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Cost effective depression patient thought record categorization via self-taught learning

Cost effective depression patient thought record categorizat...

引用

4th ACM international conference on PErvasive Technologies Related to Assistive Environments, PETRA 2011

作者： Wang, Hua Huang, Heng Lopez, Molly Makedon, Fillia Department of Computer Science and Engineering University of Texas Arlington TX United States Department of Psychology University of Texas Arlington TX United States School of Social Work University of Texas Austin TX United States

ISBN: (纸本)9781450307727

Automatic patient though record categorization (TR) is important in Cognitive Behavior therapy (CBT), which is an useful augmentation of standard clinic treatment for Major Depressive Disorder (MDD). Because both collection and labeling of TR data are expensive, it is cost prohibitive to require a large amount of TR data, as well as their corresponding category labels, to train a classification model with high classification accuracy. As in practice we only have very limited amount of labeled and unlabeled training TR data, traditional semi-supervised learning methods and transfer learning methods, which are the most commonly used strategies to deal with the lack of training data in statistical learning, can not work well in the task of automatic TR categorization. With the recognition of these challenges, in this paper we propose to approach the TR categorization problem from a new perspective via self-taught learning, an emerging topic in machine learning. Self-taught learning is a special type of transfer learning, instead of requiring labeled data from an auxiliary domain that are relevant to the classification task of interest as in traditional transfer learning methods, it learns the inherent structures of the auxiliary data and does not require their labels. Consequently, we may learn a classifier using the limited amount of labeled TR texts to achieve decent classification accuracy, with the assistance from the large amount text data obtained from some cheap, or even no-cost, resources. that is, a cost effective TR categorization system can be built up, which is of particular use in practical diagnosis and training of new therapists. We demonstrate the proposed method in the task of classifying the real depression homework texts, where promising experimental results validate the effectiveness of our new method. © 2011 ACM.

关键词： Cost effectiveness

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Construction and analysis of enzyme centric network of A. thaliana using graph theory

Construction and analysis of enzyme centric network of A. th...

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international Workshop on Soft Computing Applications and Knowledge Discovery 2011, SCAKD 2011 - In Conjunction with the 4th international conference on pattern recognition and machine Intelligence, PReMI 2011

作者： Viswanathan, Kasthuribai Parekh, Nita Center for Computational Natural Science and Bioinformatics International Institute of Information Technology Hyderabad - 500032 India

Graph comparisons, quantitative characterizations, computation of topological indices, clustering and partitioning are some of the major computations of graph that have yielded valuable results in various disciplines. Motivated by the potential benefits of graph theory application on biological data, we discuss the reconstruction and analysis of enzyme centric network of Arabidopsis thaliana using graph theory concepts. We had earlier constructed the metabolite network of Arabidopsis thaliana and witnessed the scale free and small world nature of the network. Compared to metabolites, the enzymes are more conserved in and across many pathways. So the aim of constructing the enzyme centric network is to see if the network follows similar network properties of the metabolite network and to look for additional details that a metabolite network cannot reveal. the enzyme flat file from KEGG FTP is used as the data set for the reconstruction of the enzyme centric network. We examined the network to find the relationship between topological connections among enzymes and their functions during evolution. the enzyme sequences of high degree and high betweenness enzymes belonged to ancient fold class and ancestry value showing evidence that they evolved very slowly.

关键词： Graph theory

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An Intrusion Detection System Using Decreasing Support

An Intrusion Detection System Using Decreasing Support

引用

2011 4th IEEE international conference on Computer Science and Information Technology(ICCSIT 2011)

作者： LEI LI DONG-MEI CHONG ZHI-HUI ZHU Pattern Recognition and Intelligence System College of AutomationNanjing University of Post and Telecommunications

ISBN: (纸本)9781612848334

With the rapid development of computer network technology,the need for a credible,stable and secure network platform is *** Intrusion Detection System,as the main security defending technique,is wildly used against *** mining has been extensively applied in network intrusion detection and prevention systems by discovering user behavior patterns from the network traffic *** of association rules in data mining plays an important role for intrusion *** the classical Apriori algorithm with bottleneck of frequent itemsets mining,an Exponential-Length-Decreasing Support to detect intrusion based on data mining is *** results show that the method is effective.

关键词： Intrusion Detection Exponential-Length-Decreasing Support frequent itemsets Association Rules data mining

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A systematic approach to adaptive activity modeling and discovery in smart homes

A systematic approach to adaptive activity modeling and disc...

引用

international conference on BioMedical Engineering and Informatics (BMEI)

作者： Liming Chen George Okeyo Hui Wang Roy Sterritt Chris Nugent Computer Science Research Institute School of Computing and Mathematics University of Ulster Newtownabbey UK

Activity modelling and discovery plays a critical role in smart home based assisted living. Existing approaches to pattern recognition using data-intensive analysis suffers from various drawbacks. To address these shortcomings, this paper introduces a novel ontology-based approach to activity modelling, activity discovery and evolution. In this approach, activity modelling is undertaken through ontological engineering by leveraging domain knowledge and heuristics. the generated activity models evolve from the initial “seed” activity models through continuous activity discovery and learning. Activity discovery is performed through ontological reasoning. the paper describes the approach in the context of smart home with special emphases placed on activity discovery algorithms and evolution mechanism. the approach has been implemented in a feature-rich assistive living system in which new daily activities can be detected and further used to evolve the underlying activity models.

关键词： Ontologies Adaptation models pattern recognition Cognition Smart homes Humans

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A Cloud-Based Accessible Architecture for Large-Scale ADL Analysis Services

A Cloud-Based Accessible Architecture for Large-Scale ADL An...

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IEEE international conference on Cloud Computing, CLOUD

作者： Yu-Chiao Huang Yu-Chieh Ho Ching-Hu Lu Li-Chen Fu Department of Computer Science & Information National Taiwan University Taipei Taiwan

Recognizing Activities of Daily Living (ADL) plays an important role in healthcare. However, it is often impractical and sometimes impossible for a person to collect those useful data manually, not to mention constant long-term data maintenance and analysis. To address the above-mentioned challenges, we propose an architecture, in which many health-care applications and services can easily build upon, for collective long-term ADL pattern analysis that leverages several prominent advantages inherent in cloud computing. the core of the proposed infrastructure includes a module to perform MapReduce-assisted Bayesian activity recognition based on all collected ADL data. Better yet, the resultant data analysis can be delivered as a service from a service station which serves as a readily accessible interface to 3 rd party service providers and end-users. For the evaluation of the proposed architecture, a simulation of persuasive health engagement is presented and discussed as one potential application.

关键词： Computer architecture Medical services Sensors data mining Cloud computing data analysis Engines

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TECS: A Web Text Extraction Tool Based on Semantic Similarity Calculation

TECS: A Web Text Extraction Tool Based on Semantic Similarit...

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international Workshop on Intelligent Networks and Intelligent Systems (ICINIS)

作者： Weng Yu Lirong Qiu School of Information and Engineering Minzu University of China Beijing China

the knowledge-based information extraction approach has been proved effectively in the recognition and classification of Web texts. However, manual creation of knowledge base is time consuming and error prone, even for a small application domain. For improving the automatic construction ability of knowledge base in the process of IE, this paper presents a web text extraction tool based on case similarity calculation (named TECS). TECS facilitates the process of constructing case knowledge base and strengthens the knowledge representation ability of single case by calculating case similarity and merging the cases which have similar semantic relationships.

关键词： Semantics data mining Knowledge based systems pattern matching Knowledge representation Educational institutions Resource description framework

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Electromygraphy (EMG) signal based hand gesture recognition using artificial neural network (ANN)

Electromygraphy (EMG) signal based hand gesture recognition ...

引用

international conference on Mechatronics (ICOM)

作者： Md. Rezwanul Ahsan Muhammad Ibn Ibrahimy Othman O. Khalifa Electrical & Computer Engineering Department International Islamic University Malaysia Kuala Lumpur Malaysia

Electromyography (EMG) signal is a measure of muscles' electrical activity and usually represented as a function of time, defined in terms of amplitude, frequency and phase. this biosignal can be employed in various applications including diagnoses of neuromuscular diseases, controlling assistive devices like prosthetic/orthotic devices, controlling machines, robots, computer etc. EMG signal based reliable and efficient hand gesture identification can help to develop good human computer interface which in turn will increase the quality of life of the disabled or aged people. the purpose of this paper is to describe the process of detecting different predefined hand gestures (left, right, up and down) using artificial neural network (ANN). ANNs are particularly useful for complex pattern recognition and classification tasks. the capability of learning from examples, the ability to reproduce arbitrary non-linear functions of input, and the highly parallel and regular structure of ANNs make them especially suitable for pattern recognition tasks. the EMG pattern signatures are extracted from the signals for each movement and then ANN utilized to classify the EMG signals based on features. A back-propagation (BP) network with Levenberg-Marquardt training algorithm has been used for the detection of gesture. the conventional and most effective time and time-frequency based features (namely MAV, RMS, VAR, SD, ZC, SSC and WL) have been chosen to train the neural network.

关键词： Electromyography Artificial neural networks Feature extraction Training Noise Support vector machine classification Neurons

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A Willing Events Identification Method

A Willing Events Identification Method

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IEEE/ACM Int'l conference on & Int'l conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Yang Zhao Wen Zhou Yao Li Zongtian Liu Ying Zhu Ping Zhu School of Computer Engineering &Science Shanghai University Shanghai China International Centre for Bamboo and Rattan Beijing China

Events extraction is an important part of information extraction, at present, most events extraction aim at extracting events themselves but do not consider whether the events have already happened. this paper focuses on the events named willing events which have aleady proposed and made sure to happen but not yet happened currently. there exist quite a number of willing events in many areas, and those can provide a lot of information, thus, carries on the extraction is very meaningful. this paper presents a new feature selection method based on Dependency Parsing. Dependency parsing is used to find the syntactic relations among the words expresses willingness, event denoters and other words. then these features are used in machine learning to classify the events, and finally achieve the identification of willing events. the experiment shows that, aiming at willing events, the features found based on dependency parsing do better than those features used in traditional events identification.

关键词： data mining Hidden Markov models Semantics Feature extraction Syntactics Support vector machines machine learning

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Multi-label classification with Bayes' theorem

Multi-label classification with Bayes' theorem

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international conference on BioMedical Engineering and Informatics (BMEI)

作者： Guangzhi Qu Hui Zhang Craig T. Hartrick Computer Science and Engineering Department Oakland University Rochester MI USA State Key Laboratory of Software Development Environment School of Computer Science Beihang University Beijing China Anesthesiology Research School of Medicine Oakland University Rochester MI USA

Compared with single-label classification, multi-label classification is more general in practice, since it allows one instance to have more than one label simultaneously. Bayes' theorem has been successfully applied to deal with single-label classification. In this paper, we proposed to tackle multi-label classification using Bayes' theorem. We propose two approaches, coined as Pair-Dependency Multi-Label Bayesian Classifier (PDMLBC) and Complete-Dependency Multi-Label Bayesian Classifier (CDMLBC). PDMLBC takes advantage of label dependency between any two labels, while CDMLBC considers the dependency among a set of labels. In the experiments, we evaluate the performance of PDMLBC and CDMLBC on real medical data, the results show that both PDMLBC and CDMLBC methods outperform NB+BR on all metrics, and CDMLBC works best among the three methods.

关键词： Correlation Bayesian methods Logistics data mining machine learning Support vector machines Decision trees

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the Rock Physical Phase Partition Method of Multilayer Sandstone Reservoir Based on Fuzzy Inference Networks

The Rock Physical Phase Partition Method of Multilayer Sands...

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2011 4th IEEE international conference on Computer Science and Information Technology(ICCSIT 2011)

作者： JunHui PAN Hui WANG Department of Computer and Information Technology North East Petroleum University Daqing,Heilongjiang,China

A kind of discrimination method based on weighted fuzzy inference networks is put forward in this paper according to the rock physical phase division problem of heterogeneous multilayer sandstone *** this method,the fuzziness of rock physical phase classification and meeting character of characteristic parameters are fully considered,and the cumulated expert experience in research are combined with the quantitative indexes which reflect the rock physical phase *** rock physical phase classification types and deviding indexes are defined by the core analysis data of coring well and explaining results of expert,then the classification standard pattern library of rock physical phase is *** rock physical appearance discrimination model of heterogeneous multilayer sandstone reservoir based on weighted fuzzy inference networks is established by adopting the adaptive learning *** experimental results demonstrate that the method has good application effect by processing the actual data from Daqing oilfield.

关键词： rock physical phase heterogeneous sandstone reservoir fuzzy inference network pattern recognition

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