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Particle swarm optimization with a modified sigmoid function for gene selection from gene expression data

Particle swarm optimization with a modified sigmoid function...

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15th International Symposium on Artificial Life and Robotics, AROB '10

作者： Mohamad, M.S. Omatu, S. Deris, S. Yoshioka, M. Department of Computer Science and Intelligent Systems Graduate School of Engineering Osaka Prefecture University Sakai Osaka 599-8531 Japan Department of Software Engineering Faculty of Computer Science and Information Systems Universiti Teknologi Malaysia 81310 Skudai Johore Malaysia

ISBN: (纸本)9784990288044

In order to select a small subset of informative genes from gene expression data for cancer classification, recently, many researchers are analyzing gene expression data using various computational intelligence methods. However, due to the small number of samples compared to the huge number of genes (high-dimension), irrelevant genes, and noisy genes, many of the computational methods face difficulties to select the small subset. Thus, we propose an enhancement of binary particle swarm optimization to select a small subset of informative genes that is relevant for classifying cancer samples more accurately. In this proposed method, three approaches have been introduced to increase the probability of bits in particle's positions to be zero. By performing experiments on two gene expression data sets, we have found that the performance of the proposed method is superior to previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also produces lower running times compared to BPSO. © 2010 ISAROB.

关键词： Particle swarm optimization (PSO)

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Informative gene for cancer classification by using particle swarm optimization

Informative gene for cancer classification by using particle...

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15th International Symposium on Artificial Life and Robotics, AROB '10

ISBN: (纸本)9784990288044

Microarrays technology offers the ability to measure the expression levels of thousands of genes simultaneously in biological organisms. Gene expression data that produced by the technology are expected to be of significant help in the development of efficient cancer diagnoses and classification platforms. The main problem that needs to be addressed is the selection of a small subset of genes from the thousands of genes in the data that contributes to a cancer disease. Therefore, this article proposes particle swarm optimization (PSO) with the constraint of particle's velocities to select a near-optimal (small) subset of informative genes that is relevant for cancer classification. The performance of the proposed method was evaluated by two well-known gene expression data sets and obtained encouraging results as compared with the standard version of binary PSO. © 2010 ISAROB.

关键词： Particle swarm optimization (PSO)

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A review selection approach for accurate feature rating estimation

A review selection approach for accurate feature rating esti...

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23rd International Conference on Computational Linguistics, Coling 2010

作者： Long, Chong Zhang, Jie Zhu, Xiaoyan Department of Computer Science State Key Laboratory on Intelligent Technology and Systems Tsinghua University China School of Computer Engineering Nanyang Technological University Singapore

In this paper, we propose a review selection approach towards accurate estimation of feature ratings for services on participatory websites where users write textual reviews for these services. Our approach selects reviews that comprehensively talk about a feature of a service by using information distance of the reviews on the feature. The rating estimation of the feature for these selected reviews using machine learning techniques provides more accurate results than that for other reviews. The average of these estimated feature ratings also better represents an accurate overall rating for the feature of the service, which provides useful feedback for other users to choose their satisfactory services.

关键词： Learning systems

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Structured local binary Haar pattern for graphics retrieval

Structured local binary Haar pattern for graphics retrieval

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2010 IEEE International Conference on intelligent Computing and intelligent systems, ICIS 2010

作者： Su, Song-Zhi Chen, Shu-Yuan Li, Shang-An Li, Shao-Zi Duh, Der-Jyh Cognitive Science Department Xiamen University Fujian Key Laboratory of the Brain-like Intelligent Systems Xiamen China Department of Computer Engineering and Science Yuan-Ze University Taiwan Department of Computer Science and Information Engineering Ching-Yun University Taiwan

ISBN: (纸本)9781424465835

Feature extraction is an important issue in graphics retrieval. Local feature based descriptors are currently the predominate method used in image retrieval and object recognition. Inspired by the success of Haar feature and Local Binary Pattern (LBP), a novel feature named structured local binary Haar pattern (SLBHP) is proposed for graphics retrieval in this paper. SLBHP encodes the polarity instead of the magnitude of the difference between accumulated gray values of adjacent rectangles. Experimental results on graphics retrieval show that the discriminative power of SLBHP is better than those of using edge points (EP), Haar feature, and LBP even in noisy condition. ©2010 IEEE.

关键词： Local binary pattern

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STLF using neural networks and fuzzy for anomalous load scenarios - A case study for Hajj

STLF using neural networks and fuzzy for anomalous load scen...

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6th International Conference on Electrical and computer engineering, ICECE 2010

作者： Ahmmed, Suman Khan, Mohammad Asif Ashraf Hasan, Md. Khairul Saber, Ahmed Yousuf Huda, Mohammad Nurul Rahman, Mohammad Zahidur Department of Computer Science and Engineering United International University Dhaka Bangladesh Department of Computer Science and Engineering Ahsanullah University of Science and Technology Bangladesh Real-Time Power and Intelligent Systems Laboratory Missouri University of Science and Technology United States Department of Computer Science and Engineering Jahangirnagar University Savar Dhaka Bangladesh

ISBN: (纸本)9781424462797

Load Forecasting has an important role in load generation, scheduling, planning etc. in power system. Different computational intelligent techniques are used in Short Term Load Forecasting (STLF) to make it more effective. Neural Networks (NN) is an effective mapping algorithm that can map complex input-output relationships, which is an important technique to do STLF having existing dataset. Usually a proper NN is sufficient to achieve accepted level of performance. But different load dataset may bear some irregular nature of load demand scenario due to having special events, where accuracy of NN suffers significantly. To enhance the performance for those situations, the authors propose a hybrid STLF approach-Neural Networks and Fuzzy (NNF) method. The authors first try to select the best possible trained NN and do STLF. Considering historical data trend and of existing errors of NN solution, for those special days, NNF determine the Load Change trend. Fuzzy Inference Rules (FIR) have been developed to further improvement by fuzzy method. In fuzzy part the NNF apply FIR on two inputs: STLF of NN and Load Change trend, to enhance the performance of STLF for special events. To evaluate the proposed method it is applied on the dataset of Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA). Since the authors deal with the daily load dataset of Saudia Arabia of Hijri years (Arabian years), Hajj has been chosen as one of the anomalous load scenario. Empirical results show that for Hajj event of Hijri 1428 year, the accuracy of STLF by NN is approximately 6.37%, whereas proposed NNF can decline the error at only 1.92%. ©2010 IEEE.

关键词： Fuzzy inference

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Comparative analysis of motor imagery on different scales based on brain computer interface

Comparative analysis of motor imagery on different scales ba...

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作者： Li, Junhua Zhang, Liqing Department of Computer Science and Engineering MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University Shanghai 200240 China

The event-related brain oscillatory responses have a substantial role in motor imagery experiments. There are two patterns, i.e. event-related desynchronization (ERD) and eventrelated synchronization (ERS). In clinical applications and scientific experiments, an obvious event-related potential (ERP) is pivotal to achieve required tasks. Therefore, we explore how subjects generate the obvious ERP more easily. Eight healthy subjects participated in the experiments and completed motor imagery on two different scales (index finger versus arm). The results indicate that the motor imagery of arm reaches the better performance compared with the motor imagery of index finger. Furthermore, we make a comparative analysis in averaged power spectrum in order to reveal inherent phenomenon. The analysis of power spectrum further confirms that arm motor imagery has an advantage over index finger motor imagery.

关键词： Brain computer interface

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A taxonomy of autonomic application management in grids

A taxonomy of autonomic application management in grids

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16th IEEE International Conference on Parallel and Distributed systems, ICPADS 2010

作者： Rahman, Mustafizur Ranjan, Rajiv Buyya, Rajkumar Cloud Computing and Distributed Systems Laboratory Department of Computer Science and Software Engineering University of Melbourne Australia Service Oriented Computing Research Group School of Computer Science and Engineering University of New South Wales Australia

ISBN: (纸本)9780769543079

In this paper, we propose a taxonomy that characterizes and classifies different components of autonomic application management in Grids. We also survey several representative Grid systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the similarities and differences of state-of-the-art technologies utilized in autonomic application management from the perspective of Grid computing, but also identifies the areas that require further research initiatives. © 2010 IEEE.

关键词： Surveys

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Business modeling with the support of multiple notations in requirements engineering

Business modeling with the support of multiple notations in ...

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14th Pacific Asia Conference on Information systems, PACIS 2010

作者： Bhuiyan, Moshiur Krishna, Aneesh OptInfra Systems Australia Decision Systems Laboratory School of Computer Science and Software Engineering University of Wollongong NSW 2522 Australia Department of Computing Faculty of Science and Engineering Curtin University of Technology Perth WA 6102 Australia

Requirements specification should not be concerned only with the software specification, but should also be able to integrate with the organizational models describing the environment in which the system will function. Agent-oriented conceptual modeling notations such as i* represents an interesting approach for modeling early phase requirements which includes organizational contexts, stakeholder intentions and rationale. Business Process Modeling notations such as BPMN are used to effectively conceptualize and communicate important process characteristics to relevant stakeholders. On the other hand, Unified Modeling Language (UML) is suitable for later phases of requirement capture which usually focus on completeness, consistency, and automated verification of functional requirements for the new system. In this paper, we illustrate the use of a methodology that facilitate and support the combined use of notation for modeling requirement engineering process in a synergistic fashion in a complex project for a large government department. The notations we used were i*, BPMN and UML Use Case.

关键词： Unified Modeling Language

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Flatten hierarchies for large-scale hierarchical text categorization

Flatten hierarchies for large-scale hierarchical text catego...

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International Conference on Digital Information Management

作者： Wang, Xiao-Lin Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University China

ISBN: (纸本)9781424475728

Hierarchies are very popular in organizing documents and web pages, hence automated hierarchical classification techniques are desired. However, the current dominant hierarchical approach of top-down method suffers accuracy decrease compared with flat classification approaches, because of error propagation and bottom nodes' data sparsity. In this paper we flatten hierarchies to relieve such accuracy decrease in top-down method, which aims to make hierarchies both effective enough to make large-scale classification tasks feasible, and simple enough to ensure high classification accuracy. We propose two flattening strategies based on these two causes of the accuracy decrease, and experimental results show that the flattening strategy designed for error propagation is more effective, which suggests that hierarchies with lots of branches at top layers can provide high classification accuracy. Besides, we analyze the computational complexity before and after flattening, which approximately agree with the experimental results. ©2010 IEEE.

关键词： Websites

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Structured local edge pattern moment for pedestrian detection

Structured local edge pattern moment for pedestrian detectio...

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2nd International Conference on Image Analysis and Signal Processing, IASP'2010

作者： Su, Song-Zhi Chen, Shu-Yuan Li, Shao-Zi Duh, Der-Jyh Cognitive Science Department Xiamen University Xiamen China Fujian Key Laboratory of the Brain-like Intelligent Systems Xiamen University Xiamen China Department of Computer Science and Engineering Yuanze University Taiwan Department of Computer Science and Information Engineering Ching Yun University Taiwan

ISBN: (纸本)9781424455553

Local feature based approaches have gotten great success in object detection and recognition in recent years. In this paper, a novel local based feature, Structured Local Edge Pattern Moment (SLEPm), is proposed for pedestrian detection in the sliding window framework. SLEPm encodes not only the statistical information but also the structure and spatial information of object for pedestrian detection. Linear Support Vector Machine (SVM) is used as a binary classifier to determine whether a sub-window contains pedestrian. Experimental results in INRIA pedestrian database show that performance of SLEPm is better than that of Histogram of Oriented Gradient (HOG). © 2010 IEEE.

关键词： Object detection

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