguous discriminating rhythms. After spectral filtering using the discriminating rhythms, a channel selection algorithm is used to select only relevant channels. (iv) Feature vectors are extracted based on the inter-class diversity and time-varying dynamic characteristics of the signals. (v) Finally, a support vector machine is employed for four-class classification. We tested our proposed algorithm on experimental data that was obtained from dataset 2a of BCI competition IV (2008). The overall four-class kappa values (between 0.41 and 0.80) were comparable to other models but without requiring any artifact-contaminated trial removal. The performance showed that multi-class MI tasks can be reliably discriminated using artifact-contaminated EEG recordings from a few channels. This may be a promising avenue for online robust EEG-based BCI applications. PMID:23087607

  17. Independent Comparison of Popular DPI Tools for Traffic Classification

    DEFF Research Database (Denmark)

    Bujlow, Tomasz; Carela-Español, Valentín; Barlet-Ros, Pere

    2015-01-01

    Deep Packet Inspection (DPI) is the state-of-the-art technology for traffic classification. According to the conventional wisdom, DPI is the most accurate classification technique. Consequently, most popular products, either commercial or open-source, rely on some sort of DPI for traffic classifi......Deep Packet Inspection (DPI) is the state-of-the-art technology for traffic classification. According to the conventional wisdom, DPI is the most accurate classification technique. Consequently, most popular products, either commercial or open-source, rely on some sort of DPI for traffic......, application and web service). We carefully built a labeled dataset with more than 750K flows, which contains traffic from popular applications. We used the Volunteer-Based System (VBS), developed at Aalborg University, to guarantee the correct labeling of the dataset. We released this dataset, including full...

  18. A Pareto-based Ensemble with Feature and Instance Selection for Learning from Multi-Class Imbalanced Datasets.

    Science.gov (United States)

    Fernández, Alberto; Carmona, Cristobal José; José Del Jesus, María; Herrera, Francisco

    2017-09-01

    Imbalanced classification is related to those problems that have an uneven distribution among classes. In addition to the former, when instances are located into the overlapped areas, the correct modeling of the problem becomes harder. Current solutions for both issues are often focused on the binary case study, as multi-class datasets require an additional effort to be addressed. In this research, we overcome these problems by carrying out a combination between feature and instance selections. Feature selection will allow simplifying the overlapping areas easing the generation of rules to distinguish among the classes. Selection of instances from all classes will address the imbalance itself by finding the most appropriate class distribution for the learning task, as well as possibly removing noise and difficult borderline examples. For the sake of obtaining an optimal joint set of features and instances, we embedded the searching for both parameters in a Multi-Objective Evolutionary Algorithm, using the C4.5 decision tree as baseline classifier in this wrapper approach. The multi-objective scheme allows taking a double advantage: the search space becomes broader, and we may provide a set of different solutions in order to build an ensemble of classifiers. This proposal has been contrasted versus several state-of-the-art solutions on imbalanced classification showing excellent results in both binary and multi-class problems.

  19. An intelligent condition monitoring system for on-line classification of machine tool wear

    Energy Technology Data Exchange (ETDEWEB)

    Pan, Fu; Hope, A D; Javed, M [Systems Engineering Faculty, Southampton Institute (United Kingdom)

    1998-12-31

    The development of intelligent tool condition monitoring systems is a necessary requirement for successful automation of manufacturing processes. This presentation introduces a tool wear monitoring system for milling operations. The system utilizes power, force, acoustic emission and vibration sensors to monitor tool condition comprehensively. Features relevant to tool wear are drawn from time and frequency domain signals and a fuzzy pattern recognition technique is applied to combine the multisensor information and provide reliable classification results of tool wear states. (orig.) 10 refs.

  20. An intelligent condition monitoring system for on-line classification of machine tool wear

    Energy Technology Data Exchange (ETDEWEB)

    Fu Pan; Hope, A.D.; Javed, M. [Systems Engineering Faculty, Southampton Institute (United Kingdom)

    1997-12-31

    The development of intelligent tool condition monitoring systems is a necessary requirement for successful automation of manufacturing processes. This presentation introduces a tool wear monitoring system for milling operations. The system utilizes power, force, acoustic emission and vibration sensors to monitor tool condition comprehensively. Features relevant to tool wear are drawn from time and frequency domain signals and a fuzzy pattern recognition technique is applied to combine the multisensor information and provide reliable classification results of tool wear states. (orig.) 10 refs.

  1. The Pattern Recognition in Cattle Brand using Bag of Visual Words and Support Vector Machines Multi-Class

    Directory of Open Access Journals (Sweden)

    Carlos Silva, Mr

    2018-03-01

    Full Text Available The recognition images of cattle brand in an automatic way is a necessity to governmental organs responsible for this activity. To help this process, this work presents a method that consists in using Bag of Visual Words for extracting of characteristics from images of cattle brand and Support Vector Machines Multi-Class for classification. This method consists of six stages: a select database of images; b extract points of interest (SURF; c create vocabulary (K-means; d create vector of image characteristics (visual words; e train and sort images (SVM; f evaluate the classification results. The accuracy of the method was tested on database of municipal city hall, where it achieved satisfactory results, reporting 86.02% of accuracy and 56.705 seconds of processing time, respectively.

  2. Performance of rapid subtyping tools used for the classification of ...

    African Journals Online (AJOL)

    HIV-1 genetic diversity in sub-Saharan Africa is broad and the AIDS epidemic is driven predominantly by recombinants in Central and West Africa. The classification of HIV-1 strains is therefore necessary to understand diagnostic efficiency, individual treatment responses as well as options for designing vaccines and ...

  3. Case based reasoning applied to medical diagnosis using multi-class classifier: A preliminary study

    Directory of Open Access Journals (Sweden)

    D. Viveros-Melo

    2017-02-01

    Full Text Available Case-based reasoning (CBR is a process used for computer processing that tries to mimic the behavior of a human expert in making decisions regarding a subject and learn from the experience of past cases. CBR has demonstrated to be appropriate for working with unstructured domains data or difficult knowledge acquisition situations, such as medical diagnosis, where it is possible to identify diseases such as: cancer diagnosis, epilepsy prediction and appendicitis diagnosis. Some of the trends that may be developed for CBR in the health science are oriented to reduce the number of features in highly dimensional data. An important contribution may be the estimation of probabilities of belonging to each class for new cases. In this paper, in order to adequately represent the database and to avoid the inconveniences caused by the high dimensionality, noise and redundancy, a number of algorithms are used in the preprocessing stage for performing both variable selection and dimension reduction procedures. Also, a comparison of the performance of some representative multi-class classifiers is carried out to identify the most effective one to include within a CBR scheme. Particularly, four classification techniques and two reduction techniques are employed to make a comparative study of multiclass classifiers on CBR

  4. Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery

    Science.gov (United States)

    Zhong, Yanfei; Han, Xiaobing; Zhang, Liangpei

    2018-04-01

    Multi-class geospatial object detection from high spatial resolution (HSR) remote sensing imagery is attracting increasing attention in a wide range of object-related civil and engineering applications. However, the distribution of objects in HSR remote sensing imagery is location-variable and complicated, and how to accurately detect the objects in HSR remote sensing imagery is a critical problem. Due to the powerful feature extraction and representation capability of deep learning, the deep learning based region proposal generation and object detection integrated framework has greatly promoted the performance of multi-class geospatial object detection for HSR remote sensing imagery. However, due to the translation caused by the convolution operation in the convolutional neural network (CNN), although the performance of the classification stage is seldom influenced, the localization accuracies of the predicted bounding boxes in the detection stage are easily influenced. The dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage has not been addressed for HSR remote sensing imagery, and causes position accuracy problems for multi-class geospatial object detection with region proposal generation and object detection. In order to further improve the performance of the region proposal generation and object detection integrated framework for HSR remote sensing imagery object detection, a position-sensitive balancing (PSB) framework is proposed in this paper for multi-class geospatial object detection from HSR remote sensing imagery. The proposed PSB framework takes full advantage of the fully convolutional network (FCN), on the basis of a residual network, and adopts the PSB framework to solve the dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage. In addition, a pre-training mechanism is utilized to accelerate the training procedure

  5. Hydrological Classification, a Practical Tool for Mangrove Restoration.

    Science.gov (United States)

    Van Loon, Anne F; Te Brake, Bram; Van Huijgevoort, Marjolein H J; Dijksma, Roel

    2016-01-01

    Mangrove restoration projects, aimed at restoring important values of mangrove forests after degradation, often fail because hydrological conditions are disregarded. We present a simple, but robust methodology to determine hydrological suitability for mangrove species, which can guide restoration practice. In 15 natural and 8 disturbed sites (i.e. disused shrimp ponds) in three case study regions in south-east Asia, water levels were measured and vegetation species composition was determined. Using an existing hydrological classification for mangroves, sites were classified into hydrological classes, based on duration of inundation, and vegetation classes, based on occurrence of mangrove species. For the natural sites hydrological and vegetation classes were similar, showing clear distribution of mangrove species from wet to dry sites. Application of the classification to disturbed sites showed that in some locations hydrological conditions had been restored enough for mangrove vegetation to establish, in some locations hydrological conditions were suitable for various mangrove species but vegetation had not established naturally, and in some locations hydrological conditions were too wet for any mangrove species (natural or planted) to grow. We quantified the effect that removal of obstructions such as dams would have on the hydrology and found that failure of planting at one site could have been prevented. The hydrological classification needs relatively little data, i.e. water levels for a period of only one lunar tidal cycle without additional measurements, and uncertainties in the measurements and analysis are relatively small. For the study locations, the application of the hydrological classification gave important information about how to restore the hydrology to suitable conditions to improve natural regeneration or to plant mangrove species, which could not have been obtained by estimating elevation only. Based on this research a number of recommendations

  6. Support Vector Machine Based Tool for Plant Species Taxonomic Classification

    OpenAIRE

    Manimekalai .K; Vijaya.MS

    2014-01-01

    Plant species are living things and are generally categorized in terms of Domain, Kingdom, Phylum, Class, Order, Family, Genus and name of Species in a hierarchical fashion. This paper formulates the taxonomic leaf categorization problem as the hierarchical classification task and provides a suitable solution using a supervised learning technique namely support vector machine. Features are extracted from scanned images of plant leaves and trained using SVM. Only class, order, family of plants...

  7. Hydrological Classification, a Practical Tool for Mangrove Restoration.

    Directory of Open Access Journals (Sweden)

    Anne F Van Loon

    Full Text Available Mangrove restoration projects, aimed at restoring important values of mangrove forests after degradation, often fail because hydrological conditions are disregarded. We present a simple, but robust methodology to determine hydrological suitability for mangrove species, which can guide restoration practice. In 15 natural and 8 disturbed sites (i.e. disused shrimp ponds in three case study regions in south-east Asia, water levels were measured and vegetation species composition was determined. Using an existing hydrological classification for mangroves, sites were classified into hydrological classes, based on duration of inundation, and vegetation classes, based on occurrence of mangrove species. For the natural sites hydrological and vegetation classes were similar, showing clear distribution of mangrove species from wet to dry sites. Application of the classification to disturbed sites showed that in some locations hydrological conditions had been restored enough for mangrove vegetation to establish, in some locations hydrological conditions were suitable for various mangrove species but vegetation had not established naturally, and in some locations hydrological conditions were too wet for any mangrove species (natural or planted to grow. We quantified the effect that removal of obstructions such as dams would have on the hydrology and found that failure of planting at one site could have been prevented. The hydrological classification needs relatively little data, i.e. water levels for a period of only one lunar tidal cycle without additional measurements, and uncertainties in the measurements and analysis are relatively small. For the study locations, the application of the hydrological classification gave important information about how to restore the hydrology to suitable conditions to improve natural regeneration or to plant mangrove species, which could not have been obtained by estimating elevation only. Based on this research a number

  8. BIOPHARMACEUTICS CLASSIFICATION SYSTEM: A STRATEGIC TOOL FOR CLASSIFYING DRUG SUBSTANCES

    OpenAIRE

    Rohilla Seema; Rohilla Ankur; Marwaha RK; Nanda Arun

    2011-01-01

    The biopharmaceutical classification system (BCS) is a scientific approach for classifying drug substances based on their dose/solubility ratio and intestinal permeability. The BCS has been developed to allow prediction of in vivo pharmacokinetic performance of drug products from measurements of permeability and solubility. Moreover, the drugs can be categorized into four classes of BCS on the basis of permeability and solubility namely; high permeability high solubility, high permeability lo...

  9. U.S. Geological Survey ArcMap Sediment Classification tool

    Science.gov (United States)

    O'Malley, John

    2007-01-01

    The U.S. Geological Survey (USGS) ArcMap Sediment Classification tool is a custom toolbar that extends the Environmental Systems Research Institute, Inc. (ESRI) ArcGIS 9.2 Desktop application to aid in the analysis of seabed sediment classification. The tool uses as input either a point data layer with field attributes containing percentage of gravel, sand, silt, and clay or four raster data layers representing a percentage of sediment (0-100%) for the various sediment grain size analysis: sand, gravel, silt and clay. This tool is designed to analyze the percent of sediment at a given location and classify the sediments according to either the Folk (1954, 1974) or Shepard (1954) as modified by Schlee(1973) classification schemes. The sediment analysis tool is based upon the USGS SEDCLASS program (Poppe, et al. 2004).

  10. A Proposed Functional Abilities Classification Tool for Developmental Disorders Affecting Learning and Behaviour

    Directory of Open Access Journals (Sweden)

    Benjamin Klein

    2018-02-01

    Full Text Available Children with developmental disorders affecting learning and behaviour (DDALB (e.g., attention, social communication, language, and learning disabilities, etc. require individualized support across multiple environments to promote participation, quality of life, and developmental outcomes. Support to enhance participation is based largely on individual profiles of functioning (e.g., communication, cognitive, social skills, executive functioning, etc., which are highly heterogeneous within medical diagnoses. Currently educators, clinicians, and parents encounter widespread difficulties in meeting children’s needs as there is lack of universal classification of functioning and disability for use in school environments. Objective: a practical tool for functional classification broadly applicable for children with DDALB could facilitate the collaboration, identification of points of entry of support, individual program planning, and reassessment in a transparent, equitable process based on functional need and context. We propose such a tool, the Functional Abilities Classification Tool (FACT based on the concepts of the ICF (International Classification of Functioning, Disability and Health. FACT is intended to provide ability and participation classification that is complementary to medical diagnosis. For children presenting with difficulties, the proposed tool initially classifies participation over several environments. Then, functional abilities are classified and personal factors and environment are described. Points of entry for support are identified given an analysis of functional ability profile, personal factors, environmental features, and pattern of participation. Conclusion: case examples, use of the tool and implications for children, agencies, and the system are described.

  11. Comparison of Deep Packet Inspection (DPI) Tools for Traffic Classification

    DEFF Research Database (Denmark)

    Bujlow, Tomasz; Carela-Español, Valentín; Barlet-Ros, Pere

    the researchers do not only have the full payloads, but also they are provided the information which application created the flow. Therefore, the dataset is useful for testing Deep Packet Inspection (DPI) tools, as well as statistical, and port-based classifiers. The dataset was created in a fully manual way...

  12. AutoFACT: An Automatic Functional Annotation and Classification Tool

    Directory of Open Access Journals (Sweden)

    Lang B Franz

    2005-06-01

    Full Text Available Abstract Background Assignment of function to new molecular sequence data is an essential step in genomics projects. The usual process involves similarity searches of a given sequence against one or more databases, an arduous process for large datasets. Results We present AutoFACT, a fully automated and customizable annotation tool that assigns biologically informative functions to a sequence. Key features of this tool are that it (1 analyzes nucleotide and protein sequence data; (2 determines the most informative functional description by combining multiple BLAST reports from several user-selected databases; (3 assigns putative metabolic pathways, functional classes, enzyme classes, GeneOntology terms and locus names; and (4 generates output in HTML, text and GFF formats for the user's convenience. We have compared AutoFACT to four well-established annotation pipelines. The error rate of functional annotation is estimated to be only between 1–2%. Comparison of AutoFACT to the traditional top-BLAST-hit annotation method shows that our procedure increases the number of functionally informative annotations by approximately 50%. Conclusion AutoFACT will serve as a useful annotation tool for smaller sequencing groups lacking dedicated bioinformatics staff. It is implemented in PERL and runs on LINUX/UNIX platforms. AutoFACT is available at http://megasun.bch.umontreal.ca/Software/AutoFACT.htm.

  13. A Novel Approach for Multi Class Fault Diagnosis in Induction Machine Based on Statistical Time Features and Random Forest Classifier

    Science.gov (United States)

    Sonje, M. Deepak; Kundu, P.; Chowdhury, A.

    2017-08-01

    Fault diagnosis and detection is the important area in health monitoring of electrical machines. This paper proposes the recently developed machine learning classifier for multi class fault diagnosis in induction machine. The classification is based on random forest (RF) algorithm. Initially, stator currents are acquired from the induction machine under various conditions. After preprocessing the currents, fourteen statistical time features are estimated for each phase of the current. These parameters are considered as inputs to the classifier. The main scope of the paper is to evaluate effectiveness of RF classifier for individual and mixed fault diagnosis in induction machine. The stator, rotor and mixed faults (stator and rotor faults) are classified using the proposed classifier. The obtained performance measures are compared with the multilayer perceptron neural network (MLPNN) classifier. The results show the much better performance measures and more accurate than MLPNN classifier. For demonstration of planned fault diagnosis algorithm, experimentally obtained results are considered to build the classifier more practical.

  14. Building an asynchronous web-based tool for machine learning classification.

    Science.gov (United States)

    Weber, Griffin; Vinterbo, Staal; Ohno-Machado, Lucila

    2002-01-01

    Various unsupervised and supervised learning methods including support vector machines, classification trees, linear discriminant analysis and nearest neighbor classifiers have been used to classify high-throughput gene expression data. Simpler and more widely accepted statistical tools have not yet been used for this purpose, hence proper comparisons between classification methods have not been conducted. We developed free software that implements logistic regression with stepwise variable selection as a quick and simple method for initial exploration of important genetic markers in disease classification. To implement the algorithm and allow our collaborators in remote locations to evaluate and compare its results against those of other methods, we developed a user-friendly asynchronous web-based application with a minimal amount of programming using free, downloadable software tools. With this program, we show that classification using logistic regression can perform as well as other more sophisticated algorithms, and it has the advantages of being easy to interpret and reproduce. By making the tool freely and easily available, we hope to promote the comparison of classification methods. In addition, we believe our web application can be used as a model for other bioinformatics laboratories that need to develop web-based analysis tools in a short amount of time and on a limited budget.

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

    Science.gov (United States)

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

    2014-06-01

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

  16. A Systematic Approach to Food Variety Classification as a Tool in ...

    African Journals Online (AJOL)

    A Systematic Approach to Food Variety Classification as a Tool in Dietary ... and food variety (count of all dietary items consumed during the recall period up to the ... This paper presents a pilot study carried out with an aim of demonstrating the ...

  17. DisoMCS: Accurately Predicting Protein Intrinsically Disordered Regions Using a Multi-Class Conservative Score Approach.

    Directory of Open Access Journals (Sweden)

    Zhiheng Wang

    Full Text Available The precise prediction of protein intrinsically disordered regions, which play a crucial role in biological procedures, is a necessary prerequisite to further the understanding of the principles and mechanisms of protein function. Here, we propose a novel predictor, DisoMCS, which is a more accurate predictor of protein intrinsically disordered regions. The DisoMCS bases on an original multi-class conservative score (MCS obtained by sequence-order/disorder alignment. Initially, near-disorder regions are defined on fragments located at both the terminus of an ordered region connecting a disordered region. Then the multi-class conservative score is generated by sequence alignment against a known structure database and represented as order, near-disorder and disorder conservative scores. The MCS of each amino acid has three elements: order, near-disorder and disorder profiles. Finally, the MCS is exploited as features to identify disordered regions in sequences. DisoMCS utilizes a non-redundant data set as the training set, MCS and predicted secondary structure as features, and a conditional random field as the classification algorithm. In predicted near-disorder regions a residue is determined as an order or a disorder according to the optimized decision threshold. DisoMCS was evaluated by cross-validation, large-scale prediction, independent tests and CASP (Critical Assessment of Techniques for Protein Structure Prediction tests. All results confirmed that DisoMCS was very competitive in terms of accuracy of prediction when compared with well-established publicly available disordered region predictors. It also indicated our approach was more accurate when a query has higher homologous with the knowledge database.The DisoMCS is available at http://cal.tongji.edu.cn/disorder/.

  18. On the role of cost-sensitive learning in multi-class brain-computer interfaces.

    Science.gov (United States)

    Devlaminck, Dieter; Waegeman, Willem; Wyns, Bart; Otte, Georges; Santens, Patrick

    2010-06-01

    Brain-computer interfaces (BCIs) present an alternative way of communication for people with severe disabilities. One of the shortcomings in current BCI systems, recently put forward in the fourth BCI competition, is the asynchronous detection of motor imagery versus resting state. We investigated this extension to the three-class case, in which the resting state is considered virtually lying between two motor classes, resulting in a large penalty when one motor task is misclassified into the other motor class. We particularly focus on the behavior of different machine-learning techniques and on the role of multi-class cost-sensitive learning in such a context. To this end, four different kernel methods are empirically compared, namely pairwise multi-class support vector machines (SVMs), two cost-sensitive multi-class SVMs and kernel-based ordinal regression. The experimental results illustrate that ordinal regression performs better than the other three approaches when a cost-sensitive performance measure such as the mean-squared error is considered. By contrast, multi-class cost-sensitive learning enables us to control the number of large errors made between two motor tasks.

  19. Multi-Class load balancing scheme for QoS and energy ...

    African Journals Online (AJOL)

    Multi-Class load balancing scheme for QoS and energy conservation in cloud computing. ... If you would like more information about how to print, save, and work with PDFs, Highwire Press provides a helpful Frequently Asked Questions about PDFs. Alternatively, you can download the PDF file directly to your computer, from ...

  20. ncRNA-class Web Tool: Non-coding RNA feature extraction and pre-miRNA classification web tool

    KAUST Repository

    Kleftogiannis, Dimitrios A.; Theofilatos, Konstantinos A.; Papadimitriou, Stergios; Tsakalidis, Athanasios K.; Likothanassis, Spiridon D.; Mavroudi, Seferina P.

    2012-01-01

    Until recently, it was commonly accepted that most genetic information is transacted by proteins. Recent evidence suggests that the majority of the genomes of mammals and other complex organisms are in fact transcribed into non-coding RNAs (ncRNAs), many of which are alternatively spliced and/or processed into smaller products. Non coding RNA genes analysis requires the calculation of several sequential, thermodynamical and structural features. Many independent tools have already been developed for the efficient calculation of such features but to the best of our knowledge there does not exist any integrative approach for this task. The most significant amount of existing work is related to the miRNA class of non-coding RNAs. MicroRNAs (miRNAs) are small non-coding RNAs that play a significant role in gene regulation and their prediction is a challenging bioinformatics problem. Non-coding RNA feature extraction and pre-miRNA classification Web Tool (ncRNA-class Web Tool) is a publicly available web tool ( http://150.140.142.24:82/Default.aspx ) which provides a user friendly and efficient environment for the effective calculation of a set of 58 sequential, thermodynamical and structural features of non-coding RNAs, plus a tool for the accurate prediction of miRNAs. © 2012 IFIP International Federation for Information Processing.

  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. 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...

  3. HClass: Automatic classification tool for health pathologies using artificial intelligence techniques.

    Science.gov (United States)

    Garcia-Chimeno, Yolanda; Garcia-Zapirain, Begonya

    2015-01-01

    The classification of subjects' pathologies enables a rigorousness to be applied to the treatment of certain pathologies, as doctors on occasions play with so many variables that they can end up confusing some illnesses with others. Thanks to Machine Learning techniques applied to a health-record database, it is possible to make using our algorithm. hClass contains a non-linear classification of either a supervised, non-supervised or semi-supervised type. The machine is configured using other techniques such as validation of the set to be classified (cross-validation), reduction in features (PCA) and committees for assessing the various classifiers. The tool is easy to use, and the sample matrix and features that one wishes to classify, the number of iterations and the subjects who are going to be used to train the machine all need to be introduced as inputs. As a result, the success rate is shown either via a classifier or via a committee if one has been formed. A 90% success rate is obtained in the ADABoost classifier and 89.7% in the case of a committee (comprising three classifiers) when PCA is applied. This tool can be expanded to allow the user to totally characterise the classifiers by adjusting them to each classification use.

  4. iTools: a framework for classification, categorization and integration of computational biology resources.

    Directory of Open Access Journals (Sweden)

    Ivo D Dinov

    2008-05-01

    Full Text Available The advancement of the computational biology field hinges on progress in three fundamental directions--the development of new computational algorithms, the availability of informatics resource management infrastructures and the capability of tools to interoperate and synergize. There is an explosion in algorithms and tools for computational biology, which makes it difficult for biologists to find, compare and integrate such resources. We describe a new infrastructure, iTools, for managing the query, traversal and comparison of diverse computational biology resources. Specifically, iTools stores information about three types of resources--data, software tools and web-services. The iTools design, implementation and resource meta-data content reflect the broad research, computational, applied and scientific expertise available at the seven National Centers for Biomedical Computing. iTools provides a system for classification, categorization and integration of different computational biology resources across space-and-time scales, biomedical problems, computational infrastructures and mathematical foundations. A large number of resources are already iTools-accessible to the community and this infrastructure is rapidly growing. iTools includes human and machine interfaces to its resource meta-data repository. Investigators or computer programs may utilize these interfaces to search, compare, expand, revise and mine meta-data descriptions of existent computational biology resources. We propose two ways to browse and display the iTools dynamic collection of resources. The first one is based on an ontology of computational biology resources, and the second one is derived from hyperbolic projections of manifolds or complex structures onto planar discs. iTools is an open source project both in terms of the source code development as well as its meta-data content. iTools employs a decentralized, portable, scalable and lightweight framework for long

  5. A software tool for automatic classification and segmentation of 2D/3D medical images

    International Nuclear Information System (INIS)

    Strzelecki, Michal; Szczypinski, Piotr; Materka, Andrzej; Klepaczko, Artur

    2013-01-01

    Modern medical diagnosis utilizes techniques of visualization of human internal organs (CT, MRI) or of its metabolism (PET). However, evaluation of acquired images made by human experts is usually subjective and qualitative only. Quantitative analysis of MR data, including tissue classification and segmentation, is necessary to perform e.g. attenuation compensation, motion detection, and correction of partial volume effect in PET images, acquired with PET/MR scanners. This article presents briefly a MaZda software package, which supports 2D and 3D medical image analysis aiming at quantification of image texture. MaZda implements procedures for evaluation, selection and extraction of highly discriminative texture attributes combined with various classification, visualization and segmentation tools. Examples of MaZda application in medical studies are also provided

  6. A software tool for automatic classification and segmentation of 2D/3D medical images

    Energy Technology Data Exchange (ETDEWEB)

    Strzelecki, Michal, E-mail: michal.strzelecki@p.lodz.pl [Institute of Electronics, Technical University of Lodz, Wolczanska 211/215, 90-924 Lodz (Poland); Szczypinski, Piotr; Materka, Andrzej; Klepaczko, Artur [Institute of Electronics, Technical University of Lodz, Wolczanska 211/215, 90-924 Lodz (Poland)

    2013-02-21

    Modern medical diagnosis utilizes techniques of visualization of human internal organs (CT, MRI) or of its metabolism (PET). However, evaluation of acquired images made by human experts is usually subjective and qualitative only. Quantitative analysis of MR data, including tissue classification and segmentation, is necessary to perform e.g. attenuation compensation, motion detection, and correction of partial volume effect in PET images, acquired with PET/MR scanners. This article presents briefly a MaZda software package, which supports 2D and 3D medical image analysis aiming at quantification of image texture. MaZda implements procedures for evaluation, selection and extraction of highly discriminative texture attributes combined with various classification, visualization and segmentation tools. Examples of MaZda application in medical studies are also provided.

  7. SDT: a virus classification tool based on pairwise sequence alignment and identity calculation.

    Directory of Open Access Journals (Sweden)

    Brejnev Muhizi Muhire

    Full Text Available The perpetually increasing rate at which viral full-genome sequences are being determined is creating a pressing demand for computational tools that will aid the objective classification of these genome sequences. Taxonomic classification approaches that are based on pairwise genetic identity measures are potentially highly automatable and are progressively gaining favour with the International Committee on Taxonomy of Viruses (ICTV. There are, however, various issues with the calculation of such measures that could potentially undermine the accuracy and consistency with which they can be applied to virus classification. Firstly, pairwise sequence identities computed based on multiple sequence alignments rather than on multiple independent pairwise alignments can lead to the deflation of identity scores with increasing dataset sizes. Also, when gap-characters need to be introduced during sequence alignments to account for insertions and deletions, methodological variations in the way that these characters are introduced and handled during pairwise genetic identity calculations can cause high degrees of inconsistency in the way that different methods classify the same sets of sequences. Here we present Sequence Demarcation Tool (SDT, a free user-friendly computer program that aims to provide a robust and highly reproducible means of objectively using pairwise genetic identity calculations to classify any set of nucleotide or amino acid sequences. SDT can produce publication quality pairwise identity plots and colour-coded distance matrices to further aid the classification of sequences according to ICTV approved taxonomic demarcation criteria. Besides a graphical interface version of the program for Windows computers, command-line versions of the program are available for a variety of different operating systems (including a parallel version for cluster computing platforms.

  8. A Java-based tool for the design of classification microarrays.

    Science.gov (United States)

    Meng, Da; Broschat, Shira L; Call, Douglas R

    2008-08-04

    Classification microarrays are used for purposes such as identifying strains of bacteria and determining genetic relationships to understand the epidemiology of an infectious disease. For these cases, mixed microarrays, which are composed of DNA from more than one organism, are more effective than conventional microarrays composed of DNA from a single organism. Selection of probes is a key factor in designing successful mixed microarrays because redundant sequences are inefficient and limited representation of diversity can restrict application of the microarray. We have developed a Java-based software tool, called PLASMID, for use in selecting the minimum set of probe sequences needed to classify different groups of plasmids or bacteria. The software program was successfully applied to several different sets of data. The utility of PLASMID was illustrated using existing mixed-plasmid microarray data as well as data from a virtual mixed-genome microarray constructed from different strains of Streptococcus. Moreover, use of data from expression microarray experiments demonstrated the generality of PLASMID. In this paper we describe a new software tool for selecting a set of probes for a classification microarray. While the tool was developed for the design of mixed microarrays-and mixed-plasmid microarrays in particular-it can also be used to design expression arrays. The user can choose from several clustering methods (including hierarchical, non-hierarchical, and a model-based genetic algorithm), several probe ranking methods, and several different display methods. A novel approach is used for probe redundancy reduction, and probe selection is accomplished via stepwise discriminant analysis. Data can be entered in different formats (including Excel and comma-delimited text), and dendrogram, heat map, and scatter plot images can be saved in several different formats (including jpeg and tiff). Weights generated using stepwise discriminant analysis can be stored for

  9. MR/PET quantification tools: Registration, segmentation, classification, and MR-based attenuation correction

    Science.gov (United States)

    Fei, Baowei; Yang, Xiaofeng; Nye, Jonathon A.; Aarsvold, John N.; Raghunath, Nivedita; Cervo, Morgan; Stark, Rebecca; Meltzer, Carolyn C.; Votaw, John R.

    2012-01-01

    Purpose: Combined MR/PET is a relatively new, hybrid imaging modality. A human MR/PET prototype system consisting of a Siemens 3T Trio MR and brain PET insert was installed and tested at our institution. Its present design does not offer measured attenuation correction (AC) using traditional transmission imaging. This study is the development of quantification tools including MR-based AC for quantification in combined MR/PET for brain imaging. Methods: The developed quantification tools include image registration, segmentation, classification, and MR-based AC. These components were integrated into a single scheme for processing MR/PET data. The segmentation method is multiscale and based on the Radon transform of brain MR images. It was developed to segment the skull on T1-weighted MR images. A modified fuzzy C-means classification scheme was developed to classify brain tissue into gray matter, white matter, and cerebrospinal fluid. Classified tissue is assigned an attenuation coefficient so that AC factors can be generated. PET emission data are then reconstructed using a three-dimensional ordered sets expectation maximization method with the MR-based AC map. Ten subjects had separate MR and PET scans. The PET with [11C]PIB was acquired using a high-resolution research tomography (HRRT) PET. MR-based AC was compared with transmission (TX)-based AC on the HRRT. Seventeen volumes of interest were drawn manually on each subject image to compare the PET activities between the MR-based and TX-based AC methods. Results: For skull segmentation, the overlap ratio between our segmented results and the ground truth is 85.2 ± 2.6%. Attenuation correction results from the ten subjects show that the difference between the MR and TX-based methods was <6.5%. Conclusions: MR-based AC compared favorably with conventional transmission-based AC. Quantitative tools including registration, segmentation, classification, and MR-based AC have been developed for use in combined MR

  10. MR/PET quantification tools: Registration, segmentation, classification, and MR-based attenuation correction

    Energy Technology Data Exchange (ETDEWEB)

    Fei, Baowei, E-mail: bfei@emory.edu [Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1841 Clifton Road Northeast, Atlanta, Georgia 30329 (United States); Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia 30322 (United States); Department of Mathematics and Computer Sciences, Emory University, Atlanta, Georgia 30322 (United States); Yang, Xiaofeng; Nye, Jonathon A.; Raghunath, Nivedita; Votaw, John R. [Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia 30329 (United States); Aarsvold, John N. [Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia 30329 (United States); Nuclear Medicine Service, Atlanta Veterans Affairs Medical Center, Atlanta, Georgia 30033 (United States); Cervo, Morgan; Stark, Rebecca [The Medical Physics Graduate Program in the George W. Woodruff School, Georgia Institute of Technology, Atlanta, Georgia 30332 (United States); Meltzer, Carolyn C. [Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia 30329 (United States); Department of Neurology and Department of Psychiatry and Behavior Sciences, Emory University School of Medicine, Atlanta, Georgia 30322 (United States)

    2012-10-15

    Purpose: Combined MR/PET is a relatively new, hybrid imaging modality. A human MR/PET prototype system consisting of a Siemens 3T Trio MR and brain PET insert was installed and tested at our institution. Its present design does not offer measured attenuation correction (AC) using traditional transmission imaging. This study is the development of quantification tools including MR-based AC for quantification in combined MR/PET for brain imaging. Methods: The developed quantification tools include image registration, segmentation, classification, and MR-based AC. These components were integrated into a single scheme for processing MR/PET data. The segmentation method is multiscale and based on the Radon transform of brain MR images. It was developed to segment the skull on T1-weighted MR images. A modified fuzzy C-means classification scheme was developed to classify brain tissue into gray matter, white matter, and cerebrospinal fluid. Classified tissue is assigned an attenuation coefficient so that AC factors can be generated. PET emission data are then reconstructed using a three-dimensional ordered sets expectation maximization method with the MR-based AC map. Ten subjects had separate MR and PET scans. The PET with [{sup 11}C]PIB was acquired using a high-resolution research tomography (HRRT) PET. MR-based AC was compared with transmission (TX)-based AC on the HRRT. Seventeen volumes of interest were drawn manually on each subject image to compare the PET activities between the MR-based and TX-based AC methods. Results: For skull segmentation, the overlap ratio between our segmented results and the ground truth is 85.2 ± 2.6%. Attenuation correction results from the ten subjects show that the difference between the MR and TX-based methods was <6.5%. Conclusions: MR-based AC compared favorably with conventional transmission-based AC. Quantitative tools including registration, segmentation, classification, and MR-based AC have been developed for use in combined MR/PET.

  11. MR/PET quantification tools: Registration, segmentation, classification, and MR-based attenuation correction

    International Nuclear Information System (INIS)

    Fei, Baowei; Yang, Xiaofeng; Nye, Jonathon A.; Raghunath, Nivedita; Votaw, John R.; Aarsvold, John N.; Cervo, Morgan; Stark, Rebecca; Meltzer, Carolyn C.

    2012-01-01

    Purpose: Combined MR/PET is a relatively new, hybrid imaging modality. A human MR/PET prototype system consisting of a Siemens 3T Trio MR and brain PET insert was installed and tested at our institution. Its present design does not offer measured attenuation correction (AC) using traditional transmission imaging. This study is the development of quantification tools including MR-based AC for quantification in combined MR/PET for brain imaging. Methods: The developed quantification tools include image registration, segmentation, classification, and MR-based AC. These components were integrated into a single scheme for processing MR/PET data. The segmentation method is multiscale and based on the Radon transform of brain MR images. It was developed to segment the skull on T1-weighted MR images. A modified fuzzy C-means classification scheme was developed to classify brain tissue into gray matter, white matter, and cerebrospinal fluid. Classified tissue is assigned an attenuation coefficient so that AC factors can be generated. PET emission data are then reconstructed using a three-dimensional ordered sets expectation maximization method with the MR-based AC map. Ten subjects had separate MR and PET scans. The PET with ["1"1C]PIB was acquired using a high-resolution research tomography (HRRT) PET. MR-based AC was compared with transmission (TX)-based AC on the HRRT. Seventeen volumes of interest were drawn manually on each subject image to compare the PET activities between the MR-based and TX-based AC methods. Results: For skull segmentation, the overlap ratio between our segmented results and the ground truth is 85.2 ± 2.6%. Attenuation correction results from the ten subjects show that the difference between the MR and TX-based methods was <6.5%. Conclusions: MR-based AC compared favorably with conventional transmission-based AC. Quantitative tools including registration, segmentation, classification, and MR-based AC have been developed for use in combined MR/PET.

  12. An Artificial Intelligence Classification Tool and Its Application to Gamma-Ray Bursts

    Science.gov (United States)

    Hakkila, Jon; Haglin, David J.; Roiger, Richard J.; Giblin, Timothy; Paciesas, William S.; Pendleton, Geoffrey N.; Mallozzi, Robert S.

    2004-01-01

    Despite being the most energetic phenomenon in the known universe, the astrophysics of gamma-ray bursts (GRBs) has still proven difficult to understand. It has only been within the past five years that the GRB distance scale has been firmly established, on the basis of a few dozen bursts with x-ray, optical, and radio afterglows. The afterglows indicate source redshifts of z=1 to z=5, total energy outputs of roughly 10(exp 52) ergs, and energy confined to the far x-ray to near gamma-ray regime of the electromagnetic spectrum. The multi-wavelength afterglow observations have thus far provided more insight on the nature of the GRB mechanism than the GRB observations; far more papers have been written about the few observed gamma-ray burst afterglows in the past few years than about the thousands of detected gamma-ray bursts. One reason the GRB central engine is still so poorly understood is that GRBs have complex, overlapping characteristics that do not appear to be produced by one homogeneous process. At least two subclasses have been found on the basis of duration, spectral hardness, and fluence (time integrated flux); Class 1 bursts are softer, longer, and brighter than Class 2 bursts (with two second durations indicating a rough division). A third GRB subclass, overlapping the other two, has been identified using statistical clustering techniques; Class 3 bursts are intermediate between Class 1 and Class 2 bursts in brightness and duration, but are softer than Class 1 bursts. We are developing a tool to aid scientists in the study of GRB properties. In the process of developing this tool, we are building a large gamma-ray burst classification database. We are also scientifically analyzing some GRB data as we develop the tool. Tool development thus proceeds in tandem with the dataset for which it is being designed. The tool invokes a modified KDD (Knowledge Discovery in Databases) process, which is described as follows.

  13. NMD Classifier: A reliable and systematic classification tool for nonsense-mediated decay events.

    Directory of Open Access Journals (Sweden)

    Min-Kung Hsu

    Full Text Available Nonsense-mediated decay (NMD degrades mRNAs that include premature termination codons to avoid the translation and accumulation of truncated proteins. This mechanism has been found to participate in gene regulation and a wide spectrum of biological processes. However, the evolutionary and regulatory origins of NMD-targeted transcripts (NMDTs have been less studied, partly because of the complexity in analyzing NMD events. Here we report NMD Classifier, a tool for systematic classification of NMD events for either annotated or de novo assembled transcripts. This tool is based on the assumption of minimal evolution/regulation-an event that leads to the least change is the most likely to occur. Our simulation results indicate that NMD Classifier can correctly identify an average of 99.3% of the NMD-causing transcript structural changes, particularly exon inclusions/exclusions and exon boundary alterations. Researchers can apply NMD Classifier to evolutionary and regulatory studies by comparing NMD events of different biological conditions or in different organisms.

  14. A Java-based tool for the design of classification microarrays

    Directory of Open Access Journals (Sweden)

    Broschat Shira L

    2008-08-01

    Full Text Available Abstract Background Classification microarrays are used for purposes such as identifying strains of bacteria and determining genetic relationships to understand the epidemiology of an infectious disease. For these cases, mixed microarrays, which are composed of DNA from more than one organism, are more effective than conventional microarrays composed of DNA from a single organism. Selection of probes is a key factor in designing successful mixed microarrays because redundant sequences are inefficient and limited representation of diversity can restrict application of the microarray. We have developed a Java-based software tool, called PLASMID, for use in selecting the minimum set of probe sequences needed to classify different groups of plasmids or bacteria. Results The software program was successfully applied to several different sets of data. The utility of PLASMID was illustrated using existing mixed-plasmid microarray data as well as data from a virtual mixed-genome microarray constructed from different strains of Streptococcus. Moreover, use of data from expression microarray experiments demonstrated the generality of PLASMID. Conclusion In this paper we describe a new software tool for selecting a set of probes for a classification microarray. While the tool was developed for the design of mixed microarrays–and mixed-plasmid microarrays in particular–it can also be used to design expression arrays. The user can choose from several clustering methods (including hierarchical, non-hierarchical, and a model-based genetic algorithm, several probe ranking methods, and several different display methods. A novel approach is used for probe redundancy reduction, and probe selection is accomplished via stepwise discriminant analysis. Data can be entered in different formats (including Excel and comma-delimited text, and dendrogram, heat map, and scatter plot images can be saved in several different formats (including jpeg and tiff. Weights

  15. A new tool for supervised classification of satellite images available on web servers: Google Maps as a case study

    Science.gov (United States)

    García-Flores, Agustín.; Paz-Gallardo, Abel; Plaza, Antonio; Li, Jun

    2016-10-01

    This paper describes a new web platform dedicated to the classification of satellite images called Hypergim. The current implementation of this platform enables users to perform classification of satellite images from any part of the world thanks to the worldwide maps provided by Google Maps. To perform this classification, Hypergim uses unsupervised algorithms like Isodata and K-means. Here, we present an extension of the original platform in which we adapt Hypergim in order to use supervised algorithms to improve the classification results. This involves a significant modification of the user interface, providing the user with a way to obtain samples of classes present in the images to use in the training phase of the classification process. Another main goal of this development is to improve the runtime of the image classification process. To achieve this goal, we use a parallel implementation of the Random Forest classification algorithm. This implementation is a modification of the well-known CURFIL software package. The use of this type of algorithms to perform image classification is widespread today thanks to its precision and ease of training. The actual implementation of Random Forest was developed using CUDA platform, which enables us to exploit the potential of several models of NVIDIA graphics processing units using them to execute general purpose computing tasks as image classification algorithms. As well as CUDA, we use other parallel libraries as Intel Boost, taking advantage of the multithreading capabilities of modern CPUs. To ensure the best possible results, the platform is deployed in a cluster of commodity graphics processing units (GPUs), so that multiple users can use the tool in a concurrent way. The experimental results indicate that this new algorithm widely outperform the previous unsupervised algorithms implemented in Hypergim, both in runtime as well as precision of the actual classification of the images.

  16. A New Tool for Climatic Analysis Using the Koppen Climate Classification

    Science.gov (United States)

    Larson, Paul R.; Lohrengel, C. Frederick, II

    2011-01-01

    The purpose of climate classification is to help make order of the seemingly endless spatial distribution of climates. The Koppen classification system in a modified format is the most widely applied system in use today. This system may not be the best nor most complete climate classification that can be conceived, but it has gained widespread…

  17. Performance improvement of multi-class detection using greedy algorithm for Viola-Jones cascade selection

    Science.gov (United States)

    Tereshin, Alexander A.; Usilin, Sergey A.; Arlazarov, Vladimir V.

    2018-04-01

    This paper aims to study the problem of multi-class object detection in video stream with Viola-Jones cascades. An adaptive algorithm for selecting Viola-Jones cascade based on greedy choice strategy in solution of the N-armed bandit problem is proposed. The efficiency of the algorithm on the problem of detection and recognition of the bank card logos in the video stream is shown. The proposed algorithm can be effectively used in documents localization and identification, recognition of road scene elements, localization and tracking of the lengthy objects , and for solving other problems of rigid object detection in a heterogeneous data flows. The computational efficiency of the algorithm makes it possible to use it both on personal computers and on mobile devices based on processors with low power consumption.

  18. The multi-class binomial failure rate model for the treatment of common-cause failures

    International Nuclear Information System (INIS)

    Hauptmanns, U.

    1995-01-01

    The impact of common cause failures (CCF) on PSA results for NPPs is in sharp contrast with the limited quality which can be achieved in their assessment. This is due to the dearth of observations and cannot be remedied in the short run. Therefore the methods employed for calculating failure rates should be devised such as to make the best use of the few available observations on CCF. The Multi-Class Binomial Failure Rate (MCBFR) Model achieves this by assigning observed failures to different classes according to their technical characteristics and applying the BFR formalism to each of these. The results are hence determined by a superposition of BFR type expressions for each class, each of them with its own coupling factor. The model thus obtained flexibly reproduces the dependence of CCF rates on failure multiplicity insinuated by the observed failure multiplicities. This is demonstrated by evaluating CCFs observed for combined impulse pilot valves in German NPPs. (orig.) [de

  19. Retrieving clinically relevant diabetic retinopathy images using a multi-class multiple-instance framework

    Science.gov (United States)

    Chandakkar, Parag S.; Venkatesan, Ragav; Li, Baoxin

    2013-02-01

    Diabetic retinopathy (DR) is a vision-threatening complication from diabetes mellitus, a medical condition that is rising globally. Unfortunately, many patients are unaware of this complication because of absence of symptoms. Regular screening of DR is necessary to detect the condition for timely treatment. Content-based image retrieval, using archived and diagnosed fundus (retinal) camera DR images can improve screening efficiency of DR. This content-based image retrieval study focuses on two DR clinical findings, microaneurysm and neovascularization, which are clinical signs of non-proliferative and proliferative diabetic retinopathy. The authors propose a multi-class multiple-instance image retrieval framework which deploys a modified color correlogram and statistics of steerable Gaussian Filter responses, for retrieving clinically relevant images from a database of DR fundus image database.

  20. Human Activity Recognition from Smart-Phone Sensor Data using a Multi-Class Ensemble Learning in Home Monitoring.

    Science.gov (United States)

    Ghose, Soumya; Mitra, Jhimli; Karunanithi, Mohan; Dowling, Jason

    2015-01-01

    Home monitoring of chronically ill or elderly patient can reduce frequent hospitalisations and hence provide improved quality of care at a reduced cost to the community, therefore reducing the burden on the healthcare system. Activity recognition of such patients is of high importance in such a design. In this work, a system for automatic human physical activity recognition from smart-phone inertial sensors data is proposed. An ensemble of decision trees framework is adopted to train and predict the multi-class human activity system. A comparison of our proposed method with a multi-class traditional support vector machine shows significant improvement in activity recognition accuracies.

  1. An exact solution for the state probabilities of the multi-class, multi-server queue with preemptive priorities

    NARCIS (Netherlands)

    Sleptchenko, Andrei; van Harten, Aart; van der Heijden, Matthijs C.

    2005-01-01

    We consider a multi-class, multi-server queueing system with preemptive priorities. We distinguish two groups of priority classes that consist of multiple customer types, each having their own arrival and service rate. We assume Poisson arrival processes and exponentially distributed service times.

  2. Formalization of Technological Knowledge in the Field of Metallurgy using Document Classification Tools Supported with Semantic Techniques

    Directory of Open Access Journals (Sweden)

    Regulski K.

    2017-06-01

    Full Text Available The process of knowledge formalization is an essential part of decision support systems development. Creating a technological knowledge base in the field of metallurgy encountered problems in acquisition and codifying reusable computer artifacts based on text documents. The aim of the work was to adapt the algorithms for classification of documents and to develop a method of semantic integration of a created repository. Author used artificial intelligence tools: latent semantic indexing, rough sets, association rules learning and ontologies as a tool for integration. The developed methodology allowed for the creation of semantic knowledge base on the basis of documents in natural language in the field of metallurgy.

  3. A new tool in the classification of rational conformal field theories

    International Nuclear Information System (INIS)

    Christe, P.; Ravanini, F.

    1988-10-01

    The fact that in any rational conformal field theory (RCFT) 4-point functions on the sphere must satisfy an ordinary differential equation gives a simple condition on the conformal dimensions of primary fields. We discuss how this can help in the classification program of RCFT. As an example all associative fusion rules with less than four non-trivial primary fields and N ijk <<1 are discussed. Another application to the classification of chiral algebras is briefly mentioned. (orig.)

  4. Stream Classification Tool User Manual: For Use in Applications in Hydropower-Related Evironmental Mitigation

    Energy Technology Data Exchange (ETDEWEB)

    McManamay, Ryan A. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Troia, Matthew J. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); DeRolph, Christopher R. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Samu, Nicole M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

    2016-01-01

    Stream classifications are an inventory of different types of streams. Classifications help us explore similarities and differences among different types of streams, make inferences regarding stream ecosystem behavior, and communicate the complexities of ecosystems. We developed a nested, layered, and spatially contiguous stream classification to characterize the biophysical settings of stream reaches within the Eastern United States (~ 900,000 reaches). The classification is composed of five natural characteristics (hydrology, temperature, size, confinement, and substrate) along with several disturbance regime layers, and each was selected because of their relevance to hydropower mitigation. We developed the classification at the stream reach level using the National Hydrography Dataset Plus Version 1 (1:100k scale). The stream classification is useful to environmental mitigation for hydropower dams in multiple ways. First, it creates efficiency in the regulatory process by creating an objective and data-rich means to address meaningful mitigation actions. Secondly, the SCT addresses data gaps as it quickly provides an inventory of hydrology, temperature, morphology, and ecological communities for the immediate project area, but also surrounding streams. This includes identifying potential reference streams as those that are proximate to the hydropower facility and fall within the same class. These streams can potentially be used to identify ideal environmental conditions or identify desired ecological communities. In doing so, the stream provides some context for how streams may function, respond to dam regulation, and an overview of specific mitigation needs. Herein, we describe the methodology in developing each stream classification layer and provide a tutorial to guide applications of the classification (and associated data) in regulatory settings, such as hydropower (re)licensing.

  5. MIDAS: Mining differentially activated subpaths of KEGG pathways from multi-class RNA-seq data.

    Science.gov (United States)

    Lee, Sangseon; Park, Youngjune; Kim, Sun

    2017-07-15

    Pathway based analysis of high throughput transcriptome data is a widely used approach to investigate biological mechanisms. Since a pathway consists of multiple functions, the recent approach is to determine condition specific sub-pathways or subpaths. However, there are several challenges. First, few existing methods utilize explicit gene expression information from RNA-seq. More importantly, subpath activity is usually an average of statistical scores, e.g., correlations, of edges in a candidate subpath, which fails to reflect gene expression quantity information. In addition, none of existing methods can handle multiple phenotypes. To address these technical problems, we designed and implemented an algorithm, MIDAS, that determines condition specific subpaths, each of which has different activities across multiple phenotypes. MIDAS utilizes gene expression quantity information fully and the network centrality information to determine condition specific subpaths. To test performance of our tool, we used TCGA breast cancer RNA-seq gene expression profiles with five molecular subtypes. 36 differentially activate subpaths were determined. The utility of our method, MIDAS, was demonstrated in four ways. All 36 subpaths are well supported by the literature information. Subsequently, we showed that these subpaths had a good discriminant power for five cancer subtype classification and also had a prognostic power in terms of survival analysis. Finally, in a performance comparison of MIDAS to a recent subpath prediction method, PATHOME, our method identified more subpaths and much more genes that are well supported by the literature information. http://biohealth.snu.ac.kr/software/MIDAS/. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  6. Multi-Class Simultaneous Adaptive Segmentation and Quality Control of Point Cloud Data

    Directory of Open Access Journals (Sweden)

    Ayman Habib

    2016-01-01

    Full Text Available 3D modeling of a given site is an important activity for a wide range of applications including urban planning, as-built mapping of industrial sites, heritage documentation, military simulation, and outdoor/indoor analysis of airflow. Point clouds, which could be either derived from passive or active imaging systems, are an important source for 3D modeling. Such point clouds need to undergo a sequence of data processing steps to derive the necessary information for the 3D modeling process. Segmentation is usually the first step in the data processing chain. This paper presents a region-growing multi-class simultaneous segmentation procedure, where planar, pole-like, and rough regions are identified while considering the internal characteristics (i.e., local point density/spacing and noise level of the point cloud in question. The segmentation starts with point cloud organization into a kd-tree data structure and characterization process to estimate the local point density/spacing. Then, proceeding from randomly-distributed seed points, a set of seed regions is derived through distance-based region growing, which is followed by modeling of such seed regions into planar and pole-like features. Starting from optimally-selected seed regions, planar and pole-like features are then segmented. The paper also introduces a list of hypothesized artifacts/problems that might take place during the region-growing process. Finally, a quality control process is devised to detect, quantify, and mitigate instances of partially/fully misclassified planar and pole-like features. Experimental results from airborne and terrestrial laser scanning as well as image-based point clouds are presented to illustrate the performance of the proposed segmentation and quality control framework.

  7. Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction.

    Science.gov (United States)

    Faust, Kevin; Xie, Quin; Han, Dominick; Goyle, Kartikay; Volynskaya, Zoya; Djuric, Ugljesa; Diamandis, Phedias

    2018-05-16

    There is growing interest in utilizing artificial intelligence, and particularly deep learning, for computer vision in histopathology. While accumulating studies highlight expert-level performance of convolutional neural networks (CNNs) on focused classification tasks, most studies rely on probability distribution scores with empirically defined cutoff values based on post-hoc analysis. More generalizable tools that allow humans to visualize histology-based deep learning inferences and decision making are scarce. Here, we leverage t-distributed Stochastic Neighbor Embedding (t-SNE) to reduce dimensionality and depict how CNNs organize histomorphologic information. Unique to our workflow, we develop a quantitative and transparent approach to visualizing classification decisions prior to softmax compression. By discretizing the relationships between classes on the t-SNE plot, we show we can super-impose randomly sampled regions of test images and use their distribution to render statistically-driven classifications. Therefore, in addition to providing intuitive outputs for human review, this visual approach can carry out automated and objective multi-class classifications similar to more traditional and less-transparent categorical probability distribution scores. Importantly, this novel classification approach is driven by a priori statistically defined cutoffs. It therefore serves as a generalizable classification and anomaly detection tool less reliant on post-hoc tuning. Routine incorporation of this convenient approach for quantitative visualization and error reduction in histopathology aims to accelerate early adoption of CNNs into generalized real-world applications where unanticipated and previously untrained classes are often encountered.

  8. A tool for enhancing strategic health planning: a modeled use of the International Classification of Functioning, Disability and Health.

    Science.gov (United States)

    Sinclair, Lisa Bundara; Fox, Michael H; Betts, Donald R

    2013-01-01

    This article describes use of the International Classification of Functioning, Disability and Health (ICF) as a tool for strategic planning. The ICF is the international classification system for factors that influence health, including Body Structures, Body Functions, Activities and Participation and Environmental Factors. An overview of strategic planning and the ICF are provided. Selected ICF concepts and nomenclature are used to demonstrate its utility in helping develop a classic planning framework, objectives, measures and actions. Some issues and resolutions for applying the ICF are described. Applying the ICF for strategic health planning is an innovative approach that fosters the inclusion of social ecological health determinants and broad populations. If employed from the onset of planning, the ICF can help public health organizations systematically conceptualize, organize and communicate a strategic health plan. Published 2012. This article is a US Government work and is in the public domain in the USA.

  9. An Addendum to "A New Tool for Climatic Analysis Using Köppen Climate Classification"

    Science.gov (United States)

    Larson, Paul R.; Lohrengel, C. Frederick, II

    2014-01-01

    The Köppen climatic classification system in a modified format is the most widely applied system in use today. Mapping and analysis of hundreds of arid and semiarid climate stations has made the use of the additional fourth letter in BW/BS climates essential. The addition of "s," "w," or "f" to the standard…

  10. Random Forests as a tool for estimating uncertainty at pixel-level in SAR image classification

    DEFF Research Database (Denmark)

    Loosvelt, Lien; Peters, Jan; Skriver, Henning

    2012-01-01

    , we introduce Random Forests for the probabilistic mapping of vegetation from high-dimensional remote sensing data and present a comprehensive methodology to assess and analyze classification uncertainty based on the local probabilities of class membership. We apply this method to SAR image data...

  11. Land Cover Classification from Multispectral Data Using Computational Intelligence Tools: A Comparative Study

    Directory of Open Access Journals (Sweden)

    André Mora

    2017-11-01

    Full Text Available This article discusses how computational intelligence techniques are applied to fuse spectral images into a higher level image of land cover distribution for remote sensing, specifically for satellite image classification. We compare a fuzzy-inference method with two other computational intelligence methods, decision trees and neural networks, using a case study of land cover classification from satellite images. Further, an unsupervised approach based on k-means clustering has been also taken into consideration for comparison. The fuzzy-inference method includes training the classifier with a fuzzy-fusion technique and then performing land cover classification using reinforcement aggregation operators. To assess the robustness of the four methods, a comparative study including three years of land cover maps for the district of Mandimba, Niassa province, Mozambique, was undertaken. Our results show that the fuzzy-fusion method performs similarly to decision trees, achieving reliable classifications; neural networks suffer from overfitting; while k-means clustering constitutes a promising technique to identify land cover types from unknown areas.

  12. The Development and Application of the RAND Program Classification Tool. The RAND Toolkit, Volume 1

    Science.gov (United States)

    2014-01-01

    service, reference DoD programs related to psycho - logical health and TBI, but the R-PCT can be applied to other content areas or organizations. Table...and provides a high-level description, comparison, or classification of programs. Furthermore, given its simple architecture , the R-PCT can be

  13. Performance of in silico prediction tools for the classification of rare BRCA1/2 missense variants in clinical diagnostics.

    Science.gov (United States)

    Ernst, Corinna; Hahnen, Eric; Engel, Christoph; Nothnagel, Michael; Weber, Jonas; Schmutzler, Rita K; Hauke, Jan

    2018-03-27

    The use of next-generation sequencing approaches in clinical diagnostics has led to a tremendous increase in data and a vast number of variants of uncertain significance that require interpretation. Therefore, prediction of the effects of missense mutations using in silico tools has become a frequently used approach. Aim of this study was to assess the reliability of in silico prediction as a basis for clinical decision making in the context of hereditary breast and/or ovarian cancer. We tested the performance of four prediction tools (Align-GVGD, SIFT, PolyPhen-2, MutationTaster2) using a set of 236 BRCA1/2 missense variants that had previously been classified by expert committees. However, a major pitfall in the creation of a reliable evaluation set for our purpose is the generally accepted classification of BRCA1/2 missense variants using the multifactorial likelihood model, which is partially based on Align-GVGD results. To overcome this drawback we identified 161 variants whose classification is independent of any previous in silico prediction. In addition to the performance as stand-alone tools we examined the sensitivity, specificity, accuracy and Matthews correlation coefficient (MCC) of combined approaches. PolyPhen-2 achieved the lowest sensitivity (0.67), specificity (0.67), accuracy (0.67) and MCC (0.39). Align-GVGD achieved the highest values of specificity (0.92), accuracy (0.92) and MCC (0.73), but was outperformed regarding its sensitivity (0.90) by SIFT (1.00) and MutationTaster2 (1.00). All tools suffered from poor specificities, resulting in an unacceptable proportion of false positive results in a clinical setting. This shortcoming could not be bypassed by combination of these tools. In the best case scenario, 138 families would be affected by the misclassification of neutral variants within the cohort of patients of the German Consortium for Hereditary Breast and Ovarian Cancer. We show that due to low specificities state-of-the-art in silico

  14. SPiCE : A web-based tool for sequence-based protein classification and exploration

    NARCIS (Netherlands)

    Van den Berg, B.A.; Reinders, M.J.; Roubos, J.A.; De Ridder, D.

    2014-01-01

    Background Amino acid sequences and features extracted from such sequences have been used to predict many protein properties, such as subcellular localization or solubility, using classifier algorithms. Although software tools are available for both feature extraction and classifier construction,

  15. The classification and evaluation of Computer-Aided Software Engineering tools

    OpenAIRE

    Manley, Gary W.

    1990-01-01

    Approved for public release; distribution unlimited. The use of Computer-Aided Software Engineering (CASE) tools has been viewed as a remedy for the software development crisis by achieving improved productivity and system quality via the automation of all or part of the software engineering process. The proliferation and tremendous variety of tools available have stretched the understanding of experienced practitioners and has had a profound impact on the software engineering process itse...

  16. 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.

  17. Balancing research and funding using value of information and portfolio tools for nanomaterial risk classification

    Science.gov (United States)

    Bates, Matthew E.; Keisler, Jeffrey M.; Zussblatt, Niels P.; Plourde, Kenton J.; Wender, Ben A.; Linkov, Igor

    2016-02-01

    Risk research for nanomaterials is currently prioritized by means of expert workshops and other deliberative processes. However, analytical techniques that quantify and compare alternative research investments are increasingly recommended. Here, we apply value of information and portfolio decision analysis—methods commonly applied in financial and operations management—to prioritize risk research for multiwalled carbon nanotubes and nanoparticulate silver and titanium dioxide. We modify the widely accepted CB Nanotool hazard evaluation framework, which combines nano- and bulk-material properties into a hazard score, to operate probabilistically with uncertain inputs. Literature is reviewed to develop uncertain estimates for each input parameter, and a Monte Carlo simulation is applied to assess how different research strategies can improve hazard classification. The relative cost of each research experiment is elicited from experts, which enables identification of efficient research portfolios—combinations of experiments that lead to the greatest improvement in hazard classification at the lowest cost. Nanoparticle shape, diameter, solubility and surface reactivity were most frequently identified within efficient portfolios in our results.

  18. A web-based neurological pain classifier tool utilizing Bayesian decision theory for pain classification in spinal cord injury patients

    Science.gov (United States)

    Verma, Sneha K.; Chun, Sophia; Liu, Brent J.

    2014-03-01

    Pain is a common complication after spinal cord injury with prevalence estimates ranging 77% to 81%, which highly affects a patient's lifestyle and well-being. In the current clinical setting paper-based forms are used to classify pain correctly, however, the accuracy of diagnoses and optimal management of pain largely depend on the expert reviewer, which in many cases is not possible because of very few experts in this field. The need for a clinical decision support system that can be used by expert and non-expert clinicians has been cited in literature, but such a system has not been developed. We have designed and developed a stand-alone tool for correctly classifying pain type in spinal cord injury (SCI) patients, using Bayesian decision theory. Various machine learning simulation methods are used to verify the algorithm using a pilot study data set, which consists of 48 patients data set. The data set consists of the paper-based forms, collected at Long Beach VA clinic with pain classification done by expert in the field. Using the WEKA as the machine learning tool we have tested on the 48 patient dataset that the hypothesis that attributes collected on the forms and the pain location marked by patients have very significant impact on the pain type classification. This tool will be integrated with an imaging informatics system to support a clinical study that will test the effectiveness of using Proton Beam radiotherapy for treating spinal cord injury (SCI) related neuropathic pain as an alternative to invasive surgical lesioning.

  19. Applying Multi-Class Support Vector Machines for performance assessment of shipping operations: The case of tanker vessels

    DEFF Research Database (Denmark)

    Pagoropoulos, Aris; Møller, Anders H.; McAloone, Tim C.

    2017-01-01

    of feature selection algorithms. Afterwards, a model based on Multi- Class Support Vector Machines (SVM) was constructed and the efficacy of the approach is shown through the application of a test set. The results demonstrate the importance and benefits of machine learning algorithms in driving energy....... Identifying the potential of behavioural savings can be challenging, due to the inherent difficulty in analysing the data and operationalizing energy efficiency within the dynamic operating environment of the vessels. This article proposes a supervised learning model for identifying the presence of energy...

  20. Artificial neural networks as classification and diagnostic tools for lymph node-negative breast cancers

    Energy Technology Data Exchange (ETDEWEB)

    Eswari J, Satya; Chandrakar, Neha [National Institute of Technology Raipur, Raipur (India)

    2016-04-15

    Artificial neural networks (ANNs) can be used to develop a technique to classify lymph node negative breast cancer that is prone to distant metastases based on gene expression signatures. The neural network used is a multilayered feed forward network that employs back propagation algorithm. Once trained with DNA microarraybased gene expression profiles of genes that were predictive of distant metastasis recurrence of lymph node negative breast cancer, the ANNs became capable of correctly classifying all samples and recognizing the genes most appropriate to the classification. To test the ability of the trained ANN models in recognizing lymph node negative breast cancer, we analyzed additional idle samples that were not used beforehand for the training procedure and obtained the correctly classified result in the validation set. For more substantial result, bootstrapping of training and testing dataset was performed as external validation. This study illustrates the potential application of ANN for breast tumor diagnosis and the identification of candidate targets in patients for therapy.

  1. Classification as a generic tool for characterising status and changes of regional scale groundwater systems

    Science.gov (United States)

    Barthel, Roland; Haaf, Ezra

    2016-04-01

    the behavior of groundwater systems. It is based on the hypothesis that similar groundwater systems respond similarly to similar impacts. At its core is the classification of (i) static hydrogeological characteristics (such as aquifer geometry and hydraulic properties), (ii) dynamic changes of the boundary conditions (such as recharge, water levels in surface waters), and (iii) dynamic groundwater system responses (groundwater head and chemical parameters). The dependencies of system responses on explanatory variables are used to map knowledge from observed locations to areas without measurements. Classification of static and dynamic system features combined with information about known system properties and their dependencies provide insight into system behavior that cannot be directly derived through the analysis of raw data. Classification and dependency analysis could finally lead to a new framework for groundwater system assessment on the regional scale as a replacement or supplement to numerical groundwater models and catchment scale hydrological models. This contribution focusses on the main hydrogeological concepts underlying the approach while another EGU contribution (Haaf and Barthel, 2016) explains the methodologies used to classify groundwater systems. References: Barthel, R., 2014. A call for more fundamental science in regional hydrogeology. Hydrogeol J, 22(3): 507-510. Barthel, R., Banzhaf, S., 2016. Groundwater and Surface Water Interaction at the Regional-scale - A Review with Focus on Regional Integrated Models. Water Resour Manag, 30(1): 1-32. Haaf, E., Barthel, R., 2016. An approach for classification of hydrogeological systems at the regional scale based on groundwater hydrographs. Abstract submitted to EGU General Assembly 2016, Vienna, Austria.

  2. NEURAL NETWORKS AS A CLASSIFICATION TOOL BIOTECHNOLOGICAL SYSTEMS (FOR EXAMPLE FLOUR PRODUCTION

    Directory of Open Access Journals (Sweden)

    V. K. Bitykov

    2015-01-01

    Full Text Available Summary. To date, artificial intelligence systems are the most common type to classify objects of different quality. The proposed modeling technology to predict the quality of flour products by using artificial neural networks allows to solve problems of analysis of the factors determining the quality of the products. Interest in artificial neural networks has grown due to the fact that they can change their behavior depending on external environment. This factor more than any other responsible for the interest that they cause. After the presentation of input signals (possibly together with the desired outputs, they self-configurable to provide the desired reaction. We developed a set of training algorithms, each with their own strengths and weaknesses. The solution to the problem of classification is one of the most important applications of neural networks, which represents a problem of attributing the sample to one of several non-intersecting sets. To solve this problem developed algorithms for synthesis of NA with the use of nonlinear activation functions, the algorithms for training the network. Training the NS involves determining the weights of layers of neurons. Training the NA occurs with the teacher, that is, the network must meet the values of both input and desired output signals, and it is according to some internal algorithm adjusts the weights of their synaptic connections. The work was built an artificial neural network, multilayer perceptron example. With the help of correlation analysis in total sample revealed that the traits are correlated at the significance level of 0.01 with grade quality bread. The classification accuracy exceeds 90%.

  3. Unsupervised Full-Polarimetric SAR Data Segmentation as a Tool for Classification of Agricultural Areas

    NARCIS (Netherlands)

    Hoekman, D.H.; Vissers, M.A.M.; Tran, T.N.

    2011-01-01

    Versatile, robust and computational efficient methods for radar image segmentation, which preserve the full polarimetric information content, are of importance as research tools, as well as for practical applications in land surface monitoring. The method introduced here consists of several steps.

  4. Classification of lymph nodes in computerized tomography scans using extracting tools

    International Nuclear Information System (INIS)

    Alves, Allan F.F.; Pina, Diana R. de; Altemani, Joao M.C.

    2016-01-01

    Lymph node changes in the neck region may be originated by both inflammatory and tumor causes. Diagnostic imaging method such as computed tomography are used to distinguish between these two main causes of lymph nodes alterations. In this work we used feature extracting tools I CT scans such as the average value of pixels, wavelet entropy, skewness and kurtosis to assist the radiologist in diagnosing lymph nodes alterations. The wavelet entropy proved to be the best image characteristic parameter to differentiate between the two groups of patients evaluated in this study. The study of cervical lymph nodes changes by features extraction methods may prove to be an excellent tool to assist in differentiating between infectious / inflammatory and tumor causes in CT scans. (author)

  5. The Classification and Evaluation of Computer-Aided Software Engineering Tools

    Science.gov (United States)

    1990-09-01

    Registered Trademark of Index Technology Corporation iv FrameMaker is a Registered Trademark of Frame Technology Corp HP 9000/Laserjet/ are Registered...generators and templates to meet certain standards (i.e., DoD STD-2167A) with interfaces to technical publishing systems from Interleaf, Framemaker , etc...publishing systems (i.e., Interleaf, Framemaker , etc...). Fourth Generation Language (4GL): Tool contains a high level language providing database access

  6. PyForecastTools

    Energy Technology Data Exchange (ETDEWEB)

    2017-09-22

    The PyForecastTools package provides Python routines for calculating metrics for model validation, forecast verification and model comparison. For continuous predictands the package provides functions for calculating bias (mean error, mean percentage error, median log accuracy, symmetric signed bias), and for calculating accuracy (mean squared error, mean absolute error, mean absolute scaled error, normalized RMSE, median symmetric accuracy). Convenience routines to calculate the component parts (e.g. forecast error, scaled error) of each metric are also provided. To compare models the package provides: generic skill score; percent better. Robust measures of scale including median absolute deviation, robust standard deviation, robust coefficient of variation and the Sn estimator are all provided by the package. Finally, the package implements Python classes for NxN contingency tables. In the case of a multi-class prediction, accuracy and skill metrics such as proportion correct and the Heidke and Peirce skill scores are provided as object methods. The special case of a 2x2 contingency table inherits from the NxN class and provides many additional metrics for binary classification: probability of detection, probability of false detection, false alarm ration, threat score, equitable threat score, bias. Confidence intervals for many of these quantities can be calculated using either the Wald method or Agresti-Coull intervals.

  7. Detection of surface cracking in steel pipes based on vibration data using a multi-class support vector machine classifier

    Science.gov (United States)

    Mustapha, S.; Braytee, A.; Ye, L.

    2017-04-01

    In this study, we focused at the development and verification of a robust framework for surface crack detection in steel pipes using measured vibration responses; with the presence of multiple progressive damage occurring in different locations within the structure. Feature selection, dimensionality reduction, and multi-class support vector machine were established for this purpose. Nine damage cases, at different locations, orientations and length, were introduced into the pipe structure. The pipe was impacted 300 times using an impact hammer, after each damage case, the vibration data were collected using 3 PZT wafers which were installed on the outer surface of the pipe. At first, damage sensitive features were extracted using the frequency response function approach followed by recursive feature elimination for dimensionality reduction. Then, a multi-class support vector machine learning algorithm was employed to train the data and generate a statistical model. Once the model is established, decision values and distances from the hyper-plane were generated for the new collected data using the trained model. This process was repeated on the data collected from each sensor. Overall, using a single sensor for training and testing led to a very high accuracy reaching 98% in the assessment of the 9 damage cases used in this study.

  8. Identification and optimization of classifier genes from multi-class earthworm microarray dataset.

    Directory of Open Access Journals (Sweden)

    Ying Li

    Full Text Available Monitoring, assessment and prediction of environmental risks that chemicals pose demand rapid and accurate diagnostic assays. A variety of toxicological effects have been associated with explosive compounds TNT and RDX. One important goal of microarray experiments is to discover novel biomarkers for toxicity evaluation. We have developed an earthworm microarray containing 15,208 unique oligo probes and have used it to profile gene expression in 248 earthworms exposed to TNT, RDX or neither. We assembled a new machine learning pipeline consisting of several well-established feature filtering/selection and classification techniques to analyze the 248-array dataset in order to construct classifier models that can separate earthworm samples into three groups: control, TNT-treated, and RDX-treated. First, a total of 869 genes differentially expressed in response to TNT or RDX exposure were identified using a univariate statistical algorithm of class comparison. Then, decision tree-based algorithms were applied to select a subset of 354 classifier genes, which were ranked by their overall weight of significance. A multiclass support vector machine (MC-SVM method and an unsupervised K-mean clustering method were applied to independently refine the classifier, producing a smaller subset of 39 and 30 classifier genes, separately, with 11 common genes being potential biomarkers. The combined 58 genes were considered the refined subset and used to build MC-SVM and clustering models with classification accuracy of 83.5% and 56.9%, respectively. This study demonstrates that the machine learning approach can be used to identify and optimize a small subset of classifier/biomarker genes from high dimensional datasets and generate classification models of acceptable precision for multiple classes.

  9. [International Classification of Public Health Nursing Practices - CIPESC®: a pedagogical tool for epidemiological studies].

    Science.gov (United States)

    Nichiata, Lúcia Yasuko Izumi; Padoveze, Maria Clara; Ciosak, Suely Itsuko; Gryschek, Anna Luiza de Fátima Pinho Lins; Costa, Angela Aparecida; Takahashi, Renata Ferreira; Bertolozzi, Maria Rita; de Araújo, Núbia Virgínia D'Ávila Limeira; Pereira, Erica Gomes; Dias, Vânia Ferreira Gomes; Cubas, Marcia Regina

    2012-06-01

    The CIPESC® is a tool that informs the work of nurses in Public Health and assists in prioritizing their care in practice, management and research. It is also a powerful pedagogical instrument for the qualification of nurses within the Brazilian healthcare system. In the teaching of infectious diseases, using the CIPESC® assists in analyzing the interventions by encouraging clinical and epidemiological thinking regarding the health-illness process. With the purpose in mind of developing resources for teaching undergraduate nursing students and encouraging reflection regarding the process of nursing work, this article presents an experimental application of CIPESC®, using meningococcal meningitis as an example.

  10. Insights into an original pocket-ligand pair classification: a promising tool for ligand profile prediction.

    Directory of Open Access Journals (Sweden)

    Stéphanie Pérot

    Full Text Available Pockets are today at the cornerstones of modern drug discovery projects and at the crossroad of several research fields, from structural biology to mathematical modeling. Being able to predict if a small molecule could bind to one or more protein targets or if a protein could bind to some given ligands is very useful for drug discovery endeavors, anticipation of binding to off- and anti-targets. To date, several studies explore such questions from chemogenomic approach to reverse docking methods. Most of these studies have been performed either from the viewpoint of ligands or targets. However it seems valuable to use information from both ligands and target binding pockets. Hence, we present a multivariate approach relating ligand properties with protein pocket properties from the analysis of known ligand-protein interactions. We explored and optimized the pocket-ligand pair space by combining pocket and ligand descriptors using Principal Component Analysis and developed a classification engine on this paired space, revealing five main clusters of pocket-ligand pairs sharing specific and similar structural or physico-chemical properties. These pocket-ligand pair clusters highlight correspondences between pocket and ligand topological and physico-chemical properties and capture relevant information with respect to protein-ligand interactions. Based on these pocket-ligand correspondences, a protocol of prediction of clusters sharing similarity in terms of recognition characteristics is developed for a given pocket-ligand complex and gives high performances. It is then extended to cluster prediction for a given pocket in order to acquire knowledge about its expected ligand profile or to cluster prediction for a given ligand in order to acquire knowledge about its expected pocket profile. This prediction approach shows promising results and could contribute to predict some ligand properties critical for binding to a given pocket, and conversely

  11. Insights into an original pocket-ligand pair classification: a promising tool for ligand profile prediction.

    Science.gov (United States)

    Pérot, Stéphanie; Regad, Leslie; Reynès, Christelle; Spérandio, Olivier; Miteva, Maria A; Villoutreix, Bruno O; Camproux, Anne-Claude

    2013-01-01

    Pockets are today at the cornerstones of modern drug discovery projects and at the crossroad of several research fields, from structural biology to mathematical modeling. Being able to predict if a small molecule could bind to one or more protein targets or if a protein could bind to some given ligands is very useful for drug discovery endeavors, anticipation of binding to off- and anti-targets. To date, several studies explore such questions from chemogenomic approach to reverse docking methods. Most of these studies have been performed either from the viewpoint of ligands or targets. However it seems valuable to use information from both ligands and target binding pockets. Hence, we present a multivariate approach relating ligand properties with protein pocket properties from the analysis of known ligand-protein interactions. We explored and optimized the pocket-ligand pair space by combining pocket and ligand descriptors using Principal Component Analysis and developed a classification engine on this paired space, revealing five main clusters of pocket-ligand pairs sharing specific and similar structural or physico-chemical properties. These pocket-ligand pair clusters highlight correspondences between pocket and ligand topological and physico-chemical properties and capture relevant information with respect to protein-ligand interactions. Based on these pocket-ligand correspondences, a protocol of prediction of clusters sharing similarity in terms of recognition characteristics is developed for a given pocket-ligand complex and gives high performances. It is then extended to cluster prediction for a given pocket in order to acquire knowledge about its expected ligand profile or to cluster prediction for a given ligand in order to acquire knowledge about its expected pocket profile. This prediction approach shows promising results and could contribute to predict some ligand properties critical for binding to a given pocket, and conversely, some key pocket

  12. MODIFIED MALLAMPATI CLASSIFICATION SCORE- A SIMPLE TOOL FOR PREDICTING TOLERANCE IN UNSEDATED OESOPHAGOGASTRODUODENOSCOPY

    Directory of Open Access Journals (Sweden)

    Abishek Sasidharan

    2017-04-01

    Full Text Available BACKGROUND 40-47% of patients poorly tolerates esophagogastroduodenoscopy (EGD. Early identification of potentially intolerant patients improve procedural success and avoid patient discomfort. Modified Mallampati Classification (MMC score is a simple scoring system used to predict difficult tracheal intubation and laryngoscope insertion. As EGD involves the same level of patient discomfort during introduction, MMC may predict EGD tolerance. MATERIALS AND METHODS 100 patients with dyspeptic symptoms and no alarm features attending our department were recruited for unsedated EGD between January and July 2012. All patients had good performance status and underlying anxiety disorder was excluded. Based on MMC, patients placed into 4 classes- I: Soft palate, fauces, pillars and uvula visible. II: Soft palate, fauces and uvula visible. III: Soft palate and base of uvula visible. IV: Soft palate not visible. They were divided into good view (class I and II and poor view (class III and IV. EGD was performed by the same consultant and MMS status assessed by two independent trained personnel. All received 2 doses of topical pharyngeal spray containing 10% lidocaine hydrochloride. Outcome measurements were gag reflex, endoscopist’s assessment and patient feedback. RESULTS Of 100 patients, 52 were males. 58 in group A and 42 in group B. Gag reflex was present in 32.7% of good view group compared to 78.6% in poor view (p<0.001. From the endoscopist’s view, good tolerability observed in 72.4% of good view group compared to 21% in poor view (p<0.001. 74.1% patient reported satisfactory feedback in good view group compared to 19% in poor view group (p<0.001. CONCLUSION MMC is a good clinical indicator for predicting tolerance in unsedated EGD.

  13. Rough Sets as a Knowledge Discovery and Classification Tool for the Diagnosis of Students with Learning Disabilities

    Directory of Open Access Journals (Sweden)

    Yu-Chi Lin

    2011-02-01

    Full Text Available Due to the implicit characteristics of learning disabilities (LDs, the diagnosis of students with learning disabilities has long been a difficult issue. Artificial intelligence techniques like artificial neural network (ANN and support vector machine (SVM have been applied to the LD diagnosis problem with satisfactory outcomes. However, special education teachers or professionals tend to be skeptical to these kinds of black-box predictors. In this study, we adopt the rough set theory (RST, which can not only perform as a classifier, but may also produce meaningful explanations or rules, to the LD diagnosis application. Our experiments indicate that the RST approach is competitive as a tool for feature selection, and it performs better in term of prediction accuracy than other rulebased algorithms such as decision tree and ripper algorithms. We also propose to mix samples collected from sources with different LD diagnosis procedure and criteria. By pre-processing these mixed samples with simple and readily available clustering algorithms, we are able to improve the quality and support of rules generated by the RST. Overall, our study shows that the rough set approach, as a classification and knowledge discovery tool, may have great potential in playing an essential role in LD diagnosis.

  14. A novel algorithm of super-resolution image reconstruction based on multi-class dictionaries for natural scene

    Science.gov (United States)

    Wu, Wei; Zhao, Dewei; Zhang, Huan

    2015-12-01

    Super-resolution image reconstruction is an effective method to improve the image quality. It has important research significance in the field of image processing. However, the choice of the dictionary directly affects the efficiency of image reconstruction. A sparse representation theory is introduced into the problem of the nearest neighbor selection. Based on the sparse representation of super-resolution image reconstruction method, a super-resolution image reconstruction algorithm based on multi-class dictionary is analyzed. This method avoids the redundancy problem of only training a hyper complete dictionary, and makes the sub-dictionary more representatives, and then replaces the traditional Euclidean distance computing method to improve the quality of the whole image reconstruction. In addition, the ill-posed problem is introduced into non-local self-similarity regularization. Experimental results show that the algorithm is much better results than state-of-the-art algorithm in terms of both PSNR and visual perception.

  15. Throughput Maximization Using an SVM for Multi-Class Hypothesis-Based Spectrum Sensing in Cognitive Radio

    Directory of Open Access Journals (Sweden)

    Sana Ullah Jan

    2018-03-01

    Full Text Available A framework of spectrum sensing with a multi-class hypothesis is proposed to maximize the achievable throughput in cognitive radio networks. The energy range of a sensing signal under the hypothesis that the primary user is absent (in a conventional two-class hypothesis is further divided into quantized regions, whereas the hypothesis that the primary user is present is conserved. The non-radio frequency energy harvesting-equiped secondary user transmits, when the primary user is absent, with transmission power based on the hypothesis result (the energy level of the sensed signal and the residual energy in the battery: the lower the energy of the received signal, the higher the transmission power, and vice versa. Conversely, the lower is the residual energy in the node, the lower is the transmission power. This technique increases the throughput of a secondary link by providing a higher number of transmission events, compared to the conventional two-class hypothesis. Furthermore, transmission with low power for higher energy levels in the sensed signal reduces the probability of interference with primary users if, for instance, detection was missed. The familiar machine learning algorithm known as a support vector machine (SVM is used in a one-versus-rest approach to classify the input signal into predefined classes. The input signal to the SVM is composed of three statistical features extracted from the sensed signal and a number ranging from 0 to 100 representing the percentage of residual energy in the node’s battery. To increase the generalization of the classifier, k-fold cross-validation is utilized in the training phase. The experimental results show that an SVM with the given features performs satisfactorily for all kernels, but an SVM with a polynomial kernel outperforms linear and radial-basis function kernels in terms of accuracy. Furthermore, the proposed multi-class hypothesis achieves higher throughput compared to the

  16. Remote Sensing Image Analysis Without Expert Knowledge - A Web-Based Classification Tool On Top of Taverna Workflow Management System

    Science.gov (United States)

    Selsam, Peter; Schwartze, Christian

    2016-10-01

    Providing software solutions via internet has been known for quite some time and is now an increasing trend marketed as "software as a service". A lot of business units accept the new methods and streamlined IT strategies by offering web-based infrastructures for external software usage - but geospatial applications featuring very specialized services or functionalities on demand are still rare. Originally applied in desktop environments, the ILMSimage tool for remote sensing image analysis and classification was modified in its communicating structures and enabled for running on a high-power server and benefiting from Tavema software. On top, a GIS-like and web-based user interface guides the user through the different steps in ILMSimage. ILMSimage combines object oriented image segmentation with pattern recognition features. Basic image elements form a construction set to model for large image objects with diverse and complex appearance. There is no need for the user to set up detailed object definitions. Training is done by delineating one or more typical examples (templates) of the desired object using a simple vector polygon. The template can be large and does not need to be homogeneous. The template is completely independent from the segmentation. The object definition is done completely by the software.

  17. Ab initio and template-based prediction of multi-class distance maps by two-dimensional recursive neural networks

    Directory of Open Access Journals (Sweden)

    Martin Alberto JM

    2009-01-01

    Full Text Available Abstract Background Prediction of protein structures from their sequences is still one of the open grand challenges of computational biology. Some approaches to protein structure prediction, especially ab initio ones, rely to some extent on the prediction of residue contact maps. Residue contact map predictions have been assessed at the CASP competition for several years now. Although it has been shown that exact contact maps generally yield correct three-dimensional structures, this is true only at a relatively low resolution (3–4 Å from the native structure. Another known weakness of contact maps is that they are generally predicted ab initio, that is not exploiting information about potential homologues of known structure. Results We introduce a new class of distance restraints for protein structures: multi-class distance maps. We show that Cα trace reconstructions based on 4-class native maps are significantly better than those from residue contact maps. We then build two predictors of 4-class maps based on recursive neural networks: one ab initio, or relying on the sequence and on evolutionary information; one template-based, or in which homology information to known structures is provided as a further input. We show that virtually any level of sequence similarity to structural templates (down to less than 10% yields more accurate 4-class maps than the ab initio predictor. We show that template-based predictions by recursive neural networks are consistently better than the best template and than a number of combinations of the best available templates. We also extract binary residue contact maps at an 8 Å threshold (as per CASP assessment from the 4-class predictors and show that the template-based version is also more accurate than the best template and consistently better than the ab initio one, down to very low levels of sequence identity to structural templates. Furthermore, we test both ab-initio and template-based 8

  18. Direct immersion single drop micro-extraction method for multi-class pesticides analysis in mango using GC-MS.

    Science.gov (United States)

    Pano-Farias, Norma S; Ceballos-Magaña, Silvia G; Muñiz-Valencia, Roberto; Jurado, Jose M; Alcázar, Ángela; Aguayo-Villarreal, Ismael A

    2017-12-15

    Due the negative effects of pesticides on environment and human health, more efficient and environmentally friendly methods are needed. In this sense, a simple, fast, free from memory effects and economical direct-immersion single drop micro-extraction (SDME) method and GC-MS for multi-class pesticides determination in mango samples was developed. Sample pre-treatment using ultrasound-assisted solvent extraction and factors affecting the SDME procedure (extractant solvent, drop volume, stirring rate, ionic strength, time, pH and temperature) were optimized using factorial experimental design. This method presented high sensitive (LOD: 0.14-169.20μgkg -1 ), acceptable precision (RSD: 0.7-19.1%), satisfactory recovery (69-119%) and high enrichment factors (20-722). Several obtained LOQs are below the MRLs established by the European Commission; therefore, the method could be applied for pesticides determination in routing analysis and custom laboratories. Moreover, this method has shown to be suitable for determination of some of the studied pesticides in lime, melon, papaya, banana, tomato, and lettuce. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. Estonian soil classification as a tool for recording information on soil cover and its matching with local site types, plant covers and humus forms classifications

    Science.gov (United States)

    Kõlli, Raimo; Tõnutare, Tõnu; Rannik, Kaire; Krebstein, Kadri

    2015-04-01

    Estonian soil classification (ESC) has been used successfully during more than half of century in soil survey, teaching of soil science, generalization of soil databases, arrangement of soils sustainable management and others. The Estonian normally developed (postlithogenic) mineral soils (form 72.4% from total area) are characterized by mean of genetic-functional schema, where the pedo-ecological position of soils (ie. location among other soils) is given by means of three scalars: (i) 8 stage lithic-genetic scalar (from rendzina to podzols) separates soils each from other by parent material, lithic properties, calcareousness, character of soil processes and others, (ii) 6 stage moisture and aeration conditions scalar (from aridic or well aerated to permanently wet or reductic conditions), and (iii) 2-3 stage soil development scalar, which characterizes the intensity of soil forming processes (accumulation of humus, podzolization). The organic soils pedo-ecological schema, which links with histic postlithogenic soils, is elaborated for characterizing of peatlands superficial mantle (form 23.7% from whole soil cover). The position each peat soil species among others on this organic (peat) soil matrix schema is determined by mean of 3 scalars: (i) peat thickness, (ii) type of paludification or peat forming peculiarities, and (iii) stage of peat decomposition or peat type. On the matrix of abnormally developed (synlithogenic) soils (all together 3.9%) the soil species are positioned (i) by proceeding in actual time geological processes as erosion, fluvial processes (at vicinity of rivers, lakes or sea) or transforming by anthropogenic and technological processes, and (ii) by 7 stage moisture conditions (from aridic to subaqual) of soils. The most important functions of soil cover are: (i) being a suitable environment for plant productivity; (ii) forming adequate conditions for decomposition, transformation and conversion of falling litter (characterized by humus

  20. Using the landform tool to calculate landforms for hydrogeomorphic wetland classification at a country-wide scale

    CSIR Research Space (South Africa)

    Van Deventer, Heidi

    2014-11-01

    Full Text Available Hydrogeomorphic approaches to wetland classification use landform classes to distinguish wetland functionality at a regional scale. Space-borne radar technology enabled faster regional surveying of surface elevations to digital elevation models...

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2008-10-01

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

  2. Trace analysis of multi-class pesticide residues in Chinese medicinal health wines using gas chromatography with electron capture detection

    Science.gov (United States)

    Kong, Wei-Jun; Liu, Qiu-Tao; Kong, Dan-Dan; Liu, Qian-Zhen; Ma, Xin-Ping; Yang, Mei-Hua

    2016-02-01

    A method is described for multi-residue, high-throughput determination of trace levels of 22 organochlorine pesticides (OCPs) and 5 pyrethroid pesticides (PYPs) in Chinese medicinal (CM) health wines using a QuEChERS (quick, easy, cheap, effective, rugged, and safe) based extraction method and gas chromatography-electron capture detection (GC-ECD). Several parameters were optimized to improve preparation and separation time while still maintaining high sensitivity. Validation tests of spiked samples showed good linearities for 27 pesticides (R = 0.9909-0.9996) over wide concentration ranges. Limits of detection (LODs) and quantification (LOQs) were measured at ng/L levels, 0.06-2 ng/L and 0.2-6 ng/L for OCPs and 0.02-3 ng/L and 0.06-7 ng/L for PYPs, respectively. Inter- and intra-day precision tests showed variations of 0.65-9.89% for OCPs and 0.98-13.99% for PYPs, respectively. Average recoveries were in the range of 47.74-120.31%, with relative standard deviations below 20%. The developed method was then applied to analyze 80 CM wine samples. Beta-BHC (Benzene hexachloride) was the most frequently detected pesticide at concentration levels of 5.67-31.55 mg/L, followed by delta-BHC, trans-chlordane, gamma-BHC, and alpha-BHC. The validated method is simple and economical, with adequate sensitivity for trace levels of multi-class pesticides. It could be adopted by laboratories for this and other types of complex matrices analysis.

  3. Combining two open source tools for neural computation (BioPatRec and Netlab) improves movement classification for prosthetic control.

    Science.gov (United States)

    Prahm, Cosima; Eckstein, Korbinian; Ortiz-Catalan, Max; Dorffner, Georg; Kaniusas, Eugenijus; Aszmann, Oskar C

    2016-08-31

    Controlling a myoelectric prosthesis for upper limbs is increasingly challenging for the user as more electrodes and joints become available. Motion classification based on pattern recognition with a multi-electrode array allows multiple joints to be controlled simultaneously. Previous pattern recognition studies are difficult to compare, because individual research groups use their own data sets. To resolve this shortcoming and to facilitate comparisons, open access data sets were analysed using components of BioPatRec and Netlab pattern recognition models. Performances of the artificial neural networks, linear models, and training program components were compared. Evaluation took place within the BioPatRec environment, a Matlab-based open source platform that provides feature extraction, processing and motion classification algorithms for prosthetic control. The algorithms were applied to myoelectric signals for individual and simultaneous classification of movements, with the aim of finding the best performing algorithm and network model. Evaluation criteria included classification accuracy and training time. Results in both the linear and the artificial neural network models demonstrated that Netlab's implementation using scaled conjugate training algorithm reached significantly higher accuracies than BioPatRec. It is concluded that the best movement classification performance would be achieved through integrating Netlab training algorithms in the BioPatRec environment so that future prosthesis training can be shortened and control made more reliable. Netlab was therefore included into the newest release of BioPatRec (v4.0).

  4. Structural knowledge learning from maps for supervised land cover/use classification: Application to the monitoring of land cover/use maps in French Guiana

    Science.gov (United States)

    Bayoudh, Meriam; Roux, Emmanuel; Richard, Gilles; Nock, Richard

    2015-03-01

    The number of satellites and sensors devoted to Earth observation has become increasingly elevated, delivering extensive data, especially images. At the same time, the access to such data and the tools needed to process them has considerably improved. In the presence of such data flow, we need automatic image interpretation methods, especially when it comes to the monitoring and prediction of environmental and societal changes in highly dynamic socio-environmental contexts. This could be accomplished via artificial intelligence. The concept described here relies on the induction of classification rules that explicitly take into account structural knowledge, using Aleph, an Inductive Logic Programming (ILP) system, combined with a multi-class classification procedure. This methodology was used to monitor changes in land cover/use of the French Guiana coastline. One hundred and fifty-eight classification rules were induced from 3 diachronic land cover/use maps including 38 classes. These rules were expressed in first order logic language, which makes them easily understandable by non-experts. A 10-fold cross-validation gave significant average values of 84.62%, 99.57% and 77.22% for classification accuracy, specificity and sensitivity, respectively. Our methodology could be beneficial to automatically classify new objects and to facilitate object-based classification procedures.

  5. A systematic literature review of the situation of the International Classification of Functioning, Disability, and Health and the International Classification of Functioning, Disability, and Health-Children and Youth version in education: a useful tool or a flight of fancy?

    Science.gov (United States)

    Moretti, Marta; Alves, Ines; Maxwell, Gregor

    2012-02-01

    This article presents the outcome of a systematic literature review exploring the applicability of the International Classification of Functioning, Disability, and Health (ICF) and its Children and Youth version (ICF-CY) at various levels and in processes within the education systems in different countries. A systematic database search using selected search terms has been used. The selection of studies was then refined further using four protocols: inclusion and exclusion protocols at abstract and full text and extraction levels along with a quality protocol. Studies exploring the direct relationship between education and the ICF/ICF-CY were sought.As expected, the results show a strong presence of studies from English-speaking countries, namely from Europe and North America. The articles were mainly published in noneducational journals. The most used ICF/ICF-CY components are activity and participation, participation, and environmental factors. From the analysis of the papers included, the results show that the ICF/ICF-CY is currently used as a research tool, theoretical framework, and tool for implementing educational processes. The ICF/ICF-CY can provide a useful language to the education field where there is currently a lot of disparity in theoretical, praxis, and research issues. Although the systematic literature review does not report a high incidence of the use of the ICF/ICF-CY in education, the results show that the ICF/ICF-CY model and classification have potential to be applied in education systems.

  6. Exploration and classification of chromatographic fingerprints as additional tool for identification and quality control of several Artemisia species.

    Science.gov (United States)

    Alaerts, Goedele; Pieters, Sigrid; Logie, Hans; Van Erps, Jürgen; Merino-Arévalo, Maria; Dejaegher, Bieke; Smeyers-Verbeke, Johanna; Vander Heyden, Yvan

    2014-07-01

    The World Health Organization accepts chromatographic fingerprints as a tool for identification and quality control of herbal medicines. This is the first study in which the distinction, identification and quality control of four different Artemisia species, i.e. Artemisia vulgaris, A. absinthium, A. annua and A. capillaris samples, is performed based on the evaluation of entire chromatographic fingerprint profiles developed with identical experimental conditions. High-Performance Liquid Chromatography (HPLC) with Diode Array Detection (DAD) was used to develop the fingerprints. Application of factorial designs leads to methanol/water (80:20 (v/v)) as the best extraction solvent for the pulverised plant material and to a shaking bath for 30 min as extraction method. Further, so-called screening, optimisation and fine-tuning phases were performed during fingerprint development. Most information about the different Artemisia species, i.e. the highest number of separated peaks in the fingerprint, was acquired on four coupled Chromolith columns (100 mm × 4.6 mm I.D.). Trifluoroacetic acid 0.05% (v/v) was used as mobile-phase additive in a stepwise linear methanol/water gradient, i.e. 5, 34, 41, 72 and 95% (v/v) methanol at 0, 9, 30, 44 and 51 min, where the last mobile phase composition was kept isocratic till 60 min. One detection wavelength was selected to perform data analysis. The lowest similarity between the fingerprints of the four species was present at 214 nm. The HPLC/DAD method was applied on 199 herbal samples of the four Artemisia species, resulting in 357 fingerprints. The within- and between-day variation of the entire method, as well as the quality control fingerprints obtained during routine analysis, were found acceptable. The distinction of these Artemisia species was evaluated based on the entire chromatographic profiles, developed by a shared method, and visualised in score plots by means of the Principal Component Analysis (PCA) exploratory data

  7. An alternative approach to the determination of scaling law expressions for the L–H transition in Tokamaks utilizing classification tools instead of regression

    International Nuclear Information System (INIS)

    Gaudio, P; Gelfusa, M; Lupelli, I; Murari, A; Vega, J

    2014-01-01

    A new approach to determine the power law expressions for the threshold between the H and L mode of confinement is presented. The method is based on two powerful machine learning tools for classification: neural networks and support vector machines. Using as inputs clear examples of the systems on either side of the transition, the machine learning tools learn the input–output mapping corresponding to the equations of the boundary separating the confinement regimes. Systematic tests with synthetic data show that the machine learning tools provide results competitive with traditional statistical regression and more robust against random noise and systematic errors. The developed tools have then been applied to the multi-machine International Tokamak Physics Activity International Global Threshold Database of validated ITER-like Tokamak discharges. The machine learning tools converge on the same scaling law parameters obtained with non-linear regression. On the other hand, the developed tools allow a reduction of 50% of the uncertainty in the extrapolations to ITER. Therefore the proposed approach can effectively complement traditional regression since its application poses much less stringent requirements on the experimental data, to be used to determine the scaling laws, because they do not require examples exactly at the moment of the transition. (paper)

  8. Evaluation of data discretization methods to derive platform independent isoform expression signatures for multi-class tumor subtyping.

    Science.gov (United States)

    Jung, Segun; Bi, Yingtao; Davuluri, Ramana V

    2015-01-01

    Many supervised learning algorithms have been applied in deriving gene signatures for patient stratification from gene expression data. However, transferring the multi-gene signatures from one analytical platform to another without loss of classification accuracy is a major challenge. Here, we compared three unsupervised data discretization methods--Equal-width binning, Equal-frequency binning, and k-means clustering--in accurately classifying the four known subtypes of glioblastoma multiforme (GBM) when the classification algorithms were trained on the isoform-level gene expression profiles from exon-array platform and tested on the corresponding profiles from RNA-seq data. We applied an integrated machine learning framework that involves three sequential steps; feature selection, data discretization, and classification. For models trained and tested on exon-array data, the addition of data discretization step led to robust and accurate predictive models with fewer number of variables in the final models. For models trained on exon-array data and tested on RNA-seq data, the addition of data discretization step dramatically improved the classification accuracies with Equal-frequency binning showing the highest improvement with more than 90% accuracies for all the models with features chosen by Random Forest based feature selection. Overall, SVM classifier coupled with Equal-frequency binning achieved the best accuracy (> 95%). Without data discretization, however, only 73.6% accuracy was achieved at most. The classification algorithms, trained and tested on data from the same platform, yielded similar accuracies in predicting the four GBM subgroups. However, when dealing with cross-platform data, from exon-array to RNA-seq, the classifiers yielded stable models with highest classification accuracies on data transformed by Equal frequency binning. The approach presented here is generally applicable to other cancer types for classification and identification of

  9. Automatic classification and detection of clinically relevant images for diabetic retinopathy

    Science.gov (United States)

    Xu, Xinyu; Li, Baoxin

    2008-03-01

    We proposed a novel approach to automatic classification of Diabetic Retinopathy (DR) images and retrieval of clinically-relevant DR images from a database. Given a query image, our approach first classifies the image into one of the three categories: microaneurysm (MA), neovascularization (NV) and normal, and then it retrieves DR images that are clinically-relevant to the query image from an archival image database. In the classification stage, the query DR images are classified by the Multi-class Multiple-Instance Learning (McMIL) approach, where images are viewed as bags, each of which contains a number of instances corresponding to non-overlapping blocks, and each block is characterized by low-level features including color, texture, histogram of edge directions, and shape. McMIL first learns a collection of instance prototypes for each class that maximizes the Diverse Density function using Expectation- Maximization algorithm. A nonlinear mapping is then defined using the instance prototypes and maps every bag to a point in a new multi-class bag feature space. Finally a multi-class Support Vector Machine is trained in the multi-class bag feature space. In the retrieval stage, we retrieve images from the archival database who bear the same label with the query image, and who are the top K nearest neighbors of the query image in terms of similarity in the multi-class bag feature space. The classification approach achieves high classification accuracy, and the retrieval of clinically-relevant images not only facilitates utilization of the vast amount of hidden diagnostic knowledge in the database, but also improves the efficiency and accuracy of DR lesion diagnosis and assessment.

  10. Image Processing Tools for Improved Visualization and Analysis of Remotely Sensed Images for Agriculture and Forest Classifications

    OpenAIRE

    SINHA G. R.

    2017-01-01

    This paper suggests Image Processing tools for improved visualization and better analysis of remotely sensed images. There are methods already available in literature for the purpose but the most important challenge among the limitations is lack of robustness. We propose an optimal method for image enhancement of the images using fuzzy based approaches and few optimization tools. The segmentation images subsequently obtained after de-noising will be classified into distinct information and th...

  11. Development of Tier 1 screening tool for soil and groundwater vulnerability assessment in Korea using classification algorithm in a neural network

    Science.gov (United States)

    Shin, K. H.; Kim, K. H.; Ki, S. J.; Lee, H. G.

    2017-12-01

    The vulnerability assessment tool at a Tier 1 level, although not often used for regulatory purposes, helps establish pollution prevention and management strategies in the areas of potential environmental concern such as soil and ground water. In this study, the Neural Network Pattern Recognition Tool embedded in MATLAB was used to allow the initial screening of soil and groundwater pollution based on data compiled across about 1000 previously contaminated sites in Korea. The input variables included a series of parameters which were tightly related to downward movement of water and contaminants through soil and ground water, whereas multiple classes were assigned to the sum of concentrations of major pollutants detected. Results showed that in accordance with diverse pollution indices for soil and ground water, pollution levels in both media were strongly modulated by site-specific characteristics such as intrinsic soil and other geologic properties, in addition to pollution sources and rainfall. However, classification accuracy was very sensitive to the number of classes defined as well as the types of the variables incorporated, requiring careful selection of input variables and output categories. Therefore, we believe that the proposed methodology is used not only to modify existing pollution indices so that they are more suitable for addressing local vulnerability, but also to develop a unique assessment tool to support decision making based on locally or nationally available data. This study was funded by a grant from the GAIA project(2016000560002), Korea Environmental Industry & Technology Institute, Republic of Korea.

  12. Eco-friendly LC-MS/MS method for analysis of multi-class micropollutants in tap, fountain, and well water from northern Portugal.

    Science.gov (United States)

    Barbosa, Marta O; Ribeiro, Ana R; Pereira, Manuel F R; Silva, Adrián M T

    2016-11-01

    Organic micropollutants present in drinking water (DW) may cause adverse effects for public health, and so reliable analytical methods are required to detect these pollutants at trace levels in DW. This work describes the first green analytical methodology for multi-class determination of 21 pollutants in DW: seven pesticides, an industrial compound, 12 pharmaceuticals, and a metabolite (some included in Directive 2013/39/EU or Decision 2015/495/EU). A solid-phase extraction procedure followed by ultra-high-performance liquid chromatography coupled to tandem mass spectrometry (offline SPE-UHPLC-MS/MS) method was optimized using eco-friendly solvents, achieving detection limits below 0.20 ng L -1 . The validated analytical method was successfully applied to DW samples from different sources (tap, fountain, and well waters) from different locations in the north of Portugal, as well as before and after bench-scale UV and ozonation experiments in spiked tap water samples. Thirteen compounds were detected, many of them not regulated yet, in the following order of frequency: diclofenac > norfluoxetine > atrazine > simazine > warfarin > metoprolol > alachlor > chlorfenvinphos > trimethoprim > clarithromycin ≈ carbamazepine ≈ PFOS > citalopram. Hazard quotients were also estimated for the quantified substances and suggested no adverse effects to humans. Graphical Abstract Occurrence and removal of multi-class micropollutants in drinking water, analyzed by an eco-friendly LC-MS/MS method.

  13. Correlation of Estradiol Serum Levels with Classification of Osteoporosis Risk OSTA (Osteoporosis Self-Assessment Tools for Asian in Menopause Women

    Directory of Open Access Journals (Sweden)

    Eva Maya Puspita

    2017-01-01

    Full Text Available Background: In postmenopausal women, decreasing estrogen levels is a marker of ovarian dysfunction. Hypoestrogenic state has known increasing the risk of osteoporosis. Objective: To determine the correlation between estradiol serum levels with classification of osteoporosis risk OSTA (Osteoporosis Self-Assessment Tools for Asian in menopausal women. Methods: This study was case series study which examined estradiol serum in menopausal women by ELISA and assess the osteoporosis risk using osteoporosis risk classification OSTA. Total 47 samples was collected at Dr. H.Adam malik, dr. Pirngadi, and RSU Networking in Medan. This research was conducted from May to December 2016. Data were statistically analyzed, and presented with Spearman test. Results: In this study, we found the mean levels of estradiol in menopausal women was 18.62 ± 16.85 ng / ml with OSTA osteoporosis risk score of 2.09 ± 2.45. There was a significant positive correlation between estradiol and risk of osteoporosis OSTA with correlation coefficient r = 0.825 and p <0.05. Conclusion: There is a strong positive correlation between serum levels of estradiol with OSTA osteoporosis risk assessment in menopausal women.

  14. Artificial Neural Network approach to develop unique Classification and Raga identification tools for Pattern Recognition in Carnatic Music

    Science.gov (United States)

    Srimani, P. K.; Parimala, Y. G.

    2011-12-01

    A unique approach has been developed to study patterns in ragas of Carnatic Classical music based on artificial neural networks. Ragas in Carnatic music which have found their roots in the Vedic period, have grown on a Scientific foundation over thousands of years. However owing to its vastness and complexities it has always been a challenge for scientists and musicologists to give an all encompassing perspective both qualitatively and quantitatively. Cognition, comprehension and perception of ragas in Indian classical music have always been the subject of intensive research, highly intriguing and many facets of these are hitherto not unravelled. This paper is an attempt to view the melakartha ragas with a cognitive perspective using artificial neural network based approach which has given raise to very interesting results. The 72 ragas of the melakartha system were defined through the combination of frequencies occurring in each of them. The data sets were trained using several neural networks. 100% accurate pattern recognition and classification was obtained using linear regression, TLRN, MLP and RBF networks. Performance of the different network topologies, by varying various network parameters, were compared. Linear regression was found to be the best performing network.

  15. Graph Theory-Based Brain Connectivity for Automatic Classification of Multiple Sclerosis Clinical Courses

    Directory of Open Access Journals (Sweden)

    Gabriel Kocevar

    2016-10-01

    Full Text Available Purpose: In this work, we introduce a method to classify Multiple Sclerosis (MS patients into four clinical profiles using structural connectivity information. For the first time, we try to solve this question in a fully automated way using a computer-based method. The main goal is to show how the combination of graph-derived metrics with machine learning techniques constitutes a powerful tool for a better characterization and classification of MS clinical profiles.Materials and methods: Sixty-four MS patients (12 Clinical Isolated Syndrome (CIS, 24 Relapsing Remitting (RR, 24 Secondary Progressive (SP, and 17 Primary Progressive (PP along with 26 healthy controls (HC underwent MR examination. T1 and diffusion tensor imaging (DTI were used to obtain structural connectivity matrices for each subject. Global graph metrics, such as density and modularity, were estimated and compared between subjects’ groups. These metrics were further used to classify patients using tuned Support Vector Machine (SVM combined with Radial Basic Function (RBF kernel.Results: When comparing MS patients to HC subjects, a greater assortativity, transitivity and characteristic path length as well as a lower global efficiency were found. Using all graph metrics, the best F-Measures (91.8%, 91.8%, 75.6% and 70.6% were obtained for binary (HC-CIS, CIS-RR, RR-PP and multi-class (CIS-RR-SP classification tasks, respectively. When using only one graph metric, the best F-Measures (83.6%, 88.9% and 70.7% were achieved for modularity with previous binary classification tasks.Conclusion: Based on a simple DTI acquisition associated with structural brain connectivity analysis, this automatic method allowed an accurate classification of different MS patients’ clinical profiles.

  16. A Comparative Study with RapidMiner and WEKA Tools over some Classification Techniques for SMS Spam

    Science.gov (United States)

    Foozy, Cik Feresa Mohd; Ahmad, Rabiah; Faizal Abdollah, M. A.; Chai Wen, Chuah

    2017-08-01

    SMS Spamming is a serious attack that can manipulate the use of the SMS by spreading the advertisement in bulk. By sending the unwanted SMS that contain advertisement can make the users feeling disturb and this against the privacy of the mobile users. To overcome these issues, many studies have proposed to detect SMS Spam by using data mining tools. This paper will do a comparative study using five machine learning techniques such as Naïve Bayes, K-NN (K-Nearest Neighbour Algorithm), Decision Tree, Random Forest and Decision Stumps to observe the accuracy result between RapidMiner and WEKA for dataset SMS Spam UCI Machine Learning repository.

  17. Assessment of MultiLocus Sequence Analysis As a Valuable Tool for the Classification of the Genus Salinivibrio

    Directory of Open Access Journals (Sweden)

    Clara López-Hermoso

    2017-06-01

    future studies regarding the classification and identification of new Salinivibrio strains we recommend the following strategy: (i initial partial sequencing of the 16S rRNA gene for genus-level identification; (ii sequencing and concatenation of the four before mentioned housekeeping genes for species-level discrimination; (iii DDH experiments, only required when the concatenated MLSA similarity values among a new isolate and other Salinivibrio strains are above the 97% cut-off.

  18. Identifying clinically disruptive International Classification of Diseases 10th Revision Clinical Modification conversions to mitigate financial costs using an online tool.

    Science.gov (United States)

    Venepalli, Neeta K; Qamruzzaman, Yusuf; Li, Jianrong John; Lussier, Yves A; Boyd, Andrew D

    2014-03-01

    To quantify coding ambiguity in International Classification of Diseases Ninth Revision Clinical Modification conversions (ICD-9-CM) to ICD-10-CM mappings for hematology-oncology diagnoses within an Illinois Medicaid database and an academic cancer center database (University of Illinois Cancer Center [UICC]) with the goal of anticipating challenges during ICD-10-CM transition. One data set of ICD-9-CM diagnosis codes came from the 2010 Illinois Department of Medicaid, filtered for diagnoses generated by hematology-oncology providers. The other data set of ICD-9-CM diagnosis codes came from UICC. Using a translational methodology via the Motif Web portal ICD-9-CM conversion tool, ICD-9-CM to ICD-10-CM code conversions were graphically mapped and evaluated for clinical loss of information. The transition to ICD-10-CM led to significant information loss, affecting 8% of total Medicaid codes and 1% of UICC codes; 39 ICD-9-CM codes with information loss accounted for 2.9% of total Medicaid reimbursements and 5.3% of UICC billing charges. Prior work stated hematology-oncology would be the least affected medical specialty. However, information loss affecting 5% of billing costs could evaporate the operating margin of a practice. By identifying codes at risk for complex transitions, the analytic tools described can be replicated for oncology practices to forecast areas requiring additional training and resource allocation. In summary, complex transitions and diagnosis codes associated with information loss within clinical oncology require additional attention during the transition to ICD-10-CM.

  19. A case study on the application of International Classification of Functioning, Disability and Health (ICF)-based tools for vocational rehabilitation in spinal cord injury.

    Science.gov (United States)

    Glässel, Andrea; Rauch, Alexandra; Selb, Melissa; Emmenegger, Karl; Lückenkemper, Miriam; Escorpizo, Reuben

    2012-01-01

    Vocational rehabilitation (VR) plays a key role in bringing persons with acquired disabilities back to work, while encouraging employment participation. The purpose of this case study is to illustrate the systematic application of International Classification of Functioning, Disability, and Health (ICF)-based documentation tools by using ICF Core Sets in VR shown with a case example of a client with traumatic spinal cord injury (SCI). The client was a 26-year-old male with paraplegia (7th thoracic level), working in the past as a mover. This case study describes the integration of the ICF Core Sets for VR into an interdisciplinary rehabilitation program by using ICF-based documentation tools. Improvements in the client's impairments, activity limitations, and participation restrictions were observed following rehabilitation. Goals in different areas of functioning were achieved. The use of the ICF Core Sets in VR allows a comprehensive assessment of the client's level of functioning and intervention planning. Specifically, the Brief ICF Core Set in VR can provide domains for intervention relevant to each member of an interdisciplinary team and hence, can facilitate the VR management process in a SCI center in Switzerland.

  20. Use of Multi-class Empirical Orthogonal Function for Identification of Hydrogeological Parameters and Spatiotemporal Pattern of Multiple Recharges in Groundwater Modeling

    Science.gov (United States)

    Huang, C. L.; Hsu, N. S.; Yeh, W. W. G.; Hsieh, I. H.

    2017-12-01

    This study develops an innovative calibration method for regional groundwater modeling by using multi-class empirical orthogonal functions (EOFs). The developed method is an iterative approach. Prior to carrying out the iterative procedures, the groundwater storage hydrographs associated with the observation wells are calculated. The combined multi-class EOF amplitudes and EOF expansion coefficients of the storage hydrographs are then used to compute the initial gauss of the temporal and spatial pattern of multiple recharges. The initial guess of the hydrogeological parameters are also assigned according to in-situ pumping experiment. The recharges include net rainfall recharge and boundary recharge, and the hydrogeological parameters are riverbed leakage conductivity, horizontal hydraulic conductivity, vertical hydraulic conductivity, storage coefficient, and specific yield. The first step of the iterative algorithm is to conduct the numerical model (i.e. MODFLOW) by the initial guess / adjusted values of the recharges and parameters. Second, in order to determine the best EOF combination of the error storage hydrographs for determining the correction vectors, the objective function is devised as minimizing the root mean square error (RMSE) of the simulated storage hydrographs. The error storage hydrograph are the differences between the storage hydrographs computed from observed and simulated groundwater level fluctuations. Third, adjust the values of recharges and parameters and repeat the iterative procedures until the stopping criterion is reached. The established methodology was applied to the groundwater system of Ming-Chu Basin, Taiwan. The study period is from January 1st to December 2ed in 2012. Results showed that the optimal EOF combination for the multiple recharges and hydrogeological parameters can decrease the RMSE of the simulated storage hydrographs dramatically within three calibration iterations. It represents that the iterative approach that

  1. Classifying Classifications

    DEFF Research Database (Denmark)

    Debus, Michael S.

    2017-01-01

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

  2. 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....

  3. Discovery of dominant and dormant genes from expression data using a novel generalization of SNR for multi-class problems

    Directory of Open Access Journals (Sweden)

    Chung I-Fang

    2008-10-01

    Full Text Available Abstract Background The Signal-to-Noise-Ratio (SNR is often used for identification of biomarkers for two-class problems and no formal and useful generalization of SNR is available for multiclass problems. We propose innovative generalizations of SNR for multiclass cancer discrimination through introduction of two indices, Gene Dominant Index and Gene Dormant Index (GDIs. These two indices lead to the concepts of dominant and dormant genes with biological significance. We use these indices to develop methodologies for discovery of dominant and dormant biomarkers with interesting biological significance. The dominancy and dormancy of the identified biomarkers and their excellent discriminating power are also demonstrated pictorially using the scatterplot of individual gene and 2-D Sammon's projection of the selected set of genes. Using information from the literature we have shown that the GDI based method can identify dominant and dormant genes that play significant roles in cancer biology. These biomarkers are also used to design diagnostic prediction systems. Results and discussion To evaluate the effectiveness of the GDIs, we have used four multiclass cancer data sets (Small Round Blue Cell Tumors, Leukemia, Central Nervous System Tumors, and Lung Cancer. For each data set we demonstrate that the new indices can find biologically meaningful genes that can act as biomarkers. We then use six machine learning tools, Nearest Neighbor Classifier (NNC, Nearest Mean Classifier (NMC, Support Vector Machine (SVM classifier with linear kernel, and SVM classifier with Gaussian kernel, where both SVMs are used in conjunction with one-vs-all (OVA and one-vs-one (OVO strategies. We found GDIs to be very effective in identifying biomarkers with strong class specific signatures. With all six tools and for all data sets we could achieve better or comparable prediction accuracies usually with fewer marker genes than results reported in the literature using the

  4. Evaluation of Diagnostic Tests Using Information Theory for Multi-Class Diagnostic Problems and its Application for the Detection of Occlusal Caries Lesions

    Directory of Open Access Journals (Sweden)

    Umut Arslan

    2014-09-01

    Full Text Available Background: Several methods are available to evaluate the performance of the tests when the purpose of the diagnostic test is to discriminate between two possible disease states. However multi-class diagnostic problems frequently appear in many areas of medical science. Hence, there is a need for methods which will enable us to characterize the accuracy of diagnostic tests when there are more than two possible disease states. Aims: To show that two information theory measures, information content (IC and proportional reduction in diagnostic uncertainty (PRDU, can be used for the evaluation of the performance of diagnostic tests for multi-class diagnostic problems that may appear in different areas of medical science. Study Design: Diagnostic accuracy study. Methods: Sixty freshly extracted permanent human molar and premolar teeth suspected to have occlusal caries lesions were selected for the study and were assessed by two experienced examiners. Each examiner performed two evaluations. Histological examination was used as the gold standard. The scores of the histological examination were defined as sound (n=11, enamel caries (n=22 and dentin caries (n=27. Diagnostic performance of i visual inspection, ii radiography, iii laser fluorescence (LF and iv micro-computed tomography (M-CT caries detection methods was evaluated by calculating IC and PRDU. Results: Micro-computed tomography examination was the best method among the diagnostic techniques for the diagnosis of occlusal caries in terms of both IC and PRDU. M-CT examination supplied the maximum diagnostic information about the diagnosis of occlusal caries in the first (IC: 1.056; p<0.05, (PRDU: 70.5% and second evaluation (IC: 1.105; p<0.05, (PRDU: 73.8% for the first examiner. M-CT examination was the best method among the diagnostic techniques for the second examiner in both the first (IC:1.105; p<0.05, (PRDU:73.8% and second evaluation (IC:1.061; p<0.05, (PRDU:70.8%. IC and PRDU were

  5. Development of a Multi-class Steroid Hormone Screening Method using Liquid Chromatography/Tandem Mass Spectrometry (LC-MS/MS)

    Science.gov (United States)

    Boggs, Ashley S. P.; Bowden, John A.; Galligan, Thomas M.; Guillette, Louis J.; Kucklick, John R.

    2016-01-01

    Monitoring complex endocrine pathways is often limited by indirect measurement or measurement of a single hormone class per analysis. There is a burgeoning need to develop specific direct-detection methods capable of providing simultaneous measurement of biologically relevant concentrations of multiple classes of hormones (estrogens, androgens, progestogens, and corticosteroids). The objectives of this study were to develop a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for multi-class steroid hormone detection using biologically relevant concentrations, then test limits of detection (LOD) in a high-background matrix by spiking charcoal-stripped fetal bovine serum (FBS) extract. Accuracy was tested with National Institute of Standards and Technology Standard Reference Materials (SRMs) with certified concentrations of cortisol, testosterone, and progesterone. 11-Deoxycorticosterone, 11-deoxycortisol, 17-hydroxypregnenolone, 17-hydroxyprogesterone, adrenosterone, androstenedione, cortisol, corticosterone, dehydroepiandrosterone, dihydrotestosterone, estradiol, estriol, estrone, equilin, pregnenolone, progesterone, and testosterone were also measured using isotopic dilution. Dansyl chloride (DC) derivatization was investigated maintaining the same method to improve and expedite estrogen analysis. Biologically relevant LODs were determined for 15 hormones. DC derivatization improved estrogen response two- to eight-fold, and improved chromatographic separation. All measurements had an accuracy ≤ 14 % difference from certified values (not accounting for uncertainty) and relative standard deviation ≤ 14 %. This method chromatographically separated and quantified biologically relevant concentrations of four hormone classes using highly specific fragmentation patterns and measured certified values of hormones that were previously split into three separate chromatographic methods. PMID:27039201

  6. Modeling nearshore dispersal of river-derived multi-class suspended sediments and radionuclides during a flood event around the mouth of Niida River, Fukushima, Japan

    Science.gov (United States)

    Uchiyama, Y.; Yamanishi, T.; Iwasaki, T.; Shimizu, Y.; Tsumune, D.; Misumi, K.; Onda, Y.

    2016-12-01

    A quadruple nested synoptic oceanic downscale modeling based on ROMS was carried out to investigate hydrodynamics, multi-class non-cohesive sediment transport and associated dispersal of suspended radionuclides (cesium-137; 137Cs) originated from the nuclear accident occurred at the Fukushima Dai-ichi Power Plant in March 2011. The innermost model has horizontal grid resolution of 50 m to marginally resolve the topography around the river mouth including the surf zone. The model is forced by the JCOPE2 oceanic reanalysis as the outermost boundary conditions, the GPV-MSM atmospheric reanalysis, and an in-house SWAN spectral wave hindcast embedded in the operational GPV-CWM wave reanalysis. A particular attention is paid to nearshore behaviors and inventory of the nuclides attached to terrestrial minerals with grain sizes ranging from 5 to 79 micrometers that have been occasionally discharged out to the coastal ocean through hydrological processes within the river basin even after several years since the accident. We examine oceanic dispersal of sediment and suspended 137Cs influxes from Niida River, Fukushima, evaluated with the iRIC-Nays2DH river model. Our focus is on the first flood event in late May of 2011 after the accident. Alongshore asymmetry in transport of suspended sediments and 137Cs is exhibited, comprising storm-driven southward transport confined in the shallow area due to shoreward Ekman transport associated with strong northerly wind, followed by northwestward wide-spread transport under mild southerly wind condition. About 70 % of the Niida River-derived suspended 137Cs remains near the mouth for 20 days after the flood event. Nevertheless, our model results as well as an observation suggest that the area is dominated by erosion as for high bed shear stress all the time, thus suspended radionuclides are redistributed to dissipate away in long term.

  7. Multi-class multi-residue analysis of veterinary drugs in meat using enhanced matrix removal lipid cleanup and liquid chromatography-tandem mass spectrometry.

    Science.gov (United States)

    Zhao, Limian; Lucas, Derick; Long, David; Richter, Bruce; Stevens, Joan

    2018-05-11

    This study presents the development and validation of a quantitation method for the analysis of multi-class, multi-residue veterinary drugs using lipid removal cleanup cartridges, enhanced matrix removal lipid (EMR-Lipid), for different meat matrices by liquid chromatography tandem mass spectrometry detection. Meat samples were extracted using a two-step solid-liquid extraction followed by pass-through sample cleanup. The method was optimized based on the buffer and solvent composition, solvent additive additions, and EMR-Lipid cartridge cleanup. The developed method was then validated in five meat matrices, porcine muscle, bovine muscle, bovine liver, bovine kidney and chicken liver to evaluate the method performance characteristics, such as absolute recoveries and precision at three spiking levels, calibration curve linearity, limit of quantitation (LOQ) and matrix effect. The results showed that >90% of veterinary drug analytes achieved satisfactory recovery results of 60-120%. Over 97% analytes achieved excellent reproducibility results (relative standard deviation (RSD) meat matrices. The matrix co-extractive removal efficiency by weight provided by EMR-lipid cartridge cleanup was 42-58% in samples. The post column infusion study showed that the matrix ion suppression was reduced for samples with the EMR-Lipid cartridge cleanup. The reduced matrix ion suppression effect was also confirmed with 30%) for all tested veterinary drugs in all of meat matrices. The results showed that the two-step solid-liquid extraction provides efficient extraction for the entire spectrum of veterinary drugs, including the difficult classes such as tetracyclines, beta-lactams etc. EMR-Lipid cartridges after extraction provided efficient sample cleanup with easy streamlined protocol and minimal impacts on analytes recovery, improving method reliability and consistency. Copyright © 2018 Elsevier B.V. All rights reserved.

  8. A Fast SVM-Based Tongue’s Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis

    Directory of Open Access Journals (Sweden)

    Nur Diyana Kamarudin

    2017-01-01

    Full Text Available In tongue diagnosis, colour information of tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. Qualitatively, practitioners may have difficulty in their judgement due to the instable lighting condition and naked eye’s ability to capture the exact colour distribution on the tongue especially the tongue with multicolour substance. To overcome this ambiguity, this paper presents a two-stage tongue’s multicolour classification based on a support vector machine (SVM whose support vectors are reduced by our proposed k-means clustering identifiers and red colour range for precise tongue colour diagnosis. In the first stage, k-means clustering is used to cluster a tongue image into four clusters of image background (black, deep red region, red/light red region, and transitional region. In the second-stage classification, red/light red tongue images are further classified into red tongue or light red tongue based on the red colour range derived in our work. Overall, true rate classification accuracy of the proposed two-stage classification to diagnose red, light red, and deep red tongue colours is 94%. The number of support vectors in SVM is improved by 41.2%, and the execution time for one image is recorded as 48 seconds.

  9. A Fast SVM-Based Tongue's Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis

    Science.gov (United States)

    Ooi, Chia Yee; Kawanabe, Tadaaki; Odaguchi, Hiroshi; Kobayashi, Fuminori

    2017-01-01

    In tongue diagnosis, colour information of tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. Qualitatively, practitioners may have difficulty in their judgement due to the instable lighting condition and naked eye's ability to capture the exact colour distribution on the tongue especially the tongue with multicolour substance. To overcome this ambiguity, this paper presents a two-stage tongue's multicolour classification based on a support vector machine (SVM) whose support vectors are reduced by our proposed k-means clustering identifiers and red colour range for precise tongue colour diagnosis. In the first stage, k-means clustering is used to cluster a tongue image into four clusters of image background (black), deep red region, red/light red region, and transitional region. In the second-stage classification, red/light red tongue images are further classified into red tongue or light red tongue based on the red colour range derived in our work. Overall, true rate classification accuracy of the proposed two-stage classification to diagnose red, light red, and deep red tongue colours is 94%. The number of support vectors in SVM is improved by 41.2%, and the execution time for one image is recorded as 48 seconds. PMID:29065640

  10. A Fast SVM-Based Tongue's Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis.

    Science.gov (United States)

    Kamarudin, Nur Diyana; Ooi, Chia Yee; Kawanabe, Tadaaki; Odaguchi, Hiroshi; Kobayashi, Fuminori

    2017-01-01

    In tongue diagnosis, colour information of tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. Qualitatively, practitioners may have difficulty in their judgement due to the instable lighting condition and naked eye's ability to capture the exact colour distribution on the tongue especially the tongue with multicolour substance. To overcome this ambiguity, this paper presents a two-stage tongue's multicolour classification based on a support vector machine (SVM) whose support vectors are reduced by our proposed k -means clustering identifiers and red colour range for precise tongue colour diagnosis. In the first stage, k -means clustering is used to cluster a tongue image into four clusters of image background (black), deep red region, red/light red region, and transitional region. In the second-stage classification, red/light red tongue images are further classified into red tongue or light red tongue based on the red colour range derived in our work. Overall, true rate classification accuracy of the proposed two-stage classification to diagnose red, light red, and deep red tongue colours is 94%. The number of support vectors in SVM is improved by 41.2%, and the execution time for one image is recorded as 48 seconds.

  11. Raman spectroscopy as a tool for the characterization and classification of pollen; Raman-Spektroskopie als Werkzeug fuer die Charakterisierung und Klassifizierung von Pollen

    Energy Technology Data Exchange (ETDEWEB)

    Schulte, Franziska

    2010-09-20

    The chemical composition of pollen, the physiological containers that produce the male gametophytes of seed plants, has been a subject of research of plant physiologists, biochemists, and lately even material scientists for various reasons. The aim of this work was the analysis of whole pollen grains and pollen components by Raman Spectroscopy. These experiments were complemented by other techniques such as Enviromental Scanning Electron Microscopy (ESEM), High-Performance- Thin-Layer-Chromatography (HPTLC), Infrared Spectroscopy (IR) and Nuclear-Magnetic-Resonance Spectroscopy (NMR). As reported here, individual fresh pollen grains and their morphological constituents can be characterized and also classified in situ without prior preparation. Classification of pollen is based on their biochemical fingerprint revealed in their Raman spectrum. Raman spectroscopy is nondestructive and can be carried out with single pollen grains or fragments. It could be shown that the biochemical makeup of the pollen (as a part of the recognition/mating system) is altered during formation of a new biological species and that the species-specific chemical similarities and dissimilarities indeed reflect in the Raman spectral fingerprint. On the basis of the chemical information, unsupervised multivariate analysis consisting of hierarchical clustering revealed in most cases chemical similarities between species that were indicative of both phylogenetic relationship and matin behavior. Therefore experiments were conducted that gave the in situ Raman spectroscopic signatures ot the carotenoid molecules. As the data indicates, the in situ Raman spectra of the carotenoid molecules measured in single intact pollen grains provide in situ evidence of interspecies variations in pollen carotenoid content, structure, and/or assembly without prior purification. Results from HPTLC confirmed that carotenoid composition varied greatly between species and that the different in situ spectral

  12. A real-time classification algorithm for EEG-based BCI driven by self-induced emotions.

    Science.gov (United States)

    Iacoviello, Daniela; Petracca, Andrea; Spezialetti, Matteo; Placidi, Giuseppe

    2015-12-01

    The aim of this paper is to provide an efficient, parametric, general, and completely automatic real time classification method of electroencephalography (EEG) signals obtained from self-induced emotions. The particular characteristics of the considered low-amplitude signals (a self-induced emotion produces a signal whose amplitude is about 15% of a really experienced emotion) require exploring and adapting strategies like the Wavelet Transform, the Principal Component Analysis (PCA) and the Support Vector Machine (SVM) for signal processing, analysis and classification. Moreover, the method is thought to be used in a multi-emotions based Brain Computer Interface (BCI) and, for this reason, an ad hoc shrewdness is assumed. The peculiarity of the brain activation requires ad-hoc signal processing by wavelet decomposition, and the definition of a set of features for signal characterization in order to discriminate different self-induced emotions. The proposed method is a two stages algorithm, completely parameterized, aiming at a multi-class classification and may be considered in the framework of machine learning. The first stage, the calibration, is off-line and is devoted at the signal processing, the determination of the features and at the training of a classifier. The second stage, the real-time one, is the test on new data. The PCA theory is applied to avoid redundancy in the set of features whereas the classification of the selected features, and therefore of the signals, is obtained by the SVM. Some experimental tests have been conducted on EEG signals proposing a binary BCI, based on the self-induced disgust produced by remembering an unpleasant odor. Since in literature it has been shown that this emotion mainly involves the right hemisphere and in particular the T8 channel, the classification procedure is tested by using just T8, though the average accuracy is calculated and reported also for the whole set of the measured channels. The obtained

  13. Predicting Assignment Submissions in a Multiclass Classification Problem

    Directory of Open Access Journals (Sweden)

    Bogdan Drăgulescu

    2015-08-01

    Full Text Available Predicting student failure is an important task that can empower educators to counteract the factors that affect student performance. In this paper, a part of the bigger problem of predicting student failure is addressed: predicting the students that do not complete their assignment tasks. For solving this problem, real data collected by our university’s educational platform was used. Because the problem consisted of predicting one of three possible classes (multi-class classification, the appropriate algorithms and methods were selected. Several experiments were carried out to find the best approach for this prediction problem and the used data set. An approach of time segmentation is proposed in order to facilitate the prediction from early on. Methods that address the problems of high dimensionality and imbalanced data were also evaluated. The outcome of each approach is shown and compared in order to select the best performing classification algorithm for the problem at hand.

  14. Automatic Picking of Foraminifera: Design of the Foraminifera Image Recognition and Sorting Tool (FIRST) Prototype and Results of the Image Classification Scheme

    Science.gov (United States)

    de Garidel-Thoron, T.; Marchant, R.; Soto, E.; Gally, Y.; Beaufort, L.; Bolton, C. T.; Bouslama, M.; Licari, L.; Mazur, J. C.; Brutti, J. M.; Norsa, F.

    2017-12-01

    Foraminifera tests are the main proxy carriers for paleoceanographic reconstructions. Both geochemical and taxonomical studies require large numbers of tests to achieve statistical relevance. To date, the extraction of foraminifera from the sediment coarse fraction is still done by hand and thus time-consuming. Moreover, the recognition of morphotypes, ecologically relevant, requires some taxonomical skills not easily taught. The automatic recognition and extraction of foraminifera would largely help paleoceanographers to overcome these issues. Recent advances in automatic image classification using machine learning opens the way to automatic extraction of foraminifera. Here we detail progress on the design of an automatic picking machine as part of the FIRST project. The machine handles 30 pre-sieved samples (100-1000µm), separating them into individual particles (including foraminifera) and imaging each in pseudo-3D. The particles are classified and specimens of interest are sorted either for Individual Foraminifera Analyses (44 per slide) and/or for classical multiple analyses (8 morphological classes per slide, up to 1000 individuals per hole). The classification is based on machine learning using Convolutional Neural Networks (CNNs), similar to the approach used in the coccolithophorid imaging system SYRACO. To prove its feasibility, we built two training image datasets of modern planktonic foraminifera containing approximately 2000 and 5000 images each, corresponding to 15 & 25 morphological classes. Using a CNN with a residual topology (ResNet) we achieve over 95% correct classification for each dataset. We tested the network on 160,000 images from 45 depths of a sediment core from the Pacific ocean, for which we have human counts. The current algorithm is able to reproduce the downcore variability in both Globigerinoides ruber and the fragmentation index (r2 = 0.58 and 0.88 respectively). The FIRST prototype yields some promising results for high

  15. Stellar Spectral Classification with Locality Preserving Projections ...

    Indian Academy of Sciences (India)

    With the help of computer tools and algorithms, automatic stellar spectral classification has become an area of current interest. The process of stellar spectral classification mainly includes two steps: dimension reduction and classification. As a popular dimensionality reduction technique, Principal Component Analysis (PCA) ...

  16. Multivariate analysis of volatile compounds detected by headspace solid-phase microextraction/gas chromatography: A tool for sensory classification of cork stoppers.

    Science.gov (United States)

    Prat, Chantal; Besalú, Emili; Bañeras, Lluís; Anticó, Enriqueta

    2011-06-15

    The volatile fraction of aqueous cork macerates of tainted and non-tainted agglomerate cork stoppers was analysed by headspace solid-phase microextraction (HS-SPME)/gas chromatography. Twenty compounds containing terpenoids, aliphatic alcohols, lignin-related compounds and others were selected and analysed in individual corks. Cork stoppers were previously classified in six different classes according to sensory descriptions including, 2,4,6-trichloroanisole taint and other frequent, non-characteristic odours found in cork. A multivariate analysis of the chromatographic data of 20 selected chemical compounds using linear discriminant analysis models helped in the differentiation of the a priori made groups. The discriminant model selected five compounds as the best combination. Selected compounds appear in the model in the following order; 2,4,6 TCA, fenchyl alcohol, 1-octen-3-ol, benzyl alcohol and benzothiazole. Unfortunately, not all six a priori differentiated sensory classes were clearly discriminated in the model, probably indicating that no measurable differences exist in the chromatographic data for some categories. The predictive analyses of a refined model in which two sensory classes were fused together resulted in a good classification. Prediction rates of control (non-tainted), TCA, musty-earthy-vegetative, vegetative and chemical descriptions were 100%, 100%, 85%, 67.3% and 100%, respectively, when the modified model was used. The multivariate analysis of chromatographic data will help in the classification of stoppers and provide a perfect complement to sensory analyses. Copyright © 2010 Elsevier Ltd. All rights reserved.

  17. Synoptic tool for reporting of hematological and lymphoid neoplasms based on World Health Organization classification and College of American Pathologists checklist

    Directory of Open Access Journals (Sweden)

    Winters Sharon B

    2007-07-01

    Full Text Available Abstract Background Synoptic reporting, either as part of the pathology report or replacing some free text component incorporates standardized data elements in the form of checklists for pathology reporting. This ensures the pathologists make note of these findings in their reports, thereby improving the quality and uniformity of information in the pathology reports. Methods The purpose of this project is to develop the entire set of elements in the synoptic templates or "worksheets" for hematologic and lymphoid neoplasms using the World Health Organization (WHO Classification and the College of American Pathologists (CAP Cancer Checklists. The CAP checklists' content was supplemented with the most updated classification scheme (WHO classification, specimen details, staging as well as information on various ancillary techniques such as cytochemical studies, immunophenotyping, cytogenetics including Fluorescent In-situ Hybridization (FISH studies and genotyping. We have used a digital synoptic reporting system as part of an existing laboratory information system (LIS, CoPathPlus, from Cerner DHT, Inc. The synoptic elements are presented as discrete data points, so that a data element such as tumor type is assigned from the synoptic value dictionary under the value of tumor type, allowing the user to search for just those cases that have that value point populated. Results These synoptic worksheets are implemented for use in our LIS. The data is stored as discrete data elements appear as an accession summary within the final pathology report. In addition, the synoptic data can be exported to research databases for linking pathological details on banked tissues. Conclusion Synoptic reporting provides a structured method for entering the diagnostic as well as prognostic information for a particular pathology specimen or sample, thereby reducing transcription services and reducing specimen turnaround time. Furthermore, it provides accurate and

  18. Bosniak Classification system

    DEFF Research Database (Denmark)

    Graumann, Ole; Osther, Susanne Sloth; Karstoft, Jens

    2014-01-01

    Background: The Bosniak classification is a diagnostic tool for the differentiation of cystic changes in the kidney. The process of categorizing renal cysts may be challenging, involving a series of decisions that may affect the final diagnosis and clinical outcome such as surgical management....... Purpose: To investigate the inter- and intra-observer agreement among experienced uroradiologists when categorizing complex renal cysts according to the Bosniak classification. Material and Methods: The original categories of 100 cystic renal masses were chosen as “Gold Standard” (GS), established...... to the calculated weighted κ all readers performed “very good” for both inter-observer and intra-observer variation. Most variation was seen in cysts catagorized as Bosniak II, IIF, and III. These results show that radiologists who evaluate complex renal cysts routinely may apply the Bosniak classification...

  19. A method of neighbor classes based SVM classification for optical printed Chinese character recognition.

    Science.gov (United States)

    Zhang, Jie; Wu, Xiaohong; Yu, Yanmei; Luo, Daisheng

    2013-01-01

    In optical printed Chinese character recognition (OPCCR), many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM) might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM) to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR.

  20. Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination.

    Science.gov (United States)

    Sørensen, Lauge; Nielsen, Mads

    2018-05-15

    The International Challenge for Automated Prediction of MCI from MRI data offered independent, standardized comparison of machine learning algorithms for multi-class classification of normal control (NC), mild cognitive impairment (MCI), converting MCI (cMCI), and Alzheimer's disease (AD) using brain imaging and general cognition. We proposed to use an ensemble of support vector machines (SVMs) that combined bagging without replacement and feature selection. SVM is the most commonly used algorithm in multivariate classification of dementia, and it was therefore valuable to evaluate the potential benefit of ensembling this type of classifier. The ensemble SVM, using either a linear or a radial basis function (RBF) kernel, achieved multi-class classification accuracies of 55.6% and 55.0% in the challenge test set (60 NC, 60 MCI, 60 cMCI, 60 AD), resulting in a third place in the challenge. Similar feature subset sizes were obtained for both kernels, and the most frequently selected MRI features were the volumes of the two hippocampal subregions left presubiculum and right subiculum. Post-challenge analysis revealed that enforcing a minimum number of selected features and increasing the number of ensemble classifiers improved classification accuracy up to 59.1%. The ensemble SVM outperformed single SVM classifications consistently in the challenge test set. Ensemble methods using bagging and feature selection can improve the performance of the commonly applied SVM classifier in dementia classification. This resulted in competitive classification accuracies in the International Challenge for Automated Prediction of MCI from MRI data. Copyright © 2018 Elsevier B.V. All rights reserved.

  1. Interval prediction for graded multi-label classification

    CERN Document Server

    Lastra, Gerardo; Bahamonde, Antonio

    2014-01-01

    Multi-label was introduced as an extension of multi-class classification. The aim is to predict a set of classes (called labels in this context) instead of a single one, namely the set of relevant labels. If membership to the set of relevant labels is defined to a certain degree, the learning task is called graded multi-label classification. These learning tasks can be seen as a set of ordinal classifications. Hence, recommender systems can be considered as multi-label classification tasks. In this paper, we present a new type of nondeterministic learner that, for each instance, tries to predict at the same time the true grade for each label. When the classification is uncertain for a label, however, the hypotheses predict a set of consecutive grades, i.e., an interval. The goal is to keep the set of predicted grades as small as possible; while still containing the true grade. We shall see that these classifiers take advantage of the interrelations of labels. The result is that, with quite narrow intervals, i...

  2. Seismic texture classification. Final report

    Energy Technology Data Exchange (ETDEWEB)

    Vinther, R.

    1997-12-31

    The seismic texture classification method, is a seismic attribute that can both recognize the general reflectivity styles and locate variations from these. The seismic texture classification performs a statistic analysis for the seismic section (or volume) aiming at describing the reflectivity. Based on a set of reference reflectivities the seismic textures are classified. The result of the seismic texture classification is a display of seismic texture categories showing both the styles of reflectivity from the reference set and interpolations and extrapolations from these. The display is interpreted as statistical variations in the seismic data. The seismic texture classification is applied to seismic sections and volumes from the Danish North Sea representing both horizontal stratifications and salt diapers. The attribute succeeded in recognizing both general structure of successions and variations from these. Also, the seismic texture classification is not only able to display variations in prospective areas (1-7 sec. TWT) but can also be applied to deep seismic sections. The seismic texture classification is tested on a deep reflection seismic section (13-18 sec. TWT) from the Baltic Sea. Applied to this section the seismic texture classification succeeded in locating the Moho, which could not be located using conventional interpretation tools. The seismic texture classification is a seismic attribute which can display general reflectivity styles and deviations from these and enhance variations not found by conventional interpretation tools. (LN)

  3. Identification of high-risk cutaneous melanoma tumors is improved when combining the online American Joint Committee on Cancer Individualized Melanoma Patient Outcome Prediction Tool with a 31-gene expression profile-based classification.

    Science.gov (United States)

    Ferris, Laura K; Farberg, Aaron S; Middlebrook, Brooke; Johnson, Clare E; Lassen, Natalie; Oelschlager, Kristen M; Maetzold, Derek J; Cook, Robert W; Rigel, Darrell S; Gerami, Pedram

    2017-05-01

    A significant proportion of patients with American Joint Committee on Cancer (AJCC)-defined early-stage cutaneous melanoma have disease recurrence and die. A 31-gene expression profile (GEP) that accurately assesses metastatic risk associated with primary cutaneous melanomas has been described. We sought to compare accuracy of the GEP in combination with risk determined using the web-based AJCC Individualized Melanoma Patient Outcome Prediction Tool. GEP results from 205 stage I/II cutaneous melanomas with sufficient clinical data for prognostication using the AJCC tool were classified as low (class 1) or high (class 2) risk. Two 5-year overall survival cutoffs (AJCC 79% and 68%), reflecting survival for patients with stage IIA or IIB disease, respectively, were assigned for binary AJCC risk. Cox univariate analysis revealed significant risk classification of distant metastasis-free and overall survival (hazard ratio range 3.2-9.4, P risk by GEP but low risk by AJCC. Specimens reflect tertiary care center referrals; more effective therapies have been approved for clinical use after accrual. The GEP provides valuable prognostic information and improves identification of high-risk melanomas when used together with the AJCC online prediction tool. Copyright © 2016 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

  4. Advanced Rotator Cuff Tear Score (ARoCuS): a multi-scaled tool for the classification and description of rotator cuff tears.

    Science.gov (United States)

    Walter, S G; Stadler, T; Thomas, T S; Thomas, W

    2018-03-02

    To introduce a (semi-)quantitative surgical score for the classification of rotator cuff tears. A total of 146 consecutive patients underwent rotator cuff repair and were assessed using the previously defined Advanced Rotator Cuff Tear Score (ARoCuS) criteria: muscle tendon, size, tissue quality, pattern as well as mobilization of the tear. The data set was split into a training (125 patients) and a testing set (21 patients). The training data set fitted a nonlinear predictive model of the tear score based on the ARoCuS criteria, while the testing data served as control. Based on the scoring results, rotator cuff tears were assigned to one of four categories (ΔV I-IV) and received a stage-adapted treatment. For statistical analysis, mean values ± standard deviation, interclass correlation coefficients (ICC) and kappa values were calculated. Overall, 32 patients were classified as ΔV I, 68 as ΔV II and 37 as ΔV III. Nine patients showed ΔV IV tears. Patients of all ΔV groups improved significantly their Constant scores (p tears in a standardized and reproducible manner.

  5. A Directed Acyclic Graph-Large Margin Distribution Machine Model for Music Symbol Classification.

    Directory of Open Access Journals (Sweden)

    Cuihong Wen

    Full Text Available Optical Music Recognition (OMR has received increasing attention in recent years. In this paper, we propose a classifier based on a new method named Directed Acyclic Graph-Large margin Distribution Machine (DAG-LDM. The DAG-LDM is an improvement of the Large margin Distribution Machine (LDM, which is a binary classifier that optimizes the margin distribution by maximizing the margin mean and minimizing the margin variance simultaneously. We modify the LDM to the DAG-LDM to solve the multi-class music symbol classification problem. Tests are conducted on more than 10000 music symbol images, obtained from handwritten and printed images of music scores. The proposed method provides superior classification capability and achieves much higher classification accuracy than the state-of-the-art algorithms such as Support Vector Machines (SVMs and Neural Networks (NNs.

  6. A Directed Acyclic Graph-Large Margin Distribution Machine Model for Music Symbol Classification.

    Science.gov (United States)

    Wen, Cuihong; Zhang, Jing; Rebelo, Ana; Cheng, Fanyong

    2016-01-01

    Optical Music Recognition (OMR) has received increasing attention in recent years. In this paper, we propose a classifier based on a new method named Directed Acyclic Graph-Large margin Distribution Machine (DAG-LDM). The DAG-LDM is an improvement of the Large margin Distribution Machine (LDM), which is a binary classifier that optimizes the margin distribution by maximizing the margin mean and minimizing the margin variance simultaneously. We modify the LDM to the DAG-LDM to solve the multi-class music symbol classification problem. Tests are conducted on more than 10000 music symbol images, obtained from handwritten and printed images of music scores. The proposed method provides superior classification capability and achieves much higher classification accuracy than the state-of-the-art algorithms such as Support Vector Machines (SVMs) and Neural Networks (NNs).

  7. 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...

  8. 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)...

  9. Evaluation of the Melanocytic Pathology Assessment Tool and Hierarchy for Diagnosis (MPATH-Dx) classification scheme for diagnosis of cutaneous melanocytic neoplasms: Results from the International Melanoma Pathology Study Group.

    Science.gov (United States)

    Lott, Jason P; Elmore, Joann G; Zhao, Ge A; Knezevich, Stevan R; Frederick, Paul D; Reisch, Lisa M; Chu, Emily Y; Cook, Martin G; Duncan, Lyn M; Elenitsas, Rosalie; Gerami, Pedram; Landman, Gilles; Lowe, Lori; Messina, Jane L; Mihm, Martin C; van den Oord, Joost J; Rabkin, Michael S; Schmidt, Birgitta; Shea, Christopher R; Yun, Sook Jung; Xu, George X; Piepkorn, Michael W; Elder, David E; Barnhill, Raymond L

    2016-08-01

    Pathologists use diverse terminology when interpreting melanocytic neoplasms, potentially compromising quality of care. We sought to evaluate the Melanocytic Pathology Assessment Tool and Hierarchy for Diagnosis (MPATH-Dx) scheme, a 5-category classification system for melanocytic lesions. Participants (n = 16) of the 2013 International Melanoma Pathology Study Group Workshop provided independent case-level diagnoses and treatment suggestions for 48 melanocytic lesions. Individual diagnoses (including, when necessary, least and most severe diagnoses) were mapped to corresponding MPATH-Dx classes. Interrater agreement and correlation between MPATH-Dx categorization and treatment suggestions were evaluated. Most participants were board-certified dermatopathologists (n = 15), age 50 years or older (n = 12), male (n = 9), based in the United States (n = 11), and primary academic faculty (n = 14). Overall, participants generated 634 case-level diagnoses with treatment suggestions. Mean weighted kappa coefficients for diagnostic agreement after MPATH-Dx mapping (assuming least and most severe diagnoses, when necessary) were 0.70 (95% confidence interval 0.68-0.71) and 0.72 (95% confidence interval 0.71-0.73), respectively, whereas correlation between MPATH-Dx categorization and treatment suggestions was 0.91. This was a small sample size of experienced pathologists in a testing situation. Varying diagnostic nomenclature can be classified into a concise hierarchy using the MPATH-Dx scheme. Further research is needed to determine whether this classification system can facilitate diagnostic concordance in general pathology practice and improve patient care. Copyright © 2016 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

  10. From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification

    Directory of Open Access Journals (Sweden)

    Dawyndt Peter

    2010-01-01

    Full Text Available Abstract Background Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification. Results In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model. Conclusions FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the

  11. From learning taxonomies to phylogenetic learning: integration of 16S rRNA gene data into FAME-based bacterial classification.

    Science.gov (United States)

    Slabbinck, Bram; Waegeman, Willem; Dawyndt, Peter; De Vos, Paul; De Baets, Bernard

    2010-01-30

    Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification. In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model. FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for the discrimination of bacterial

  12. From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification

    Science.gov (United States)

    2010-01-01

    Background Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification. Results In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model. Conclusions FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for

  13. New guidelines for dam safety classification

    International Nuclear Information System (INIS)

    Dascal, O.

    1999-01-01

    Elements are outlined of recommended new guidelines for safety classification of dams. Arguments are provided for the view that dam classification systems should require more than one system as follows: (a) classification for selection of design criteria, operation procedures and emergency measures plans, based on potential consequences of a dam failure - the hazard classification of water retaining structures; (b) classification for establishment of surveillance activities and for safety evaluation of dams, based on the probability and consequences of failure - the risk classification of water retaining structures; and (c) classification for establishment of water management plans, for safety evaluation of the entire project, for preparation of emergency measures plans, for definition of the frequency and extent of maintenance operations, and for evaluation of changes and modifications required - the hazard classification of the project. The hazard classification of the dam considers, as consequence, mainly the loss of lives or persons in jeopardy and the property damages to third parties. Difficulties in determining the risk classification of the dam lie in the fact that no tool exists to evaluate the probability of the dam's failure. To overcome this, the probability of failure can be substituted for by a set of dam characteristics that express the failure potential of the dam and its foundation. The hazard classification of the entire project is based on the probable consequences of dam failure influencing: loss of life, persons in jeopardy, property and environmental damage. The classification scheme is illustrated for dam threatening events such as earthquakes and floods. 17 refs., 5 tabs

  14. Overfitting Reduction of Text Classification Based on AdaBELM

    Directory of Open Access Journals (Sweden)

    Xiaoyue Feng

    2017-07-01

    Full Text Available Overfitting is an important problem in machine learning. Several algorithms, such as the extreme learning machine (ELM, suffer from this issue when facing high-dimensional sparse data, e.g., in text classification. One common issue is that the extent of overfitting is not well quantified. In this paper, we propose a quantitative measure of overfitting referred to as the rate of overfitting (RO and a novel model, named AdaBELM, to reduce the overfitting. With RO, the overfitting problem can be quantitatively measured and identified. The newly proposed model can achieve high performance on multi-class text classification. To evaluate the generalizability of the new model, we designed experiments based on three datasets, i.e., the 20 Newsgroups, Reuters-21578, and BioMed corpora, which represent balanced, unbalanced, and real application data, respectively. Experiment results demonstrate that AdaBELM can reduce overfitting and outperform classical ELM, decision tree, random forests, and AdaBoost on all three text-classification datasets; for example, it can achieve 62.2% higher accuracy than ELM. Therefore, the proposed model has a good generalizability.

  15. adabag: An R Package for Classification with Boosting and Bagging

    Directory of Open Access Journals (Sweden)

    Esteban Alfaro

    2013-09-01

    Full Text Available Boosting and bagging are two widely used ensemble methods for classification. Their common goal is to improve the accuracy of a classifier combining single classifiers which are slightly better than random guessing. Among the family of boosting algorithms, AdaBoost (adaptive boosting is the best known, although it is suitable only for dichotomous tasks. AdaBoost.M1 and SAMME (stagewise additive modeling using a multi-class exponential loss function are two easy and natural extensions to the general case of two or more classes. In this paper, the adabag R package is introduced. This version implements AdaBoost.M1, SAMME and bagging algorithms with classification trees as base classifiers. Once the ensembles have been trained, they can be used to predict the class of new samples. The accuracy of these classifiers can be estimated in a separated data set or through cross validation. Moreover, the evolution of the error as the ensemble grows can be analysed and the ensemble can be pruned. In addition, the margin in the class prediction and the probability of each class for the observations can be calculated. Finally, several classic examples in classification literature are shown to illustrate the use of this package.

  16. Improved Classification by Non Iterative and Ensemble Classifiers in Motor Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    PANIGRAHY, P. S.

    2018-02-01

    Full Text Available Data driven approach for multi-class fault diagnosis of induction motor using MCSA at steady state condition is a complex pattern classification problem. This investigation has exploited the built-in ensemble process of non-iterative classifiers to resolve the most challenging issues in this area, including bearing and stator fault detection. Non-iterative techniques exhibit with an average 15% of increased fault classification accuracy against their iterative counterparts. Particularly RF has shown outstanding performance even at less number of training samples and noisy feature space because of its distributive feature model. The robustness of the results, backed by the experimental verification shows that the non-iterative individual classifiers like RF is the optimum choice in the area of automatic fault diagnosis of induction motor.

  17. Classification and Analysis of Computer Network Traffic

    DEFF Research Database (Denmark)

    Bujlow, Tomasz

    2014-01-01

    various classification modes (decision trees, rulesets, boosting, softening thresholds) regarding the classification accuracy and the time required to create the classifier. We showed how to use our VBS tool to obtain per-flow, per-application, and per-content statistics of traffic in computer networks...

  18. Emotions Classification for Arabic Tweets

    African Journals Online (AJOL)

    pc

    2018-03-05

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

  19. Using Machine Learning for Land Suitability Classification

    African Journals Online (AJOL)

    User

    West African Journal of Applied Ecology, vol. ... evidence for the utility of machine learning methods in land suitability classification especially MCS methods. ... Artificial intelligence tools. ..... Numerical values of index for the various classes.

  20. Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification.

    Science.gov (United States)

    Alshamlan, Hala M; Badr, Ghada H; Alohali, Yousef A

    2015-06-01

    Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

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

    2018-04-11

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

  2. Acute pesticide poisoning: a proposed classification tool

    OpenAIRE

    Thundiyil, Josef G; Stober, Judy; Besbelli, Nida; Pronczuk, Jenny

    2008-01-01

    Cases of acute pesticide poisoning (APP) account for significant morbidity and mortality worldwide. Developing countries are particularly susceptible due to poorer regulation, lack of surveillance systems, less enforcement, lack of training and inadequate access to information systems. Previous research has demonstrated wide variability in incidence rates for APP. This is possibly due to inconsistent reporting methodology and exclusion of occupational and non-intentional poisonings. The purpo...

  3. 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....

  4. Classification of the web

    DEFF Research Database (Denmark)

    Mai, Jens Erik

    2004-01-01

    This paper discusses the challenges faced by investigations into the classification of the Web and outlines inquiries that are needed to use principles for bibliographic classification to construct classifications of the Web. This paper suggests that the classification of the Web meets challenges...... that call for inquiries into the theoretical foundation of bibliographic classification theory....

  5. 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

  6. Lagrangian Multi-Class Traffic State Estimation

    NARCIS (Netherlands)

    Yuan, Y.

    2013-01-01

    Road traffic is important to everybody in the world. People travel and commute everyday. For those who travel by cars (or other types of road vehicles), traffic congestion is a daily experience. One essential goal of traffic researchers is to reduce traffic congestion and to improve the whole

  7. Histological image classification using biologically interpretable shape-based features

    International Nuclear Information System (INIS)

    Kothari, Sonal; Phan, John H; Young, Andrew N; Wang, May D

    2013-01-01

    Automatic cancer diagnostic systems based on histological image classification are important for improving therapeutic decisions. Previous studies propose textural and morphological features for such systems. These features capture patterns in histological images that are useful for both cancer grading and subtyping. However, because many of these features lack a clear biological interpretation, pathologists may be reluctant to adopt these features for clinical diagnosis. We examine the utility of biologically interpretable shape-based features for classification of histological renal tumor images. Using Fourier shape descriptors, we extract shape-based features that capture the distribution of stain-enhanced cellular and tissue structures in each image and evaluate these features using a multi-class prediction model. We compare the predictive performance of the shape-based diagnostic model to that of traditional models, i.e., using textural, morphological and topological features. The shape-based model, with an average accuracy of 77%, outperforms or complements traditional models. We identify the most informative shapes for each renal tumor subtype from the top-selected features. Results suggest that these shapes are not only accurate diagnostic features, but also correlate with known biological characteristics of renal tumors. Shape-based analysis of histological renal tumor images accurately classifies disease subtypes and reveals biologically insightful discriminatory features. This method for shape-based analysis can be extended to other histological datasets to aid pathologists in diagnostic and therapeutic decisions

  8. Scientific and General Subject Classifications in the Digital World

    CERN Document Server

    De Robbio, Antonella; Marini, A

    2001-01-01

    In the present work we discuss opportunities, problems, tools and techniques encountered when interconnecting discipline-specific subject classifications, primarily organized as search devices in bibliographic databases, with general classifications originally devised for book shelving in public libraries. We first state the fundamental distinction between topical (or subject) classifications and object classifications. Then we trace the structural limitations that have constrained subject classifications since their library origins, and the devices that were used to overcome the gap with genuine knowledge representation. After recalling some general notions on structure, dynamics and interferences of subject classifications and of the objects they refer to, we sketch a synthetic overview on discipline-specific classifications in Mathematics, Computing and Physics, on one hand, and on general classifications on the other. In this setting we present The Scientific Classifications Page, which collects groups of...

  9. Supervised remote sensing image classification: An example of a ...

    African Journals Online (AJOL)

    These conventional multi-class classifiers/algorithms are usually written in programming languages such as C, C++, and python. The objective of this research is to experiment the use of a binary classifier/algorithm for multi-class remote sensing task, implemented in MATLAB. MATLAB is a programming language just like C ...

  10. 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...

  11. Multivariate Approaches to Classification in Extragalactic Astronomy

    Directory of Open Access Journals (Sweden)

    Didier eFraix-Burnet

    2015-08-01

    Full Text Available Clustering objects into synthetic groups is a natural activity of any science. Astrophysics is not an exception and is now facing a deluge of data. For galaxies, the one-century old Hubble classification and the Hubble tuning fork are still largely in use, together with numerous mono- or bivariate classifications most often made by eye. However, a classification must be driven by the data, and sophisticated multivariate statistical tools are used more and more often. In this paper we review these different approaches in order to situate them in the general context of unsupervised and supervised learning. We insist on the astrophysical outcomes of these studies to show that multivariate analyses provide an obvious path toward a renewal of our classification of galaxies and are invaluable tools to investigate the physics and evolution of galaxies.

  12. A chemometric evaluation of the underlying physical and chemical patterns that support near infrared spectroscopy of barley seeds as a tool for explorative classification of endosperm genes and gene combinations

    DEFF Research Database (Denmark)

    Jacobsen, Susanne; Søndergaard, Ib; Møller, Birthe

    2005-01-01

    Analysis (PCA). Riso mutants R-13, R-29 high (I -> 3, 1 -> 4)-beta-glucan, low starch and R-1508 (high lysine, reduced starch), near isogeneic controls and normal lines and recombinants were studied. Based on proteome analysis results, six antimicrobial proteins were followed during endosperm development...... revealing pleiotropic gene effects in expression timing that supporting the gene classification. To verify that NIR spectroscopy data represents a physio-chemical fingerprint of the barley seed, physical and chemical spectral components were partially separated by Multiple Scatter Correction...... and their genetic classification ability verified. Wavelength bands with known water binding and (I -> 3, 1 -> 4)-beta-glucan assignments were successfully predicted by partial least squares regression giving insight into how NIR-data works in classification. Highly reproducible gene-specific, covariate...

  13. Training echo state networks for rotation-invariant bone marrow cell classification.

    Science.gov (United States)

    Kainz, Philipp; Burgsteiner, Harald; Asslaber, Martin; Ahammer, Helmut

    2017-01-01

    The main principle of diagnostic pathology is the reliable interpretation of individual cells in context of the tissue architecture. Especially a confident examination of bone marrow specimen is dependent on a valid classification of myeloid cells. In this work, we propose a novel rotation-invariant learning scheme for multi-class echo state networks (ESNs), which achieves very high performance in automated bone marrow cell classification. Based on representing static images as temporal sequence of rotations, we show how ESNs robustly recognize cells of arbitrary rotations by taking advantage of their short-term memory capacity. The performance of our approach is compared to a classification random forest that learns rotation-invariance in a conventional way by exhaustively training on multiple rotations of individual samples. The methods were evaluated on a human bone marrow image database consisting of granulopoietic and erythropoietic cells in different maturation stages. Our ESN approach to cell classification does not rely on segmentation of cells or manual feature extraction and can therefore directly be applied to image data.

  14. Linear Subpixel Learning Algorithm for Land Cover Classification from WELD using High Performance Computing

    Science.gov (United States)

    Ganguly, S.; Kumar, U.; Nemani, R. R.; Kalia, S.; Michaelis, A.

    2017-12-01

    In this work, we use a Fully Constrained Least Squares Subpixel Learning Algorithm to unmix global WELD (Web Enabled Landsat Data) to obtain fractions or abundances of substrate (S), vegetation (V) and dark objects (D) classes. Because of the sheer nature of data and compute needs, we leveraged the NASA Earth Exchange (NEX) high performance computing architecture to optimize and scale our algorithm for large-scale processing. Subsequently, the S-V-D abundance maps were characterized into 4 classes namely, forest, farmland, water and urban areas (with NPP-VIIRS - national polar orbiting partnership visible infrared imaging radiometer suite nighttime lights data) over California, USA using Random Forest classifier. Validation of these land cover maps with NLCD (National Land Cover Database) 2011 products and NAFD (North American Forest Dynamics) static forest cover maps showed that an overall classification accuracy of over 91% was achieved, which is a 6% improvement in unmixing based classification relative to per-pixel based classification. As such, abundance maps continue to offer an useful alternative to high-spatial resolution data derived classification maps for forest inventory analysis, multi-class mapping for eco-climatic models and applications, fast multi-temporal trend analysis and for societal and policy-relevant applications needed at the watershed scale.

  15. Multi-class, multi-residue analysis of pesticides, polychlorinated biphenyls, polycyclic aromatic hydrocarbons, polybrominated diphenyl ethers and novel flame retardants in fish using fast, low-pressure gas chromatography–tandem mass spectrometry

    International Nuclear Information System (INIS)

    Sapozhnikova, Yelena; Lehotay, Steven J.

    2013-01-01

    Highlights: ► A method for analysis of POPs and novel flame retardants in catfish was developed. ► The method is based on a QuEChERS extraction, d-SPE clean-up and low pressure GC/MS–MS. ► The method validation demonstrated good recoveries and low detection limits. ► The method was successfully applied for analysis of catfish samples from the market. - Abstract: A multi-class, multi-residue method for the analysis of 13 novel flame retardants, 18 representative pesticides, 14 polychlorinated biphenyl (PCB) congeners, 16 polycyclic aromatic hydrocarbons (PAHs), and 7 polybrominated diphenyl ether (PBDE) congeners in catfish muscle was developed and evaluated using fast low pressure gas chromatography triple quadrupole tandem mass spectrometry (LP-GC/MS–MS). The method was based on a QuEChERS (quick, easy, cheap, effective, rugged, safe) extraction with acetonitrile and dispersive solid-phase extraction (d-SPE) clean-up with zirconium-based sorbent prior to LP-GC/MS–MS analysis. The developed method was evaluated at 4 spiking levels and further validated by analysis of NIST Standard Reference Materials (SRMs) 1974B and 1947. Sample preparation for a batch of 10 homogenized samples took about 1 h/analyst, and LP-GC/MS–MS analysis provided fast separation of multiple analytes within 9 min achieving high throughput. With the use of isotopically labeled internal standards, recoveries of all but one analyte were between 70 and 120% with relative standard deviations less than 20% (n = 5). The measured values for both SRMs agreed with certified/reference values (72–119% accuracy) for the majority of analytes. The detection limits were 0.1–0.5 ng g −1 for PCBs, 0.5–10 ng g −1 for PBDEs, 0.5–5 ng g −1 for select pesticides and PAHs and 1–10 ng g −1 for flame retardants. The developed method was successfully applied for analysis of catfish samples from the market.

  16. Remote sensing as a tool for monitoring plant invasions: testing the effects of data resolution and image classification approach on the detection of a model plant species Heracleum mantegazzianum (giant hogweed)

    Czech Academy of Sciences Publication Activity Database

    Müllerová, Jana; Pergl, Jan; Pyšek, Petr

    2013-01-01

    Roč. 25, Dec.2013 (2013), s. 55-65 ISSN 0303-2434 R&D Projects: GA AV ČR IAA600050811 Institutional support: RVO:67985939 Keywords : historical aerial VHR photography * invasion progress * object and pixel-based image classification Subject RIV: EF - Botanics Impact factor: 2.539, year: 2013

  17. Asteroid taxonomic classifications

    International Nuclear Information System (INIS)

    Tholen, D.J.

    1989-01-01

    This paper reports on three taxonomic classification schemes developed and applied to the body of available color and albedo data. Asteroid taxonomic classifications according to two of these schemes are reproduced

  18. Hand eczema classification

    DEFF Research Database (Denmark)

    Diepgen, T L; Andersen, Klaus Ejner; Brandao, F M

    2008-01-01

    of the disease is rarely evidence based, and a classification system for different subdiagnoses of hand eczema is not agreed upon. Randomized controlled trials investigating the treatment of hand eczema are called for. For this, as well as for clinical purposes, a generally accepted classification system...... A classification system for hand eczema is proposed. Conclusions It is suggested that this classification be used in clinical work and in clinical trials....

  19. Classification with support hyperplanes

    NARCIS (Netherlands)

    G.I. Nalbantov (Georgi); J.C. Bioch (Cor); P.J.F. Groenen (Patrick)

    2006-01-01

    textabstractA new classification method is proposed, called Support Hy- perplanes (SHs). To solve the binary classification task, SHs consider the set of all hyperplanes that do not make classification mistakes, referred to as semi-consistent hyperplanes. A test object is classified using

  20. Standard classification: Physics

    International Nuclear Information System (INIS)

    1977-01-01

    This is a draft standard classification of physics. The conception is based on the physics part of the systematic catalogue of the Bayerische Staatsbibliothek and on the classification given in standard textbooks. The ICSU-AB classification now used worldwide by physics information services was not taken into account. (BJ) [de

  1. Classification of refrigerants; Classification des fluides frigorigenes

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2001-07-01

    This document was made from the US standard ANSI/ASHRAE 34 published in 2001 and entitled 'designation and safety classification of refrigerants'. This classification allows to clearly organize in an international way the overall refrigerants used in the world thanks to a codification of the refrigerants in correspondence with their chemical composition. This note explains this codification: prefix, suffixes (hydrocarbons and derived fluids, azeotropic and non-azeotropic mixtures, various organic compounds, non-organic compounds), safety classification (toxicity, flammability, case of mixtures). (J.S.)

  2. Odor Classification using Agent Technology

    Directory of Open Access Journals (Sweden)

    Sigeru OMATU

    2014-03-01

    Full Text Available In order to measure and classify odors, Quartz Crystal Microbalance (QCM can be used. In the present study, seven QCM sensors and three different odors are used. The system has been developed as a virtual organization of agents using an agent platform called PANGEA (Platform for Automatic coNstruction of orGanizations of intElligent Agents. This is a platform for developing open multi-agent systems, specifically those including organizational aspects. The main reason for the use of agents is the scalability of the platform, i.e. the way in which it models the services. The system models functionalities as services inside the agents, or as Service Oriented Approach (SOA architecture compliant services using Web Services. This way the adaptation of the odor classification systems with new algorithms, tools and classification techniques is allowed.

  3. The International Classification of Functioning, Disability and Health and the version for children and youth as a tool in child habilitation/early childhood intervention--feasibility and usefulness as a common language and frame of reference for practice.

    Science.gov (United States)

    Björck-Åkesson, Eva; Wilder, Jenny; Granlund, Mats; Pless, Mia; Simeonsson, Rune; Adolfsson, Margareta; Almqvist, Lena; Augustine, Lilly; Klang, Nina; Lillvist, Anne

    2010-01-01

    Early childhood intervention and habilitation services for children with disabilities operate on an interdisciplinary basis. It requires a common language between professionals, and a shared framework for intervention goals and intervention implementation. The International Classification of Functioning, Disability and Health (ICF) and the version for children and youth (ICF-CY) may serve as this common framework and language. This overview of studies implemented by our research group is based on three research questions: Do the ICF-CY conceptual model have a valid content and is it logically coherent when investigated empirically? Is the ICF-CY classification useful for documenting child characteristics in services? What difficulties and benefits are related to using ICF-CY model as a basis for intervention when it is implemented in services? A series of studies, undertaken by the CHILD researchers are analysed. The analysis is based on data sets from published studies or master theses. Results and conclusion show that the ICF-CY has a useful content and is logically coherent on model level. Professionals find it useful for documenting children's body functions and activities. Guidelines for separating activity and participation are needed. ICF-CY is a complex classification, implementing it in services is a long-term project.

  4. 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.

  5. The research on business rules classification and specification methods

    OpenAIRE

    Baltrušaitis, Egidijus

    2005-01-01

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

  6. Inter Genre Similarity Modelling For Automatic Music Genre Classification

    OpenAIRE

    Bagci, Ulas; Erzin, Engin

    2009-01-01

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

  7. Exploring diversity in ensemble classification: Applications in large area land cover mapping

    Science.gov (United States)

    Mellor, Andrew; Boukir, Samia

    2017-07-01

    Ensemble classifiers, such as random forests, are now commonly applied in the field of remote sensing, and have been shown to perform better than single classifier systems, resulting in reduced generalisation error. Diversity across the members of ensemble classifiers is known to have a strong influence on classification performance - whereby classifier errors are uncorrelated and more uniformly distributed across ensemble members. The relationship between ensemble diversity and classification performance has not yet been fully explored in the fields of information science and machine learning and has never been examined in the field of remote sensing. This study is a novel exploration of ensemble diversity and its link to classification performance, applied to a multi-class canopy cover classification problem using random forests and multisource remote sensing and ancillary GIS data, across seven million hectares of diverse dry-sclerophyll dominated public forests in Victoria Australia. A particular emphasis is placed on analysing the relationship between ensemble diversity and ensemble margin - two key concepts in ensemble learning. The main novelty of our work is on boosting diversity by emphasizing the contribution of lower margin instances used in the learning process. Exploring the influence of tree pruning on diversity is also a new empirical analysis that contributes to a better understanding of ensemble performance. Results reveal insights into the trade-off between ensemble classification accuracy and diversity, and through the ensemble margin, demonstrate how inducing diversity by targeting lower margin training samples is a means of achieving better classifier performance for more difficult or rarer classes and reducing information redundancy in classification problems. Our findings inform strategies for collecting training data and designing and parameterising ensemble classifiers, such as random forests. This is particularly important in large area

  8. 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.

  9. A hierarchical anatomical classification schema for prediction of phenotypic side effects.

    Science.gov (United States)

    Wadhwa, Somin; Gupta, Aishwarya; Dokania, Shubham; Kanji, Rakesh; Bagler, Ganesh

    2018-01-01

    Prediction of adverse drug reactions is an important problem in drug discovery endeavors which can be addressed with data-driven strategies. SIDER is one of the most reliable and frequently used datasets for identification of key features as well as building machine learning models for side effects prediction. The inherently unbalanced nature of this data presents with a difficult multi-label multi-class problem towards prediction of drug side effects. We highlight the intrinsic issue with SIDER data and methodological flaws in relying on performance measures such as AUC while attempting to predict side effects.We argue for the use of metrics that are robust to class imbalance for evaluation of classifiers. Importantly, we present a 'hierarchical anatomical classification schema' which aggregates side effects into organs, sub-systems, and systems. With the help of a weighted performance measure, using 5-fold cross-validation we show that this strategy facilitates biologically meaningful side effects prediction at different levels of anatomical hierarchy. By implementing various machine learning classifiers we show that Random Forest model yields best classification accuracy at each level of coarse-graining. The manually curated, hierarchical schema for side effects can also serve as the basis of future studies towards prediction of adverse reactions and identification of key features linked to specific organ systems. Our study provides a strategy for hierarchical classification of side effects rooted in the anatomy and can pave the way for calibrated expert systems for multi-level prediction of side effects.

  10. ICF-CY as a Tool in Elementary School : An interview study of teacher experiences and perceptions of the International Classification of Functioning, Disability and Health for Children and Youth in their Work in Elementary School

    OpenAIRE

    Tulinius, Halla Kristín

    2008-01-01

    AIM OF THE STUDY The aim of this study was to explore if ICF-CY can support teachers in elementary schools in their work in promoting children’s health, development and learning. A further aim was to bring forward what teachers experience as benefits and disadvantages in using the classification.   METHOD After an introduction to ICF-CY, six elementary school teachers filled in questionnaires based on ICF-CY for 94 children. In conjunction with this, the teachers were interviewed about their ...

  11. Hydrologic Landscape Classification to Estimate Bristol Bay Watershed Hydrology

    Science.gov (United States)

    The use of hydrologic landscapes has proven to be a useful tool for broad scale assessment and classification of landscapes across the United States. These classification systems help organize larger geographical areas into areas of similar hydrologic characteristics based on cl...

  12. A hierarchical classification scheme of psoriasis images

    DEFF Research Database (Denmark)

    Maletti, Gabriela Mariel; Ersbøll, Bjarne Kjær

    2003-01-01

    A two-stage hierarchical classification scheme of psoriasis lesion images is proposed. These images are basically composed of three classes: normal skin, lesion and background. The scheme combines conventional tools to separate the skin from the background in the first stage, and the lesion from...

  13. Diagnosis of periodontal diseases using different classification ...

    African Journals Online (AJOL)

    2014-11-29

    Nov 29, 2014 ... Nigerian Journal of Clinical Practice • May-Jun 2015 • Vol 18 • Issue 3 ... Materials and Methods: A total of 150 patients was divided into two groups such as training ... functions from training data and DT learning is one of ... were represented as numerical codings for classification ..... tool within dentistry.

  14. 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.

  15. Scene Classification Using High Spatial Resolution Multispectral Data

    National Research Council Canada - National Science Library

    Garner, Jamada

    2002-01-01

    ...), High-spatial resolution (8-meter), 4-color MSI data from IKONOS provide a new tool for scene classification, The utility of these data are studied for the purpose of classifying the Elkhorn Slough and surrounding wetlands in central...

  16. Automatic classification of blank substrate defects

    Science.gov (United States)

    Boettiger, Tom; Buck, Peter; Paninjath, Sankaranarayanan; Pereira, Mark; Ronald, Rob; Rost, Dan; Samir, Bhamidipati

    2014-10-01

    Mask preparation stages are crucial in mask manufacturing, since this mask is to later act as a template for considerable number of dies on wafer. Defects on the initial blank substrate, and subsequent cleaned and coated substrates, can have a profound impact on the usability of the finished mask. This emphasizes the need for early and accurate identification of blank substrate defects and the risk they pose to the patterned reticle. While Automatic Defect Classification (ADC) is a well-developed technology for inspection and analysis of defects on patterned wafers and masks in the semiconductors industry, ADC for mask blanks is still in the early stages of adoption and development. Calibre ADC is a powerful analysis tool for fast, accurate, consistent and automatic classification of defects on mask blanks. Accurate, automated classification of mask blanks leads to better usability of blanks by enabling defect avoidance technologies during mask writing. Detailed information on blank defects can help to select appropriate job-decks to be written on the mask by defect avoidance tools [1][4][5]. Smart algorithms separate critical defects from the potentially large number of non-critical defects or false defects detected at various stages during mask blank preparation. Mechanisms used by Calibre ADC to identify and characterize defects include defect location and size, signal polarity (dark, bright) in both transmitted and reflected review images, distinguishing defect signals from background noise in defect images. The Calibre ADC engine then uses a decision tree to translate this information into a defect classification code. Using this automated process improves classification accuracy, repeatability and speed, while avoiding the subjectivity of human judgment compared to the alternative of manual defect classification by trained personnel [2]. This paper focuses on the results from the evaluation of Automatic Defect Classification (ADC) product at MP Mask

  17. The Periodic Table and the Philosophy of Classification

    DEFF Research Database (Denmark)

    Hjørland, Birger

    2011-01-01

    This paper discusses some problems in the philosophy of classification based on a discussion of the periodic system of chemistry and physics. The emerging interdisciplinary field ‘philosophy of classification’ is briefly introduced and related to the field of knowledge organization (KO) within...... Library and Information Science (LIS). It is argued that KO needs to be better integrated with the broader field of classification theory and research. The paper considers some core issues such as whether classifications are pragmatic human tools or neutral reflections of nature, how classifications...

  18. Classification of radiological procedures

    International Nuclear Information System (INIS)

    1989-01-01

    A classification for departments in Danish hospitals which use radiological procedures. The classification codes consist of 4 digits, where the first 2 are the codes for the main groups. The first digit represents the procedure's topographical object and the second the techniques. The last 2 digits describe individual procedures. (CLS)

  19. 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

  20. Munitions Classification Library

    Science.gov (United States)

    2016-04-04

    members of the community to make their own additions to any, or all, of the classification libraries . The next phase entailed data collection over less......Include area code) 04/04/2016 Final Report August 2014 - August 2015 MUNITIONS CLASSIFICATION LIBRARY Mr. Craig Murray, Parsons Dr. Thomas H. Bell, Leidos

  1. Recursive automatic classification algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Bauman, E V; Dorofeyuk, A A

    1982-03-01

    A variational statement of the automatic classification problem is given. The dependence of the form of the optimal partition surface on the form of the classification objective functional is investigated. A recursive algorithm is proposed for maximising a functional of reasonably general form. The convergence problem is analysed in connection with the proposed algorithm. 8 references.

  2. 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…

  3. 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....

  4. 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

  5. Constructing criticality by classification

    DEFF Research Database (Denmark)

    Machacek, Erika

    2017-01-01

    " in the bureaucratic practice of classification: Experts construct material criticality in assessments as they allot information on the materials to the parameters of the assessment framework. In so doing, they ascribe a new set of connotations to the materials, namely supply risk, and their importance to clean energy......, legitimizing a criticality discourse.Specifically, the paper introduces a typology delineating the inferences made by the experts from their produced recommendations in the classification of rare earth element criticality. The paper argues that the classification is a specific process of constructing risk....... It proposes that the expert bureaucratic practice of classification legitimizes (i) the valorisation that was made in the drafting of the assessment framework for the classification, and (ii) political operationalization when enacted that might have (non-)distributive implications for the allocation of public...

  6. Image Classification Workflow Using Machine Learning Methods

    Science.gov (United States)

    Christoffersen, M. S.; Roser, M.; Valadez-Vergara, R.; Fernández-Vega, J. A.; Pierce, S. A.; Arora, R.

    2016-12-01

    Recent increases in the availability and quality of remote sensing datasets have fueled an increasing number of scientifically significant discoveries based on land use classification and land use change analysis. However, much of the software made to work with remote sensing data products, specifically multispectral images, is commercial and often prohibitively expensive. The free to use solutions that are currently available come bundled up as small parts of much larger programs that are very susceptible to bugs and difficult to install and configure. What is needed is a compact, easy to use set of tools to perform land use analysis on multispectral images. To address this need, we have developed software using the Python programming language with the sole function of land use classification and land use change analysis. We chose Python to develop our software because it is relatively readable, has a large body of relevant third party libraries such as GDAL and Spectral Python, and is free to install and use on Windows, Linux, and Macintosh operating systems. In order to test our classification software, we performed a K-means unsupervised classification, Gaussian Maximum Likelihood supervised classification, and a Mahalanobis Distance based supervised classification. The images used for testing were three Landsat rasters of Austin, Texas with a spatial resolution of 60 meters for the years of 1984 and 1999, and 30 meters for the year 2015. The testing dataset was easily downloaded using the Earth Explorer application produced by the USGS. The software should be able to perform classification based on any set of multispectral rasters with little to no modification. Our software makes the ease of land use classification using commercial software available without an expensive license.

  7. Automatic classification of endogenous seismic sources within a landslide body using random forest algorithm

    Science.gov (United States)

    Provost, Floriane; Hibert, Clément; Malet, Jean-Philippe; Stumpf, André; Doubre, Cécile

    2016-04-01

    Different studies have shown the presence of microseismic activity in soft-rock landslides. The seismic signals exhibit significantly different features in the time and frequency domains which allow their classification and interpretation. Most of the classes could be associated with different mechanisms of deformation occurring within and at the surface (e.g. rockfall, slide-quake, fissure opening, fluid circulation). However, some signals remain not fully understood and some classes contain few examples that prevent any interpretation. To move toward a more complete interpretation of the links between the dynamics of soft-rock landslides and the physical processes controlling their behaviour, a complete catalog of the endogeneous seismicity is needed. 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 Forest 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 to the seismicity recorded at the Super-Sauze landslide between 2013 and 2015. We selected a dozen of seismic signal features that characterize precisely its spectral content (e.g. central frequency, spectrum width, energy in several frequency bands, spectrogram shape, spectrum local and global maxima

  8. Classification of movement disorders.

    Science.gov (United States)

    Fahn, Stanley

    2011-05-01

    The classification of movement disorders has evolved. Even the terminology has shifted, from an anatomical one of extrapyramidal disorders to a phenomenological one of movement disorders. The history of how this shift came about is described. The history of both the definitions and the classifications of the various neurologic conditions is then reviewed. First is a review of movement disorders as a group; then, the evolving classifications for 3 of them--parkinsonism, dystonia, and tremor--are covered in detail. Copyright © 2011 Movement Disorder Society.

  9. [Utility and validity of indicators from the Nursing Outcomes Classification as a support tool for diagnosing Ineffective Self Health Management in patients with chronic conditions in primary health care].

    Science.gov (United States)

    Morilla-Herrera, J C; Morales-Asencio, J M; Fernández-Gallego, M C; Cobos, E Berrobianco; Romero, A Delgado

    2011-01-01

    Self-care and management of therapeutic regime (drugs adherence, preventive behaviours and development of healthy life-styles) are key components for managing chronic diseases. Nursing has standardized languages which describe many of these situations, such as the diagnosis "Ineffective Self Health Management" (ISHM) or many of the Nursing Outcomes Classification (NOC) indicators. The aims of this study were to determine the interobserver reliability of a NOC-based instrument for assessment and aid in diagnosis of the ISHM in patients with chronic conditions in Primary Health Care, to determine its diagnostic validity and to describe the prevalence of patients with this problem. Cross-sectional validation study developed in the provinces of Málaga, Cádiz and Almería from 2006 to 2009. Each patient was assessed by 3 independent observers: the first two observers evaluated scoring of the NOC indicators and the third one acted as the "gold-standard". Two hundred and twenty-eight patients were included, 37.7% of them with more than one chronic condition. NOC indicators showed a high interobserver reliability (ICC>0,70) and a consistency (Cronbach's alpha: 0.81). With a cut-point of 10.5, sensitivity was 61% and specificity 85%, and the area under the curve was 0.81 (CI95%: 0.77 to 0.85). The prevalence of patients with ISHM was 36% (CI 95%: 34 to 40). The use of NOC indicators allows evaluation of management of the therapeutic regime in people with chronic conditions with a satisfactory validity and it provides new approaches for dealing with this problem.

  10. 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.

  11. 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.

  12. 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:

  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......, and that even though classification gets marginally better, not much is achieved by increasing the window size beyond 1 s....

  14. Towards secondary fingerprint classification

    CSIR Research Space (South Africa)

    Msiza, IS

    2011-07-01

    Full Text Available an accuracy figure of 76.8%. This small difference between the two figures is indicative of the validity of the proposed secondary classification module. Keywords?fingerprint core; fingerprint delta; primary classifi- cation; secondary classification I..., namely, the fingerprint core and the fingerprint delta. Forensically, a fingerprint core is defined as the innermost turning point where the fingerprint ridges form a loop, while the fingerprint delta is defined as the point where these ridges form a...

  15. Expected Classification Accuracy

    Directory of Open Access Journals (Sweden)

    Lawrence M. Rudner

    2005-08-01

    Full Text Available Every time we make a classification based on a test score, we should expect some number..of misclassifications. Some examinees whose true ability is within a score range will have..observed scores outside of that range. A procedure for providing a classification table of..true and expected scores is developed for polytomously scored items under item response..theory and applied to state assessment data. A simplified procedure for estimating the..table entries is also presented.

  16. 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....

  17. 78 FR 68983 - Cotton Futures Classification: Optional Classification Procedure

    Science.gov (United States)

    2013-11-18

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

  18. Materials selection for cutting tools

    International Nuclear Information System (INIS)

    Burkhis, Adel M.

    2008-01-01

    The selection of proper tool steel for a given application is a difficult task. So; the most important selection factors in choosing cutting tool materials are based on their tool material requirements, cutting tool design and service conditions which is mainly considered as functional requirements. The processability requirements concerns in heat treat ability of the material tool. The classification of these tool materials were discussed with their properties requirement and percent of alloying element which is added to give best properties with a little increase in cost that highly appear in comparison of the selection. The cutting tool materials were evaluated based on two cases; The first was in case of rough surface; the high speed steels is the best material and the other was the ceramic material is the highest performance in cutting of soft or high rate of metal removal. (author)

  19. Supernova Photometric Lightcurve Classification

    Science.gov (United States)

    Zaidi, Tayeb; Narayan, Gautham

    2016-01-01

    This is a preliminary report on photometric supernova classification. We first explore the properties of supernova light curves, and attempt to restructure the unevenly sampled and sparse data from assorted datasets to allow for processing and classification. The data was primarily drawn from the Dark Energy Survey (DES) simulated data, created for the Supernova Photometric Classification Challenge. This poster shows a method for producing a non-parametric representation of the light curve data, and applying a Random Forest classifier algorithm to distinguish between supernovae types. We examine the impact of Principal Component Analysis to reduce the dimensionality of the dataset, for future classification work. The classification code will be used in a stage of the ANTARES pipeline, created for use on the Large Synoptic Survey Telescope alert data and other wide-field surveys. The final figure-of-merit for the DES data in the r band was 60% for binary classification (Type I vs II).Zaidi was supported by the NOAO/KPNO Research Experiences for Undergraduates (REU) Program which is funded by the National Science Foundation Research Experiences for Undergraduates Program (AST-1262829).

  20. Observation versus classification in supervised category learning.

    Science.gov (United States)

    Levering, Kimery R; Kurtz, Kenneth J

    2015-02-01

    The traditional supervised classification paradigm encourages learners to acquire only the knowledge needed to predict category membership (a discriminative approach). An alternative that aligns with important aspects of real-world concept formation is learning with a broader focus to acquire knowledge of the internal structure of each category (a generative approach). Our work addresses the impact of a particular component of the traditional classification task: the guess-and-correct cycle. We compare classification learning to a supervised observational learning task in which learners are shown labeled examples but make no classification response. The goals of this work sit at two levels: (1) testing for differences in the nature of the category representations that arise from two basic learning modes; and (2) evaluating the generative/discriminative continuum as a theoretical tool for understand learning modes and their outcomes. Specifically, we view the guess-and-correct cycle as consistent with a more discriminative approach and therefore expected it to lead to narrower category knowledge. Across two experiments, the observational mode led to greater sensitivity to distributional properties of features and correlations between features. We conclude that a relatively subtle procedural difference in supervised category learning substantially impacts what learners come to know about the categories. The results demonstrate the value of the generative/discriminative continuum as a tool for advancing the psychology of category learning and also provide a valuable constraint for formal models and associated theories.

  1. 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...

  2. Classification methods to detect sleep apnea in adults based on respiratory and oximetry signals: a systematic review.

    Science.gov (United States)

    Uddin, M B; Chow, C M; Su, S W

    2018-03-26

    Sleep apnea (SA), a common sleep disorder, can significantly decrease the quality of life, and is closely associated with major health risks such as cardiovascular disease, sudden death, depression, and hypertension. The normal diagnostic process of SA using polysomnography is costly and time consuming. In addition, the accuracy of different classification methods to detect SA varies with the use of different physiological signals. If an effective, reliable, and accurate classification method is developed, then the diagnosis of SA and its associated treatment will be time-efficient and economical. This study aims to systematically review the literature and present an overview of classification methods to detect SA using respiratory and oximetry signals and address the automated detection approach. Sixty-two included studies revealed the application of single and multiple signals (respiratory and oximetry) for the diagnosis of SA. Both airflow and oxygen saturation signals alone were effective in detecting SA in the case of binary decision-making, whereas multiple signals were good for multi-class detection. In addition, some machine learning methods were superior to the other classification methods for SA detection using respiratory and oximetry signals. To deal with the respiratory and oximetry signals, a good choice of classification method as well as the consideration of associated factors would result in high accuracy in the detection of SA. An accurate classification method should provide a high detection rate with an automated (independent of human action) analysis of respiratory and oximetry signals. Future high-quality automated studies using large samples of data from multiple patient groups or record batches are recommended.

  3. Conformal radiotherapy: principles and classification

    International Nuclear Information System (INIS)

    Rosenwald, J.C.; Gaboriaud, G.; Pontvert, D.

    1999-01-01

    'Conformal radiotherapy' is the name fixed by usage and given to a new form of radiotherapy resulting from the technological improvements observed during the last ten years. While this terminology is now widely used, no precise definition can be found in the literature. Conformal radiotherapy refers to an approach in which the dose distribution is more closely 'conformed' or adapted to the actual shape of the target volume. However, the achievement of a consensus on a more specific definition is hampered by various difficulties, namely in characterizing the degree of 'conformality'. We have therefore suggested a classification scheme be established on the basis of the tools and the procedures actually used for all steps of the process, i.e., from prescription to treatment completion. Our classification consists of four levels: schematically, at level 0, there is no conformation (rectangular fields); at level 1, a simple conformation takes place, on the basis of conventional 2D imaging; at level 2, a 3D reconstruction of the structures is used for a more accurate conformation; and level 3 includes research and advanced dynamic techniques. We have used our personal experience, contacts with colleagues and data from the literature to analyze all the steps of the planning process, and to define the tools and procedures relevant to a given level. The corresponding tables have been discussed and approved at the European level within the Dynarad concerted action. It is proposed that the term 'conformal radiotherapy' be restricted to procedures where all steps are at least at level 2. (author)

  4. Classification of Patient Care Complexity: Cloud Technology.

    Science.gov (United States)

    de Oliveira Riboldi, Caren; Macedo, Andrea Barcellos Teixeira; Mergen, Thiane; Dias, Vera Lúcia Mendes; da Costa, Diovane Ghignatti; Malvezzi, Maria Luiza Falsarella; Magalhães, Ana Maria Muller; Silveira, Denise Tolfo

    2016-01-01

    Presentation of the computerized structure to implement, in a university hospital in the South of Brazil, the Patients Classification System of Perroca, which categorizes patients according to the care complexity. This solution also aims to corroborate a recent study at the hospital, which evidenced that the increasing workload presents a direct relation with the institutional quality indicators. The tools used were the Google applications with high productivity interconnecting the topic knowledge on behalf of the nursing professionals and information technology professionals.

  5. USING SAS ENTERPRISE GUIDE SOFTWARE IN CLASSIFICATION

    OpenAIRE

    Ana Maria Mihaela IORDACHE

    2011-01-01

    Data mining, also known as "discovery knowledge in large databases "is a modern and powerful information technology and communications tool that can be used to extract useful information but still unknown. This automates the process of discovery some relations and mixtures from the raw data and founded results could be incorporated into an automated decision support. This paper aims to present and perform the classification of European Union countries based on the social indicators calculated...

  6. A Data Mining Classification Approach for Behavioral Malware Detection

    Directory of Open Access Journals (Sweden)

    Monire Norouzi

    2016-01-01

    Full Text Available Data mining techniques have numerous applications in malware detection. Classification method is one of the most popular data mining techniques. In this paper we present a data mining classification approach to detect malware behavior. We proposed different classification methods in order to detect malware based on the feature and behavior of each malware. A dynamic analysis method has been presented for identifying the malware features. A suggested program has been presented for converting a malware behavior executive history XML file to a suitable WEKA tool input. To illustrate the performance efficiency as well as training data and test, we apply the proposed approaches to a real case study data set using WEKA tool. The evaluation results demonstrated the availability of the proposed data mining approach. Also our proposed data mining approach is more efficient for detecting malware and behavioral classification of malware can be useful to detect malware in a behavioral antivirus.

  7. Cellular image classification

    CERN Document Server

    Xu, Xiang; Lin, Feng

    2017-01-01

    This book introduces new techniques for cellular image feature extraction, pattern recognition and classification. The authors use the antinuclear antibodies (ANAs) in patient serum as the subjects and the Indirect Immunofluorescence (IIF) technique as the imaging protocol to illustrate the applications of the described methods. Throughout the book, the authors provide evaluations for the proposed methods on two publicly available human epithelial (HEp-2) cell datasets: ICPR2012 dataset from the ICPR'12 HEp-2 cell classification contest and ICIP2013 training dataset from the ICIP'13 Competition on cells classification by fluorescent image analysis. First, the reading of imaging results is significantly influenced by one’s qualification and reading systems, causing high intra- and inter-laboratory variance. The authors present a low-order LP21 fiber mode for optical single cell manipulation and imaging staining patterns of HEp-2 cells. A focused four-lobed mode distribution is stable and effective in optical...

  8. Bosniak classification system

    DEFF Research Database (Denmark)

    Graumann, Ole; Osther, Susanne Sloth; Karstoft, Jens

    2016-01-01

    BACKGROUND: The Bosniak classification was originally based on computed tomographic (CT) findings. Magnetic resonance (MR) and contrast-enhanced ultrasonography (CEUS) imaging may demonstrate findings that are not depicted at CT, and there may not always be a clear correlation between the findings...... at MR and CEUS imaging and those at CT. PURPOSE: To compare diagnostic accuracy of MR, CEUS, and CT when categorizing complex renal cystic masses according to the Bosniak classification. MATERIAL AND METHODS: From February 2011 to June 2012, 46 complex renal cysts were prospectively evaluated by three...... readers. Each mass was categorized according to the Bosniak classification and CT was chosen as gold standard. Kappa was calculated for diagnostic accuracy and data was compared with pathological results. RESULTS: CT images found 27 BII, six BIIF, seven BIII, and six BIV. Forty-three cysts could...

  9. Principal component analysis of normalized full spectrum mass spectrometry data in multiMS-toolbox: An effective tool to identify important factors for classification of different metabolic patterns and bacterial strains.

    Science.gov (United States)

    Cejnar, Pavel; Kuckova, Stepanka; Prochazka, Ales; Karamonova, Ludmila; Svobodova, Barbora

    2018-06-15

    Explorative statistical analysis of mass spectrometry data is still a time-consuming step. We analyzed critical factors for application of principal component analysis (PCA) in mass spectrometry and focused on two whole spectrum based normalization techniques and their application in the analysis of registered peak data and, in comparison, in full spectrum data analysis. We used this technique to identify different metabolic patterns in the bacterial culture of Cronobacter sakazakii, an important foodborne pathogen. Two software utilities, the ms-alone, a python-based utility for mass spectrometry data preprocessing and peak extraction, and the multiMS-toolbox, an R software tool for advanced peak registration and detailed explorative statistical analysis, were implemented. The bacterial culture of Cronobacter sakazakii was cultivated on Enterobacter sakazakii Isolation Agar, Blood Agar Base and Tryptone Soya Agar for 24 h and 48 h and applied by the smear method on an Autoflex speed MALDI-TOF mass spectrometer. For three tested cultivation media only two different metabolic patterns of Cronobacter sakazakii were identified using PCA applied on data normalized by two different normalization techniques. Results from matched peak data and subsequent detailed full spectrum analysis identified only two different metabolic patterns - a cultivation on Enterobacter sakazakii Isolation Agar showed significant differences to the cultivation on the other two tested media. The metabolic patterns for all tested cultivation media also proved the dependence on cultivation time. Both whole spectrum based normalization techniques together with the full spectrum PCA allow identification of important discriminative factors in experiments with several variable condition factors avoiding any problems with improper identification of peaks or emphasis on bellow threshold peak data. The amounts of processed data remain still manageable. Both implemented software utilities are available

  10. Acoustic classification of dwellings

    DEFF Research Database (Denmark)

    Berardi, Umberto; Rasmussen, Birgit

    2014-01-01

    insulation performance, national schemes for sound classification of dwellings have been developed in several European countries. These schemes define acoustic classes according to different levels of sound insulation. Due to the lack of coordination among countries, a significant diversity in terms...... exchanging experiences about constructions fulfilling different classes, reducing trade barriers, and finally increasing the sound insulation of dwellings.......Schemes for the classification of dwellings according to different building performances have been proposed in the last years worldwide. The general idea behind these schemes relates to the positive impact a higher label, and thus a better performance, should have. In particular, focusing on sound...

  11. Minimum Error Entropy Classification

    CERN Document Server

    Marques de Sá, Joaquim P; Santos, Jorge M F; Alexandre, Luís A

    2013-01-01

    This book explains the minimum error entropy (MEE) concept applied to data classification machines. Theoretical results on the inner workings of the MEE concept, in its application to solving a variety of classification problems, are presented in the wider realm of risk functionals. Researchers and practitioners also find in the book a detailed presentation of practical data classifiers using MEE. These include multi‐layer perceptrons, recurrent neural networks, complexvalued neural networks, modular neural networks, and decision trees. A clustering algorithm using a MEE‐like concept is also presented. Examples, tests, evaluation experiments and comparison with similar machines using classic approaches, complement the descriptions.

  12. Classification of iconic images

    OpenAIRE

    Zrianina, Mariia; Kopf, Stephan

    2016-01-01

    Iconic images represent an abstract topic and use a presentation that is intuitively understood within a certain cultural context. For example, the abstract topic “global warming” may be represented by a polar bear standing alone on an ice floe. Such images are widely used in media and their automatic classification can help to identify high-level semantic concepts. This paper presents a system for the classification of iconic images. It uses a variation of the Bag of Visual Words approach wi...

  13. Casemix classification systems.

    Science.gov (United States)

    Fetter, R B

    1999-01-01

    The idea of using casemix classification to manage hospital services is not new, but has been limited by available technology. It was not until after the introduction of Medicare in the United States in 1965 that serious attempts were made to measure hospital production in order to contain spiralling costs. This resulted in a system of casemix classification known as diagnosis related groups (DRGs). This paper traces the development of DRGs and their evolution from the initial version to the All Patient Refined DRGs developed in 1991.

  14. Normed kernel function-based fuzzy possibilistic C-means (NKFPCM) algorithm for high-dimensional breast cancer database classification with feature selection is based on Laplacian Score

    Science.gov (United States)

    Lestari, A. W.; Rustam, Z.

    2017-07-01

    In the last decade, breast cancer has become the focus of world attention as this disease is one of the primary leading cause of death for women. Therefore, it is necessary to have the correct precautions and treatment. In previous studies, Fuzzy Kennel K-Medoid algorithm has been used for multi-class data. This paper proposes an algorithm to classify the high dimensional data of breast cancer using Fuzzy Possibilistic C-means (FPCM) and a new method based on clustering analysis using Normed Kernel Function-Based Fuzzy Possibilistic C-Means (NKFPCM). The objective of this paper is to obtain the best accuracy in classification of breast cancer data. In order to improve the accuracy of the two methods, the features candidates are evaluated using feature selection, where Laplacian Score is used. The results show the comparison accuracy and running time of FPCM and NKFPCM with and without feature selection.

  15. Information gathering for CLP classification

    Directory of Open Access Journals (Sweden)

    Ida Marcello

    2011-01-01

    Full Text Available Regulation 1272/2008 includes provisions for two types of classification: harmonised classification and self-classification. The harmonised classification of substances is decided at Community level and a list of harmonised classifications is included in the Annex VI of the classification, labelling and packaging Regulation (CLP. If a chemical substance is not included in the harmonised classification list it must be self-classified, based on available information, according to the requirements of Annex I of the CLP Regulation. CLP appoints that the harmonised classification will be performed for carcinogenic, mutagenic or toxic to reproduction substances (CMR substances and for respiratory sensitisers category 1 and for other hazard classes on a case-by-case basis. The first step of classification is the gathering of available and relevant information. This paper presents the procedure for gathering information and to obtain data. The data quality is also discussed.

  16. The paradox of atheoretical classification

    DEFF Research Database (Denmark)

    Hjørland, Birger

    2016-01-01

    A distinction can be made between “artificial classifications” and “natural classifications,” where artificial classifications may adequately serve some limited purposes, but natural classifications are overall most fruitful by allowing inference and thus many different purposes. There is strong...... support for the view that a natural classification should be based on a theory (and, of course, that the most fruitful theory provides the most fruitful classification). Nevertheless, atheoretical (or “descriptive”) classifications are often produced. Paradoxically, atheoretical classifications may...... be very successful. The best example of a successful “atheoretical” classification is probably the prestigious Diagnostic and Statistical Manual of Mental Disorders (DSM) since its third edition from 1980. Based on such successes one may ask: Should the claim that classifications ideally are natural...

  17. Ecosystem classification, Chapter 2

    Science.gov (United States)

    M.J. Robin-Abbott; L.H. Pardo

    2011-01-01

    The ecosystem classification in this report is based on the ecoregions developed through the Commission for Environmental Cooperation (CEC) for North America (CEC 1997). Only ecosystems that occur in the United States are included. CEC ecoregions are described, with slight modifications, below (CEC 1997) and shown in Figures 2.1 and 2.2. We chose this ecosystem...

  18. The classification of phocomelia.

    Science.gov (United States)

    Tytherleigh-Strong, G; Hooper, G

    2003-06-01

    We studied 24 patients with 44 phocomelic upper limbs. Only 11 limbs could be grouped in the classification system of Frantz and O' Rahilly. The non-classifiable limbs were further studied and their characteristics identified. It is confirmed that phocomelia is not an intercalary defect.

  19. Principles for ecological classification

    Science.gov (United States)

    Dennis H. Grossman; Patrick Bourgeron; Wolf-Dieter N. Busch; David T. Cleland; William Platts; G. Ray; C. Robins; Gary Roloff

    1999-01-01

    The principal purpose of any classification is to relate common properties among different entities to facilitate understanding of evolutionary and adaptive processes. In the context of this volume, it is to facilitate ecosystem stewardship, i.e., to help support ecosystem conservation and management objectives.

  20. Mimicking human texture classification

    NARCIS (Netherlands)

    Rogowitz, B.E.; van Rikxoort, Eva M.; van den Broek, Egon; Pappas, T.N.; Schouten, Theo E.; Daly, S.J.

    2005-01-01

    In an attempt to mimic human (colorful) texture classification by a clustering algorithm three lines of research have been encountered, in which as test set 180 texture images (both their color and gray-scale equivalent) were drawn from the OuTex and VisTex databases. First, a k-means algorithm was

  1. Classification, confusion and misclassification

    African Journals Online (AJOL)

    The classification of objects and phenomena in science and nature has fascinated academics since Carl Linnaeus, the Swedish botanist and zoologist, created his binomial description of living things in the 1700s and probably long before in accounts of others in textbooks long since gone. It must have concerned human ...

  2. Classifications in popular music

    NARCIS (Netherlands)

    van Venrooij, A.; Schmutz, V.; Wright, J.D.

    2015-01-01

    The categorical system of popular music, such as genre categories, is a highly differentiated and dynamic classification system. In this article we present work that studies different aspects of these categorical systems in popular music. Following the work of Paul DiMaggio, we focus on four

  3. Shark Teeth Classification

    Science.gov (United States)

    Brown, Tom; Creel, Sally; Lee, Velda

    2009-01-01

    On a recent autumn afternoon at Harmony Leland Elementary in Mableton, Georgia, students in a fifth-grade science class investigated the essential process of classification--the act of putting things into groups according to some common characteristics or attributes. While they may have honed these skills earlier in the week by grouping their own…

  4. Text document classification

    Czech Academy of Sciences Publication Activity Database

    Novovičová, Jana

    č. 62 (2005), s. 53-54 ISSN 0926-4981 R&D Projects: GA AV ČR IAA2075302; GA AV ČR KSK1019101; GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : document representation * categorization * classification Subject RIV: BD - Theory of Information

  5. Classification in Medical Imaging

    DEFF Research Database (Denmark)

    Chen, Chen

    Classification is extensively used in the context of medical image analysis for the purpose of diagnosis or prognosis. In order to classify image content correctly, one needs to extract efficient features with discriminative properties and build classifiers based on these features. In addition...... on characterizing human faces and emphysema disease in lung CT images....

  6. Improving Student Question Classification

    Science.gov (United States)

    Heiner, Cecily; Zachary, Joseph L.

    2009-01-01

    Students in introductory programming classes often articulate their questions and information needs incompletely. Consequently, the automatic classification of student questions to provide automated tutorial responses is a challenging problem. This paper analyzes 411 questions from an introductory Java programming course by reducing the natural…

  7. NOUN CLASSIFICATION IN ESAHIE

    African Journals Online (AJOL)

    The present work deals with noun classification in Esahie (Kwa, Niger ... phonological information influences the noun (form) class system of Esahie. ... between noun classes and (grammatical) Gender is interrogated (in the light of ..... the (A) argument6 precedes the verb and the (P) argument7 follows the verb in a simple.

  8. Dynamic Latent Classification Model

    DEFF Research Database (Denmark)

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

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

  9. Classification of myocardial infarction

    DEFF Research Database (Denmark)

    Saaby, Lotte; Poulsen, Tina Svenstrup; Hosbond, Susanne Elisabeth

    2013-01-01

    The classification of myocardial infarction into 5 types was introduced in 2007 as an important component of the universal definition. In contrast to the plaque rupture-related type 1 myocardial infarction, type 2 myocardial infarction is considered to be caused by an imbalance between demand...

  10. Event Classification using Concepts

    NARCIS (Netherlands)

    Boer, M.H.T. de; Schutte, K.; Kraaij, W.

    2013-01-01

    The semantic gap is one of the challenges in the GOOSE project. In this paper a Semantic Event Classification (SEC) system is proposed as an initial step in tackling the semantic gap challenge in the GOOSE project. This system uses semantic text analysis, multiple feature detectors using the BoW

  11. Automatic feed phase identification in multivariate bioprocess profiles by sequential binary classification.

    Science.gov (United States)

    Nikzad-Langerodi, Ramin; Lughofer, Edwin; Saminger-Platz, Susanne; Zahel, Thomas; Sagmeister, Patrick; Herwig, Christoph

    2017-08-22

    In this paper, we propose a new strategy for retrospective identification of feed phases from online sensor-data enriched feed profiles of an Escherichia Coli (E. coli) fed-batch fermentation process. In contrast to conventional (static), data-driven multi-class machine learning (ML), we exploit process knowledge in order to constrain our classification system yielding more parsimonious models compared to static ML approaches. In particular, we enforce unidirectionality on a set of binary, multivariate classifiers trained to discriminate between adjacent feed phases by linking the classifiers through a one-way switch. The switch is activated when the actual classifier output changes. As a consequence, the next binary classifier in the classifier chain is used for the discrimination between the next feed phase pair etc. We allow activation of the switch only after a predefined number of consecutive predictions of a transition event in order to prevent premature activation of the switch and undertake a sensitivity analysis regarding the optimal choice of the (time) lag parameter. From a complexity/parsimony perspective the benefit of our approach is three-fold: i) The multi-class learning task is broken down into binary subproblems which usually have simpler decision surfaces and tend to be less susceptible to the class-imbalance problem. ii) We exploit the fact that the process follows a rigid feed cycle structure (i.e. batch-feed-batch-feed) which allows us to focus on the subproblems involving phase transitions as they occur during the process while discarding off-transition classifiers and iii) only one binary classifier is active at the time which keeps effective model complexity low. We further use a combination of logistic regression and Lasso (i.e. regularized logistic regression, RLR) as a wrapper to extract the most relevant features for individual subproblems from the whole set of high-dimensional sensor data. We train different soft computing classifiers

  12. NEW CLASSIFICATION OF ECOPOLICES

    Directory of Open Access Journals (Sweden)

    VOROBYOV V. V.

    2016-09-01

    Full Text Available Problem statement. Ecopolices are the newest stage of the urban planning. They have to be consideredsuchas material and energy informational structures, included to the dynamic-evolutionary matrix netsofex change processes in the ecosystems. However, there are not made the ecopolice classifications, developing on suchapproaches basis. And this determined the topicality of the article. Analysis of publications on theoretical and applied aspects of the ecopolices formation showed, that the work on them is managed mainly in the context of the latest scientific and technological achievements in the various knowledge fields. These settlements are technocratic. They are connected with the morphology of space, network structures of regional and local natural ecosystems, without independent stability, can not exist without continuous man support. Another words, they do not work in with an ecopolices idea. It is come to a head for objective, symbiotic searching of ecopolices concept with the development of their classifications. Purpose statement is to develop the objective evidence for ecopolices and to propose their new classification. Conclusion. On the base of the ecopolices classification have to lie an elements correlation idea of their general plans and men activity type according with natural mechanism of accepting, reworking and transmission of material, energy and information between geo-ecosystems, planet, man, ecopolices material part and Cosmos. New ecopolices classification should be based on the principles of multi-dimensional, time-spaced symbiotic clarity with exchange ecosystem networks. The ecopolice function with this approach comes not from the subjective anthropocentric economy but from the holistic objective of Genesis paradigm. Or, otherwise - not from the Consequence, but from the Cause.

  13. Efficient Fingercode Classification

    Science.gov (United States)

    Sun, Hong-Wei; Law, Kwok-Yan; Gollmann, Dieter; Chung, Siu-Leung; Li, Jian-Bin; Sun, Jia-Guang

    In this paper, we present an efficient fingerprint classification algorithm which is an essential component in many critical security application systems e. g. systems in the e-government and e-finance domains. Fingerprint identification is one of the most important security requirements in homeland security systems such as personnel screening and anti-money laundering. The problem of fingerprint identification involves searching (matching) the fingerprint of a person against each of the fingerprints of all registered persons. To enhance performance and reliability, a common approach is to reduce the search space by firstly classifying the fingerprints and then performing the search in the respective class. Jain et al. proposed a fingerprint classification algorithm based on a two-stage classifier, which uses a K-nearest neighbor classifier in its first stage. The fingerprint classification algorithm is based on the fingercode representation which is an encoding of fingerprints that has been demonstrated to be an effective fingerprint biometric scheme because of its ability to capture both local and global details in a fingerprint image. We enhance this approach by improving the efficiency of the K-nearest neighbor classifier for fingercode-based fingerprint classification. Our research firstly investigates the various fast search algorithms in vector quantization (VQ) and the potential application in fingerprint classification, and then proposes two efficient algorithms based on the pyramid-based search algorithms in VQ. Experimental results on DB1 of FVC 2004 demonstrate that our algorithms can outperform the full search algorithm and the original pyramid-based search algorithms in terms of computational efficiency without sacrificing accuracy.

  14. Differential Classification of Dementia

    Directory of Open Access Journals (Sweden)

    E. Mohr

    1995-01-01

    Full Text Available In the absence of biological markers, dementia classification remains complex both in terms of characterization as well as early detection of the presence or absence of dementing symptoms, particularly in diseases with possible secondary dementia. An empirical, statistical approach using neuropsychological measures was therefore developed to distinguish demented from non-demented patients and to identify differential patterns of cognitive dysfunction in neurodegenerative disease. Age-scaled neurobehavioral test results (Wechsler Adult Intelligence Scale—Revised and Wechsler Memory Scale from Alzheimer's (AD and Huntington's (HD patients, matched for intellectual disability, as well as normal controls were used to derive a classification formula. Stepwise discriminant analysis accurately (99% correct distinguished controls from demented patients, and separated the two patient groups (79% correct. Variables discriminating between HD and AD patient groups consisted of complex psychomotor tasks, visuospatial function, attention and memory. The reliability of the classification formula was demonstrated with a new, independent sample of AD and HD patients which yielded virtually identical results (classification accuracy for dementia: 96%; AD versus HD: 78%. To validate the formula, the discriminant function was applied to Parkinson's (PD patients, 38% of whom were classified as demented. The validity of the classification was demonstrated by significant PD subgroup differences on measures of dementia not included in the discriminant function. Moreover, a majority of demented PD patients (65% were classified as having an HD-like pattern of cognitive deficits, in line with previous reports of the subcortical nature of PD dementia. This approach may thus be useful in classifying presence or absence of dementia and in discriminating between dementia subtypes in cases of secondary or coincidental dementia.

  15. Simulation tools

    CERN Document Server

    Jenni, F

    2006-01-01

    In the last two decades, simulation tools made a significant contribution to the great progress in development of power electronics. Time to market was shortened and development costs were reduced drastically. Falling costs, as well as improved speed and precision, opened new fields of application. Today, continuous and switched circuits can be mixed. A comfortable number of powerful simulation tools is available. The users have to choose the best suitable for their application. Here a simple rule applies: The best available simulation tool is the tool the user is already used to (provided, it can solve the task). Abilities, speed, user friendliness and other features are continuously being improved—even though they are already powerful and comfortable. This paper aims at giving the reader an insight into the simulation of power electronics. Starting with a short description of the fundamentals of a simulation tool as well as properties of tools, several tools are presented. Starting with simplified models ...

  16. 78 FR 54970 - Cotton Futures Classification: Optional Classification Procedure

    Science.gov (United States)

    2013-09-09

    ... Service 7 CFR Part 27 [AMS-CN-13-0043] RIN 0581-AD33 Cotton Futures Classification: Optional Classification Procedure AGENCY: Agricultural Marketing Service, USDA. ACTION: Proposed rule. SUMMARY: The... optional cotton futures classification procedure--identified and known as ``registration'' by the U.S...

  17. A neural network-based optimal spatial filter design method for motor imagery classification.

    Directory of Open Access Journals (Sweden)

    Ayhan Yuksel

    Full Text Available In this study, a novel spatial filter design method is introduced. Spatial filtering is an important processing step for feature extraction in motor imagery-based brain-computer interfaces. This paper introduces a new motor imagery signal classification method combined with spatial filter optimization. We simultaneously train the spatial filter and the classifier using a neural network approach. The proposed spatial filter network (SFN is composed of two layers: a spatial filtering layer and a classifier layer. These two layers are linked to each other with non-linear mapping functions. The proposed method addresses two shortcomings of the common spatial patterns (CSP algorithm. First, CSP aims to maximize the between-classes variance while ignoring the minimization of within-classes variances. Consequently, the features obtained using the CSP method may have large within-classes variances. Second, the maximizing optimization function of CSP increases the classification accuracy indirectly because an independent classifier is used after the CSP method. With SFN, we aimed to maximize the between-classes variance while minimizing within-classes variances and simultaneously optimizing the spatial filter and the classifier. To classify motor imagery EEG signals, we modified the well-known feed-forward structure and derived forward and backward equations that correspond to the proposed structure. We tested our algorithm on simple toy data. Then, we compared the SFN with conventional CSP and its multi-class version, called one-versus-rest CSP, on two data sets from BCI competition III. The evaluation results demonstrate that SFN is a good alternative for classifying motor imagery EEG signals with increased classification accuracy.

  18. Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization

    Science.gov (United States)

    Ma, Yuliang; Ding, Xiaohui; She, Qingshan; Luo, Zhizeng; Potter, Thomas; Zhang, Yingchun

    2016-01-01

    Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals. PMID:27313656

  19. [Evaluation of new and emerging health technologies. Proposal for classification].

    Science.gov (United States)

    Prados-Torres, J D; Vidal-España, F; Barnestein-Fonseca, P; Gallo-García, C; Irastorza-Aldasoro, A; Leiva-Fernández, F

    2011-01-01

    Review and develop a proposal for the classification of health technologies (HT) evaluated by the Health Technology Assessment Agencies (HTAA). Peer review of AETS of the previous proposed classification of HT. Analysis of their input and suggestions for amendments. Construction of a new classification. Pilot study with physicians. Andalusian Public Health System. Spanish HTAA. Experts from HTAA. Tutors of family medicine residents. HT Update classification previously made by the research team. Peer review by Spanish HTAA. Qualitative and quantitative analysis of responses. Construction of a new and pilot study based on 12 evaluation reports of the HTAA. We obtained 11 thematic categories that are classified into 6 major head groups: 1, prevention technology; 2, diagnostic technology; 3, therapeutic technologies; 4, diagnostic and therapeutic technologies; 5, organizational technology, and 6, knowledge management and quality of care. In the pilot there was a good concordance in the classification of 8 of the 12 reports reviewed by physicians. Experts agree on 11 thematic categories of HT. A new classification of HT with double entry (Nature and purpose of HT) is proposed. APPLICABILITY: According to experts, the classification of the work of the HTAA may represent a useful tool to transfer and manage knowledge. Moreover, an adequate classification of the HTAA reports would help clinicians and other potential users to locate them and this can facilitate their dissemination. Copyright © 2010 SECA. Published by Elsevier Espana. All rights reserved.

  20. 32 CFR 2700.22 - Classification guides.

    Science.gov (United States)

    2010-07-01

    ... SECURITY INFORMATION REGULATIONS Derivative Classification § 2700.22 Classification guides. OMSN shall... direct derivative classification, shall identify the information to be protected in specific and uniform...

  1. Improved binary dragonfly optimization algorithm and wavelet packet based non-linear features for infant cry classification.

    Science.gov (United States)

    Hariharan, M; Sindhu, R; Vijean, Vikneswaran; Yazid, Haniza; Nadarajaw, Thiyagar; Yaacob, Sazali; Polat, Kemal

    2018-03-01

    Infant cry signal carries several levels of information about the reason for crying (hunger, pain, sleepiness and discomfort) or the pathological status (asphyxia, deaf, jaundice, premature condition and autism, etc.) of an infant and therefore suited for early diagnosis. In this work, combination of wavelet packet based features and Improved Binary Dragonfly Optimization based feature selection method was proposed to classify the different types of infant cry signals. Cry signals from 2 different databases were utilized. First database contains 507 cry samples of normal (N), 340 cry samples of asphyxia (A), 879 cry samples of deaf (D), 350 cry samples of hungry (H) and 192 cry samples of pain (P). Second database contains 513 cry samples of jaundice (J), 531 samples of premature (Prem) and 45 samples of normal (N). Wavelet packet transform based energy and non-linear entropies (496 features), Linear Predictive Coding (LPC) based cepstral features (56 features), Mel-frequency Cepstral Coefficients (MFCCs) were extracted (16 features). The combined feature set consists of 568 features. To overcome the curse of dimensionality issue, improved binary dragonfly optimization algorithm (IBDFO) was proposed to select the most salient attributes or features. Finally, Extreme Learning Machine (ELM) kernel classifier was used to classify the different types of infant cry signals using all the features and highly informative features as well. Several experiments of two-class and multi-class classification of cry signals were conducted. In binary or two-class experiments, maximum accuracy of 90.18% for H Vs P, 100% for A Vs N, 100% for D Vs N and 97.61% J Vs Prem was achieved using the features selected (only 204 features out of 568) by IBDFO. For the classification of multiple cry signals (multi-class problem), the selected features could differentiate between three classes (N, A & D) with the accuracy of 100% and seven classes with the accuracy of 97.62%. The experimental

  2. IAEA Classification of Uranium Deposits

    International Nuclear Information System (INIS)

    Bruneton, Patrice

    2014-01-01

    Classifications of uranium deposits follow two general approaches, focusing on: • descriptive features such as the geotectonic position, the host rock type, the orebody morphology, …… : « geologic classification »; • or on genetic aspects: « genetic classification »

  3. Classification of Osteogenesis Imperfecta revisited

    NARCIS (Netherlands)

    van Dijk, F. S.; Pals, G.; van Rijn, R. R.; Nikkels, P. G. J.; Cobben, J. M.

    2010-01-01

    In 1979 Sillence proposed a classification of Osteogenesis Imperfecta (OI) in OI types I, II, III and IV. In 2004 and 2007 this classification was expanded with OI types V-VIII because of distinct clinical features and/or different causative gene mutations. We propose a revised classification of OI

  4. The future of general classification

    DEFF Research Database (Denmark)

    Mai, Jens Erik

    2013-01-01

    Discusses problems related to accessing multiple collections using a single retrieval language. Surveys the concepts of interoperability and switching language. Finds that mapping between more indexing languages always will be an approximation. Surveys the issues related to general classification...... and contrasts that to special classifications. Argues for the use of general classifications to provide access to collections nationally and internationally....

  5. [Headache: classification and diagnosis].

    Science.gov (United States)

    Carbaat, P A T; Couturier, E G M

    2016-11-01

    There are many types of headache and, moreover, many people have different types of headache at the same time. Adequate treatment is possible only on the basis of the correct diagnosis. Technically and in terms of content the current diagnostics process for headache is based on the 'International Classification of Headache Disorders' (ICHD-3-beta) that was produced under the auspices of the International Headache Society. This classification is based on a distinction between primary and secondary headaches. The most common primary headache types are the tension type headache, migraine and the cluster headache. Application of uniform diagnostic concepts is essential to come to the most appropriate treatment of the various types of headache.

  6. 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...... recruited from nine different tertiary referral centres. All patients underwent examination by specialists in dermatology and were checked using relevant allergy testing. Patients were classified into one of the six diagnostic subgroups of HE: allergic contact dermatitis, irritant contact dermatitis, atopic...... system investigated in the present study was useful, being able to give an appropriate main diagnosis for 89% of HE patients, and for another 7% when using two main diagnoses. The fact that more than half of the patients had one or more additional diagnoses illustrates that HE is a multifactorial disease....

  7. Sound classification of dwellings

    DEFF Research Database (Denmark)

    Rasmussen, Birgit

    2012-01-01

    National schemes for sound classification of dwellings exist in more than ten countries in Europe, typically published as national standards. The schemes define quality classes reflecting different levels of acoustical comfort. Main criteria concern airborne and impact sound insulation between...... dwellings, facade sound insulation and installation noise. The schemes have been developed, implemented and revised gradually since the early 1990s. However, due to lack of coordination between countries, there are significant discrepancies, and new standards and revisions continue to increase the diversity...... is needed, and a European COST Action TU0901 "Integrating and Harmonizing Sound Insulation Aspects in Sustainable Urban Housing Constructions", has been established and runs 2009-2013, one of the main objectives being to prepare a proposal for a European sound classification scheme with a number of quality...

  8. Granular loess classification based

    International Nuclear Information System (INIS)

    Browzin, B.S.

    1985-01-01

    This paper discusses how loess might be identified by two index properties: the granulometric composition and the dry unit weight. These two indices are necessary but not always sufficient for identification of loess. On the basis of analyses of samples from three continents, it was concluded that the 0.01-0.5-mm fraction deserves the name loessial fraction. Based on the loessial fraction concept, a granulometric classification of loess is proposed. A triangular chart is used to classify loess

  9. Classification and regression trees

    CERN Document Server

    Breiman, Leo; Olshen, Richard A; Stone, Charles J

    1984-01-01

    The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.

  10. CLASSIFICATION OF CRIMINAL GROUPS

    OpenAIRE

    Natalia Romanova

    2013-01-01

    New types of criminal groups are emerging in modern society.  These types have their special criminal subculture. The research objective is to develop new parameters of classification of modern criminal groups, create a new typology of criminal groups and identify some features of their subculture. Research methodology is based on the system approach that includes using the method of analysis of documentary sources (materials of a criminal case), method of conversations with themembers of the...

  11. Decimal Classification Editions

    Directory of Open Access Journals (Sweden)

    Zenovia Niculescu

    2009-01-01

    Full Text Available The study approaches the evolution of Dewey Decimal Classification editions from the perspective of updating the terminology, reallocating and expanding the main and auxilary structure of Dewey indexing language. The comparative analysis of DDC editions emphasizes the efficiency of Dewey scheme from the point of view of improving the informational offer, through basic index terms, revised and developed, as well as valuing the auxilary notations.

  12. Decimal Classification Editions

    OpenAIRE

    Zenovia Niculescu

    2009-01-01

    The study approaches the evolution of Dewey Decimal Classification editions from the perspective of updating the terminology, reallocating and expanding the main and auxilary structure of Dewey indexing language. The comparative analysis of DDC editions emphasizes the efficiency of Dewey scheme from the point of view of improving the informational offer, through basic index terms, revised and developed, as well as valuing the auxilary notations.

  13. Classification of High Spatial Resolution, Hyperspectral ...

    Science.gov (United States)

    EPA announced the availability of the final report,Classification of High Spatial Resolution, Hyperspectral Remote Sensing Imagery of the Little Miami River Watershed in Southwest Ohio, USA . This report and associated land use/land cover (LULC) coverage is the result of a collaborative effort among an interdisciplinary team of scientists with the U.S. Environmental Protection Agency's (U.S. EPA's) Office of Research and Development in Cincinnati, Ohio. A primary goal of this project is to enhance the use of geography and spatial analytic tools in risk assessment, and to improve the scientific basis for risk management decisions affecting drinking water and water quality. The land use/land cover classification is derived from 82 flight lines of Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery acquired from July 24 through August 9, 2002 via fixed-wing aircraft.

  14. Prediction and classification of respiratory motion

    CERN Document Server

    Lee, Suk Jin

    2014-01-01

    This book describes recent radiotherapy technologies including tools for measuring target position during radiotherapy and tracking-based delivery systems. This book presents a customized prediction of respiratory motion with clustering from multiple patient interactions. The proposed method contributes to the improvement of patient treatments by considering breathing pattern for the accurate dose calculation in radiotherapy systems. Real-time tumor-tracking, where the prediction of irregularities becomes relevant, has yet to be clinically established. The statistical quantitative modeling for irregular breathing classification, in which commercial respiration traces are retrospectively categorized into several classes based on breathing pattern are discussed as well. The proposed statistical classification may provide clinical advantages to adjust the dose rate before and during the external beam radiotherapy for minimizing the safety margin. In the first chapter following the Introduction  to this book, we...

  15. Classifications of track structures

    International Nuclear Information System (INIS)

    Paretzke, H.G.

    1984-01-01

    When ionizing particles interact with matter they produce random topological structures of primary activations which represent the initial boundary conditions for all subsequent physical, chemical and/or biological reactions. There are two important aspects of research on such track structures, namely their experimental or theoretical determination on one hand and the quantitative classification of these complex structures which is a basic pre-requisite for the understanding of mechanisms of radiation actions. This paper deals only with the latter topic, i.e. the problems encountered in and possible approaches to quantitative ordering and grouping of these multidimensional objects by their degrees of similarity with respect to their efficiency in producing certain final radiation effects, i.e. to their ''radiation quality.'' Various attempts of taxonometric classification with respect to radiation efficiency have been made in basic and applied radiation research including macro- and microdosimetric concepts as well as track entities and stopping power based theories. In this paper no review of those well-known approaches is given but rather an outline and discussion of alternative methods new to this field of radiation research which have some very promising features and which could possibly solve at least some major classification problems

  16. Neuromuscular disease classification system

    Science.gov (United States)

    Sáez, Aurora; Acha, Begoña; Montero-Sánchez, Adoración; Rivas, Eloy; Escudero, Luis M.; Serrano, Carmen

    2013-06-01

    Diagnosis of neuromuscular diseases is based on subjective visual assessment of biopsies from patients by the pathologist specialist. A system for objective analysis and classification of muscular dystrophies and neurogenic atrophies through muscle biopsy images of fluorescence microscopy is presented. The procedure starts with an accurate segmentation of the muscle fibers using mathematical morphology and a watershed transform. A feature extraction step is carried out in two parts: 24 features that pathologists take into account to diagnose the diseases and 58 structural features that the human eye cannot see, based on the assumption that the biopsy is considered as a graph, where the nodes are represented by each fiber, and two nodes are connected if two fibers are adjacent. A feature selection using sequential forward selection and sequential backward selection methods, a classification using a Fuzzy ARTMAP neural network, and a study of grading the severity are performed on these two sets of features. A database consisting of 91 images was used: 71 images for the training step and 20 as the test. A classification error of 0% was obtained. It is concluded that the addition of features undetectable by the human visual inspection improves the categorization of atrophic patterns.

  17. An automated cirrus classification

    Science.gov (United States)

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

    2018-05-01

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

  18. Reliable classification of moving waste materials with LIBS in concrete recycling.

    Science.gov (United States)

    Xia, Han; Bakker, M C M

    2014-03-01

    Effective discrimination between different waste materials is of paramount importance for inline quality inspection of recycle concrete aggregates from demolished buildings. The moving targeted materials in the concrete waste stream are wood, PVC, gypsum block, glass, brick, steel rebar, aggregate and cement paste. For each material, up to three different types were considered, while thirty particles of each material were selected. Proposed is a reliable classification methodology based on integration of the LIBS spectral emissions in a fixed time window, starting from the deployment of the laser shot. PLS-DA (multi class) and the hybrid combination PCA-Adaboost (binary class) were investigated as efficient classifiers. In addition, mean centre and auto scaling approaches were compared for both classifiers. Using 72 training spectra and 18 test spectra per material, each averaged by ten shots, only PLS-DA achieved full discrimination, and the mean centre approach made it slightly more robust. Continuing with PLS-DA, the relation between data averaging and convergence to 0.3% average error was investigated using 9-fold cross-validations. Single-shot PLS-DA presented the highest challenge and most desirable methodology, which converged with 59 PC. The degree of success in practical testing will depend on the quality of the training set and the implications of the possibly remaining false positives. © 2013 Published by Elsevier B.V.

  19. Structure of diagnostics horizons and humus classification

    Directory of Open Access Journals (Sweden)

    Zanella A

    2008-03-01

    Full Text Available The classification of the main humus forms is generally based on the morpho-genetic characters of the A and OH diagnostic horizons. This is the case in the new European key of classification presented in Freiburg on September 2004 (Eurosoil Congress. Among the morpho-genetic characters, the soil structure covers a very important role. In this work, the structure of the diagnostic A and OH horizons has been analysed in terms of aggregation force, diameter and composition of the soil lumps (peds. In order to study the aggregation force, two disaggregating tools have been conceived and used. The diameter of the lumps has been measured by sieving the soil samples with standardised webs. Observing the samples thanks to a binocular magnifying 10X and 50X, the organic or/and mineral composition of the soil aggregates has been determined, data being investigated with ANOVA and Factorial Analysis. The article examines the argument from two points of view: crashing tools for estimating the soil structure (part 1 and the dimensions of the peds given in European key of humus forms classification (part 2. The categories of soil peds diameter and composition seem to be linked to the main humus forms. For instance, aggregates having a diamater larger than 1 mm and well amalgamate organo-mineral composition are more present in the A horizons of the Mull forms than in which of the other forms; contrary to the OH horizon of the Moder or Mor, the OH horizon of the Amphi forms shows an important percent of small organic lumps. Some propositions have been given in order to improve the European key of humus forms classification.

  20. 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

  1. Maximum mutual information regularized classification

    KAUST Repository

    Wang, Jim Jing-Yan

    2014-09-07

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

  2. Maximum mutual information regularized classification

    KAUST Repository

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

    2014-01-01

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

  3. Hydrological classification, a practical tool for mangrove restoration

    NARCIS (Netherlands)

    Loon, van Anne F.; Brake, te Bram; Huijgevoort, Van Marjolein H.J.; Dijksma, Roel

    2016-01-01

    Mangrove restoration projects, aimed at restoring important values of mangrove forests after degradation, often fail because hydrological conditions are disregarded. We present a simple, but robust methodology to determine hydrological suitability for mangrove species, which can guide restoration

  4. Hydrological Classification, a Practical Tool for Mangrove Restoration

    OpenAIRE

    Van Loon, Anne F.; Te Brake, Bram; Van Huijgevoort, Marjolein H. J.; Dijksma, Roel

    2016-01-01

    Mangrove restoration projects, aimed at restoring important values of mangrove forests after degradation, often fail because hydrological conditions are disregarded. We present a simple, but robust methodology to determine hydrological suitability for mangrove species, which can guide restoration practice. In 15 natural and 8 disturbed sites (i.e. disused shrimp ponds) in three case study regions in south-east Asia, water levels were measured and vegetation species composition was determined....

  5. 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.

  6. 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.

  7. Classification of IRAS asteroids

    International Nuclear Information System (INIS)

    Tedesco, E.F.; Matson, D.L.; Veeder, G.J.

    1989-01-01

    Albedos and spectral reflectances are essential for classifying asteroids. For example, classes E, M and P are indistinguishable without albedo data. Colorometric data are available for about 1000 asteroids but, prior to IRAS, albedo data was available for only about 200. IRAS broke this bottleneck by providing albedo data on nearly 2000 asteroids. Hence, excepting absolute magnitudes, the albedo and size are now the most common asteroid physical parameters known. In this chapter the authors present the results of analyses of IRAS-derived asteroid albedos, discuss their application to asteroid classification, and mention several studies which might be done to exploit further this data set

  8. SPORT FOOD ADDITIVE CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    I. P. Prokopenko

    2015-01-01

    Full Text Available Correctly organized nutritive and pharmacological support is an important component of an athlete's preparation for competitions, an optimal shape maintenance, fast recovery and rehabilitation after traumas and defatigation. Special products of enhanced biological value (BAS for athletes nutrition are used with this purpose. Easy-to-use energy sources are administered into athlete's organism, yielded materials and biologically active substances which regulate and activate exchange reactions which proceed with difficulties during certain physical trainings. The article presents sport supplements classification which can be used before warm-up and trainings, after trainings and in competitions breaks.

  9. Radioactive facilities classification criteria

    International Nuclear Information System (INIS)

    Briso C, H.A.; Riesle W, J.

    1992-01-01

    Appropriate classification of radioactive facilities into groups of comparable risk constitutes one of the problems faced by most Regulatory Bodies. Regarding the radiological risk, the main facts to be considered are the radioactive inventory and the processes to which these radionuclides are subjected. Normally, operations are ruled by strict safety procedures. Thus, the total activity of the radionuclides existing in a given facility is the varying feature that defines its risk. In order to rely on a quantitative criterion and, considering that the Annual Limits of Intake are widely accepted references, an index based on these limits, to support decisions related to radioactive facilities, is proposed. (author)

  10. Robust tissue classification for reproducible wound assessment in telemedicine environments

    Science.gov (United States)

    Wannous, Hazem; Treuillet, Sylvie; Lucas, Yves

    2010-04-01

    In telemedicine environments, a standardized and reproducible assessment of wounds, using a simple free-handled digital camera, is an essential requirement. However, to ensure robust tissue classification, particular attention must be paid to the complete design of the color processing chain. We introduce the key steps including color correction, merging of expert labeling, and segmentation-driven classification based on support vector machines. The tool thus developed ensures stability under lighting condition, viewpoint, and camera changes, to achieve accurate and robust classification of skin tissues. Clinical tests demonstrate that such an advanced tool, which forms part of a complete 3-D and color wound assessment system, significantly improves the monitoring of the healing process. It achieves an overlap score of 79.3 against 69.1% for a single expert, after mapping on the medical reference developed from the image labeling by a college of experts.

  11. ILAE Classification of the Epilepsies Position Paper of the ILAE Commission for Classification and Terminology

    Science.gov (United States)

    Scheffer, Ingrid E; Berkovic, Samuel; Capovilla, Giuseppe; Connolly, Mary B; French, Jacqueline; Guilhoto, Laura; Hirsch, Edouard; Jain, Satish; Mathern, Gary W.; Moshé, Solomon L; Nordli, Douglas R; Perucca, Emilio; Tomson, Torbjörn; Wiebe, Samuel; Zhang, Yue-Hua; Zuberi, Sameer M

    2017-01-01

    Summary The ILAE Classification of the Epilepsies has been updated to reflect our gain in understanding of the epilepsies and their underlying mechanisms following the major scientific advances which have taken place since the last ratified classification in 1989. As a critical tool for the practising clinician, epilepsy classification must be relevant and dynamic to changes in thinking, yet robust and translatable to all areas of the globe. Its primary purpose is for diagnosis of patients, but it is also critical for epilepsy research, development of antiepileptic therapies and communication around the world. The new classification originates from a draft document submitted for public comments in 2013 which was revised to incorporate extensive feedback from the international epilepsy community over several rounds of consultation. It presents three levels, starting with seizure type where it assumes that the patient is having epileptic seizures as defined by the new 2017 ILAE Seizure Classification. After diagnosis of the seizure type, the next step is diagnosis of epilepsy type, including focal epilepsy, generalized epilepsy, combined generalized and focal epilepsy, and also an unknown epilepsy group. The third level is that of epilepsy syndrome where a specific syndromic diagnosis can be made. The new classification incorporates etiology along each stage, emphasizing the need to consider etiology at each step of diagnosis as it often carries significant treatment implications. Etiology is broken into six subgroups, selected because of their potential therapeutic consequences. New terminology is introduced such as developmental and epileptic encephalopathy. The term benign is replaced by the terms self-limited and pharmacoresponsive, to be used where appropriate. It is hoped that this new framework will assist in improving epilepsy care and research in the 21st century. PMID:28276062

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

    Science.gov (United States)

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

    2007-01-01

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

  13. The Spinal Cord Injury-Interventions Classification System

    NARCIS (Netherlands)

    van Langeveld, A.H.B.

    2010-01-01

    Title: The Spinal Cord Injury-Interventions Classification System: development and evaluation of a documentation tool to record therapy to improve mobility and self-care in people with spinal cord injury. Background: Many rehabilitation researchers have emphasized the need to examine the actual

  14. Awareness and use of Gross Motor Function Classification System ...

    African Journals Online (AJOL)

    Introduction The degree of disability in children with Cerebral Palsy (CP) can be evaluated with the Gross Motor Function Classification System (GMFCS), a valid tool which was designed for such purposes. However, there appears to be paucity of data on the awareness and use of the GMFCS particularly in the ...

  15. Modular playware as a playful diagnosis tool for autistic children

    DEFF Research Database (Denmark)

    Lund, Henrik Hautop

    2009-01-01

    children. Using artificial neural networks for automatic classification of the individual construction practices, we may compare this classification with the diagnosis of the children, and possible obtain a supplementary diagnosis tool which is based on the autistic children's free play with the modular...

  16. Supply chain planning classification

    Science.gov (United States)

    Hvolby, Hans-Henrik; Trienekens, Jacques; Bonde, Hans

    2001-10-01

    Industry experience a need to shift in focus from internal production planning towards planning in the supply network. In this respect customer oriented thinking becomes almost a common good amongst companies in the supply network. An increase in the use of information technology is needed to enable companies to better tune their production planning with customers and suppliers. Information technology opportunities and supply chain planning systems facilitate companies to monitor and control their supplier network. In spite if these developments, most links in today's supply chains make individual plans, because the real demand information is not available throughout the chain. The current systems and processes of the supply chains are not designed to meet the requirements now placed upon them. For long term relationships with suppliers and customers, an integrated decision-making process is needed in order to obtain a satisfactory result for all parties. Especially when customized production and short lead-time is in focus. An effective value chain makes inventory available and visible among the value chain members, minimizes response time and optimizes total inventory value held throughout the chain. In this paper a supply chain planning classification grid is presented based current manufacturing classifications and supply chain planning initiatives.

  17. Waste classification sampling plan

    International Nuclear Information System (INIS)

    Landsman, S.D.

    1998-01-01

    The purpose of this sampling is to explain the method used to collect and analyze data necessary to verify and/or determine the radionuclide content of the B-Cell decontamination and decommissioning waste stream so that the correct waste classification for the waste stream can be made, and to collect samples for studies of decontamination methods that could be used to remove fixed contamination present on the waste. The scope of this plan is to establish the technical basis for collecting samples and compiling quantitative data on the radioactive constituents present in waste generated during deactivation activities in B-Cell. Sampling and radioisotopic analysis will be performed on the fixed layers of contamination present on structural material and internal surfaces of process piping and tanks. In addition, dose rate measurements on existing waste material will be performed to determine the fraction of dose rate attributable to both removable and fixed contamination. Samples will also be collected to support studies of decontamination methods that are effective in removing the fixed contamination present on the waste. Sampling performed under this plan will meet criteria established in BNF-2596, Data Quality Objectives for the B-Cell Waste Stream Classification Sampling, J. M. Barnett, May 1998

  18. Cluster Based Text Classification Model

    DEFF Research Database (Denmark)

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

    2011-01-01

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

  19. Classification of smooth Fano polytopes

    DEFF Research Database (Denmark)

    Øbro, Mikkel

    A simplicial lattice polytope containing the origin in the interior is called a smooth Fano polytope, if the vertices of every facet is a basis of the lattice. The study of smooth Fano polytopes is motivated by their connection to toric varieties. The thesis concerns the classification of smooth...... Fano polytopes up to isomorphism. A smooth Fano -polytope can have at most vertices. In case of vertices an explicit classification is known. The thesis contains the classification in case of vertices. Classifications of smooth Fano -polytopes for fixed exist only for . In the thesis an algorithm...... for the classification of smooth Fano -polytopes for any given is presented. The algorithm has been implemented and used to obtain the complete classification for ....

  20. Small-scale classification schemes

    DEFF Research Database (Denmark)

    Hertzum, Morten

    2004-01-01

    Small-scale classification schemes are used extensively in the coordination of cooperative work. This study investigates the creation and use of a classification scheme for handling the system requirements during the redevelopment of a nation-wide information system. This requirements...... classification inherited a lot of its structure from the existing system and rendered requirements that transcended the framework laid out by the existing system almost invisible. As a result, the requirements classification became a defining element of the requirements-engineering process, though its main...... effects remained largely implicit. The requirements classification contributed to constraining the requirements-engineering process by supporting the software engineers in maintaining some level of control over the process. This way, the requirements classification provided the software engineers...

  1. Active Learning for Text Classification

    OpenAIRE

    Hu, Rong

    2011-01-01

    Text classification approaches are used extensively to solve real-world challenges. The success or failure of text classification systems hangs on the datasets used to train them, without a good dataset it is impossible to build a quality system. This thesis examines the applicability of active learning in text classification for the rapid and economical creation of labelled training data. Four main contributions are made in this thesis. First, we present two novel selection strategies to cho...

  2. Unsupervised Classification Using Immune Algorithm

    OpenAIRE

    Al-Muallim, M. T.; El-Kouatly, R.

    2012-01-01

    Unsupervised classification algorithm based on clonal selection principle named Unsupervised Clonal Selection Classification (UCSC) is proposed in this paper. The new proposed algorithm is data driven and self-adaptive, it adjusts its parameters to the data to make the classification operation as fast as possible. The performance of UCSC is evaluated by comparing it with the well known K-means algorithm using several artificial and real-life data sets. The experiments show that the proposed U...

  3. Application of higher order spectral features and support vector machines for bearing faults classification.

    Science.gov (United States)

    Saidi, Lotfi; Ben Ali, Jaouher; Fnaiech, Farhat

    2015-01-01

    Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals. Copyright © 2014 ISA

  4. A kernel-based multivariate feature selection method for microarray data classification.

    Directory of Open Access Journals (Sweden)

    Shiquan Sun

    Full Text Available High dimensionality and small sample sizes, and their inherent risk of overfitting, pose great challenges for constructing efficient classifiers in microarray data classification. Therefore a feature selection technique should be conducted prior to data classification to enhance prediction performance. In general, filter methods can be considered as principal or auxiliary selection mechanism because of their simplicity, scalability, and low computational complexity. However, a series of trivial examples show that filter methods result in less accurate performance because they ignore the dependencies of features. Although few publications have devoted their attention to reveal the relationship of features by multivariate-based methods, these methods describe relationships among features only by linear methods. While simple linear combination relationship restrict the improvement in performance. In this paper, we used kernel method to discover inherent nonlinear correlations among features as well as between feature and target. Moreover, the number of orthogonal components was determined by kernel Fishers linear discriminant analysis (FLDA in a self-adaptive manner rather than by manual parameter settings. In order to reveal the effectiveness of our method we performed several experiments and compared the results between our method and other competitive multivariate-based features selectors. In our comparison, we used two classifiers (support vector machine, [Formula: see text]-nearest neighbor on two group datasets, namely two-class and multi-class datasets. Experimental results demonstrate that the performance of our method is better than others, especially on three hard-classify datasets, namely Wang's Breast Cancer, Gordon's Lung Adenocarcinoma and Pomeroy's Medulloblastoma.

  5. Reliability of Oronasal Fistula Classification.

    Science.gov (United States)

    Sitzman, Thomas J; Allori, Alexander C; Matic, Damir B; Beals, Stephen P; Fisher, David M; Samson, Thomas D; Marcus, Jeffrey R; Tse, Raymond W

    2018-01-01

    Objective Oronasal fistula is an important complication of cleft palate repair that is frequently used to evaluate surgical quality, yet reliability of fistula classification has never been examined. The objective of this study was to determine the reliability of oronasal fistula classification both within individual surgeons and between multiple surgeons. Design Using intraoral photographs of children with repaired cleft palate, surgeons rated the location of palatal fistulae using the Pittsburgh Fistula Classification System. Intrarater and interrater reliability scores were calculated for each region of the palate. Participants Eight cleft surgeons rated photographs obtained from 29 children. Results Within individual surgeons reliability for each region of the Pittsburgh classification ranged from moderate to almost perfect (κ = .60-.96). By contrast, reliability between surgeons was lower, ranging from fair to substantial (κ = .23-.70). Between-surgeon reliability was lowest for the junction of the soft and hard palates (κ = .23). Within-surgeon and between-surgeon reliability were almost perfect for the more general classification of fistula in the secondary palate (κ = .95 and κ = .83, respectively). Conclusions This is the first reliability study of fistula classification. We show that the Pittsburgh Fistula Classification System is reliable when used by an individual surgeon, but less reliable when used among multiple surgeons. Comparisons of fistula occurrence among surgeons may be subject to less bias if they use the more general classification of "presence or absence of fistula of the secondary palate" rather than the Pittsburgh Fistula Classification System.

  6. A Novel Vehicle Classification Using Embedded Strain Gauge Sensors

    Directory of Open Access Journals (Sweden)

    Qi Wang

    2008-11-01

    Full Text Available Abstract: This paper presents a new vehicle classification and develops a traffic monitoring detector to provide reliable vehicle classification to aid traffic management systems. The basic principle of this approach is based on measuring the dynamic strain caused by vehicles across pavement to obtain the corresponding vehicle parameters – wheelbase and number of axles – to then accurately classify the vehicle. A system prototype with five embedded strain sensors was developed to validate the accuracy and effectiveness of the classification method. According to the special arrangement of the sensors and the different time a vehicle arrived at the sensors one can estimate the vehicle’s speed accurately, corresponding to the estimated vehicle wheelbase and number of axles. Because of measurement errors and vehicle characteristics, there is a lot of overlap between vehicle wheelbase patterns. Therefore, directly setting up a fixed threshold for vehicle classification often leads to low-accuracy results. Using the machine learning pattern recognition method to deal with this problem is believed as one of the most effective tools. In this study, support vector machines (SVMs were used to integrate the classification features extracted from the strain sensors to automatically classify vehicles into five types, ranging from small vehicles to combination trucks, along the lines of the Federal Highway Administration vehicle classification guide. Test bench and field experiments will be introduced in this paper. Two support vector machines classification algorithms (one-against-all, one-against-one are used to classify single sensor data and multiple sensor combination data. Comparison of the two classification method results shows that the classification accuracy is very close using single data or multiple data. Our results indicate that using multiclass SVM-based fusion multiple sensor data significantly improves

  7. Event classification and optimization methods using artificial intelligence and other relevant techniques: Sharing the experiences

    Science.gov (United States)

    Mohamed, Abdul Aziz; Hasan, Abu Bakar; Ghazali, Abu Bakar Mhd.

    2017-01-01

    Classification of large data into respected classes or groups could be carried out with the help of artificial intelligence (AI) tools readily available in the market. To get the optimum or best results, optimization tool could be applied on those data. Classification and optimization have been used by researchers throughout their works, and the outcomes were very encouraging indeed. Here, the authors are trying to share what they have experienced in three different areas of applied research.

  8. Authoring Tools

    Science.gov (United States)

    Treviranus, Jutta

    Authoring tools that are accessible and that enable authors to produce accessible Web content play a critical role in web accessibility. Widespread use of authoring tools that comply to the W3C Authoring Tool Accessibility Guidelines (ATAG) would ensure that even authors who are neither knowledgeable about nor particularly motivated to produce accessible content do so by default. The principles and techniques of ATAG are discussed. Some examples of accessible authoring tools are described including authoring tool content management components such as TinyMCE. Considerations for creating an accessible collaborative environment are also covered. As part of providing accessible content, the debate between system-based personal optimization and one universally accessible site configuration is presented. The issues and potential solutions to address the accessibility crisis presented by the advent of rich internet applications are outlined. This challenge must be met to ensure that a large segment of the population is able to participate in the move toward the web as a two-way communication mechanism.

  9. Modeling self-consistent multi-class dynamic traffic flow

    Science.gov (United States)

    Cho, Hsun-Jung; Lo, Shih-Ching

    2002-09-01

    In this study, we present a systematic self-consistent multiclass multilane traffic model derived from the vehicular Boltzmann equation and the traffic dispersion model. The multilane domain is considered as a two-dimensional space and the interaction among vehicles in the domain is described by a dispersion model. The reason we consider a multilane domain as a two-dimensional space is that the driving behavior of road users may not be restricted by lanes, especially motorcyclists. The dispersion model, which is a nonlinear Poisson equation, is derived from the car-following theory and the equilibrium assumption. Under the concept that all kinds of users share the finite section, the density is distributed on a road by the dispersion model. In addition, the dynamic evolution of the traffic flow is determined by the systematic gas-kinetic model derived from the Boltzmann equation. Multiplying Boltzmann equation by the zeroth, first- and second-order moment functions, integrating both side of the equation and using chain rules, we can derive continuity, motion and variance equation, respectively. However, the second-order moment function, which is the square of the individual velocity, is employed by previous researches does not have physical meaning in traffic flow. Although the second-order expansion results in the velocity variance equation, additional terms may be generated. The velocity variance equation we propose is derived from multiplying Boltzmann equation by the individual velocity variance. It modifies the previous model and presents a new gas-kinetic traffic flow model. By coupling the gas-kinetic model and the dispersion model, a self-consistent system is presented.

  10. Classification of radioactive waste

    International Nuclear Information System (INIS)

    1994-01-01

    Radioactive wastes are generated in a number of different kinds of facilities and arise in a wide range of concentrations of radioactive materials and in a variety of physical and chemical forms. To simplify their management, a number of schemes have evolved for classifying radioactive waste according to the physical, chemical and radiological properties of significance to those facilities managing this waste. These schemes have led to a variety of terminologies, differing from country to country and even between facilities in the same country. This situation makes it difficult for those concerned to communicate with one another regarding waste management practices. This document revises and updates earlier IAEA references on radioactive waste classification systems given in IAEA Technical Reports Series and Safety Series. Guidance regarding exemption of materials from regulatory control is consistent with IAEA Safety Series and the RADWASS documents published under IAEA Safety Series. 11 refs, 2 figs, 2 tab

  11. Nonlinear estimation and classification

    CERN Document Server

    Hansen, Mark; Holmes, Christopher; Mallick, Bani; Yu, Bin

    2003-01-01

    Researchers in many disciplines face the formidable task of analyzing massive amounts of high-dimensional and highly-structured data This is due in part to recent advances in data collection and computing technologies As a result, fundamental statistical research is being undertaken in a variety of different fields Driven by the complexity of these new problems, and fueled by the explosion of available computer power, highly adaptive, non-linear procedures are now essential components of modern "data analysis," a term that we liberally interpret to include speech and pattern recognition, classification, data compression and signal processing The development of new, flexible methods combines advances from many sources, including approximation theory, numerical analysis, machine learning, signal processing and statistics The proposed workshop intends to bring together eminent experts from these fields in order to exchange ideas and forge directions for the future

  12. Automatic diabetic retinopathy classification

    Science.gov (United States)

    Bravo, María. A.; Arbeláez, Pablo A.

    2017-11-01

    Diabetic retinopathy (DR) is a disease in which the retina is damaged due to augmentation in the blood pressure of small vessels. DR is the major cause of blindness for diabetics. It has been shown that early diagnosis can play a major role in prevention of visual loss and blindness. This work proposes a computer based approach for the detection of DR in back-of-the-eye images based on the use of convolutional neural networks (CNNs). Our CNN uses deep architectures to classify Back-of-the-eye Retinal Photographs (BRP) in 5 stages of DR. Our method combines several preprocessing images of BRP to obtain an ACA score of 50.5%. Furthermore, we explore subproblems by training a larger CNN of our main classification task.

  13. LOCAL WEATHER CLASSIFICATIONS FOR ENVIRONMENTAL APPLICATIONS

    Directory of Open Access Journals (Sweden)

    Katarzyna PIOTROWICZ

    2013-03-01

    Full Text Available Two approaches of local weather type definitions are presented and illustrated for selected stations of Poland and Hungary. The subjective classification, continuing long traditions, especially in Poland, relies on diurnal values of local weather elements. The main types are defined according to temperature with some sub-types considering relative sunshine duration, diurnal precipitation totals, relative humidity and wind speed. The classification does not make a difference between the seasons of the year, but the occurrence of the classes obviously reflects the annual cycle. Another important feature of this classification is that only a minor part of the theoretically possible combination of the various types and sub-types occurs in all stations of both countries. The objective version of the classification starts from ten possible weather element which are reduced to four according to factor analysis, based on strong correlation between the elements. This analysis yields 3 to 4 factors depending on the specific criteria of selection. The further cluster analysis uses four selected weather elements belonging to different rotated factors. They are the diurnal mean values of temperature, of relative humidity, of cloudiness and of wind speed. From the possible ways of hierarchical cluster analysis (i.e. no a priori assumption on the number of classes, the method of furthest neighbours is selected, indicating the arguments of this decision in the paper. These local weather types are important tools in understanding the role of weather in various environmental indicators, in climatic generalisation of short samples by stratified sampling and in interpretation of the climate change.

  14. Tool steels

    DEFF Research Database (Denmark)

    Højerslev, C.

    2001-01-01

    On designing a tool steel, its composition and heat treatment parameters are chosen to provide a hardened and tempered martensitic matrix in which carbides are evenly distributed. In this condition the matrix has an optimum combination of hardness andtoughness, the primary carbides provide...... resistance against abrasive wear and secondary carbides (if any) increase the resistance against plastic deformation. Tool steels are alloyed with carbide forming elements (Typically: vanadium, tungsten, molybdenumand chromium) furthermore some steel types contains cobalt. Addition of alloying elements...... serves primarily two purpose (i) to improve the hardenabillity and (ii) to provide harder and thermally more stable carbides than cementite. Assuming proper heattreatment, the properties of a tool steel depends on the which alloying elements are added and their respective concentrations....

  15. Management Tools

    Science.gov (United States)

    1987-01-01

    Manugistics, Inc. (formerly AVYX, Inc.) has introduced a new programming language for IBM and IBM compatible computers called TREES-pls. It is a resource management tool originating from the space shuttle, that can be used in such applications as scheduling, resource allocation project control, information management, and artificial intelligence. Manugistics, Inc. was looking for a flexible tool that can be applied to many problems with minimal adaptation. Among the non-government markets are aerospace, other manufacturing, transportation, health care, food and beverage and professional services.

  16. Classification Systems, their Digitization and Consequences for Data-Driven Decision Making

    DEFF Research Database (Denmark)

    Stein, Mari-Klara; Newell, Sue; Galliers, Robert D.

    2013-01-01

    Classification systems are foundational in many standardized software tools. This digitization of classification systems gives them a new ‘materiality’ that, jointly with the social practices of information producers/consumers, has significant consequences on the representational quality of such ...... and the foundational role of representational quality in understanding the success and consequences of data-driven decision-making.......-narration and meta-narration), and three different information production/consumption situations. We contribute to the relational theorization of representational quality and extend classification systems research by drawing explicit attention to the importance of ‘materialization’ of classification systems...

  17. Hazard classification or risk assessment

    DEFF Research Database (Denmark)

    Hass, Ulla

    2013-01-01

    The EU classification of substances for e.g. reproductive toxicants is hazard based and does not to address the risk suchsubstances may pose through normal, or extreme, use. Such hazard classification complies with the consumer's right to know. It is also an incentive to careful use and storage...

  18. Efficient AUC optimization for classification

    NARCIS (Netherlands)

    Calders, T.; Jaroszewicz, S.; Kok, J.N.; Koronacki, J.; Lopez de Mantaras, R.; Matwin, S.; Mladenic, D.; Skowron, A.

    2007-01-01

    In this paper we show an efficient method for inducing classifiers that directly optimize the area under the ROC curve. Recently, AUC gained importance in the classification community as a mean to compare the performance of classifiers. Because most classification methods do not optimize this

  19. Dewey Decimal Classification: A Quagmire.

    Science.gov (United States)

    Gamaluddin, Ahmad Fouad

    1980-01-01

    A survey of 660 Pennsylvania school librarians indicates that, though there is limited professional interest in the Library of Congress Classification system, Dewey Decimal Classification (DDC) appears to be firmly entrenched. This article also discusses the relative merits of DDC, the need for a uniform system, librarianship preparation, and…

  20. Latent class models for classification

    NARCIS (Netherlands)

    Vermunt, J.K.; Magidson, J.

    2003-01-01

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

  1. 45 CFR 601.5 - Derivative classification.

    Science.gov (United States)

    2010-10-01

    ... CLASSIFICATION AND DECLASSIFICATION OF NATIONAL SECURITY INFORMATION § 601.5 Derivative classification. Distinct... 45 Public Welfare 3 2010-10-01 2010-10-01 false Derivative classification. 601.5 Section 601.5... classification guide, need not possess original classification authority. (a) If a person who applies derivative...

  2. 12 CFR 403.4 - Derivative classification.

    Science.gov (United States)

    2010-01-01

    ... SAFEGUARDING OF NATIONAL SECURITY INFORMATION § 403.4 Derivative classification. (a) Use of derivative classification. (1) Unlike original classification which is an initial determination, derivative classification... 12 Banks and Banking 4 2010-01-01 2010-01-01 false Derivative classification. 403.4 Section 403.4...

  3. 32 CFR 2001.15 - Classification guides.

    Science.gov (United States)

    2010-07-01

    ... 32 National Defense 6 2010-07-01 2010-07-01 false Classification guides. 2001.15 Section 2001.15..., NATIONAL ARCHIVES AND RECORDS ADMINISTRATION CLASSIFIED NATIONAL SECURITY INFORMATION Classification § 2001.15 Classification guides. (a) Preparation of classification guides. Originators of classification...

  4. Design tools

    Science.gov (United States)

    Anton TenWolde; Mark T. Bomberg

    2009-01-01

    Overall, despite the lack of exact input data, the use of design tools, including models, is much superior to the simple following of rules of thumbs, and a moisture analysis should be standard procedure for any building envelope design. Exceptions can only be made for buildings in the same climate, similar occupancy, and similar envelope construction. This chapter...

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

    Science.gov (United States)

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

    2012-07-01

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

  6. Vietnamese Document Representation and Classification

    Science.gov (United States)

    Nguyen, Giang-Son; Gao, Xiaoying; Andreae, Peter

    Vietnamese is very different from English and little research has been done on Vietnamese document classification, or indeed, on any kind of Vietnamese language processing, and only a few small corpora are available for research. We created a large Vietnamese text corpus with about 18000 documents, and manually classified them based on different criteria such as topics and styles, giving several classification tasks of different difficulty levels. This paper introduces a new syllable-based document representation at the morphological level of the language for efficient classification. We tested the representation on our corpus with different classification tasks using six classification algorithms and two feature selection techniques. Our experiments show that the new representation is effective for Vietnamese categorization, and suggest that best performance can be achieved using syllable-pair document representation, an SVM with a polynomial kernel as the learning algorithm, and using Information gain and an external dictionary for feature selection.

  7. Promoting consistent use of the communication function classification system (CFCS).

    Science.gov (United States)

    Cunningham, Barbara Jane; Rosenbaum, Peter; Hidecker, Mary Jo Cooley

    2016-01-01

    We developed a Knowledge Translation (KT) intervention to standardize the way speech-language pathologists working in Ontario Canada's Preschool Speech and Language Program (PSLP) used the Communication Function Classification System (CFCS). This tool was being used as part of a provincial program evaluation and standardizing its use was critical for establishing reliability and validity within the provincial dataset. Two theoretical foundations - Diffusion of Innovations and the Communication Persuasion Matrix - were used to develop and disseminate the intervention to standardize use of the CFCS among a cohort speech-language pathologists. A descriptive pre-test/post-test study was used to evaluate the intervention. Fifty-two participants completed an electronic pre-test survey, reviewed intervention materials online, and then immediately completed an electronic post-test survey. The intervention improved clinicians' understanding of how the CFCS should be used, their intentions to use the tool in the standardized way, and their abilities to make correct classifications using the tool. Findings from this work will be shared with representatives of the Ontario PSLP. The intervention may be disseminated to all speech-language pathologists working in the program. This study can be used as a model for developing and disseminating KT interventions for clinicians in paediatric rehabilitation. The Communication Function Classification System (CFCS) is a new tool that allows speech-language pathologists to classify children's skills into five meaningful levels of function. There is uncertainty and inconsistent practice in the field about the methods for using this tool. This study used combined two theoretical frameworks to develop an intervention to standardize use of the CFCS among a cohort of speech-language pathologists. The intervention effectively increased clinicians' understanding of the methods for using the CFCS, ability to make correct classifications, and

  8. Classification of mammographic masses using geometric symmetry and fractal analysis

    Energy Technology Data Exchange (ETDEWEB)

    Guo Qi; Ruiz, V.F. [Cybernetics, School of Systems Engineering, Univ. of Reading (United Kingdom); Shao Jiaqing [Dept. of Electronics, Univ. of Kent (United Kingdom); Guo Falei [WanDe Industrial Engineering Co. (China)

    2007-06-15

    In this paper, we propose a fuzzy symmetry measure based on geometrical operations to characterise shape irregularity of mammographic mass lesion. Group theory, a powerful tool in the investigation of geometric transformation, is employed in our work to define and describe the underlying mathematical relations. We investigate the usefulness of fuzzy symmetry measure in combination with fractal analysis for classification of masses. Comparative studies show that fuzzy symmetry measure is useful for shape characterisation of mass lesions and is a good complementary feature for benign-versus-malignant classification of masses. (orig.)

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

    DEFF Research Database (Denmark)

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

    2012-01-01

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

  10. Machine assisted histogram classification

    Science.gov (United States)

    Benyó, B.; Gaspar, C.; Somogyi, P.

    2010-04-01

    LHCb is one of the four major experiments under completion at the Large Hadron Collider (LHC). Monitoring the quality of the acquired data is important, because it allows the verification of the detector performance. Anomalies, such as missing values or unexpected distributions can be indicators of a malfunctioning detector, resulting in poor data quality. Spotting faulty or ageing components can be either done visually using instruments, such as the LHCb Histogram Presenter, or with the help of automated tools. In order to assist detector experts in handling the vast monitoring information resulting from the sheer size of the detector, we propose a graph based clustering tool combined with machine learning algorithm and demonstrate its use by processing histograms representing 2D hitmaps events. We prove the concept by detecting ion feedback events in the LHCb experiment's RICH subdetector.

  11. Machine assisted histogram classification

    Energy Technology Data Exchange (ETDEWEB)

    Benyo, B; Somogyi, P [BME-IIT, H-1117 Budapest, Magyar tudosok koerutja 2. (Hungary); Gaspar, C, E-mail: Peter.Somogyi@cern.c [CERN-PH, CH-1211 Geneve 23 (Switzerland)

    2010-04-01

    LHCb is one of the four major experiments under completion at the Large Hadron Collider (LHC). Monitoring the quality of the acquired data is important, because it allows the verification of the detector performance. Anomalies, such as missing values or unexpected distributions can be indicators of a malfunctioning detector, resulting in poor data quality. Spotting faulty or ageing components can be either done visually using instruments, such as the LHCb Histogram Presenter, or with the help of automated tools. In order to assist detector experts in handling the vast monitoring information resulting from the sheer size of the detector, we propose a graph based clustering tool combined with machine learning algorithm and demonstrate its use by processing histograms representing 2D hitmaps events. We prove the concept by detecting ion feedback events in the LHCb experiment's RICH subdetector.

  12. Validation of a new classification for periprosthetic shoulder fractures.

    Science.gov (United States)

    Kirchhoff, Chlodwig; Beirer, Marc; Brunner, Ulrich; Buchholz, Arne; Biberthaler, Peter; Crönlein, Moritz

    2018-06-01

    Successful treatment of periprosthetic shoulder fractures depends on the right strategy, starting with a well-structured classification of the fracture. Unfortunately, clinically relevant factors for treatment planning are missing in the pre-existing classifications. Therefore, the aim of the present study was to describe a new specific classification system for periprosthetic shoulder fractures including a structured treatment algorithm for this important fragility fracture issue. The classification was established, focussing on five relevant items, naming the prosthesis type, the fracture localisation, the rotator cuff status, the anatomical fracture region and the stability of the implant. After considering each single item, the individual treatment concept can be assessed in one last step. To evaluate the introduced classification, a retrospective analysis of pre- and post-operative data of patients, treated with periprosthetic shoulder fractures, was conducted by two board certified trauma surgery consultants. The data of 19 patients (8 male, 11 female) with a mean age of 74 ± five years have been analysed in our study. The suggested treatment algorithm was proven to be reliable, detected by good clinical outcome in 15 of 16 (94%) cases, where the suggested treatment was maintained. Only one case resulted in poor outcome due to post-operative wound infection and had to be revised. The newly developed six-step classification is easy to utilise and extends the pre-existing classification systems in terms of clinically-relevant information. This classification should serve as a simple tool for the surgeon to consider the optimal treatment for his patients.

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

    Science.gov (United States)

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

    2017-10-01

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

  14. Generalized Partial Least Squares Approach for Nominal Multinomial Logit Regression Models with a Functional Covariate

    Science.gov (United States)

    Albaqshi, Amani Mohammed H.

    2017-01-01

    Functional Data Analysis (FDA) has attracted substantial attention for the last two decades. Within FDA, classifying curves into two or more categories is consistently of interest to scientists, but multi-class prediction within FDA is challenged in that most classification tools have been limited to binary response applications. The functional…

  15. Classification of titanium dioxide

    International Nuclear Information System (INIS)

    Macias B, L.R.; Garcia C, R.M.; Maya M, M.E.; Ita T, A. De; Palacios G, J.

    2002-01-01

    In this work the X-ray diffraction (XRD), Scanning Electron Microscopy (Sem) and the X-ray Dispersive Energy Spectroscopy techniques are used with the purpose to achieve a complete identification of phases and mixture of phases of a crystalline material as titanium dioxide. The problem for solving consists of being able to distinguish a sample of titanium dioxide being different than a titanium dioxide pigment. A standard sample of titanium dioxide with NIST certificate is used, which indicates a purity of 99.74% for the TiO 2 . The following way is recommended to proceed: a)To make an analysis by means of X-ray diffraction technique to the sample of titanium dioxide pigment and on the standard of titanium dioxide waiting not find differences. b) To make a chemical analysis by the X-ray Dispersive Energy Spectroscopy via in a microscope, taking advantage of the high vacuum since it is oxygen which is analysed and if it is concluded that the aluminium oxide appears in a greater proportion to 1% it is established that is a titanium dioxide pigment, but if it is lesser then it will be only titanium dioxide. This type of analysis is an application of the nuclear techniques useful for the tariff classification of merchandise which is considered as of difficult recognition. (Author)

  16. Classification of new particles

    International Nuclear Information System (INIS)

    Karl, G.

    1976-01-01

    A classification of the new particles is proposed. Hadrons are constructed from quarks corresponding to several different representations of an SU 3 color group, with confined color. The new family of resonances, related to psi/J, is assigned to color-antisextet quarks Q. These new quarks Q do not form mixed mesons q-barQ with old antiquarks but can form mixed baryons Qqq. We speculate on the relation between color and mass. High-mass recurrences of the psi/J family are expected to have associated large changes in the cross section for electron-positron annihilation (ΔR > 4). A conjectured mass formula, which relates the masses of psi/J and ω, predicts the masses of possible recurrences of the psi/J particle. Other experimental implications at presently available energies are discussed, especially the necessity for an isovector partner for psi/J, and for pseudoscalar mesons at 1.8--2.2 GeV, some of which can decay into two photons

  17. A Proposal to Develop Interactive Classification Technology

    Science.gov (United States)

    deBessonet, Cary

    1998-01-01

    Research for the first year was oriented towards: 1) the design of an interactive classification tool (ICT); and 2) the development of an appropriate theory of inference for use in ICT technology. The general objective was to develop a theory of classification that could accommodate a diverse array of objects, including events and their constituent objects. Throughout this report, the term "object" is to be interpreted in a broad sense to cover any kind of object, including living beings, non-living physical things, events, even ideas and concepts. The idea was to produce a theory that could serve as the uniting fabric of a base technology capable of being implemented in a variety of automated systems. The decision was made to employ two technologies under development by the principal investigator, namely, SMS (Symbolic Manipulation System) and SL (Symbolic Language) [see debessonet, 1991, for detailed descriptions of SMS and SL]. The plan was to enhance and modify these technologies for use in an ICT environment. As a means of giving focus and direction to the proposed research, the investigators decided to design an interactive, classificatory tool for use in building accessible knowledge bases for selected domains. Accordingly, the proposed research was divisible into tasks that included: 1) the design of technology for classifying domain objects and for building knowledge bases from the results automatically; 2) the development of a scheme of inference capable of drawing upon previously processed classificatory schemes and knowledge bases; and 3) the design of a query/ search module for accessing the knowledge bases built by the inclusive system. The interactive tool for classifying domain objects was to be designed initially for textual corpora with a view to having the technology eventually be used in robots to build sentential knowledge bases that would be supported by inference engines specially designed for the natural or man-made environments in which the

  18. Music classification with MPEG-7

    Science.gov (United States)

    Crysandt, Holger; Wellhausen, Jens

    2003-01-01

    Driven by increasing amount of music available electronically the need and possibility of automatic classification systems for music becomes more and more important. Currently most search engines for music are based on textual descriptions like artist or/and title. This paper presents a system for automatic music description, classification and visualization for a set of songs. The system is designed to extract significant features of a piece of music in order to find songs of similar genre or a similar sound characteristics. The description is done with the help of MPEG-7 only. The classification and visualization is done with the self organizing map algorithm.

  19. Systema Naturae. Classification of living things.

    OpenAIRE

    Alexey Shipunov

    2007-01-01

    Original classification of living organisms containing four kingdoms (Monera, Protista, Vegetabilia and Animalia), 60 phyla and 254 classes, is presented. The classification is based on latest available information.

  20. Atmospheric circulation classification comparison based on wildfires in Portugal

    Science.gov (United States)

    Pereira, M. G.; Trigo, R. M.

    2009-04-01

    Atmospheric circulation classifications are not a simple description of atmospheric states but a tool to understand and interpret the atmospheric processes and to model the relation between atmospheric circulation and surface climate and other related variables (Radan Huth et al., 2008). Classifications were initially developed with weather forecasting purposes, however with the progress in computer processing capability, new and more robust objective methods were developed and applied to large datasets prompting atmospheric circulation classification methods to one of the most important fields in synoptic and statistical climatology. Classification studies have been extensively used in climate change studies (e.g. reconstructed past climates, recent observed changes and future climates), in bioclimatological research (e.g. relating human mortality to climatic factors) and in a wide variety of synoptic climatological applications (e.g. comparison between datasets, air pollution, snow avalanches, wine quality, fish captures and forest fires). Likewise, atmospheric circulation classifications are important for the study of the role of weather in wildfire occurrence in Portugal because the daily synoptic variability is the most important driver of local weather conditions (Pereira et al., 2005). In particular, the objective classification scheme developed by Trigo and DaCamara (2000) to classify the atmospheric circulation affecting Portugal have proved to be quite useful in discriminating the occurrence and development of wildfires as well as the distribution over Portugal of surface climatic variables with impact in wildfire activity such as maximum and minimum temperature and precipitation. This work aims to present: (i) an overview the existing circulation classification for the Iberian Peninsula, and (ii) the results of a comparison study between these atmospheric circulation classifications based on its relation with wildfires and relevant meteorological

  1. CLASSIFICATION ALGORITHMS FOR BIG DATA ANALYSIS, A MAP REDUCE APPROACH

    Directory of Open Access Journals (Sweden)

    V. A. Ayma

    2015-03-01

    Full Text Available Since many years ago, the scientific community is concerned about how to increase the accuracy of different classification methods, and major achievements have been made so far. Besides this issue, the increasing amount of data that is being generated every day by remote sensors raises more challenges to be overcome. In this work, a tool within the scope of InterIMAGE Cloud Platform (ICP, which is an open-source, distributed framework for automatic image interpretation, is presented. The tool, named ICP: Data Mining Package, is able to perform supervised classification procedures on huge amounts of data, usually referred as big data, on a distributed infrastructure using Hadoop MapReduce. The tool has four classification algorithms implemented, taken from WEKA’s machine learning library, namely: Decision Trees, Naïve Bayes, Random Forest and Support Vector Machines (SVM. The results of an experimental analysis using a SVM classifier on data sets of different sizes for different cluster configurations demonstrates the potential of the tool, as well as aspects that affect its performance.

  2. Sound classification of dwellings in the Nordic countries

    DEFF Research Database (Denmark)

    Rindel, Jens Holger; Turunen-Rise, Iiris

    1997-01-01

    be met. The classification system is based on limit values for airborne sound insulation, impact sound pressure level, reverberation time and indoor and outdoor noise levels. The purpose of the standard is to offer a tool for specification of a standardised acoustic climate and to promote constructors......A draft standard INSTA 122:1997 on sound classification of dwellings is for voting as a common national standard in the Nordic countries (Denmark, Norway, Sweden, Finland, Iceland) and in Estonia. The draft standard specifies a sound classification system with four classes A, B, C and D, where...... class C is proposed as the future minimum requirements for new dwellings. The classes B and A define criteria for dwellings with improved or very good acoustic conditions, whereas class D may be used for older, renovated dwellings in which the acoustic quality level of a new dwelling cannot reasonably...

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

    Science.gov (United States)

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

    2018-04-01

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

  4. Classification of Pulse Waveforms Using Edit Distance with Real Penalty

    Directory of Open Access Journals (Sweden)

    Zhang Dongyu

    2010-01-01

    Full Text Available Abstract Advances in sensor and signal processing techniques have provided effective tools for quantitative research in traditional Chinese pulse diagnosis (TCPD. Because of the inevitable intraclass variation of pulse patterns, the automatic classification of pulse waveforms has remained a difficult problem. In this paper, by referring to the edit distance with real penalty (ERP and the recent progress in -nearest neighbors (KNN classifiers, we propose two novel ERP-based KNN classifiers. Taking advantage of the metric property of ERP, we first develop an ERP-induced inner product and a Gaussian ERP kernel, then embed them into difference-weighted KNN classifiers, and finally develop two novel classifiers for pulse waveform classification. The experimental results show that the proposed classifiers are effective for accurate classification of pulse waveform.

  5. Link prediction boosted psychiatry disorder classification for functional connectivity network

    Science.gov (United States)

    Li, Weiwei; Mei, Xue; Wang, Hao; Zhou, Yu; Huang, Jiashuang

    2017-02-01

    Functional connectivity network (FCN) is an effective tool in psychiatry disorders classification, and represents cross-correlation of the regional blood oxygenation level dependent signal. However, FCN is often incomplete for suffering from missing and spurious edges. To accurate classify psychiatry disorders and health control with the incomplete FCN, we first `repair' the FCN with link prediction, and then exact the clustering coefficients as features to build a weak classifier for every FCN. Finally, we apply a boosting algorithm to combine these weak classifiers for improving classification accuracy. Our method tested by three datasets of psychiatry disorder, including Alzheimer's Disease, Schizophrenia and Attention Deficit Hyperactivity Disorder. The experimental results show our method not only significantly improves the classification accuracy, but also efficiently reconstructs the incomplete FCN.

  6. Progression in nuclear classification

    International Nuclear Information System (INIS)

    Wang Yuying

    1999-01-01

    In this book, summarize the author's achievements of nuclear classification by new method in latest 30 years, new foundational law of nuclear layer in matter world is found. It is explained with a hypothesis of a nucleus which it is made up of two nucleon's clusters with deuteron and triton. Its concrete content is: to advance a new method which analyze data of nuclei with natural abundance using relationship between the numbers of proton and neutron. The relationship of each nucleus increases to 4 sets: S+H=Z H+Z=N Z+N=A and S-H=K. To expand the similarity between proton and neutron to the similarity among p,n, deuteron, triton, and He-5 clusters. According to the distribution law of same kind of nuclei, it obtains that the upper limits of stable region both should be '44s'. New foundational law of nuclear system is 1,2,4,8,16,8,4,2,1. In order to explain new law, a hypothesis which nucleus is made up of deuteron and triton is developing and nuclear field of whole number is built up. And it relates that unity of matter motion, which is the most foundational form atomic nuclear systematic is similar to the most first-class form chromosome numbers of mankind. These achievements will shake the foundations of traditional nuclear science. These achievements will supply new tasks in developing nuclear theory. And shake the ground of which magic number is the basic of nuclear science. It opens up a new field on foundational research. The book will supply new knowledge for researcher, teachers and students in universities and polytechnic schools. Scientific workers read in works of research and technical exploit. It can be stored up for library and laboratory of society and universities. In nowadays of prosperity our nation by science and education, the book is readable for workers of scientific technology and amateurs of natural science

  7. Classification and clinical assessment

    Directory of Open Access Journals (Sweden)

    F. Cantini

    2012-06-01

    Full Text Available There are at least nine classification criteria for psoriatic arthritis (PsA that have been proposed and used in clinical studies. With the exception of the ESSG and Bennett rules, all of the other criteria sets have a good performance in identifying PsA patients. As the CASPAR criteria are based on a robust study methodology, they are considered the current reference standard. However, if there seems to be no doubt that they are very good to classify PsA patients (very high specificity, they might be not sensitive enough to diagnose patients with unknown early PsA. The vast clinical heterogeneity of PsA makes its assessment very challenging. Peripheral joint involvement is measured by 78/76 joint counts, spine involvement by the instruments used for ankylosing spondylitis (AS, dactylitis by involved digit count or by the Leeds dactylitis index, enthesitis by the number of affected entheses (several indices available and psoriasis by the Psoriasis Area and Severity Index (PASI. Peripheral joint damage can be assessed by a modified van der Heijde-Sharp scoring system and axial damage by the methods used for AS or by the Psoriatic Arthritis Spondylitis Radiology Index (PASRI. As in other arthritides, global evaluation of disease activity and severity by patient and physician and assessment of disability and quality of life are widely used. Finally, composite indices that capture several clinical manifestations of PsA have been proposed and a new instrument, the Psoriatic ARthritis Disease Activity Score (PASDAS, is currently being developed.

  8. The classification of easement

    Directory of Open Access Journals (Sweden)

    Popov Danica D.

    2015-01-01

    Full Text Available Easement means, a right enjoyed by the owner of land over the lands of another: such as rights of way, right of light, rights of support, rights to a flow of air or water etc. The dominant tenement is the land owned by the possessor of the easement, and the servient tenement is the land over which the right is enjoyed. An easement must exist for the accommodation and better enjoyment to which it is annexed, otherwise it may amount to mere licence. An easement benefits and binds the land itself and therefore countinious despite any change of ownership of either dominant or servient tenement, although it will be extinguished if the two tenemants come into common ownership. An easement can only be enjoyed in respect of land. This means two parcels of land. First there must be a 'dominant tenement' and a 'servient tenement'. Dominant tenement to which the benefit of the easement attaches, and another (servient tenement which bears the burden of the easement. A positive easement consist of a right to do something on the land of another; a negative easement restrict the use of owner of the serviant tenement may make of his land. An easement may be on land or on the house made on land. The next classification is on easement on the ground, and the other one under the ground. An easement shall be done in accordance with the principle of restrictions. This means that the less burden the servient tenement. When there is doubt about the extent of the actual easement shall take what easier the servient tenement. The new needs of the dominant estate does not result in the expansion of servitude. In the article is made comparison between The Draft Code of property and other real estate, and The Draft of Civil Code of Serbia.

  9. Critical Evaluation of Headache Classifications.

    Science.gov (United States)

    Özge, Aynur

    2013-08-01

    Transforming a subjective sense like headache into an objective state and establishing a common language for this complaint which can be both a symptom and a disease all by itself have kept the investigators busy for years. Each recommendation proposed has brought along a set of patients who do not meet the criteria. While almost the most ideal and most comprehensive classification studies continued at this point, this time criticisims about withdrawing from daily practice came to the fore. In this article, the classification adventure of scientists who work in the area of headache will be summarized. More specifically, 2 classifications made by the International Headache Society (IHS) and the point reached in relation with the 3rd classification which is still being worked on will be discussed together with headache subtypes. It has been presented with the wish and belief that it will contribute to the readers and young investigators who are interested in this subject.

  10. The last classification of vasculitis

    NARCIS (Netherlands)

    Kallenberg, Cees G. M.

    2008-01-01

    Systemic vasculitides are a group of diverse conditions characterized by inflammation of the blood vessels. To obtain homogeneity in clinical characteristics, prognosis, and response to treatment, patients with vasculitis should be classified into defined disease categories. Many classification

  11. Radon classification of building ground

    International Nuclear Information System (INIS)

    Slunga, E.

    1988-01-01

    The Laboratories of Building Technology and Soil Mechanics and Foundation Engineering at the Helsinki University of Technology in cooperation with The Ministry of the Environment have proposed a radon classification for building ground. The proposed classification is based on the radon concentration in soil pores and on the permeability of the foundation soil. The classification includes four radon classes: negligible, normal, high and very high. Depending on the radon class the radon-technical solution for structures is chosen. It is proposed that the classification be done in general terms in connection with the site investigations for the planning of land use and in more detail in connection with the site investigations for an individual house. (author)

  12. Deep Learning for ECG Classification

    Science.gov (United States)

    Pyakillya, B.; Kazachenko, N.; Mikhailovsky, N.

    2017-10-01

    The importance of ECG classification is very high now due to many current medical applications where this problem can be stated. Currently, there are many machine learning (ML) solutions which can be used for analyzing and classifying ECG data. However, the main disadvantages of these ML results is use of heuristic hand-crafted or engineered features with shallow feature learning architectures. The problem relies in the possibility not to find most appropriate features which will give high classification accuracy in this ECG problem. One of the proposing solution is to use deep learning architectures where first layers of convolutional neurons behave as feature extractors and in the end some fully-connected (FCN) layers are used for making final decision about ECG classes. In this work the deep learning architecture with 1D convolutional layers and FCN layers for ECG classification is presented and some classification results are showed.

  13. Vehicle classification using mobile sensors.

    Science.gov (United States)

    2013-04-01

    In this research, the feasibility of using mobile traffic sensors for binary vehicle classification on arterial roads is investigated. Features (e.g. : speed related, acceleration/deceleration related, etc.) are extracted from vehicle traces (passeng...

  14. Classification of remotely sensed images

    CSIR Research Space (South Africa)

    Dudeni, N

    2008-10-01

    Full Text Available For this research, the researchers examine various existing image classification algorithms with the aim of demonstrating how these algorithms can be applied to remote sensing images. These algorithms are broadly divided into supervised...

  15. Classification of Building Object Types

    DEFF Research Database (Denmark)

    Jørgensen, Kaj Asbjørn

    2011-01-01

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

  16. VT Biodiversity Project - Bedrock Classification

    Data.gov (United States)

    Vermont Center for Geographic Information — (Link to Metadata) This dataset is a five category, nine sub-category classification of the bedrock units appearing on the Centennial Geologic Map of Vermont. The...

  17. Classification of Cortical Brain Malformations

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2008-03-01

    Full Text Available Clinical, radiological, and genetic classifications of 113 cases of malformations of cortical development (MCD were evaluated at the Erasmus Medical Center-Sophia Children's Hospital, Rotterdam, the Netherlands.

  18. Phylogenetic classification of bony fishes.

    Science.gov (United States)

    Betancur-R, Ricardo; Wiley, Edward O; Arratia, Gloria; Acero, Arturo; Bailly, Nicolas; Miya, Masaki; Lecointre, Guillaume; Ortí, Guillermo

    2017-07-06

    Fish classifications, as those of most other taxonomic groups, are being transformed drastically as new molecular phylogenies provide support for natural groups that were unanticipated by previous studies. A brief review of the main criteria used by ichthyologists to define their classifications during the last 50 years, however, reveals slow progress towards using an explicit phylogenetic framework. Instead, the trend has been to rely, in varying degrees, on deep-rooted anatomical concepts and authority, often mixing taxa with explicit phylogenetic support with arbitrary groupings. Two leading sources in ichthyology frequently used for fish classifications (JS Nelson's volumes of Fishes of the World and W. Eschmeyer's Catalog of Fishes) fail to adopt a global phylogenetic framework despite much recent progress made towards the resolution of the fish Tree of Life. The first explicit phylogenetic classification of bony fishes was published in 2013, based on a comprehensive molecular phylogeny ( www.deepfin.org ). We here update the first version of that classification by incorporating the most recent phylogenetic results. The updated classification presented here is based on phylogenies inferred using molecular and genomic data for nearly 2000 fishes. A total of 72 orders (and 79 suborders) are recognized in this version, compared with 66 orders in version 1. The phylogeny resolves placement of 410 families, or ~80% of the total of 514 families of bony fishes currently recognized. The ordinal status of 30 percomorph families included in this study, however, remains uncertain (incertae sedis in the series Carangaria, Ovalentaria, or Eupercaria). Comments to support taxonomic decisions and comparisons with conflicting taxonomic groups proposed by others are presented. We also highlight cases were morphological support exist for the groups being classified. This version of the phylogenetic classification of bony fishes is substantially improved, providing resolution

  19. A classification of chinese culture

    OpenAIRE

    Fan, Y

    2000-01-01

    This paper presents a classification of Chinese Cultural Values (CCVs). Although there exist great differences between the Mainland China, Hong Kong and Taiwan, it is still possible to identify certain core cultural values that are shared by the Chinese people no matter where they live. Based on the original list by the Chinese Cultural Connection (1987), the paper creates a new list that contains 71 core values against 40 in the old. The implications and limitations of the classification are...

  20. Classification of pyodestructive pulmonary diseases

    International Nuclear Information System (INIS)

    Muromskij, Yu.A.; Semivolkov, V.I.; Shlenova, L.A.

    1993-01-01

    Classification of pyodestructive lungs diseases, thier complications and outcomes is proposed which makes it possible for physioians engaged in studying respiratory organs pathology to orient themselves in problems of diagnosis and treatment tactics. The above classification is developed on the basis of studying the disease anamnesis and its clinical process, as well as on the basis of roentgenological and morphological study results by more than 10000 patients

  1. Quantum computing for pattern classification

    OpenAIRE

    Schuld, Maria; Sinayskiy, Ilya; Petruccione, Francesco

    2014-01-01

    It is well known that for certain tasks, quantum computing outperforms classical computing. A growing number of contributions try to use this advantage in order to improve or extend classical machine learning algorithms by methods of quantum information theory. This paper gives a brief introduction into quantum machine learning using the example of pattern classification. We introduce a quantum pattern classification algorithm that draws on Trugenberger's proposal for measuring the Hamming di...

  2. Cancer classification using the Immunoscore: a worldwide task force.

    Science.gov (United States)

    Galon, Jérôme; Pagès, Franck; Marincola, Francesco M; Angell, Helen K; Thurin, Magdalena; Lugli, Alessandro; Zlobec, Inti; Berger, Anne; Bifulco, Carlo; Botti, Gerardo; Tatangelo, Fabiana; Britten, Cedrik M; Kreiter, Sebastian; Chouchane, Lotfi; Delrio, Paolo; Arndt, Hartmann; Asslaber, Martin; Maio, Michele; Masucci, Giuseppe V; Mihm, Martin; Vidal-Vanaclocha, Fernando; Allison, James P; Gnjatic, Sacha; Hakansson, Leif; Huber, Christoph; Singh-Jasuja, Harpreet; Ottensmeier, Christian; Zwierzina, Heinz; Laghi, Luigi; Grizzi, Fabio; Ohashi, Pamela S; Shaw, Patricia A; Clarke, Blaise A; Wouters, Bradly G; Kawakami, Yutaka; Hazama, Shoichi; Okuno, Kiyotaka; Wang, Ena; O'Donnell-Tormey, Jill; Lagorce, Christine; Pawelec, Graham; Nishimura, Michael I; Hawkins, Robert; Lapointe, Réjean; Lundqvist, Andreas; Khleif, Samir N; Ogino, Shuji; Gibbs, Peter; Waring, Paul; Sato, Noriyuki; Torigoe, Toshihiko; Itoh, Kyogo; Patel, Prabhu S; Shukla, Shilin N; Palmqvist, Richard; Nagtegaal, Iris D; Wang, Yili; D'Arrigo, Corrado; Kopetz, Scott; Sinicrope, Frank A; Trinchieri, Giorgio; Gajewski, Thomas F; Ascierto, Paolo A; Fox, Bernard A

    2012-10-03

    Prediction of clinical outcome in cancer is usually achieved by histopathological evaluation of tissue samples obtained during surgical resection of the primary tumor. Traditional tumor staging (AJCC/UICC-TNM classification) summarizes data on tumor burden (T), presence of cancer cells in draining and regional lymph nodes (N) and evidence for metastases (M). However, it is now recognized that clinical outcome can significantly vary among patients within the same stage. The current classification provides limited prognostic information, and does not predict response to therapy. Recent literature has alluded to the importance of the host immune system in controlling tumor progression. Thus, evidence supports the notion to include immunological biomarkers, implemented as a tool for the prediction of prognosis and response to therapy. Accumulating data, collected from large cohorts of human cancers, has demonstrated the impact of immune-classification, which has a prognostic value that may add to the significance of the AJCC/UICC TNM-classification. It is therefore imperative to begin to incorporate the 'Immunoscore' into traditional classification, thus providing an essential prognostic and potentially predictive tool. Introduction of this parameter as a biomarker to classify cancers, as part of routine diagnostic and prognostic assessment of tumors, will facilitate clinical decision-making including rational stratification of patient treatment. Equally, the inherent complexity of quantitative immunohistochemistry, in conjunction with protocol variation across laboratories, analysis of different immune cell types, inconsistent region selection criteria, and variable ways to quantify immune infiltration, all underline the urgent requirement to reach assay harmonization. In an effort to promote the Immunoscore in routine clinical settings, an international task force was initiated. This review represents a follow-up of the announcement of this initiative, and of the J

  3. CREST--classification resources for environmental sequence tags.

    Directory of Open Access Journals (Sweden)

    Anders Lanzén

    Full Text Available Sequencing of taxonomic or phylogenetic markers is becoming a fast and efficient method for studying environmental microbial communities. This has resulted in a steadily growing collection of marker sequences, most notably of the small-subunit (SSU ribosomal RNA gene, and an increased understanding of microbial phylogeny, diversity and community composition patterns. However, to utilize these large datasets together with new sequencing technologies, a reliable and flexible system for taxonomic classification is critical. We developed CREST (Classification Resources for Environmental Sequence Tags, a set of resources and tools for generating and utilizing custom taxonomies and reference datasets for classification of environmental sequences. CREST uses an alignment-based classification method with the lowest common ancestor algorithm. It also uses explicit rank similarity criteria to reduce false positives and identify novel taxa. We implemented this method in a web server, a command line tool and the graphical user interfaced program MEGAN. Further, we provide the SSU rRNA reference database and taxonomy SilvaMod, derived from the publicly available SILVA SSURef, for classification of sequences from bacteria, archaea and eukaryotes. Using cross-validation and environmental datasets, we compared the performance of CREST and SilvaMod to the RDP Classifier. We also utilized Greengenes as a reference database, both with CREST and the RDP Classifier. These analyses indicate that CREST performs better than alignment-free methods with higher recall rate (sensitivity as well as precision, and with the ability to accurately identify most sequences from novel taxa. Classification using SilvaMod performed better than with Greengenes, particularly when applied to environmental sequences. CREST is freely available under a GNU General Public License (v3 from http://apps.cbu.uib.no/crest and http://lcaclassifier.googlecode.com.

  4. Information Classification on University Websites

    DEFF Research Database (Denmark)

    Nawaz, Ather; Clemmensen, Torkil; Hertzum, Morten

    2011-01-01

    Websites are increasingly used as a medium for providing information to university students. The quality of a university website depends on how well the students’ information classification fits with the structure of the information on the website. This paper investigates the information classifi......Websites are increasingly used as a medium for providing information to university students. The quality of a university website depends on how well the students’ information classification fits with the structure of the information on the website. This paper investigates the information...... classification of 14 Danish and 14 Pakistani students and compares it with the information classification of their university website. Brainstorming, card sorting, and task exploration activities were used to discover similarities and differences in the participating students’ classification of website...... information and their ability to navigate the websites. The results of the study indicate group differences in user classification and related taskperformance differences. The main implications of the study are that (a) the edit distance appears a useful measure in cross-country HCI research and practice...

  5. Ototoxicity (cochleotoxicity) classifications: A review.

    Science.gov (United States)

    Crundwell, Gemma; Gomersall, Phil; Baguley, David M

    2016-01-01

    Drug-mediated ototoxicity, specifically cochleotoxicity, is a concern for patients receiving medications for the treatment of serious illness. A number of classification schemes exist, most of which are based on pure-tone audiometry, in order to assist non-audiological/non-otological specialists in the identification and monitoring of iatrogenic hearing loss. This review identifies the primary classification systems used in cochleototoxicity monitoring. By bringing together classifications published in discipline-specific literature, the paper aims to increase awareness of their relative strengths and limitations in the assessment and monitoring of ototoxic hearing loss and to indicate how future classification systems may improve upon the status-quo. Literature review. PubMed identified 4878 articles containing the search term ototox*. A systematic search identified 13 key classification systems. Cochleotoxicity classification systems can be divided into those which focus on hearing change from a baseline audiogram and those that focus on the functional impact of the hearing loss. Common weaknesses of these grading scales included a lack of sensitivity to small adverse changes in hearing thresholds, a lack of high-frequency audiometry (>8 kHz), and lack of indication of which changes are likely to be clinically significant for communication and quality of life.

  6. Information Classification on University Websites

    DEFF Research Database (Denmark)

    Nawaz, Ather; Clemmensen, Torkil; Hertzum, Morten

    2011-01-01

    Websites are increasingly used as a medium for providing information to university students. The quality of a university website depends on how well the students’ information classification fits with the structure of the information on the website. This paper investigates the information classifi......Websites are increasingly used as a medium for providing information to university students. The quality of a university website depends on how well the students’ information classification fits with the structure of the information on the website. This paper investigates the information...... classification of 14 Danish and 14 Pakistani students and compares it with the information classification of their university website. Brainstorming, card sorting, and task exploration activities were used to discover similarities and differences in the participating students’ classification of website...... information and their ability to navigate the websites. The results of the study indicate group differences in user classification and related task-performance differences. The main implications of the study are that (a) the edit distance appears a useful measure in cross-country HCI research and practice...

  7. Gear cutting tools fundamentals of design and computation

    CERN Document Server

    Radzevich, Stephen P

    2010-01-01

    Presents the DG/K-based method of surface generation, a novel and practical mathematical method for designing gear cutting tools with optimal parameters. This book proposes a scientific classification for the various kinds of the gear machining meshes, discussing optimal designs of gear cutting tools.

  8. A revised 3-column classification approach for the surgical planning of extended lateral tibial plateau fractures.

    Science.gov (United States)

    Hoekstra, H; Kempenaers, K; Nijs, S

    2017-10-01

    Variable angle locking compression plates allow for lateral buttress and support of the posterolateral joint surface of tibial plateau fractures. This gives room for improvement of the surgical 3-column classification approach. Our aim was to revise and validate the 3-column classification approach to better guide the surgical planning of tibial plateau fractures extending into the posterolateral corner. In contrast to the 3-column classification approach, in the revised approach the posterior border of the lateral column in the revised approach lies posterior instead of anterior of the fibula. According to the revised 3-column classification approach, extended lateral column fractures are defined as single lateral column fractures extending posteriorly into the posterolateral corner. CT-images of 36 patients were reviewed and classified twice online according to Schatzker and revised 3-column classification approach by five observers. The intraobserver reliability was calculated using the Cohen's kappa and the interobserver reliability was calculated using the Fleiss' kappa. The intraobserver reliability showed substantial agreement according to Landis and Koch for both Schatzker and the revised 3-column classification approach (0.746 vs. 0.782 p = 0.37, Schatzker vs. revised 3-column, respectively). However, the interobserver reliability of the revised 3-column classification approach was significantly higher as compared to the Schatzker classification (0.531 vs. 0.669 p column, respectively). With the introduction of variable angle locking compression plates, the revised 3-column classification approach is a very helpful tool in the preoperative surgical planning of tibial plateau fractures, in particular, lateral column fractures that extend into the posterolateral corner. The revised 3-column classification approach is rather a practical supplement to the Schatzker classification. It has a significantly higher interobserver reliability as compared to the

  9. THE LOW BACKSCATTERING TARGETS CLASSIFICATION IN URBAN AREAS

    Directory of Open Access Journals (Sweden)

    L. Shi

    2012-07-01

    Full Text Available The Polarimetric and Interferometric Synthetic Aperture Radar (POLINSAR is widely used in urban area nowadays. Because of the physical and geometric sensitivity, the POLINSAR is suitable for the city classification, power-lines detection, building extraction, etc. As the new X-band POLINSAR radar, the china prototype airborne system, XSAR works with high spatial resolution in azimuth (0.1 m and slant range (0.4 m. In land applications, SAR image classification is a useful tool to distinguish the interesting area and obtain the target information. The bare soil, the cement road, the water and the building shadow are common scenes in the urban area. As it always exists low backscattering sign objects (LBO with the similar scattering mechanism (all odd bounce except for shadow in the XSAR images, classes are usually confused in Wishart-H-Alpha and Freeman-Durden methods. It is very hard to distinguish those targets only using the general information. To overcome the shortage, this paper explores an improved algorithm for LBO refined classification based on the Pre-Classification in urban areas. Firstly, the Pre-Classification is applied in the polarimetric datum and the mixture class is marked which contains LBO. Then, the polarimetric covariance matrix C3 is re-estimated on the Pre-Classification results to get more reliable results. Finally, the occurrence space which combining the entropy and the phase-diff standard deviation between HH and VV channel is used to refine the Pre-Classification results. The XSAR airborne experiments show the improved method is potential to distinguish the mixture classes in the low backscattering objects.

  10. Insights into the classification of small GTPases

    Directory of Open Access Journals (Sweden)

    Dominik Heider

    2010-05-01

    Full Text Available Dominik Heider1, Sascha Hauke3, Martin Pyka4, Daniel Kessler21Department of Bioinformatics, Center for Medical Biotechnology, 2Institute of Cell Biology (Cancer Research, University of Duisburg-Essen, Essen, Germany; 3Institute of Computer Science, University of Münster, Münster, Germany; 4Interdisciplinary Center for Clinical Research, University Hospital of Münster, Münster, GermanyAbstract: In this study we used a Random Forest-based approach for an assignment of small guanosine triphosphate proteins (GTPases to specific subgroups. Small GTPases represent an important functional group of proteins that serve as molecular switches in a wide range of fundamental cellular processes, including intracellular transport, movement and signaling events. These proteins have further gained a special emphasis in cancer research, because within the last decades a huge variety of small GTPases from different subgroups could be related to the development of all types of tumors. Using a random forest approach, we were able to identify the most important amino acid positions for the classification process within the small GTPases superfamily and its subgroups. These positions are in line with the results of earlier studies and have been shown to be the essential elements for the different functionalities of the GTPase families. Furthermore, we provide an accurate and reliable software tool (GTPasePred to identify potential novel GTPases and demonstrate its application to genome sequences.Keywords: cancer, machine learning, classification, Random Forests, proteins

  11. HIV classification using coalescent theory

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Ming [Los Alamos National Laboratory; Letiner, Thomas K [Los Alamos National Laboratory; Korber, Bette T [Los Alamos National Laboratory

    2008-01-01

    Algorithms for subtype classification and breakpoint detection of HIV-I sequences are based on a classification system of HIV-l. Hence, their quality highly depend on this system. Due to the history of creation of the current HIV-I nomenclature, the current one contains inconsistencies like: The phylogenetic distance between the subtype B and D is remarkably small compared with other pairs of subtypes. In fact, it is more like the distance of a pair of subsubtypes Robertson et al. (2000); Subtypes E and I do not exist any more since they were discovered to be composed of recombinants Robertson et al. (2000); It is currently discussed whether -- instead of CRF02 being a recombinant of subtype A and G -- subtype G should be designated as a circulating recombination form (CRF) nd CRF02 as a subtype Abecasis et al. (2007); There are 8 complete and over 400 partial HIV genomes in the LANL-database which belong neither to a subtype nor to a CRF (denoted by U). Moreover, the current classification system is somehow arbitrary like all complex classification systems that were created manually. To this end, it is desirable to deduce the classification system of HIV systematically by an algorithm. Of course, this problem is not restricted to HIV, but applies to all fast mutating and recombining viruses. Our work addresses the simpler subproblem to score classifications of given input sequences of some virus species (classification denotes a partition of the input sequences in several subtypes and CRFs). To this end, we reconstruct ancestral recombination graphs (ARG) of the input sequences under restrictions determined by the given classification. These restritions are imposed in order to ensure that the reconstructed ARGs do not contradict the classification under consideration. Then, we find the ARG with maximal probability by means of Markov Chain Monte Carlo methods. The probability of the most probable ARG is interpreted as a score for the classification. To our

  12. Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Martin Längkvist

    2016-04-01

    Full Text Available The availability of high-resolution remote sensing (HRRS data has opened up the possibility for new interesting applications, such as per-pixel classification of individual objects in greater detail. This paper shows how a convolutional neural network (CNN can be applied to multispectral orthoimagery and a digital surface model (DSM of a small city for a full, fast and accurate per-pixel classification. The predicted low-level pixel classes are then used to improve the high-level segmentation. Various design choices of the CNN architecture are evaluated and analyzed. The investigated land area is fully manually labeled into five categories (vegetation, ground, roads, buildings and water, and the classification accuracy is compared to other per-pixel classification works on other land areas that have a similar choice of categories. The results of the full classification and segmentation on selected segments of the map show that CNNs are a viable tool for solving both the segmentation and object recognition task for remote sensing data.

  13. ICF-based classification and measurement of functioning.

    Science.gov (United States)

    Stucki, G; Kostanjsek, N; Ustün, B; Cieza, A

    2008-09-01

    If we aim towards a comprehensive understanding of human functioning and the development of comprehensive programs to optimize functioning of individuals and populations we need to develop suitable measures. The approval of the International Classification, Disability and Health (ICF) in 2001 by the 54th World Health Assembly as the first universally shared model and classification of functioning, disability and health marks, therefore an important step in the development of measurement instruments and ultimately for our understanding of functioning, disability and health. The acceptance and use of the ICF as a reference framework and classification has been facilitated by its development in a worldwide, comprehensive consensus process and the increasing evidence regarding its validity. However, the broad acceptance and use of the ICF as a reference framework and classification will also depend on the resolution of conceptual and methodological challenges relevant for the classification and measurement of functioning. This paper therefore describes first how the ICF categories can serve as building blocks for the measurement of functioning and then the current state of the development of ICF based practical tools and international standards such as the ICF Core Sets. Finally it illustrates how to map the world of measures to the ICF and vice versa and the methodological principles relevant for the transformation of information obtained with a clinical test or a patient-oriented instrument to the ICF as well as the development of ICF-based clinical and self-reported measurement instruments.

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

    Directory of Open Access Journals (Sweden)

    Mehdi Khashei

    2015-09-01

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

  15. A NEW CLASSIFICATION OF SMES IN THE DIGITAL ECONOMY CONTEXT

    Directory of Open Access Journals (Sweden)

    Maximilian ROBU

    2013-06-01

    Full Text Available In a highly dynamic and competitive environment as the online one, SMEs need to adapt and change their behavior, requiring a rethinking of classification criteria. Social media is changing the way people interact, but also changing organizations and how they operate. Social networks are no longer just a simple tool to create a network of friends; they have become a destination for business. We can also talk of a new world of business, a new way of working; the freelancers have a share of the increasingly significant. Cloud computing is the support for all changes in the current environment, providing the tools necessary to conduct activities anywhere. Given the mentioned arguments, we consider that the classification of SMEs according to a new set of criteria: operating environment, geographical area, type of employees in the company or how they organize marketing activities.

  16. Development of an intelligent ultrasonic welding defect classification software

    International Nuclear Information System (INIS)

    Song, Sung Jin; Kim, Hak Joon; Jeong, Hee Don

    1997-01-01

    Ultrasonic pattern recognition is the most effective approach to the problem of discriminating types of flaws in weldments based on ultrasonic flaw signals. In spite of significant progress in the research on this methodology, it has not been widely used in many practical ultrasonic inspections of weldments in industry. Hence, for the convenient application of this approach in many practical situations, we develop an intelligent ultrasonic signature classification software which can discriminate types of flaws in weldments based on their ultrasonic signals using various tools in artificial intelligence such as neural networks. This software shows the excellent performance in an experimental problem where flaws in weldments are classified into two categories of cracks and non-cracks. This performance demonstrates the high possibility of this software as a practical tool for ultrasonic flaw classification in weldments.

  17. 5 CFR 1312.7 - Derivative classification.

    Science.gov (United States)

    2010-01-01

    ..., DOWNGRADING, DECLASSIFICATION AND SAFEGUARDING OF NATIONAL SECURITY INFORMATION Classification and Declassification of National Security Information § 1312.7 Derivative classification. A derivative classification... 5 Administrative Personnel 3 2010-01-01 2010-01-01 false Derivative classification. 1312.7 Section...

  18. 32 CFR 2400.15 - Classification guides.

    Science.gov (United States)

    2010-07-01

    ... REGULATIONS TO IMPLEMENT E.O. 12356; OFFICE OF SCIENCE AND TECHNOLOGY POLICY INFORMATION SECURITY PROGRAM Derivative Classification § 2400.15 Classification guides. (a) OSTP shall issue and maintain classification guides to facilitate the proper and uniform derivative classification of information. These guides shall...

  19. 7 CFR 51.1860 - Color classification.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 2 2010-01-01 2010-01-01 false Color classification. 51.1860 Section 51.1860... STANDARDS) United States Standards for Fresh Tomatoes 1 Color Classification § 51.1860 Color classification... illustrating the color classification requirements, as set forth in this section. This visual aid may be...

  20. 22 CFR 42.11 - Classification symbols.

    Science.gov (United States)

    2010-04-01

    ... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Classification symbols. 42.11 Section 42.11... NATIONALITY ACT, AS AMENDED Classification and Foreign State Chargeability § 42.11 Classification symbols. A... visa symbol to show the classification of the alien. Immigrants Symbol Class Section of law Immediate...

  1. 28 CFR 345.20 - Position classification.

    Science.gov (United States)

    2010-07-01

    ... 28 Judicial Administration 2 2010-07-01 2010-07-01 false Position classification. 345.20 Section... INDUSTRIES (FPI) INMATE WORK PROGRAMS Position Classification § 345.20 Position classification. (a) Inmate... the objectives and principles of pay classification as a part of the routine orientation of new FPI...

  2. 7 CFR 51.2284 - Size classification.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 2 2010-01-01 2010-01-01 false Size classification. 51.2284 Section 51.2284... Size classification. The following classifications are provided to describe the size of any lot... shall conform to the requirements of the specified classification as defined below: (a) Halves. Lot...

  3. 22 CFR 9.8 - Classification challenges.

    Science.gov (United States)

    2010-04-01

    ... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Classification challenges. 9.8 Section 9.8 Foreign Relations DEPARTMENT OF STATE GENERAL SECURITY INFORMATION REGULATIONS § 9.8 Classification... classification status is improper are expected and encouraged to challenge the classification status of the...

  4. 7 CFR 28.911 - Review classification.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 2 2010-01-01 2010-01-01 false Review classification. 28.911 Section 28.911... REGULATIONS COTTON CLASSING, TESTING, AND STANDARDS Cotton Classification and Market News Service for Producers Classification § 28.911 Review classification. (a) A producer may request one review...

  5. 46 CFR 503.54 - Original classification.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 9 2010-10-01 2010-10-01 false Original classification. 503.54 Section 503.54 Shipping... Program § 503.54 Original classification. (a) No Commission Member or employee has the authority to... classification, it shall be sent to the appropriate agency with original classification authority over the...

  6. 32 CFR 2001.21 - Original classification.

    Science.gov (United States)

    2010-07-01

    ... 32 National Defense 6 2010-07-01 2010-07-01 false Original classification. 2001.21 Section 2001.21... Markings § 2001.21 Original classification. (a) Primary markings. At the time of original classification... authority. The name and position, or personal identifier, of the original classification authority shall...

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

    Directory of Open Access Journals (Sweden)

    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.

  8. Voxel-based plaque classification in coronary intravascular optical coherence tomography images using decision trees

    Science.gov (United States)

    Kolluru, Chaitanya; Prabhu, David; Gharaibeh, Yazan; Wu, Hao; Wilson, David L.

    2018-02-01

    Intravascular Optical Coherence Tomography (IVOCT) is a high contrast, 3D microscopic imaging technique that can be used to assess atherosclerosis and guide stent interventions. Despite its advantages, IVOCT image interpretation is challenging and time consuming with over 500 image frames generated in a single pullback volume. We have developed a method to classify voxel plaque types in IVOCT images using machine learning. To train and test the classifier, we have used our unique database of labeled cadaver vessel IVOCT images accurately registered to gold standard cryoimages. This database currently contains 300 images and is growing. Each voxel is labeled as fibrotic, lipid-rich, calcified or other. Optical attenuation, intensity and texture features were extracted for each voxel and were used to build a decision tree classifier for multi-class classification. Five-fold cross-validation across images gave accuracies of 96 % +/- 0.01 %, 90 +/- 0.02% and 90 % +/- 0.01 % for fibrotic, lipid-rich and calcified classes respectively. To rectify performance degradation seen in left out vessel specimens as opposed to left out images, we are adding data and reducing features to limit overfitting. Following spatial noise cleaning, important vascular regions were unambiguous in display. We developed displays that enable physicians to make rapid determination of calcified and lipid regions. This will inform treatment decisions such as the need for devices (e.g., atherectomy or scoring balloon in the case of calcifications) or extended stent lengths to ensure coverage of lipid regions prone to injury at the edge of a stent.

  9. What is new in genetics and osteogenesis imperfecta classification?

    Directory of Open Access Journals (Sweden)

    Eugênia R. Valadares

    2014-11-01

    Conclusions: Considering the discovery of new genes and limited genotype‐phenotype correlation, the use of next‐generation sequencing tools has become useful in molecular studies of OI cases. The recommendation of the Nosology Group of the International Society of Skeletal Dysplasias is to maintain the classification of Sillence as the prototypical form, universally accepted to classify the degree of severity in OI, while maintaining it free from direct molecular reference.

  10. Featureless classification of light curves

    Science.gov (United States)

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

    2015-08-01

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

  11. A Semisupervised Cascade Classification Algorithm

    Directory of Open Access Journals (Sweden)

    Stamatis Karlos

    2016-01-01

    Full Text Available Classification is one of the most important tasks of data mining techniques, which have been adopted by several modern applications. The shortage of enough labeled data in the majority of these applications has shifted the interest towards using semisupervised methods. Under such schemes, the use of collected unlabeled data combined with a clearly smaller set of labeled examples leads to similar or even better classification accuracy against supervised algorithms, which use labeled examples exclusively during the training phase. A novel approach for increasing semisupervised classification using Cascade Classifier technique is presented in this paper. The main characteristic of Cascade Classifier strategy is the use of a base classifier for increasing the feature space by adding either the predicted class or the probability class distribution of the initial data. The classifier of the second level is supplied with the new dataset and extracts the decision for each instance. In this work, a self-trained NB∇C4.5 classifier algorithm is presented, which combines the characteristics of Naive Bayes as a base classifier and the speed of C4.5 for final classification. We performed an in-depth comparison with other well-known semisupervised classification methods on standard benchmark datasets and we finally reached to the point that the presented technique has better accuracy in most cases.

  12. What lies beneath: detecting sub-canopy changes in savanna woodlands using a three-dimensional classification method

    CSIR Research Space (South Africa)

    Fisher, JT

    2015-07-01

    Full Text Available structural diversity. A 3D classification approach was successful in detecting fine-scale, short-term changes between land uses, and can thus be used as amonitoring tool for savannawoody vegetation structure....

  13. EEG source space analysis of the supervised factor analytic approach for the classification of multi-directional arm movement

    Science.gov (United States)

    Shenoy Handiru, Vikram; Vinod, A. P.; Guan, Cuntai

    2017-08-01

    Objective. In electroencephalography (EEG)-based brain-computer interface (BCI) systems for motor control tasks the conventional practice is to decode motor intentions by using scalp EEG. However, scalp EEG only reveals certain limited information about the complex tasks of movement with a higher degree of freedom. Therefore, our objective is to investigate the effectiveness of source-space EEG in extracting relevant features that discriminate arm movement in multiple directions. Approach. We have proposed a novel feature extraction algorithm based on supervised factor analysis that models the data from source-space EEG. To this end, we computed the features from the source dipoles confined to Brodmann areas of interest (BA4a, BA4p and BA6). Further, we embedded class-wise labels of multi-direction (multi-class) source-space EEG to an unsupervised factor analysis to make it into a supervised learning method. Main Results. Our approach provided an average decoding accuracy of 71% for the classification of hand movement in four orthogonal directions, that is significantly higher (>10%) than the classification accuracy obtained using state-of-the-art spatial pattern features in sensor space. Also, the group analysis on the spectral characteristics of source-space EEG indicates that the slow cortical potentials from a set of cortical source dipoles reveal discriminative information regarding the movement parameter, direction. Significance. This study presents evidence that low-frequency components in the source space play an important role in movement kinematics, and thus it may lead to new strategies for BCI-based neurorehabilitation.

  14. Rock suitability classification RSC 2012

    Energy Technology Data Exchange (ETDEWEB)

    McEwen, T. (ed.) [McEwen Consulting, Leicester (United Kingdom); Kapyaho, A. [Geological Survey of Finland, Espoo (Finland); Hella, P. [Saanio and Riekkola, Helsinki (Finland); Aro, S.; Kosunen, P.; Mattila, J.; Pere, T.

    2012-12-15

    This report presents Posiva's Rock Suitability Classification (RSC) system, developed for locating suitable rock volumes for repository design and construction. The RSC system comprises both the revised rock suitability criteria and the procedure for the suitability classification during the construction of the repository. The aim of the classification is to avoid such features of the host rock that may be detrimental to the favourable conditions within the repository, either initially or in the long term. This report also discusses the implications of applying the RSC system for the fulfilment of the regulatory requirements concerning the host rock as a natural barrier and the site's overall suitability for hosting a final repository of spent nuclear fuel.

  15. Rock suitability classification RSC 2012

    International Nuclear Information System (INIS)

    McEwen, T.; Kapyaho, A.; Hella, P.; Aro, S.; Kosunen, P.; Mattila, J.; Pere, T.

    2012-12-01

    This report presents Posiva's Rock Suitability Classification (RSC) system, developed for locating suitable rock volumes for repository design and construction. The RSC system comprises both the revised rock suitability criteria and the procedure for the suitability classification during the construction of the repository. The aim of the classification is to avoid such features of the host rock that may be detrimental to the favourable conditions within the repository, either initially or in the long term. This report also discusses the implications of applying the RSC system for the fulfilment of the regulatory requirements concerning the host rock as a natural barrier and the site's overall suitability for hosting a final repository of spent nuclear fuel

  16. Automatic Hierarchical Color Image Classification

    Directory of Open Access Journals (Sweden)

    Jing Huang

    2003-02-01

    Full Text Available Organizing images into semantic categories can be extremely useful for content-based image retrieval and image annotation. Grouping images into semantic classes is a difficult problem, however. Image classification attempts to solve this hard problem by using low-level image features. In this paper, we propose a method for hierarchical classification of images via supervised learning. This scheme relies on using a good low-level feature and subsequently performing feature-space reconfiguration using singular value decomposition to reduce noise and dimensionality. We use the training data to obtain a hierarchical classification tree that can be used to categorize new images. Our experimental results suggest that this scheme not only performs better than standard nearest-neighbor techniques, but also has both storage and computational advantages.

  17. Oral epithelial dysplasia classification systems

    DEFF Research Database (Denmark)

    Warnakulasuriya, S; Reibel, J; Bouquot, J

    2008-01-01

    At a workshop coordinated by the WHO Collaborating Centre for Oral Cancer and Precancer in the United Kingdom issues related to potentially malignant disorders of the oral cavity were discussed by an expert group. The consensus views of the Working Group are presented in a series of papers....... 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...... use. Although most oral pathologists possibly recognize and accept the criteria for grading epithelial dysplasia, firstly based on architectural features and then of cytology, there is great variability in their interpretation of the presence, degree and significance of the individual criteria...

  18. Polsar Land Cover Classification Based on Hidden Polarimetric Features in Rotation Domain and Svm Classifier

    Science.gov (United States)

    Tao, C.-S.; Chen, S.-W.; Li, Y.-Z.; Xiao, S.-P.

    2017-09-01

    Land cover classification is an important application for polarimetric synthetic aperture radar (PolSAR) data utilization. Rollinvariant polarimetric features such as H / Ani / text-decoration: overline">α / Span are commonly adopted in PolSAR land cover classification. However, target orientation diversity effect makes PolSAR images understanding and interpretation difficult. Only using the roll-invariant polarimetric features may introduce ambiguity in the interpretation of targets' scattering mechanisms and limit the followed classification accuracy. To address this problem, this work firstly focuses on hidden polarimetric feature mining in the rotation domain along the radar line of sight using the recently reported uniform polarimetric matrix rotation theory and the visualization and characterization tool of polarimetric coherence pattern. The former rotates the acquired polarimetric matrix along the radar line of sight and fully describes the rotation characteristics of each entry of the matrix. Sets of new polarimetric features are derived to describe the hidden scattering information of the target in the rotation domain. The latter extends the traditional polarimetric coherence at a given rotation angle to the rotation domain for complete interpretation. A visualization and characterization tool is established to derive new polarimetric features for hidden information exploration. Then, a classification scheme is developed combing both the selected new hidden polarimetric features in rotation domain and the commonly used roll-invariant polarimetric features with a support vector machine (SVM) classifier. Comparison experiments based on AIRSAR and multi-temporal UAVSAR data demonstrate that compared with the conventional classification scheme which only uses the roll-invariant polarimetric features, the proposed classification scheme achieves both higher classification accuracy and better robustness. For AIRSAR data, the overall classification

  19. POLSAR LAND COVER CLASSIFICATION BASED ON HIDDEN POLARIMETRIC FEATURES IN ROTATION DOMAIN AND SVM CLASSIFIER

    Directory of Open Access Journals (Sweden)

    C.-S. Tao

    2017-09-01

    Full Text Available Land cover classification is an important application for polarimetric synthetic aperture radar (PolSAR data utilization. Rollinvariant polarimetric features such as H / Ani / α / Span are commonly adopted in PolSAR land cover classification. However, target orientation diversity effect makes PolSAR images understanding and interpretation difficult. Only using the roll-invariant polarimetric features may introduce ambiguity in the interpretation of targets’ scattering mechanisms and limit the followed classification accuracy. To address this problem, this work firstly focuses on hidden polarimetric feature mining in the rotation domain along the radar line of sight using the recently reported uniform polarimetric matrix rotation theory and the visualization and characterization tool of polarimetric coherence pattern. The former rotates the acquired polarimetric matrix along the radar line of sight and fully describes the rotation characteristics of each entry of the matrix. Sets of new polarimetric features are derived to describe the hidden scattering information of the target in the rotation domain. The latter extends the traditional polarimetric coherence at a given rotation angle to the rotation domain for complete interpretation. A visualization and characterization tool is established to derive new polarimetric features for hidden information exploration. Then, a classification scheme is developed combing both the selected new hidden polarimetric features in rotation domain and the commonly used roll-invariant polarimetric features with a support vector machine (SVM classifier. Comparison experiments based on AIRSAR and multi-temporal UAVSAR data demonstrate that compared with the conventional classification scheme which only uses the roll-invariant polarimetric features, the proposed classification scheme achieves both higher classification accuracy and better robustness. For AIRSAR data, the overall classification accuracy

  20. SHIP CLASSIFICATION FROM MULTISPECTRAL VIDEOS

    Directory of Open Access Journals (Sweden)

    Frederique Robert-Inacio

    2012-05-01

    Full Text Available Surveillance of a seaport can be achieved by different means: radar, sonar, cameras, radio communications and so on. Such a surveillance aims, on the one hand, to manage cargo and tanker traffic, and, on the other hand, to prevent terrorist attacks in sensitive areas. In this paper an application to video-surveillance of a seaport entrance is presented, and more particularly, the different steps enabling to classify mobile shapes. This classification is based on a parameter measuring the similarity degree between the shape under study and a set of reference shapes. The classification result describes the considered mobile in terms of shape and speed.

  1. Proteomic classification of breast cancer.

    LENUS (Irish Health Repository)

    Kamel, Dalia

    2012-11-01

    Being a significant health problem that affects patients in various age groups, breast cancer has been extensively studied to date. Recently, molecular breast cancer classification has advanced significantly with the availability of genomic profiling technologies. Proteomic technologies have also advanced from traditional protein assays including enzyme-linked immunosorbent assay, immunoblotting and immunohistochemistry to more comprehensive approaches including mass spectrometry and reverse phase protein lysate arrays (RPPA). The purpose of this manuscript is to review the current protein markers that influence breast cancer prediction and prognosis and to focus on novel advances in proteomic classification of breast cancer.

  2. Deep learning for image classification

    Science.gov (United States)

    McCoppin, Ryan; Rizki, Mateen

    2014-06-01

    This paper provides an overview of deep learning and introduces the several subfields of deep learning including a specific tutorial of convolutional neural networks. Traditional methods for learning image features are compared to deep learning techniques. In addition, we present our preliminary classification results, our basic implementation of a convolutional restricted Boltzmann machine on the Mixed National Institute of Standards and Technology database (MNIST), and we explain how to use deep learning networks to assist in our development of a robust gender classification system.

  3. Facial aging: A clinical classification

    Directory of Open Access Journals (Sweden)

    Shiffman Melvin

    2007-01-01

    Full Text Available The purpose of this classification of facial aging is to have a simple clinical method to determine the severity of the aging process in the face. This allows a quick estimate as to the types of procedures that the patient would need to have the best results. Procedures that are presently used for facial rejuvenation include laser, chemical peels, suture lifts, fillers, modified facelift and full facelift. The physician is already using his best judgment to determine which procedure would be best for any particular patient. This classification may help to refine these decisions.

  4. Project implementation : classification of organic soils and classification of marls - training of INDOT personnel.

    Science.gov (United States)

    2012-09-01

    This is an implementation project for the research completed as part of the following projects: SPR3005 Classification of Organic Soils : and SPR3227 Classification of Marl Soils. The methods developed for the classification of both soi...

  5. SCOWLP classification: Structural comparison and analysis of protein binding regions

    Directory of Open Access Journals (Sweden)

    Anders Gerd

    2008-01-01

    Full Text Available Abstract Background Detailed information about protein interactions is critical for our understanding of the principles governing protein recognition mechanisms. The structures of many proteins have been experimentally determined in complex with different ligands bound either in the same or different binding regions. Thus, the structural interactome requires the development of tools to classify protein binding regions. A proper classification may provide a general view of the regions that a protein uses to bind others and also facilitate a detailed comparative analysis of the interacting information for specific protein binding regions at atomic level. Such classification might be of potential use for deciphering protein interaction networks, understanding protein function, rational engineering and design. Description Protein binding regions (PBRs might be ideally described as well-defined separated regions that share no interacting residues one another. However, PBRs are often irregular, discontinuous and can share a wide range of interacting residues among them. The criteria to define an individual binding region can be often arbitrary and may differ from other binding regions within a protein family. Therefore, the rational behind protein interface classification should aim to fulfil the requirements of the analysis to be performed. We extract detailed interaction information of protein domains, peptides and interfacial solvent from the SCOWLP database and we classify the PBRs of each domain family. For this purpose, we define a similarity index based on the overlapping of interacting residues mapped in pair-wise structural alignments. We perform our classification with agglomerative hierarchical clustering using the complete-linkage method. Our classification is calculated at different similarity cut-offs to allow flexibility in the analysis of PBRs, feature especially interesting for those protein families with conflictive binding regions

  6. Seafloor classification using echo- waveforms: A method employing hybrid neural network architecture

    Digital Repository Service at National Institute of Oceanography (India)

    Chakraborty, B.; Mahale, V.; DeSouza, C.; Das, P.

    , neural network architecture, seafloor classification, self-organizing feature map (SOFM). I. INTRODUCTION S EAFLOOR classification and characterization using re- mote high-frequency acoustic system has been recognized as a useful tool (see [1...] and references therein). The seafloor’s characteristics are extremely complicated due to variations of the many parameters at different scales. The parameters include sediment grain size, relief height at the water–sediment inter- face, and variations within...

  7. Inter- and intraobserver reliability of the MTM-classification for proximal humeral fractures

    DEFF Research Database (Denmark)

    Bahrs, Christian; Schmal, Hagen; Lingenfelter, Erich

    2008-01-01

    tool. METHODS: Three observers classified plain radiographs of 22 fractures using both a simple version (fracture displacement, number of parts) and an extensive version (individual topographic fracture type and morphology) of the MTM classification. Kappa-statistics were used to determine reliability....... RESULTS: An acceptable reliability was found for the simple version classifying fracture displacement and fractured main parts. Fair interobserver agreement was found for the extensive version with individual topographic fracture type and morphology. CONCLUSION: Although the MTM-classification covers...

  8. Classification of bacterial contamination using image processing and distributed computing.

    Science.gov (United States)

    Ahmed, W M; Bayraktar, B; Bhunia, A; Hirleman, E D; Robinson, J P; Rajwa, B

    2013-01-01

    Disease outbreaks due to contaminated food are a major concern not only for the food-processing industry but also for the public at large. Techniques for automated detection and classification of microorganisms can be a great help in preventing outbreaks and maintaining the safety of the nations food supply. Identification and classification of foodborne pathogens using colony scatter patterns is a promising new label-free technique that utilizes image-analysis and machine-learning tools. However, the feature-extraction tools employed for this approach are computationally complex, and choosing the right combination of scatter-related features requires extensive testing with different feature combinations. In the presented work we used computer clusters to speed up the feature-extraction process, which enables us to analyze the contribution of different scatter-based features to the overall classification accuracy. A set of 1000 scatter patterns representing ten different bacterial strains was used. Zernike and Chebyshev moments as well as Haralick texture features were computed from the available light-scatter patterns. The most promising features were first selected using Fishers discriminant analysis, and subsequently a support-vector-machine (SVM) classifier with a linear kernel was used. With extensive testing we were able to identify a small subset of features that produced the desired results in terms of classification accuracy and execution speed. The use of distributed computing for scatter-pattern analysis, feature extraction, and selection provides a feasible mechanism for large-scale deployment of a light scatter-based approach to bacterial classification.

  9. Influence of nuclei segmentation on breast cancer malignancy classification

    Science.gov (United States)

    Jelen, Lukasz; Fevens, Thomas; Krzyzak, Adam

    2009-02-01

    Breast Cancer is one of the most deadly cancers affecting middle-aged women. Accurate diagnosis and prognosis are crucial to reduce the high death rate. Nowadays there are numerous diagnostic tools for breast cancer diagnosis. In this paper we discuss a role of nuclear segmentation from fine needle aspiration biopsy (FNA) slides and its influence on malignancy classification. Classification of malignancy plays a very important role during the diagnosis process of breast cancer. Out of all cancer diagnostic tools, FNA slides provide the most valuable information about the cancer malignancy grade which helps to choose an appropriate treatment. This process involves assessing numerous nuclear features and therefore precise segmentation of nuclei is very important. In this work we compare three powerful segmentation approaches and test their impact on the classification of breast cancer malignancy. The studied approaches involve level set segmentation, fuzzy c-means segmentation and textural segmentation based on co-occurrence matrix. Segmented nuclei were used to extract nuclear features for malignancy classification. For classification purposes four different classifiers were trained and tested with previously extracted features. The compared classifiers are Multilayer Perceptron (MLP), Self-Organizing Maps (SOM), Principal Component-based Neural Network (PCA) and Support Vector Machines (SVM). The presented results show that level set segmentation yields the best results over the three compared approaches and leads to a good feature extraction with a lowest average error rate of 6.51% over four different classifiers. The best performance was recorded for multilayer perceptron with an error rate of 3.07% using fuzzy c-means segmentation.

  10. Definition and classification of epilepsy. Classification of epileptic seizures 2016

    Directory of Open Access Journals (Sweden)

    K. Yu. Mukhin

    2017-01-01

    Full Text Available Epilepsy is one of the most common neurological diseases, especially in childhood and adolescence. The incidence varies from 15 to 113 cases per 100 000 population with the maximum among children under 1 year old. The prevalence of epilepsy is high, ranging from 5 to 8 cases (in some regions – 10 cases per 1000 children under 15 years old. Classification of the disease has great importance for diagnosis, treatment and prognosis. The article presents a novel strategy for classification of epileptic seizures, developed in 2016. It contains a number of brand new concepts, including a very important one, saying that some seizures, previously considered as generalized or focal only, can be, in fact, both focal and generalized. They include tonic, atonic, myoclonic seizures and epileptic spasms. The term “secondarily generalized seizure” is replace by the term “bilateral tonic-clonic seizure” (as soon as it is not a separate type of epileptic seizures, and the term reflects the spread of discharge from any area of cerebral cortex and evolution of any types of focal seizures. International League Against Epilepsy recommends to abandon the term “pseudo-epileptic seizures” and replace it by the term “psychogenic non-epileptic seizures”. If a doctor is not sure that seizures have epileptic nature, the term “paroxysmal event” should be used without specifying the disease. The conception of childhood epileptic encephalopathies, developed within this novel classification project, is one of the most significant achievements, since in this case not only the seizures, but even epileptiform activity can induce severe disorders of higher mental functions. In addition to detailed description of the new strategy for classification of epileptic seizures, the article contains a comprehensive review of the existing principles of epilepsy and epileptic seizures classification.

  11. Semantic aspects of the International Classification of Functioning, Disability and Health: towards sharing knowledge and unifying information.

    Science.gov (United States)

    Andronache, Adrian Stefan; Simoncello, Andrea; Della Mea, Vincenzo; Daffara, Carlo; Francescutti, Carlo

    2012-02-01

    During the last decade, under the World Health Organization's direction, the International Classification of Functioning, Disability and Health (ICF) has become a reference tool for monitoring and developing various policies addressing people with disability. This article presents three steps to increase the semantic interoperability of ICF: first, the representation of ICF using ontology tools; second, the alignment to upper-level ontologies; and third, the use of these tools to implement semantic mappings between ICF and other tools, such as disability assessment instruments, health classifications, and at least partially formalized terminologies.

  12. The World Health Organization Classification of dontogenic Lesions: A Summary of the Changes of the 2017 (4th Edition

    Directory of Open Access Journals (Sweden)

    Merva SOLUK-TEKKEŞİN

    2018-01-01

    Full Text Available The 4th edition of the World Health Organization (WHO Classification of Head and Neck Tumors was published in January 2017. The edition serves to provide an updated classification scheme, and extended genetic and molecular data that are useful as diagnostic tools for the lesions of the head and neck region. This review focuses on the most current update of odontogenic cysts and tumors based on the 2017 WHO edition. The updated classification has some important differences from the 3rd edition (2005, including a new classification of odontogenic cysts, ‘reclassified’ odontogenic tumors, and some new entities.

  13. Classification of soil samples according to their geographic origin using gamma-ray spectrometry and principal component analysis

    International Nuclear Information System (INIS)

    Dragovic, Snezana; Onjia, Antonije

    2006-01-01

    A principal component analysis (PCA) was used for classification of soil samples from different locations in Serbia and Montenegro. Based on activities of radionuclides ( 226 Ra, 238 U, 235 U, 4 K, 134 Cs, 137 Cs, 232 Th and 7 Be) detected by gamma-ray spectrometry, the classification of soils according to their geographical origin was performed. Application of PCA to our experimental data resulted in satisfactory classification rate (86.0% correctly classified samples). The obtained results indicate that gamma-ray spectrometry in conjunction with PCA is a viable tool for soil classification

  14. Seizure classification in EEG signals utilizing Hilbert-Huang transform

    Directory of Open Access Journals (Sweden)

    Abdulhay Enas W

    2011-05-01

    Full Text Available Abstract Background Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG signals. Method Discrimination in this work is achieved by analyzing EEG signals obtained from freely accessible databases. MATLAB has been used to implement and test the proposed classification algorithm. The analysis in question presents a classification of normal and ictal activities using a feature relied on Hilbert-Huang Transform. Through this method, information related to the intrinsic functions contained in the EEG signal has been extracted to track the local amplitude and the frequency of the signal. Based on this local information, weighted frequencies are calculated and a comparison between ictal and seizure-free determinant intrinsic functions is then performed. Methods of comparison used are the t-test and the Euclidean clustering. Results The t-test results in a P-value Conclusion An original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper. The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly. Furthermore, the classification of mode mixing can be achieved using the extracted instantaneous information of every IMF, but it would be most likely a hard task if only the average value is used. Extra benefits of this proposed system include low cost, and ease of interface. All of that indicate the usefulness of the tool and its use as an efficient diagnostic tool.

  15. Seizure classification in EEG signals utilizing Hilbert-Huang transform.

    Science.gov (United States)

    Oweis, Rami J; Abdulhay, Enas W

    2011-05-24

    Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG) signals. Discrimination in this work is achieved by analyzing EEG signals obtained from freely accessible databases. MATLAB has been used to implement and test the proposed classification algorithm. The analysis in question presents a classification of normal and ictal activities using a feature relied on Hilbert-Huang Transform. Through this method, information related to the intrinsic functions contained in the EEG signal has been extracted to track the local amplitude and the frequency of the signal. Based on this local information, weighted frequencies are calculated and a comparison between ictal and seizure-free determinant intrinsic functions is then performed. Methods of comparison used are the t-test and the Euclidean clustering. The t-test results in a P-value with respect to its fast response and ease to use. An original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper. The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly. Furthermore, the classification of mode mixing can be achieved using the extracted instantaneous information of every IMF, but it would be most likely a hard task if only the average value is used. Extra benefits of this proposed system include low cost, and ease of interface. All of that indicate the usefulness of the tool and its use as an efficient diagnostic tool.

  16. Agriculture classification using POLSAR data

    DEFF Research Database (Denmark)

    Skriver, Henning; Dall, Jørgen; Ferro-Famil, Laurent

    2005-01-01

    of their components) show strongly preferred orientations, such as the stalks or ears of cereals. The importance of SAR polarimetry in crop classification arises principally because polarisation is sen-sitive to orientation. Hence it provides a means to distinguish crops with different canopy archi-tectures. Detailed...

  17. Urogenital tuberculosis: definition and classification.

    Science.gov (United States)

    Kulchavenya, Ekaterina

    2014-10-01

    To improve the approach to the diagnosis and management of urogenital tuberculosis (UGTB), we need clear and unique classification. UGTB remains an important problem, especially in developing countries, but it is often an overlooked disease. As with any other infection, UGTB should be cured by antibacterial therapy, but because of late diagnosis it may often require surgery. Scientific literature dedicated to this problem was critically analyzed and juxtaposed with the author's own more than 30 years' experience in tuberculosis urology. The conception, terms and definition were consolidated into one system; classification stage by stage as well as complications are presented. Classification of any disease includes dispersion on forms and stages and exact definitions for each stage. Clinical features and symptoms significantly vary between different forms and stages of UGTB. A simple diagnostic algorithm was constructed. UGTB is multivariant disease and a standard unified approach to it is impossible. Clear definition as well as unique classification are necessary for real estimation of epidemiology and the optimization of therapy. The term 'UGTB' has insufficient information in order to estimate therapy, surgery and prognosis, or to evaluate the epidemiology.

  18. Real time automatic scene classification

    NARCIS (Netherlands)

    Verbrugge, R.; Israël, Menno; Taatgen, N.; van den Broek, Egon; van der Putten, Peter; Schomaker, L.; den Uyl, Marten J.

    2004-01-01

    This work has been done as part of the EU VICAR (IST) project and the EU SCOFI project (IAP). The aim of the first project was to develop a real time video indexing classification annotation and retrieval system. For our systems, we have adapted the approach of Picard and Minka [3], who categorized

  19. Unsupervised classification of variable stars

    Science.gov (United States)

    Valenzuela, Lucas; Pichara, Karim

    2018-03-01

    During the past 10 years, a considerable amount of effort has been made to develop algorithms for automatic classification of variable stars. That has been primarily achieved by applying machine learning methods to photometric data sets where objects are represented as light curves. Classifiers require training sets to learn the underlying patterns that allow the separation among classes. Unfortunately, building training sets is an expensive process that demands a lot of human efforts. Every time data come from new surveys; the only available training instances are the ones that have a cross-match with previously labelled objects, consequently generating insufficient training sets compared with the large amounts of unlabelled sources. In this work, we present an algorithm that performs unsupervised classification of variable stars, relying only on the similarity among light curves. We tackle the unsupervised classification problem by proposing an untraditional approach. Instead of trying to match classes of stars with clusters found by a clustering algorithm, we propose a query-based method where astronomers can find groups of variable stars ranked by similarity. We also develop a fast similarity function specific for light curves, based on a novel data structure that allows scaling the search over the entire data set of unlabelled objects. Experiments show that our unsupervised model achieves high accuracy in the classification of different types of variable stars and that the proposed algorithm scales up to massive amounts of light curves.

  20. Automatic indexing, compiling and classification

    International Nuclear Information System (INIS)

    Andreewsky, Alexandre; Fluhr, Christian.

    1975-06-01

    A review of the principles of automatic indexing, is followed by a comparison and summing-up of work by the authors and by a Soviet staff from the Moscou INFORM-ELECTRO Institute. The mathematical and linguistic problems of the automatic building of thesaurus and automatic classification are examined [fr

  1. Aphasia Classification Using Neural Networks

    DEFF Research Database (Denmark)

    Axer, H.; Jantzen, Jan; Berks, G.

    2000-01-01

    A web-based software model (http://fuzzy.iau.dtu.dk/aphasia.nsf) was developed as an example for classification of aphasia using neural networks. Two multilayer perceptrons were used to classify the type of aphasia (Broca, Wernicke, anomic, global) according to the results in some subtests...

  2. Classification Accuracy Is Not Enough

    DEFF Research Database (Denmark)

    Sturm, Bob L.

    2013-01-01

    A recent review of the research literature evaluating music genre recognition (MGR) systems over the past two decades shows that most works (81\\%) measure the capacity of a system to recognize genre by its classification accuracy. We show here, by implementing and testing three categorically...

  3. Functions in Biological Kind Classification

    Science.gov (United States)

    Lombrozo, Tania; Rehder, Bob

    2012-01-01

    Biological traits that serve functions, such as a zebra's coloration (for camouflage) or a kangaroo's tail (for balance), seem to have a special role in conceptual representations for biological kinds. In five experiments, we investigate whether and why functional features are privileged in biological kind classification. Experiment 1…

  4. Is classification necessary after Google?

    DEFF Research Database (Denmark)

    Hjørland, Birger

    2012-01-01

    believe that the activity of “classification” is not worth the effort, as search engines can be improved without the heavy cost of providing metadata. Design/methodology/approach – The basic issue in classification is seen as providing criteria for deciding whether A should be classified as X...

  5. Data Augmentation for Plant Classification

    NARCIS (Netherlands)

    Pawara, Pornntiwa; Okafor, Emmanuel; Schomaker, Lambertus; Wiering, Marco

    2017-01-01

    Data augmentation plays a crucial role in increasing the number of training images, which often aids to improve classification performances of deep learning techniques for computer vision problems. In this paper, we employ the deep learning framework and determine the effects of several

  6. Climatic classification of the Karst

    International Nuclear Information System (INIS)

    Eslava Ramirez Jesus Antonio; Bahamon Ayala, Sandra Marcela; Lopez Romero Maria Ines

    2000-01-01

    Climate is one the main factors in forming or modifying Karsts, or its resulting forms. The determining climatic elements of Karst characteristics are humidity, air circulation and temperature. Many Karstic processes show characteristics corresponding to a given climate sequence. In the present article we discuss the relation between climate and Karst as well as a climate classification based on the structure of the Karsts

  7. 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.

  8. Crop Classification by Polarimetric SAR

    DEFF Research Database (Denmark)

    Skriver, Henning; Svendsen, Morten Thougaard; Nielsen, Flemming

    1999-01-01

    Polarimetric SAR-data of agricultural fields have been acquired by the Danish polarimetric L- and C-band SAR (EMISAR) during a number of missions at the Danish agricultural test site Foulum during 1995. The data are used to study the classification potential of polarimetric SAR data using...

  9. Correlation Dimension Estimation for Classification

    Czech Academy of Sciences Publication Activity Database

    Jiřina, Marcel; Jiřina jr., M.

    2006-01-01

    Roč. 1, č. 3 (2006), s. 547-557 ISSN 1895-8648 R&D Projects: GA MŠk(CZ) 1M0567 Institutional research plan: CEZ:AV0Z10300504 Keywords : correlation dimension * probability density estimation * classification * UCI MLR Subject RIV: BA - General Mathematics

  10. Whewell on classification and consilience.

    Science.gov (United States)

    Quinn, Aleta

    2017-08-01

    In this paper I sketch William Whewell's attempts to impose order on classificatory mineralogy, which was in Whewell's day (1794-1866) a confused science of uncertain prospects. Whewell argued that progress was impeded by the crude reductionist assumption that all macroproperties of crystals could be straightforwardly explained by reference to the crystals' chemical constituents. By comparison with biological classification, Whewell proposed methodological reforms that he claimed would lead to a natural classification of minerals, which in turn would support advances in causal understanding of the properties of minerals. Whewell's comparison to successful biological classification is particularly striking given that classificatory biologists did not share an understanding of the causal structure underlying the natural classification of life (the common descent with modification of all organisms). Whewell's key proposed methodological reform is consideration of multiple, distinct principles of classification. The most powerful evidence in support of a natural classificatory claim is the consilience of claims arrived at through distinct lines of reasoning, rooted in distinct conceptual approaches to the target objects. Mineralogists must consider not only elemental composition and chemical affinities, but also symmetry and polarity. Geometrical properties are central to what makes an individual mineral the type of mineral that it is. In Whewell's view, function and organization jointly define life, and so are the keys to understanding what makes an organism the type of organism that it is. I explain the relationship between Whewell's teleological account of life and his natural theology. I conclude with brief comments about the importance of Whewell's classificatory theory for the further development of his philosophy of science and in particular his account of consilience. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. Functional Basis of Microorganism Classification.

    Science.gov (United States)

    Zhu, Chengsheng; Delmont, Tom O; Vogel, Timothy M; Bromberg, Yana

    2015-08-01

    Correctly identifying nearest "neighbors" of a given microorganism is important in industrial and clinical applications where close relationships imply similar treatment. Microbial classification based on similarity of physiological and genetic organism traits (polyphasic similarity) is experimentally difficult and, arguably, subjective. Evolutionary relatedness, inferred from phylogenetic markers, facilitates classification but does not guarantee functional identity between members of the same taxon or lack of similarity between different taxa. Using over thirteen hundred sequenced bacterial genomes, we built a novel function-based microorganism classification scheme, functional-repertoire similarity-based organism network (FuSiON; flattened to fusion). Our scheme is phenetic, based on a network of quantitatively defined organism relationships across the known prokaryotic space. It correlates significantly with the current taxonomy, but the observed discrepancies reveal both (1) the inconsistency of functional diversity levels among different taxa and (2) an (unsurprising) bias towards prioritizing, for classification purposes, relatively minor traits of particular interest to humans. Our dynamic network-based organism classification is independent of the arbitrary pairwise organism similarity cut-offs traditionally applied to establish taxonomic identity. Instead, it reveals natural, functionally defined organism groupings and is thus robust in handling organism diversity. Additionally, fusion can use organism meta-data to highlight the specific environmental factors that drive microbial diversification. Our approach provides a complementary view to cladistic assignments and holds important clues for further exploration of microbial lifestyles. Fusion is a more practical fit for biomedical, industrial, and ecological applications, as many of these rely on understanding the functional capabilities of the microbes in their environment and are less concerned with

  12. Functional Basis of Microorganism Classification

    Science.gov (United States)

    Zhu, Chengsheng; Delmont, Tom O.; Vogel, Timothy M.; Bromberg, Yana

    2015-01-01

    Correctly identifying nearest “neighbors” of a given microorganism is important in industrial and clinical applications where close relationships imply similar treatment. Microbial classification based on similarity of physiological and genetic organism traits (polyphasic similarity) is experimentally difficult and, arguably, subjective. Evolutionary relatedness, inferred from phylogenetic markers, facilitates classification but does not guarantee functional identity between members of the same taxon or lack of similarity between different taxa. Using over thirteen hundred sequenced bacterial genomes, we built a novel function-based microorganism classification scheme, functional-repertoire similarity-based organism network (FuSiON; flattened to fusion). Our scheme is phenetic, based on a network of quantitatively defined organism relationships across the known prokaryotic space. It correlates significantly with the current taxonomy, but the observed discrepancies reveal both (1) the inconsistency of functional diversity levels among different taxa and (2) an (unsurprising) bias towards prioritizing, for classification purposes, relatively minor traits of particular interest to humans. Our dynamic network-based organism classification is independent of the arbitrary pairwise organism similarity cut-offs traditionally applied to establish taxonomic identity. Instead, it reveals natural, functionally defined organism groupings and is thus robust in handling organism diversity. Additionally, fusion can use organism meta-data to highlight the specific environmental factors that drive microbial diversification. Our approach provides a complementary view to cladistic assignments and holds important clues for further exploration of microbial lifestyles. Fusion is a more practical fit for biomedical, industrial, and ecological applications, as many of these rely on understanding the functional capabilities of the microbes in their environment and are less concerned

  13. New tools for evaluating LQAS survey designs

    OpenAIRE

    Hund, Lauren

    2014-01-01

    Lot Quality Assurance Sampling (LQAS) surveys have become increasingly popular in global health care applications. Incorporating Bayesian ideas into LQAS survey design, such as using reasonable prior beliefs about the distribution of an indicator, can improve the selection of design parameters and decision rules. In this paper, a joint frequentist and Bayesian framework is proposed for evaluating LQAS classification accuracy and informing survey design parameters. Simple software tools are pr...

  14. A novel, fast and efficient single-sensor automatic sleep-stage classification based on complementary cross-frequency coupling estimates.

    Science.gov (United States)

    Dimitriadis, Stavros I; Salis, Christos; Linden, David

    2018-04-01

    Limitations of the manual scoring of polysomnograms, which include data from electroencephalogram (EEG), electro-oculogram (EOG), electrocardiogram (ECG) and electromyogram (EMG) channels have long been recognized. Manual staging is resource intensive and time consuming, and thus considerable effort must be spent to ensure inter-rater reliability. As a result, there is a great interest in techniques based on signal processing and machine learning for a completely Automatic Sleep Stage Classification (ASSC). In this paper, we present a single-EEG-sensor ASSC technique based on the dynamic reconfiguration of different aspects of cross-frequency coupling (CFC) estimated between predefined frequency pairs over 5 s epoch lengths. The proposed analytic scheme is demonstrated using the PhysioNet Sleep European Data Format (EDF) Database with repeat recordings from 20 healthy young adults. We validate our methodology in a second sleep dataset. We achieved very high classification sensitivity, specificity and accuracy of 96.2 ± 2.2%, 94.2 ± 2.3%, and 94.4 ± 2.2% across 20 folds, respectively, and also a high mean F1 score (92%, range 90-94%) when a multi-class Naive Bayes classifier was applied. High classification performance has been achieved also in the second sleep dataset. Our method outperformed the accuracy of previous studies not only on different datasets but also on the same database. Single-sensor ASSC makes the entire methodology appropriate for longitudinal monitoring using wearable EEG in real-world and laboratory-oriented environments. Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.

  15. An Efficient Optimization Method for Solving Unsupervised Data Classification Problems

    Directory of Open Access Journals (Sweden)

    Parvaneh Shabanzadeh

    2015-01-01

    Full Text Available Unsupervised data classification (or clustering analysis is one of the most useful tools and a descriptive task in data mining that seeks to classify homogeneous groups of objects based on similarity and is used in many medical disciplines and various applications. In general, there is no single algorithm that is suitable for all types of data, conditions, and applications. Each algorithm has its own advantages, limitations, and deficiencies. Hence, research for novel and effective approaches for unsupervised data classification is still active. In this paper a heuristic algorithm, Biogeography-Based Optimization (BBO algorithm, was adapted for data clustering problems by modifying the main operators of BBO algorithm, which is inspired from the natural biogeography distribution of different species. Similar to other population-based algorithms, BBO algorithm starts with an initial population of candidate solutions to an optimization problem and an objective function that is calculated for them. To evaluate the performance of the proposed algorithm assessment was carried on six medical and real life datasets and was compared with eight well known and recent unsupervised data classification algorithms. Numerical results demonstrate that the proposed evolutionary optimization algorithm is efficient for unsupervised data classification.

  16. Adaptive phase k-means algorithm for waveform classification

    Science.gov (United States)

    Song, Chengyun; Liu, Zhining; Wang, Yaojun; Xu, Feng; Li, Xingming; Hu, Guangmin

    2018-01-01

    Waveform classification is a powerful technique for seismic facies analysis that describes the heterogeneity and compartments within a reservoir. Horizon interpretation is a critical step in waveform classification. However, the horizon often produces inconsistent waveform phase, and thus results in an unsatisfied classification. To alleviate this problem, an adaptive phase waveform classification method called the adaptive phase k-means is introduced in this paper. Our method improves the traditional k-means algorithm using an adaptive phase distance for waveform similarity measure. The proposed distance is a measure with variable phases as it moves from sample to sample along the traces. Model traces are also updated with the best phase interference in the iterative process. Therefore, our method is robust to phase variations caused by the interpretation horizon. We tested the effectiveness of our algorithm by applying it to synthetic and real data. The satisfactory results reveal that the proposed method tolerates certain waveform phase variation and is a good tool for seismic facies analysis.

  17. Screening and classification of ceramic powders

    Science.gov (United States)

    Miwa, S.

    1983-01-01

    A summary is given of the classification technology of ceramic powders. Advantages and disadvantages of the wet and dry screening and classification methods are discussed. Improvements of wind force screening devices are described.

  18. 5 CFR 1312.3 - Classification requirements.

    Science.gov (United States)

    2010-01-01

    ..., DOWNGRADING, DECLASSIFICATION AND SAFEGUARDING OF NATIONAL SECURITY INFORMATION Classification and Declassification of National Security Information § 1312.3 Classification requirements. United States citizens must...; (5) Scientific, technological, or economic matters relating to the national security; (6) United...

  19. 14 CFR 1203.412 - Classification guides.

    Science.gov (United States)

    2010-01-01

    ... of the classification designations (i.e., Top Secret, Secret or Confidential) apply to the identified... writing by an official with original Top Secret classification authority; the identity of the official...

  20. Classification guide: Paralympic Games London 2012

    OpenAIRE

    2013-01-01

    The London 2012 Paralympic Games Classification Guide is designed to provide National Paralympic Committees (NPCs) and International Paralympic Sport Federations (IPSFs) with information about the classification policies and procedures that will apply to the London 2012 Paralympic Games.

  1. Classification guide: Sochi 2014 Paralympic Winter Games

    OpenAIRE

    2014-01-01

    The Sochi 2014 Paralympic Winter Games classification guide is designed to provide National Paralympic Committees (NPCs) and International Federations (IFs) with information about the classification policies and procedures that will apply to the Sochi 2014 Paralympic Winter Games.

  2. New tools for evaluating LQAS survey designs.

    Science.gov (United States)

    Hund, Lauren

    2014-02-15

    Lot Quality Assurance Sampling (LQAS) surveys have become increasingly popular in global health care applications. Incorporating Bayesian ideas into LQAS survey design, such as using reasonable prior beliefs about the distribution of an indicator, can improve the selection of design parameters and decision rules. In this paper, a joint frequentist and Bayesian framework is proposed for evaluating LQAS classification accuracy and informing survey design parameters. Simple software tools are provided for calculating the positive and negative predictive value of a design with respect to an underlying coverage distribution and the selected design parameters. These tools are illustrated using a data example from two consecutive LQAS surveys measuring Oral Rehydration Solution (ORS) preparation. Using the survey tools, the dependence of classification accuracy on benchmark selection and the width of the 'grey region' are clarified in the context of ORS preparation across seven supervision areas. Following the completion of an LQAS survey, estimation of the distribution of coverage across areas facilitates quantifying classification accuracy and can help guide intervention decisions.

  3. Tool path in torus tool CNC machining

    Directory of Open Access Journals (Sweden)

    XU Ying

    2016-10-01

    Full Text Available This paper is about tool path in torus tool CNC machining.The mathematical model of torus tool is established.The tool path planning algorithm is determined through calculation of the cutter location,boundary discretization,calculation of adjacent tool path and so on,according to the conversion formula,the cutter contact point will be converted to the cutter location point and then these points fit a toolpath.Lastly,the path planning algorithm is implemented by using Matlab programming.The cutter location points for torus tool are calculated by Matlab,and then fit these points to a toolpath.While using UG software,another tool path of free surface is simulated of the same data.It is drew compared the two tool paths that using torus tool is more efficient.

  4. Improvement of Classification of Enterprise Circulating Funds

    OpenAIRE

    Rohanova Hanna O.

    2014-01-01

    The goal of the article lies in revelation of possibilities of increase of efficiency of managing enterprise circulating funds by means of improvement of their classification features. Having analysed approaches of many economists to classification of enterprise circulating funds, systemised and supplementing them, the article offers grouping classification features of enterprise circulating funds. In the result of the study the article offers an expanded classification of circulating funds, ...

  5. A Classification Scheme for Glaciological AVA Responses

    Science.gov (United States)

    Booth, A.; Emir, E.

    2014-12-01

    A classification scheme is proposed for amplitude vs. angle (AVA) responses as an aid to the interpretation of seismic reflectivity in glaciological research campaigns. AVA responses are a powerful tool in characterising the material properties of glacier ice and its substrate. However, before interpreting AVA data, careful true amplitude processing is required to constrain basal reflectivity and compensate amplitude decay mechanisms, including anelastic attenuation and spherical divergence. These fundamental processing steps can be difficult to design in cases of noisy data, e.g. where a target reflection is contaminated by surface wave energy (in the case of shallow glaciers) or by energy reflected from out of the survey plane. AVA methods have equally powerful usage in estimating the fluid fill of potential hydrocarbon reservoirs. However, such applications seldom use true amplitude data and instead consider qualitative AVA responses using a well-defined classification scheme. Such schemes are often defined in terms of the characteristics of best-fit responses to the observed reflectivity, e.g. the intercept (I) and gradient (G) of a linear approximation to the AVA data. The position of the response on a cross-plot of I and G then offers a diagnostic attribute for certain fluid types. We investigate the advantages in glaciology of emulating this practice, and develop a cross-plot based on the 3-term Shuey AVA approximation (using I, G, and a curvature term C). Model AVA curves define a clear lithification trend: AVA responses to stiff (lithified) substrates fall discretely into one quadrant of the cross-plot, with positive I and negative G, whereas those to fluid-rich substrates plot diagonally opposite (in the negative I and positive G quadrant). The remaining quadrants are unoccupied by plausible single-layer responses and may therefore be diagnostic of complex thin-layer reflectivity, and the magnitude and polarity of the C term serves as a further indicator

  6. An interobserver reliability comparison between the Orthopaedic Trauma Association's open fracture classification and the Gustilo and Anderson classification.

    Science.gov (United States)

    Ghoshal, A; Enninghorst, N; Sisak, K; Balogh, Z J

    2018-02-01

    To evaluate interobserver reliability of the Orthopaedic Trauma Association's open fracture classification system (OTA-OFC). Patients of any age with a first presentation of an open long bone fracture were included. Standard radiographs, wound photographs, and a short clinical description were given to eight orthopaedic surgeons, who independently evaluated the injury using both the Gustilo and Anderson (GA) and OTA-OFC classifications. The responses were compared for variability using Cohen's kappa. The overall interobserver agreement was ĸ = 0.44 for the GA classification and ĸ = 0.49 for OTA-OFC, which reflects moderate agreement (0.41 to 0.60) for both classifications. The agreement in the five categories of OTA-OFC was: for skin, ĸ = 0.55 (moderate); for muscle, ĸ = 0.44 (moderate); for arterial injury, ĸ = 0.74 (substantial); for contamination, ĸ = 0.35 (fair); and for bone loss, ĸ = 0.41 (moderate). Although the OTA-OFC, with similar interobserver agreement to GA, offers a more detailed description of open fractures, further development may be needed to make it a reliable and robust tool. Cite this article: Bone Joint J 2018;100-B:242-6. ©2018 The British Editorial Society of Bone & Joint Surgery.

  7. 10 CFR 61.55 - Waste classification.

    Science.gov (United States)

    2010-01-01

    ... REGULATORY COMMISSION (CONTINUED) LICENSING REQUIREMENTS FOR LAND DISPOSAL OF RADIOACTIVE WASTE Technical Requirements for Land Disposal Facilities § 61.55 Waste classification. (a) Classification of waste for near surface disposal—(1) Considerations. Determination of the classification of radioactive waste involves two...

  8. 6 CFR 7.26 - Derivative classification.

    Science.gov (United States)

    2010-01-01

    ... 6 Domestic Security 1 2010-01-01 2010-01-01 false Derivative classification. 7.26 Section 7.26 Domestic Security DEPARTMENT OF HOMELAND SECURITY, OFFICE OF THE SECRETARY CLASSIFIED NATIONAL SECURITY INFORMATION Classified Information § 7.26 Derivative classification. (a) Derivative classification is defined...

  9. 22 CFR 9.6 - Derivative classification.

    Science.gov (United States)

    2010-04-01

    ... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Derivative classification. 9.6 Section 9.6 Foreign Relations DEPARTMENT OF STATE GENERAL SECURITY INFORMATION REGULATIONS § 9.6 Derivative classification. (a) Definition. Derivative classification is the incorporating, paraphrasing, restating or...

  10. 46 CFR 76.50-5 - Classification.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 3 2010-10-01 2010-10-01 false Classification. 76.50-5 Section 76.50-5 Shipping COAST... Classification. (a) Hand portable fire extinguishers and semiportable fire extinguishing systems shall be... extinguishing systems are set forth in table 76.50-5(c). Table 76.50-5(c) Classification Type Size Soda acid and...

  11. 12 CFR 560.160 - Asset classification.

    Science.gov (United States)

    2010-01-01

    ... 12 Banks and Banking 5 2010-01-01 2010-01-01 false Asset classification. 560.160 Section 560.160... Lending and Investment Provisions Applicable to all Savings Associations § 560.160 Asset classification... consistent with, or reconcilable to, the asset classification system used by OTS in its Thrift Activities...

  12. 14 CFR 298.3 - Classification.

    Science.gov (United States)

    2010-01-01

    ... 14 Aeronautics and Space 4 2010-01-01 2010-01-01 false Classification. 298.3 Section 298.3... REGULATIONS EXEMPTIONS FOR AIR TAXI AND COMMUTER AIR CARRIER OPERATIONS General § 298.3 Classification. (a) There is hereby established a classification of air carriers, designated as “air taxi operators,” which...

  13. 6 CFR 7.30 - Classification challenges.

    Science.gov (United States)

    2010-01-01

    ... 6 Domestic Security 1 2010-01-01 2010-01-01 false Classification challenges. 7.30 Section 7.30... INFORMATION Classified Information § 7.30 Classification challenges. (a) Authorized holders of information... classified are encouraged and expected to challenge the classification status of that information pursuant to...

  14. 14 CFR 1203.701 - Classification.

    Science.gov (United States)

    2010-01-01

    ... 14 Aeronautics and Space 5 2010-01-01 2010-01-01 false Classification. 1203.701 Section 1203.701... Government Information § 1203.701 Classification. (a) Foreign government information that is classified by a foreign entity shall either retain its original classification designation or be marked with a United...

  15. 32 CFR 1602.7 - Classification.

    Science.gov (United States)

    2010-07-01

    ... 32 National Defense 6 2010-07-01 2010-07-01 false Classification. 1602.7 Section 1602.7 National Defense Other Regulations Relating to National Defense SELECTIVE SERVICE SYSTEM DEFINITIONS § 1602.7 Classification. Classification is the exercise of the power to determine claims or questions with respect to...

  16. 32 CFR 644.426 - Classification.

    Science.gov (United States)

    2010-07-01

    ... 32 National Defense 4 2010-07-01 2010-07-01 true Classification. 644.426 Section 644.426 National... HANDBOOK Disposal Disposal of Fee-Owned Real Property and Easement Interests § 644.426 Classification... required by the special acts, classification will be coordinated with the interested Federal agency. The...

  17. 46 CFR Sec. 18 - Group classification.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 8 2010-10-01 2010-10-01 false Group classification. Sec. 18 Section 18 Shipping... Sec. 18 Group classification. In the preparation of specifications, Job Orders, Supplemental Job... inserted thereon: Number Classification 41 Maintenance Repairs (deck, engine and stewards department...

  18. 10 CFR 1045.37 - Classification guides.

    Science.gov (United States)

    2010-01-01

    ... 10 Energy 4 2010-01-01 2010-01-01 false Classification guides. 1045.37 Section 1045.37 Energy DEPARTMENT OF ENERGY (GENERAL PROVISIONS) NUCLEAR CLASSIFICATION AND DECLASSIFICATION Generation and Review of Documents Containing Restricted Data and Formerly Restricted Data § 1045.37 Classification guides...

  19. 46 CFR 193.50-5 - Classification.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 7 2010-10-01 2010-10-01 false Classification. 193.50-5 Section 193.50-5 Shipping COAST... Details § 193.50-5 Classification. (a) Hand portable fire extinguishers and semiportable fire...) Classification Type Size Soda-acid and water, gals. Foam, gals. Carbon dioxide, lbs. Dry chemical, lbs. A II 21/2...

  20. Border Lakes land-cover classification

    Science.gov (United States)

    Marvin Bauer; Brian Loeffelholz; Doug. Shinneman

    2009-01-01

    This document contains metadata and description of land-cover classification of approximately 5.1 million acres of land bordering Minnesota, U.S.A. and Ontario, Canada. The classification focused on the separation and identification of specific forest-cover types. Some separation of the nonforest classes also was performed. The classification was derived from multi-...

  1. 22 CFR 9a.4 - Classification.

    Science.gov (United States)

    2010-04-01

    ... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Classification. 9a.4 Section 9a.4 Foreign... ENERGY PROGRAMS; RELATED MATERIAL § 9a.4 Classification. (a) Section 1 of E.O. 11932, August 4, 1976.... If the officer determines that the information or material warrants classification, he shall assign...

  2. 75 FR 10529 - Mail Classification Change

    Science.gov (United States)

    2010-03-08

    ... POSTAL REGULATORY COMMISSION [Docket Nos. MC2010-19; Order No. 415] Mail Classification Change...-filed Postal Service request to make a minor modification to the Mail Classification Schedule. The.... concerning a change in classification which reflects a change in terminology from Bulk Mailing Center (BMC...

  3. 7 CFR 51.1903 - Size classification.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 2 2010-01-01 2010-01-01 false Size classification. 51.1903 Section 51.1903... STANDARDS) United States Consumer Standards for Fresh Tomatoes Size and Maturity Classification § 51.1903 Size classification. The following terms may be used for describing the size of the tomatoes in any lot...

  4. 33 CFR 154.1216 - Facility classification.

    Science.gov (United States)

    2010-07-01

    ... 33 Navigation and Navigable Waters 2 2010-07-01 2010-07-01 false Facility classification. 154.1216... Vegetable Oils Facilities § 154.1216 Facility classification. (a) The Coast Guard classifies facilities that... classification of a facility that handles, stores, or transports animal fats or vegetable oils. The COTP may...

  5. 7 CFR 1794.31 - Classification.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 12 2010-01-01 2010-01-01 false Classification. 1794.31 Section 1794.31 Agriculture... Classification. (a) Electric and telecommunications programs. RUS will normally determine the proper environmental classification of projects based on its evaluation of the project description set forth in the...

  6. 76 FR 47614 - Mail Classification Change

    Science.gov (United States)

    2011-08-05

    ... POSTAL REGULATORY COMMISSION [Docket No. MC2011-27; Order No. 785] Mail Classification Change...-filed Postal Service request for a change in classification to the ``Reply Rides Free'' program. The... Service filed a notice of classification change pursuant to 39 CFR 3020.90 and 3020.91 concerning the...

  7. 32 CFR 1602.13 - Judgmental Classification.

    Science.gov (United States)

    2010-07-01

    ... 32 National Defense 6 2010-07-01 2010-07-01 false Judgmental Classification. 1602.13 Section 1602.13 National Defense Other Regulations Relating to National Defense SELECTIVE SERVICE SYSTEM DEFINITIONS § 1602.13 Judgmental Classification. A classification action relating to a registrant's claim for...

  8. 7 CFR 51.1904 - Maturity classification.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 2 2010-01-01 2010-01-01 false Maturity classification. 51.1904 Section 51.1904... STANDARDS) United States Consumer Standards for Fresh Tomatoes Size and Maturity Classification § 51.1904 Maturity classification. Tomatoes which are characteristically red when ripe, but are not overripe or soft...

  9. Pattern Classification with Memristive Crossbar Circuits

    Science.gov (United States)

    2016-03-31

    Pattern Classification with Memristive Crossbar Circuits Dmitri B. Strukov Department of Electrical and Computer Engineering Department UC Santa...pattern classification ; deep learning; convolutional neural network networks. Introduction Deep-learning convolutional neural networks (DLCNN), which...the best classification performances on a variety of benchmark tasks [1]. The major challenge in building fast and energy- efficient networks of this

  10. 46 CFR 132.210 - Classification.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 4 2010-10-01 2010-10-01 false Classification. 132.210 Section 132.210 Shipping COAST... Portable and Semiportable Fire Extinguishers § 132.210 Classification. (a) Each portable fire extinguisher... Classification Type Size Halon 1211, 1301, and 1211-1301 mixtures kgs. (lbs.) Foam, liters (gallons) Carbon...

  11. 32 CFR 2400.34 - Classification.

    Science.gov (United States)

    2010-07-01

    ... 32 National Defense 6 2010-07-01 2010-07-01 false Classification. 2400.34 Section 2400.34 National... Government Information § 2400.34 Classification. (a) Foreign government information classified by a foreign government or international organization of governments shall retain its original classification designation...

  12. 7 CFR 51.1402 - Size classification.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 2 2010-01-01 2010-01-01 false Size classification. 51.1402 Section 51.1402... STANDARDS) United States Standards for Grades of Pecans in the Shell 1 Size Classification § 51.1402 Size classification. Size of pecans may be specified in connection with the grade in accordance with one of the...

  13. Maxillectomy defects: a suggested classification scheme.

    Science.gov (United States)

    Akinmoladun, V I; Dosumu, O O; Olusanya, A A; Ikusika, O F

    2013-06-01

    The term "maxillectomy" has been used to describe a variety of surgical procedures for a spectrum of diseases involving a diverse anatomical site. Hence, classifications of maxillectomy defects have often made communication difficult. This article highlights this problem, emphasises the need for a uniform system of classification and suggests a classification system which is simple and comprehensive. Articles related to this subject, especially those with specified classifications of maxillary surgical defects were sourced from the internet through Google, Scopus and PubMed using the search terms maxillectomy defects classification. A manual search through available literature was also done. The review of the materials revealed many classifications and modifications of classifications from the descriptive, reconstructive and prosthodontic perspectives. No globally acceptable classification exists among practitioners involved in the management of diseases in the mid-facial region. There were over 14 classifications of maxillary defects found in the English literature. Attempts made to address the inadequacies of previous classifications have tended to result in cumbersome and relatively complex classifications. A single classification that is based on both surgical and prosthetic considerations is most desirable and is hereby proposed.

  14. Angle′s Molar Classification Revisited

    Directory of Open Access Journals (Sweden)

    Devanshi Yadav

    2014-01-01

    Results: Of the 500 pretreatment study casts assessed 52.4% were definitive Class I, 23.6% were Class II, 2.6% were Class III and the ambiguous cases were 21%. These could be easily classified with our method of classification. Conclusion: This improvised classification technique will help orthodontists in making classification of malocclusion accurate and simple.

  15. Classification of high resolution satellite images

    OpenAIRE

    Karlsson, Anders

    2003-01-01

    In this thesis the Support Vector Machine (SVM)is applied on classification of high resolution satellite images. Sveral different measures for classification, including texture mesasures, 1st order statistics, and simple contextual information were evaluated. Additionnally, the image was segmented, using an enhanced watershed method, in order to improve the classification accuracy.

  16. The Classification of Romanian High-Schools

    Science.gov (United States)

    Ivan, Ion; Milodin, Daniel; Naie, Lucian

    2006-01-01

    The article tries to tackle the issue of high-schools classification from one city, district or from Romania. The classification criteria are presented. The National Database of Education is also presented and the application of criteria is illustrated. An algorithm for high-school multi-rang classification is proposed in order to build classes of…

  17. Hydropedological insights when considering catchment classification

    NARCIS (Netherlands)

    Bouma, J.; Droogers, P.; Sonneveld, M.P.W.; Ritsema, C.J.; Hunink, J.E.; Immerzeel, W.W.; Kauffman, S.

    2011-01-01

    Soil classification systems are analysed to explore the potential of developing classification systems for catchments. Soil classifications are useful to create systematic order in the overwhelming quantity of different soils in the world and to extrapolate data available for a given soil type to

  18. 28 CFR 524.73 - Classification procedures.

    Science.gov (United States)

    2010-07-01

    ... 28 Judicial Administration 2 2010-07-01 2010-07-01 false Classification procedures. 524.73 Section 524.73 Judicial Administration BUREAU OF PRISONS, DEPARTMENT OF JUSTICE INMATE ADMISSION, CLASSIFICATION, AND TRANSFER CLASSIFICATION OF INMATES Central Inmate Monitoring (CIM) System § 524.73...

  19. 22 CFR 9.4 - Original classification.

    Science.gov (United States)

    2010-04-01

    ... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Original classification. 9.4 Section 9.4 Foreign Relations DEPARTMENT OF STATE GENERAL SECURITY INFORMATION REGULATIONS § 9.4 Original classification. (a) Definition. Original classification is the initial determination that certain information...

  20. 5 CFR 2500.3 - Original classification.

    Science.gov (United States)

    2010-01-01

    ... 5 Administrative Personnel 3 2010-01-01 2010-01-01 false Original classification. 2500.3 Section... SECURITY REGULATION § 2500.3 Original classification. No one in the Office of Administration has been granted authority for original classification of information. ...