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Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2011 12th ACIS International Conference on

Date 6-8 July 2011

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Displaying Results 1 - 25 of 47
  • [Front cover]

    Page(s): C1
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    Freely Available from IEEE
  • [Title page i]

    Page(s): i
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  • [Title page iii]

    Page(s): iii
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  • [Copyright notice]

    Page(s): iv
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  • Table of contents

    Page(s): v - viii
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  • Message from Conference Chairs

    Page(s): ix
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  • Message from Program Chair

    Page(s): x
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  • Organizing Committee

    Page(s): xi
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  • Program Committee

    Page(s): xii - xiii
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  • Reviewers

    Page(s): xiv
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  • Keynote Abstract

    Page(s): xv
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    Summary form only given. Provides an abstract for each of the keynote presentations and a brief professional biography of each presenter. View full abstract»

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  • A Domain Specific Expert System Model for Diagnostic Consultation in Psychiatry

    Page(s): 3 - 6
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (223 KB) |  | HTML iconHTML  

    Medical Experts Systems have been one of the earliest and ongoing pursuits of the Artificial Intelligence (AI) community. Unfortunately, they still remain a largely unrealized goal. The systems that have been developed are either prototypes, or involve only small knowledge domains. One of the main reasons for this situation is explained as the lack of domain specific models. While it is true that there are significant differences in the nature of the clinical knowledge and the diagnostic strategies used by expert clinicians across different medical subspecialties, this fact has been largely overlooked. In addition, Knowledge engineers who are not the domain experts have failed to capture the uniqueness, depth and the complexity of clinical reasoning. This has resulted in expert system models that are too generic and non-intuitive to clinicians. Psychiatry is characterized by its highly subjective and vague knowledge and reasoning process. Therefore generic models, which have not taken this into consideration, are particularly unsuitable for psychiatry. The authors have introduced an expert system model specific for psychiatry, in which diagnostic knowledge is described as a hierarchically organized set of entities through which diagnostic inference is made via a bottom-up approach. The relationships between the entities in diagnostic knowledge are described in terms of likelihoods and the degrees of severity using approximated mathematical functions. While this model highlights the need for domain specific models, it also gives insight to the implementation of similar approaches in other medical subspecialties. It is the intention that this model will be implemented as a web-based diagnostic consultation system. View full abstract»

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  • A Neural Network Ensemble Incorporated with Dynamic Variable Selection for Rainfall Forecast

    Page(s): 7 - 12
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (279 KB) |  | HTML iconHTML  

    This paper presents a novel ensemble model of artificial neural networks for rainfall forecast incorporating dynamic variable selection. In the first phase of the model, meteorological variables optimal to the response (here rainfall) are selected with the optimal lag value of the response variable. A dynamic variable selection method named, time series least angle regression (TS-LARS) is applied in this phase. In the second phase, an ensemble comprising artificial neural network (ANN) is constructed. The number of hidden neurons in each ANN are selected randomly to speed up the training of the ensemble. The optimization of each ANN is done by Levenberg Marquart Gradient Descent method. In the third phase of the ensemble, the component ANN models are ranked based on mutual information (MI) between the outputs of the base models and the original output. Before applying MI, we have used independent component analysis (ICA) to extract the base models which are independent with each other. Finally the highest ranked base models are combined to construct the ensemble model. A real world case study has been setup in Fukuoka city, Japan. Daily rainfall data from 1990 to 2010 with relevant meteorological variables are extracted to construct the data. The empirical results reveal that, the use of TS-LARS to select most relevant dynamic variables increase the efficiency of the ensemble model, where as the ICA-MI method reduce the number of base models hence reduce the complexity of the ensemble. View full abstract»

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  • Abnormal Crowd Behavior Detection Using Topological Methods

    Page(s): 13 - 18
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1965 KB) |  | HTML iconHTML  

    In this paper we present a novel framework for abnormal behavior detection in crowded scenes. For this purpose, the theory of topological simplification on the dense field is extended to the sparse particle motion field, which is used to describe the dynamics of the crowd. We propose two new methods for analysis of boundary point structure and extraction of critical point from the particle motion field. Both methods can be used to describe the global topological structure of the crowd motion, which is the kernel idea of our work. Various types of abnormal behaviors, including crowd formation/dispersal, crowds splitting/merging, can be detected by monitoring the changes of the topological structure. The advantage of our method is that each kind of abnormal event can be described as a specific topological structure change, therefore we do not need a complex classifier to detect these anomalies. Experiments are conducted on known datasets and results show that our method is effective in detecting and locating these kinds of abnormal behaviors. View full abstract»

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  • Image Retrieval Techniques, Analysis and Interpretation for Leukemia Data Sets

    Page(s): 19 - 23
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1138 KB) |  | HTML iconHTML  

    The research was carried out to build a patient management system and decision support system (considering certain types of Leukaemia as the domain) for the Hematology Department of Hospital Kuala Lumpur (HKL), Malaysia. The objective of this paper is to describe the techniques used for syntactical and contextual Image retrieval for leukemic images. The system contains an image database containing digitized specimens which belong to classes of lymphocytic, myelocytic and megakaryocytic disorders and a class of healthy leukocytes. The developed system is based on open source. Pages were designed and developed using Java server pages and Java with MySQL as the database for the domain and image repository. Several Java-based tools were used for image processing, neural network based pattern classification and recognition. View full abstract»

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  • Reinforcement Learning Approach to AIBO Robot's Decision Making Process in Robosoccer's Goal Keeper Problem

    Page(s): 24 - 30
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (228 KB) |  | HTML iconHTML  

    Robocup is a popular test bed for AI programs around the world. Robosoccer is one of the two major parts of Robocup, in which AIBO entertainment robots take part in the middle sized soccer event. The three key challenges that robots need to face in this event are manoeuvrability, image recognition and decision making skills. This paper focuses on the decision making problem in Robosoccer -- The goal keeper problem. We investigate whether reinforcement learning (RL) as a form of semi-supervised learning can effectively contribute to the goal keeper's decision making process when penalty shot and two attacker problem are considered. Currently, the decision making process in Robosoccer is carried out using rule-base system. RL also is used for quadruped locomotion and navigation purpose in Robosoccer using AIBO. In this paper, we propose a reinforcement learning based approach that uses a dynamic state-action mapping using back propagation of reward and space quantized Q-learning (SQQL) for the choice of high level functions in order to save the goal. The novelty of our approach is that the agent learns while playing and can take independent decision which overcomes the limitations of rule-base system due to fixed and limited predefined decision rules. Performance of the proposed method has been verified against the bench mark data set made with Upenn'03 code logic. It was found that the efficiency of our SQQL approach in goalkeeping was better than the rule based approach. The SQQL develops a semi-supervised learning process over the rule-base system's input-output mapping process, given in the Upenn'03 code. View full abstract»

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  • A MAC Layer Protocol Supporting the Application of WSNs in Medicine and Healthcare Domains

    Page(s): 33 - 37
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (153 KB) |  | HTML iconHTML  

    Application of Wireless Sensor Networks (WSN) in many fields has achieved a significant advances, many research can be seen in military, industrial control surveillance, and bush fire and wild habitant monitoring, However, it is a bit too early to claim the application of WSN in the domain of Medicine and Healthcare (M&HC) a big success. The major obstacle is the concern raised by the users that how secure the collected data is stored or transmitted over the underlying WSN and how to protect privacy while taking advantage of the technology. In this paper, a sensible solution is proposed to improve the security by augmenting the IEEE 802.15.4 standard with intrusion detection and counterattack mechanism. View full abstract»

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  • An Integrated Model Supporting Billing and QOS in the Internet

    Page(s): 38 - 44
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (270 KB) |  | HTML iconHTML  

    This article develops a charging scheme that is simple and easily usable for the users and provides them with the incentives to use only the resources they need. This scheme is developed on the time-volume charging approach to show how the contributing providers can share the total charge earned by each mobile and wireless service instance in a fair way, with each provider collecting the portion of charge that corresponds to the consumption of its own resources for the service. This is also an important issue for the commercial viability of mobile services to mobile users, given that its provision spans multiple domains. Our proposed architecture is compliant to the relevant standards and can serve as a basis for applying other Internet charging schemes as well. View full abstract»

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  • Performance Evaluation of the MMS Framework for Internet Applications

    Page(s): 45 - 50
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1142 KB) |  | HTML iconHTML  

    This research focuses on the development and performance evaluation of MMS framework for internet applications. The details of the architecture and algorithms employed in the MMS framework for message delivery are presented. We implement the proposed MMS framework for internet application by building a mobile telemedicine system. On the client side, we develop MMS application that implements data compression and split techniques and on the server side, we implement merge and decompression techniques. The compression and split techniques are required in order to send large-size data files which is not possible with the existing MMS technology. The proposed technique ensures that the quality of data is perserved which is critical for accomplishing the diagnosis process. The performance measure, 'Peak Signal to Noise Ratio (PSNR)' is used to evaluate the quality of data. The experimental results show that the proposed framework can be used to develop internet applications for a practical mobile telemedicine system. View full abstract»

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  • An Improvement on Hsiang and Shih's Remote User Authentication Scheme Using Smart Cards

    Page(s): 53 - 57
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (165 KB) |  | HTML iconHTML  

    Recently, Hsiang and Shih proposed remote user authentication scheme using smart cards, they claims that their schemes defended against parallel session attack, and password guessing attacks. In this paper, we show that Hsiang and Shih's schemes are still vulnerable to off-line password guessing attacks and undetectable on-line password guessing attacks. Notably, problems remain in situations where the user loses a smart card. To remedy these flaws, this paper proposes an improvement on Hsiang and Shih's remote user authentication schemes using smart cards. View full abstract»

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  • Security of RFID Systems - A Hybrid Approach

    Page(s): 58 - 63
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (184 KB) |  | HTML iconHTML  

    The use of RFID (Radio Frequency Identification) technology can be employed for tracking and detecting each container, pallet, case, and product uniquely in the supply chain. It connects the supply chain stakeholders (i.e., suppliers, manufacturers, wholesalers/distributors, retailers and customers) and allows them to exchange data and product information. Despite these potential benefits, security issues are the key factor in the deployment of a RFID-enabled system in the global supply chain. This paper proposes a hybrid approach to secure RFID transmission in Supply Chain Management (SCM) systems using modified Wired Equivalent Encryption (WEP) and Rivest, Shamir and Adleman (RSA) cryptosystem. The proposed system also addresses the common loop hole of WEP key algorithm and makes it more secure compare to the existing modified WEP key process. View full abstract»

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  • Smart RFID Reader Protocol for Malware Detection

    Page(s): 64 - 69
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (163 KB) |  | HTML iconHTML  

    Radio frequency identification (RFID) is a remote identification technique promises to revolutionize the way a specific object use to identify in our industry. However, large scale implementation of RFID sought for protection, against Malware threat, information privacy and un-traceability, for low cost RFID tag. In this paper, we propose a framework to provide privacy for tag data and to provide protection for RFID system from malware. In the proposed framework, malware infected tag is detected by analysing individual component of the RFID tag. It uses sanitization technique for analysing individual component. Here authentication based shared unique parameters is used as a method to protect privacy. This authentication protocol will be capable of handling forward and backward security and identifying rogue reader better than existing protocols. Using this framework, the RFID system will be protected from malware and the privacy of the tag will be ensured as well. View full abstract»

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  • Feature Selection of Imbalanced Gene Expression Microarray Data

    Page(s): 73 - 78
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (193 KB) |  | HTML iconHTML  

    Gene expression data is a very complex data set characterised by abundant numbers of features but with a low number of observations. However, only a small number of these features are relevant to an outcome of interest. With this kind of data set, feature selection becomes a real prerequisite. This paper proposes a methodology for feature selection for an imbalanced leukaemia gene expression data based on random forest algorithm. It presents the importance of feature selection in terms of reducing the number of features, enhancing the quality of machine learning and providing better understanding for biologists in diagnosis and prediction. Algorithms are presented to show the methodology and strategy for feature selection taking care to avoid over fitting. Moreover, experiments are done using imbalanced Leukaemia gene expression data and special measurement is used to evaluate the quality of feature selection and performance of classification. View full abstract»

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  • Features Extraction Using Free Score of Words for Classifying Conotoxin Superfamily

    Page(s): 79 - 84
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (195 KB) |  | HTML iconHTML  

    Interest in Conotoxin has been rapidly growing over the past number of years due to its potential for effective use in the design of drugs to treat a myriad of conditions including, neuromuscular disorders, chronic pain and schizophrenia. As a result it is necessary to develop powerful and efficient techniques which can accurately classify conotoxin super families. In this paper, we propose a novel technique which makes use of support vector machines for classification. The method which considers suboptimal alignments of words with restricted length and computes local alignment partition functions to produce free scores for the alignments plays the key role in the feature extraction step of support vector machine classification. In the classification of conotoxin proteins, the proposed approach, SVM-Freescore, demonstrates its potential use by yielding an improved sensitivity and specificity of approximately 5.864% and 3.76%, respectively. View full abstract»

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  • A Study of Key Success Factors for Supply Chain Management System in Semiconductor Industry

    Page(s): 87 - 92
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (235 KB) |  | HTML iconHTML  

    Developing a supply chain management (SCM) system is costly, but important. However, because of its complicated nature, not many of such projects are considered successful. Few research publications directly relate to key success factors (KSFs) for implementing and operating a SCM system. Motivated by the above, this research proposes two hierarchies of KSFs for SCM system implementation and operation phase respectively in the semiconductor industry by using a two-step approach. First, a literature review indicates the initial hierarchy. The second step includes a focus group approach to finalize the proposed KSF hierarchies by extracting valuable experiences from executives and managers that actively participated in a project, which successfully establish a seamless SCM integration between the world's largest semiconductor foundry manufacturing company and the world's largest assembly and testing company. Finally, this research compared the KSF's between the two phases and made a conclusion. Future project executives may refer the resulting KSF hierarchies as a checklist for SCM system implementation and operation in semiconductor or related industries. View full abstract»

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