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Intelligent Systems, Modelling and Simulation (ISMS), 2010 International Conference on

Date 27-29 Jan. 2010

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Displaying Results 1 - 25 of 96
  • [Front cover]

    Page(s): C1
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  • [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 - xi
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  • Welcome message from the Chairs

    Page(s): xii - xiii
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  • Conference organization

    Page(s): xiv
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  • International/Technical Program Committee

    Page(s): xv
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  • International reviewers

    Page(s): xvi - xvii
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  • Technical Sponsors

    Page(s): xviii
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  • Plenary Abstract 1 - Structure Evolving Systems and Control

    Page(s): xix - xxi
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    The need for integrating process and control design issues has been recognised in the Process, Aerospace and other areas of applications, but with a few exceptions (early work in Process Control ,EU project SESDIP), little attention has been given in the development of an integrated Systems and Control Theory Based Framework that may integrate the overall process. The integration of traditional design stages, such as Process Synthesis (PS), Global Instrumentation (GS) and finally Control Design (CD) is a complex problem that is characterised by different forms of system evolution. This evolution has two main features: The first is linked to the natural evolution of the system structure as this is shaped through the design stages of process synthesis and global instrumentation and it is referred to as structural evolution. The second stems from the need to address design and decision problems at "early" and "late" stages of system design (as part of an iterative design cycle) using models with a variability in their complexity and referred to as design time evolution. The paper aims to describe those two forms of system evolution from a systems and control theoretic viewpoint, review and unify existing results and define a road map for research in structural system methodologies; this is essential for the development of the control theoretic aspects of the complex problem of systems integration and opens up a new field for research for Structural Control Methodologies. The paper addresses a number of control theory issues intimately linked to overall system design, which have a clear structural nature and express aspects and stages of the evolutionary design process. View full abstract»

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  • Plenary Abstract 2

    Page(s): xxii - xxiii
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    Provides an abstract of the keynote presentation and a brief professional biography of the presenter. The complete presentation was not made available for publication as part of the conference proceedings. View full abstract»

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  • Plenary Abstract 3

    Page(s): xxiv - xxv
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    The author discusses the development of new Information Communications Technologies (ICT) in the areas of e-Learning, e-Health, e-Government, e-Journalism, etc. The current Web 2.0 combined with the Semantic Web technologies provide support to creation of social networks, content retrieval and analysis, which creates foundation for the future fully automated cyberspace. View full abstract»

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  • Intrusion Detection by New Data Description Method

    Page(s): 1 - 5
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (353 KB) |  | HTML iconHTML  

    This paper presents a new approach in data description for intrusion detection based on Support Vector Data Description (SVDD). The SVDD is a well-known kernel method which tries to fit a hypersphere around the target objects and more precise boundary is depending on using proper kernel functions. In the proposed method we find a minimal hyperellipse around the normal objects to describe them. The overall experiments show prominence of our proposed method in comparison with the standard SVDD. View full abstract»

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  • Beck Depression Inventory Test Assessment Using Fuzzy Inference System

    Page(s): 6 - 9
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (287 KB) |  | HTML iconHTML  

    In this paper, the fuzzy Takagi-Sugeno method, one of fuzzy inference system, is proposed to evaluate beck depression inventory II test (BDI II-Test). BDI II-Test is an assessment tool by healthcare professionals and researchers to diagnose someone's depression level in the fields of psychology and psychiatry. In this case, all the depressed factors; emotional, cognitive, motivational, physical, and delusional; are compiled as fuzzy rules input variables. The sample test case has shown that the fuzzy inference system may help in diagnosing all the components resulted by BDI II Test, 21 questions to get membership values, to support the medical treatment decision. View full abstract»

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  • A New Support Vector Data Description with Fuzzy Constraints

    Page(s): 10 - 14
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (346 KB) |  | HTML iconHTML  

    This paper presents a novel approach to eliminate the effect of noisy samples from the learning step of support vector data description (SVDD) method. SVDD is a popular kernel method which tries to fit a hypersphere around the target object and can obtain more flexible and more accurate data descriptions by using proper kernel functions. Nonetheless, the SVDD could sometimes generate such a loose decision boundary while some noisy samples (outliers) exist in the training set. In order to solve this problem we define fuzzy constraints and two new concepts for each learning sample. Duo to the usage of fuzzy constraints, we called this method fuzzy constraints SVDD (FCSVDD). The overall experiments show prominence of our proposed method in comparison with the standard SVDD. View full abstract»

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  • Selecting Informative Genes from Microarray Data by Using a Cyclic GA-Based Method

    Page(s): 15 - 20
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (389 KB) |  | HTML iconHTML  

    Microarray data 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. This selection process is difficult due to the availability of a small number of samples compared to the huge number of genes, many irrelevant genes, and noisy genes. Therefore, this paper proposes a cyclic method based on genetic algorithms (GA) 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 three benchmark microarray data sets and obtained encouraging results as compared with other experimented methods and previous related works. View full abstract»

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  • The Use of Location Based Services for Very Fast and Precise Accidents' Reporting and Locating

    Page(s): 21 - 24
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (294 KB) |  | HTML iconHTML  

    Most people are stressed out and overstrained after accidents even if no one is hurt. Consequently, they may face some difficulty in reporting the accident to the police and civil defense, or they may provide them with inaccurate information about the location of the accident. Moreover, even if they were able to provide the necessary information it may take them some time to deliver it to a human counterpart and hence it will take the police and civil defense more time to reach the accident location in the appropriate time to rescue people. This paper proposes the use of location based services to develop a system that can be used easily to report and locate an accident more quickly and precisely. View full abstract»

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  • An Intelligent Classification Model for Rubber Seed Clones Based on Shape Features through Imaging Techniques

    Page(s): 25 - 31
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (623 KB) |  | HTML iconHTML  

    This paper describes research work in developing an intelligent model for classifying selected rubber tree series clones based on shape features using image processing techniques. Sample of rubber tree seeds are captured using digital camera where the RGB color image are processed involving segmentation algorithm which includes thresholding and morphological technique. Shape features such as area, perimeter and radius are extracted from each image. Two models are being designed. Model 1 is represented by 38 input features while Model 2 is represented by a reduction of input size using Principle Component Analysis (PCA). The inputs for both models are then used to train a multi-layer perceptron Artificial Neural Network (ANN) using Levenberg-Marquardt algorithm. 160 samples are used as training set while another 100 samples are used for testing. The optimized ANN models are then evaluated and validated through analysis of performance indicators regularly applied in classification research work via pattern recognition. Findings in this work have shown that the optimized Model 2 has the best accuracy of 84% with more than 70% achievement for sensitivity and specificity. View full abstract»

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  • Sequential Modeling of D_st Dynamics with SEEk Trained Recurrent Neural Networks

    Page(s): 32 - 37
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (338 KB) |  | HTML iconHTML  

    A sequential framework for modeling magnetospheric plasma interactions with a SEEK trained recurrent neural network is proposed. An overview of the state-space modeling framework is provided, along with a review of previous Kalman trained neural models. The proposed algorithm is described and is evaluated against an EKF trained RNN and a gradient based model. The exogenous inputs to the RNNs consist of three parameters, bz, b2, and by 2, where b, bz, and by represent the magnitude, the southward and azimuthal components of the interplanetary magnetic field (IMF) respectively. It was found that the SEEK trained recurrent neural network outperforms other neural time series models trained with the extended Kalman filter, and gradient descent learning. The numerical simulations suggest that the SEEK filter provides superior tracking capabilities than the EKF, resulting in accurate forecast of the Dst index. View full abstract»

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  • Intelligent Agent System Architecture for Presenting Health Grid Contents from Complex Database

    Page(s): 38 - 42
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (444 KB) |  | HTML iconHTML  

    Health grid contents determine a number of contents activities located in a number of different health institutions, whose contents information need to save in different storage units. To simplify the health contents representation in term of processing, segmentation and analyzing, we proposed intelligent agent system architecture to manage and retrieve healthcare contents in real world environment. This paper presents three structural processes which namely: (1) Agent for retrieving and managing complex database contents, (2) Agent for processing queries within frequently processing cycle, and (3) Agent for approving contents to be presented. View full abstract»

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  • Solving Ceramic Grinding Optimization Problem by Adaptive Quantum Evolutionary Algorithm

    Page(s): 43 - 48
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (315 KB) |  | HTML iconHTML  

    Advanced structural ceramics are widely used in engineering applications. They are useful due to high chemical inertness and sublimation temperature. Optimization of ceramic grinding process is essential as ceramics have high hardness and low surface toughness which can lead to large number of defects. The ceramic grinding optimization problem is formulated as non-linear constrained optimization problem. This paper proposes to solve the optimization problem by using a recently proposed real coded Adaptive Quantum inspired Evolutionary Algorithm. It is free from user selectable parameters in evolutionary operators as the same is determined adaptively. The proposed algorithm does not require mutation for maintaining diversity. The results also show that the proposed algorithm is fast and robust in comparison to the known state of art methods available for solving such problems. View full abstract»

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  • Recognition of Ingredients and Recipes of Cookery Shows for Value Added Service (VAS) of an Interactive Set Top Box (iSTB)

    Page(s): 49 - 53
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (356 KB) |  | HTML iconHTML  

    In this paper, authors have proposed a VAS for personal video recorder (PVR) enabled iSTB. The proposed solution can recognize the regions in the frames where the recipe and ingredients are shown and can save them in image/text format for further use. We have used a hybrid approach where the text regions are localized using the compress domain features of the streaming video in real time and then some pixel domain processing is performed on that reduced region of interest to recognize the text. The novelty of the proposed approach lies in using the compressed domain features of H.264 video in text localization and applying some pixel domain post processing on the ROI to perform the entire recognition process in real time. The system is tested over a video upto CIF resolution and can recognize the text successfully with an accuracy of nearly 80%. View full abstract»

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  • Automated Method for Reducing False Positives

    Page(s): 54 - 59
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (313 KB) |  | HTML iconHTML  

    False positives are critical problems of network intrusion detection systems that use pattern matching algorithm to detect network intrusions. The algorithm is unable to eliminate false packets with short lifespan. Secondly, the algorithm lacks the capability to manage the trade-offs between false and true positives. Consequently, system administrators are frequently swamped with massive false alerts from intrusive packets that cannot achieve their objectives and unfortunately, such alerts are often mixed with few true positives. However, how to substantiate these two generic groups of alerts without incurring additional overheads are classical research issues. Therefore, we present clustering-based adaptive P-filter model to investigate false positives. Alerts from Snort were the input to the P-filter model and they were clustered with some sequential filtering criteria. Extensive evaluations that we performed have demonstrated high efficacy of our approach to collaborate with pattern matching algorithm in achieving significant reduction of false positives during intrusion detections. View full abstract»

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  • The Development of an Advanced Autonomous Integrated Mission System for Uninhabited Air Systems to Meet UK Airspace Requirements

    Page(s): 60 - 64
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (253 KB) |  | HTML iconHTML  

    The routine use of Uninhabited Air Systems (UAS) in non-segregated airspace presents many technological and regulatory challenges to the Aerospace Industry. This paper describes the development and key features of a new UAS avionic system designed to meet these challenges, the Advanced Autonomous Integrated Mission System (AAIMS). The paper gives an overview of the systems design process followed for AAIMS, its architectural design and its system components including autonomous decision making, sense and avoid, weather sensing and processing, forced landing and system health management. View full abstract»

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