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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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Practical applications of intelligent systems :Porceedings of the sixth international conference on intelligent systems and knowledge engineering, Shanghai, China, Dec 2011 (ISKE2011) /

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丛书名： Advances in Intelligent and Soft Computing

2011年

作者： international conference on intelligent systems and knowledge engineering(6th :2011 :Shanghai China)editors Yinglin Wang and Tianrui Li. Wang Yinglin. Li Tianrui.

ISBN: (纸本)9783642256578

Proceedings oftheSixthinternational conference on Intelligent System and Knowledge Engineering presents selected papers from the conference ISKE 2011, held December 15-17 in Shanghai, China. this proceedings doesnt only examine original research and approaches in the broad areas of intelligent systems and knowledge engineering, but also present new methodologies and practices in intelligent computing paradigms. the book introduces the current scientific and technical advances in the fields of artificial intelligence, machine learning, pattern recognition, data mining, information retrieval, knowledge-based systems, knowledge representation and reasoning, multi-agent systems, natural-language processing, etc. Furthermore, new computing methodologies are presented, including cloud computing, service computing and pervasive computing with traditional intelligent methods. the proceedings will be beneficial for both researchers and practitioners who want to utilize intelligent methods in their specific research fields. Dr. Yinglin Wang is a professor at the Department of Computer Science and Engineering, Shanghai Jiao Tong University, China; Dr. Tianrui Li is a professor at the School of Information Science and Technology, Southwest Jiaotong University, China.

关键词： Intellegent Systems.Knowledge engineering

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Cost effective depression patient thought record categorization via self-taught learning 11

Cost effective depression patient thought record categorizat...

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Proceedings of the 4th international conference on PErvasive Technologies Related to Assistive Environments

作者： Hua Wang Heng Huang Monica Basco Molly Lopez Fillia Makedon University of Texas at Arlington TX The University of Texas at Austin TX

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 cor-responding 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.

关键词： cost effective classification self-taught learning major depressive disorder thought record cognitive behavior therapy

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Extracting Biomarker Information applying Natural Language Processing and machine learning

Extracting Biomarker Information applying Natural Language P...

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4th international conference on Bioinformatics and Biomedical Engineering (iCBBE)

作者： Islam, Md Tawhidul Shaikh, Mostafa Nayak, Abhaya Ranganathan, Shoba Macquarie Univ Biotechnol Res Inst Dept Chem & Biomol Sci Sydney NSW Australia Univ Tokyo Dept Informat & Commun Engn Tokyo Japan Macquarie Univ Dept Comp Sydney NSW Australia

ISBN: (纸本)9781424447138

In this paper, we detail an approach to a very specific task of information extraction namely, extracting biomarker information in biomedical literature. Starting with the abstract of a given publication, we first identify the evaluative sentence(s) among other sentences by recognizing words and phrases in the text belonging to semantic categories of interest to bio-medical entities (i.e., semantic category recognition). For the entities like, protein, gene and disease, we determine whether the statement refers to biomarker relationship (i.e., assertion classification). Finally, we identify the biomarker relationship among the biomedical entities (i.e., semantic relationship classification). the system, Biomarker Information Extraction Tool (BIET) implements machine learning-based biomarker extraction using support vector machines (SVM). the system is trained and tested on a corpus of oncology related PubMed/MEDLINE literatures hand-annotated with biomarker information. We investigate the effectiveness of different features for this task and examine the amount of training data needed to learn the biomarker relationship with the entities. Our system achieved an average F-score of 86% for the task of biomarker information extraction comparing

关键词： Biomarker relationship extraction text mining literature mining

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pattern recognition Using Artificial Neural Network: A Review

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4th international conference on Information Security and Assurance

作者： Kim, Tai-hoon Hannam Univ Multimedia Engn Dept Taejon South Korea

ISBN: (纸本)9783642133640

Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, artificial neural network techniques theory have been receiving increasing attention. the design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. the objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system using ANN and identify research topics and applications which are at the forefront of this exciting and challenging field.

关键词： pattern recognition correlation Neural Network

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