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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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Malware detection on mobile devices using distributed machine learning

Malware detection on mobile devices using distributed machin...

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international conference on pattern recognition

作者： Shamili, Ashkan Sharifi Bauckhage, Christian Alpcan, Tansu Bonn-Aachen International Center for Information Technology Germany Deutsche Telekom Laboratories Technical University of Berlin

ISBN: (纸本)9780769541099

this paper presents a distributed Support Vector machine (SVM) algorithm in order to detect malicious software (malware) on a network of mobile devices. the light-weight system monitors mobile user activity in a distributed and privacy-preserving way using a statistical classification model which is evolved by training with examples of both normal usage patterns and unusual behavior. the system is evaluated using the MIT reality mining data set. the results indicate that the distributed learning system trains quickly and performs reliably. Moreover, it is robust against failures of individual components. © 2010 IEEE.

关键词： Malware

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Sentiment mining within Social Media for Topic Identification

Sentiment Mining within Social Media for Topic Identificatio...

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4th Annual IEEE international conference on Semantic Computing (ICSC)

作者： Ostrowski, David Alfred Ford Motor Co Res & Innovat Ctr Syst Analyt Dearborn MI 48121 USA

ISBN: (纸本)9780769541549

Social media has demonstrated itself to be a proven source of information towards the marketing of products. this unique source of data provides a rapid means of customer feedback that is used to support a number of business areas. Towards this purpose, we describe a methodology for the identification of topics associated with customer sentiment. this process first employs a Fisher Classification based approach towards sentiment analysis. By considering specific mutual information and word frequency distribution, topics are then identified within sentiment categories. the goal is to provide overall trends in sentiment along with associated subject matter (ie. why) as it supports a company's business. We demonstrate this methodology against data collected among a particular product line as obtained from Twitter advanced search.

关键词： Fisher classification machine learning Social Media Analytics Twitter advanced search Web mining customer feedback customer satisfaction customer sentiment data mining data source mutual information pattern classification product marketing sentiment analysis sentiment category sentiment mining social media topic identification word frequency distribution

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Uncertainty in Clustering and Classification

Uncertainty in Clustering and Classification

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4th Annual international conference on Scalable Uncertainty Management (SUM)

作者： Huellermeier, Eyke Univ Marburg Dept Math & Comp Sci D-35032 Marburg Germany

ISBN: (纸本)9783642159503

Clustering and classification are among the most important problem tasks in the realm of data analysis, data mining and machine learning. In fact, while clustering can be seen as the most popular representative of unsupervised learning, classification (together with regression) is arguably the most frequently considered task in supervised learning. Even though the literature on clustering and classification abounds, the interest in these topics seems to be unwaning, both from a research and application point of view. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： data mining

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Artificial immune system based machine learning for voltage stability prediction in power system

Artificial immune system based machine learning for voltage ...

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4th international Power Engineering and Optimization conference, PEOCO 2010

作者： Suliman, S.I. Abdul Rahman, T.K. Faculty of Electrical Engineering Universiti Teknologi Mara Malaysia

ISBN: (纸本)9781424471287

Voltage instability has recently become a challenging problem for many power system operators. this phenomenon has been reported to be responsible for severe low voltage condition leading to major blackouts. this paper presents the application of Artificial Immune Systems (AIS) for online voltage stability evaluation that could be used as early warning system to the power system operator so that necessary action could be taken in order to avoid the occurrence of voltage collapse. Key features of the proposed method are the implementation of clonal selection principle that has the capability in performing pattern recognition task. the proposed technique was tested on the IEEE 30 bus power system and the results shows that fast performance with accurate evaluation for voltage stability condition has been obtained. ©2010 IEEE.

关键词： pattern recognition

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Logic-based representation, reasoning and machine learning for event recognition 10

Logic-based representation, reasoning and machine learning f...

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4th ACM international conference on Distributed Event-Based Systems, DEBS 2010

作者： Artikis, Alexander Paliouras, Georgios Portet, François Skarlatidis, Anastasios Institute of Informatics and Telecommunications NCSR Demokritos Athens 15310 Greece Laboratoire D'informatique de Grenoble Grenoble Universités 38400 Saint Martin d'Hères France Department of Information and Communication Systems Engineering University of the Aegean Greece

ISBN: (纸本)9781605589275

Today's organisations require techniques for automated transformation of the large data volumes they collect during their operations into operational knowledge. this requirement may be addressed by employing event recognition systems that detect activities/events of special significance within an organisation, given streams of 'low-level' information that is very difficult to be utilised by humans. Numerous event recognition systems have been proposed in the literature. recognition systems with a logic-based representation of event structures, in particular, have been attracting considerable attention because, among others, they exhibit a formal, declarative semantics, they haven proven to be efficient and scalable, and they are supported by machine learning tools automating the construction and refinement of event structures. In this paper we review representative approaches of logic-based event recognition, and discuss open research issues of this field. © 2010 ACM.

关键词： Metadata

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Proceedings of the 4th ACM international conference on Distributed Event-Based Systems, DEBS 2010

Proceedings of the 4th ACM International Conference on Distr...

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4th ACM international conference on Distributed Event-Based Systems, DEBS 2010

ISBN: (纸本)9781605589275

the proceedings contain 38 papers. the topics discussed include: context aware computing and its utilization in event-based systems;logic-based representation, reasoning and machine learning for event recognition;an event view model and DSL for engineering an event-based SOA monitoring infrastructure;an RFID architecture based on an event-oriented component model;content-based rendezvous with upgraph combination;event semantics in event dissemination architectures for massive multiuser virtual environments;complex event processing synergies with predictive analytics;event processing for large-scale distributed games;reliable fault-tolerant sensors for distributed systems;business-oriented development methodology for complex event processing: demonstration of an integrated approach for process monitoring;placement of replicated tasks for distributed stream processing systems;and an approach for iterative event pattern recommendation.

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