Tools And Applications With Artificial Intelligence Studies In Computational Intelligence

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<ul><li><p>Constantinos Koutsojannis and Spiros Sirmakessis (Eds.)</p><p>Tools and Applications with Artificial Intelligence</p></li><li><p>Studies in Computational Intelligence,Volume 166</p><p>Editor-in-ChiefProf. Janusz KacprzykSystems Research InstitutePolish Academy of Sciencesul. Newelska 601-447WarsawPolandE-mail: kacprzyk@ibspan.waw.pl</p><p>Further volumes of this series can be found on our homepage:springer.com</p><p>Vol. 144.Andreas Fink and Franz Rothlauf (Eds.)Advances in Computational Intelligence in Transport, Logistics,and Supply Chain Management, 2008ISBN 978-3-540-69024-5</p><p>Vol. 145.Mikhail Ju.Moshkov,Marcin Piliszczukand Beata ZieloskoPartial Covers, Reducts and Decision Rules in Rough Sets, 2008ISBN 978-3-540-69027-6</p><p>Vol. 146. Fatos Xhafa and Ajith Abraham (Eds.)Metaheuristics for Scheduling in Distributed ComputingEnvironments, 2008ISBN 978-3-540-69260-7</p><p>Vol. 147. Oliver KramerSelf-Adaptive Heuristics for Evolutionary Computation, 2008ISBN 978-3-540-69280-5</p><p>Vol. 148. Philipp LimbourgDependabilityModelling under Uncertainty, 2008ISBN 978-3-540-69286-7</p><p>Vol. 149. Roger Lee (Ed.)Software Engineering,Artificial Intelligence, Networking andParallel/DistributedComputing, 2008ISBN 978-3-540-70559-8</p><p>Vol. 150. Roger Lee (Ed.)Software Engineering Research, Management andApplications, 2008ISBN 978-3-540-70774-5</p><p>Vol. 151. Tomasz G. Smolinski,Mariofanna G.Milanovaand Aboul-Ella Hassanien (Eds.)Computational Intelligence in Biomedicine and Bioinformatics,2008ISBN 978-3-540-70776-9</p><p>Vol. 152. Jaroslaw StepaniukRough Granular Computing in Knowledge Discovery and DataMining, 2008ISBN 978-3-540-70800-1</p><p>Vol. 153. Carlos Cotta and Jano van Hemert (Eds.)Recent Advances in Evolutionary Computation forCombinatorial Optimization, 2008ISBN 978-3-540-70806-3</p><p>Vol. 154. Oscar Castillo, Patricia Melin, Janusz Kacprzyk andWitold Pedrycz (Eds.)Soft Computing for Hybrid Intelligent Systems, 2008ISBN 978-3-540-70811-7</p><p>Vol. 155. Hamid R. Tizhoosh and M.Ventresca (Eds.)Oppositional Concepts in Computational Intelligence, 2008ISBN 978-3-540-70826-1</p><p>Vol. 156. Dawn E.Holmes and Lakhmi C. Jain (Eds.)Innovations in Bayesian Networks, 2008ISBN 978-3-540-85065-6</p><p>Vol. 157.Ying-ping Chen and Meng-Hiot Lim (Eds.)Linkage in Evolutionary Computation, 2008ISBN 978-3-540-85067-0</p><p>Vol. 158.Marina Gavrilova (Ed.)Generalized Voronoi Diagram: A Geometry-Based Approach toComputational Intelligence, 2009ISBN 978-3-540-85125-7</p><p>Vol. 159. Dimitri Plemenos and Georgios Miaoulis (Eds.)Artificial Intelligence Techniques for Computer Graphics, 2009ISBN 978-3-540-85127-1</p><p>Vol. 160. P. Rajasekaran andVasantha Kalyani DavidPattern Recognition using Neural and Functional Networks,2009ISBN 978-3-540-85129-5</p><p>Vol. 161. Francisco Baptista Pereira and Jorge Tavares (Eds.)Bio-inspired Algorithms for the Vehicle Routing Problem, 2009ISBN 978-3-540-85151-6</p><p>Vol. 162. Costin Badica, Giuseppe Mangioni,Vincenza Carchiolo and Dumitru Dan Burdescu (Eds.)Intelligent Distributed Computing, Systems and Applications,2008ISBN 978-3-540-85256-8</p><p>Vol. 163. Pawel Delimata,Mikhail Ju.Moshkov,Andrzej Skowron and Zbigniew SurajInhibitory Rules in Data Analysis, 2009ISBN 978-3-540-85637-5</p><p>Vol. 164. Nadia Nedjah, Luiza de Macedo Mourelle,Janusz Kacprzyk, Felipe M.G. Francaand Alberto Ferreira de Souza (Eds.)Intelligent Text Categorization and Clustering, 2009ISBN 978-3-540-85643-6</p><p>Vol. 165. Djamel A. Zighed, Shusaku Tsumoto,ZbigniewW. Ras and Hakim Hacid (Eds.)Mining Complex Data, 2009ISBN 978-3-540-88066-0</p><p>Vol. 166. Constantinos Koutsojannis and Spiros Sirmakessis(Eds.)Tools and Applications with Artificial Intelligence, 2009ISBN 978-3-540-88068-4</p></li><li><p>Constantinos KoutsojannisSpiros Sirmakessis(Eds.)</p><p>Tools and Applications withArtificial Intelligence</p><p>123</p></li><li><p>Prof. Spiros SirmakessisResearch Academic Computer TechnologyInstitute "D.Maritsas" Building N.Kazantzaki str.Patras University Campus26500 PatrasGreece</p><p>Prof. Constantinos KoutsojannisComputer Engineering and Informatics DepartmentUniversity of Patras26500 PatrasGreece</p><p>ISBN 978-3-540-88068-4 e-ISBN 978-3-540-88069-1</p><p>DOI 10.1007/978-3-540-88069-1</p><p>Studies in Computational Intelligence ISSN 1860949X</p><p>Library of Congress Control Number: 2008935499</p><p>c 2009 Springer-Verlag Berlin Heidelberg</p><p>This work is subject to copyright. All rights are reserved, whether the whole or part of thematerial is concerned, specifically the rights of translation, reprinting, reuse of illustrations,recitation, broadcasting, reproduction on microfilm or in any other way, and storage in databanks.Duplication of this publication or parts thereof is permitted only under the provisions ofthe German Copyright Law of September 9, 1965, in its current version, and permission for usemust always be obtained from Springer.Violations are liable to prosecution under the GermanCopyright Law.</p><p>The use of general descriptive names, registered names, trademarks, etc. in this publicationdoes not imply, even in the absence of a specific statement, that such names are exempt fromthe relevant protective laws and regulations and therefore free for general use.</p><p>Typeset &amp; Cover Design: Scientific Publishing Services Pvt. Ltd., Chennai, India.</p><p>Printed in acid-free paper</p><p>9 8 7 6 5 4 3 2 1</p><p>springer.com</p></li><li><p>Preface </p><p>In recent years, the use of Artificial Intelligence (AI) techniques has been greatly increased. The term intelligence seems to be a must in a large number of European and International project calls. AI Techniques have been used in almost any domain. Application-oriented systems usually incorporate some kind of intelligence by using techniques stemming from intelligent search, knowledge representation, machine learning, knowledge discovery, intelligent agents, computational intelligence etc. The Workshop on "Applications with Artificial Intelligence" seeks for quality papers on computer applications that incorporate some kind of AI technique. The objective of the workshop was to bring together scientists, engineers and practitioners, who work on designing or developing applications that use intelligent techniques or work on intelligent techniques and apply them to application domains (like medicine, biology, education etc), to present and discuss their research works and exchange ideas. Results of project-based works were very welcome. </p><p>Topics of interest for the workshop as far as application domains were concerned included (but not limited to) the following: </p><p> Biology Computer Networks Computer Security E-commerce Education Engineering Finance Health Care &amp; Medicine Logistics Multimedia Psychology Power Systems Sociology Web Applications </p><p>Topics of interest as far as intelligent techniques were concerned included (but are not limited to) the following: </p><p> Evolutionary Algorithms Fuzzy Logic Hybrid Techniques </p></li><li><p> Preface </p><p>VI</p><p> Heuristic Search Intelligent Agents Knowledge-Based Systems Knowledge Representation Knowledge Discovery Machine Learning Neural Networks Planning Semantic Web Techniques </p><p>We would like to express our appreciation to all authors of submitted papers, to the members of the program committee and all the people that have worked for this event. </p><p>This workshop could not have been held without the outstanding efforts of Marios Katsis Finally, recognition and acknowledgement is due to all members of the Internet and Multimedia Research Unit at Research Academic Computer Technology Institute and the eBusiness Lab staff. Constantinos Koutsojannis </p><p>Spiros Sirmakessis </p></li><li><p>Organization </p><p>Program Committee </p><p>Constantinos Koutsojannis (Co-chair) </p><p>Department of Physiotherapy, Technological Educational Institution of Patras, Greece </p><p>Spiros Sirmakessis (Co-chair) Department of Applied Informatics in Administration and Economy, Technological Educational Institution of Messolongi, Greece </p><p>Spiros Likothanasis Computer Engineering and Informatics Department, University of Patras, Greece </p><p>Maria Rigkou Computer Engineering and Informatics Department, University of Patras, Greece </p><p>Martin Rajman Global Computing Center, Ecole Polytechnique Federale de Lausanne, Switzerland </p><p>Michalis Xenos Hellenic Open University, Greece Thrasyvoulos Tsiatsos Department of Informatics, Aristotle University of </p><p>Thessaloniki, Greece Georgios Miaoulis Department of Informatics, TEI of Athens Greece Nikiktas Karanikolas Department of Informatics, TEI of Athens Greece Dimitri Plemenos XLIM laboratory, University of Limoges, France Pierre-Franois Bonnefoi XLIM laboratory, University of Limoges, France Grigorios Beligiannis Department of Business Administration in Food and </p><p>Agricultural Enterprises, University of Ioannina, Greece </p><p>Vassilios Tampakas Department of Accounting, TEI of Patras, Greece </p></li><li><p>Contents</p><p>Evaluation of Morphological Features for Breast CellsClassification Using Neural NetworksHarsa Amylia Mat Sakim, Nuryanti Mohd Salleh, Mohd Rizal Arshad,Nor Hayati Othman . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1</p><p>A BP-Neural Network Improvement to Hop-Counting forLocalization in Wireless Sensor NetworksYurong Xu, James Ford, Eric Becker, Fillia S. Makedon . . . . . . . . . . . . . . . 11</p><p>SudokuSatA Tool for Analyzing Difficult Sudoku PuzzlesMartin Henz, Hoang-Minh Truong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25</p><p>Understanding and Forecasting Air Pollution with the Aid ofArtificial Intelligence Methods in Athens, GreeceKostas D. Karatzas, George Papadourakis, Ioannis Kyriakidis . . . . . . . . . . . 37</p><p>Multiple Target Tracking for Mobile Robots Using the JPDAFAlgorithmAliakbar Gorji, Mohammad Bagher Menhaj, Saeed Shiry . . . . . . . . . . . . . . . 51</p><p>An Artificial Market for Emission PermitsIoannis Mourtos, Spyros Xanthopoulos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69</p><p>Application of Neural Networks for InvestigatingDay-of-the-Week Effect in Stock MarketVirgilijus Sakalauskas, Dalia Kriksciuniene . . . . . . . . . . . . . . . . . . . . . . . . . . . 77</p><p>WSRPS: A Weaning Success Rate Prediction System Basedon Artificial Neural Network AlgorithmsAustin H. Chen, Guan Ting Chen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89</p><p>Dealing with Large Datasets Using an Artificial IntelligenceClustering ToolCharalampos N. Moschopoulos, Panagiotis Tsiatsis,Grigorios N. Beligiannis, Dimitrios Fotakis, Spiridon D. Likothanassis . . . 105</p></li><li><p>X Contents</p><p>An Application of Fuzzy Measure and Integral for DiagnosingFaults in Rotating MachinesMasahiro Tsunoyama, Hirokazu Jinno, Masayuki Ogawa, Tatsuo Sato . . . 121</p><p>A Multi-agent Architecture for Sensors and Actuators FaultDetection and Isolation in Case of Uncertain ParameterSystemsSalma Bouslama Bouabdallah, Ramla Saddam, Moncef Tagina . . . . . . . . . . 135</p><p>A User-Friendly Evolutionary Tool for High-SchoolTimetablingCharalampos N. Moschopoulos, Christos E. Alexakos, Christina Dosi,Grigorios N. Beligiannis, Spiridon D. Likothanassis . . . . . . . . . . . . . . . . . . . 149</p><p>HEPAR: An Intelligent System for Hepatitis Prognosis andLiver Transplantation Decision SupportConstantinos Koutsojannis, Andrew Koupparis,Ioannis Hatzilygeroudis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163</p><p>Improving Web Content Delivery in eGoverment ApplicationsKostas Markellos, Marios Katsis, Spiros Sirmakessis . . . . . . . . . . . . . . . . . . 181</p><p>Improving Text-Dependent Speaker Recognition PerformanceDonato Impedovo, Mario Refice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199</p><p>Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213</p></li><li><p>C. Koutsojannis and S. Sirmakessis (Eds.): Tools &amp; Appli. with Artificial Intel., SCI 166, pp. 19. springerlink.com Springer-Verlag Berlin Heidelberg 2009 </p><p>Evaluation of Morphological Features for Breast Cells Classification Using Neural Networks </p><p>Harsa Amylia Mat Sakim1, Nuryanti Mohd Salleh1, Mohd Rizal Arshad1, and Nor Hayati Othman2 </p><p>1 School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Seberang Perai Selatan, Pulau Pinang, Malaysia harsaamylia@ieee.org 2 School of Medical Sciences, Universiti Sains Malaysia, Health Campus, 16150 Kubang Kerian, Kelantan, Malaysia hayati@kb.usm.my </p><p>Abstract. Rapid technology advancement has contributed towards achievements in medical applications. Cancer detection in its earliest stage is definitely very important for effective treatments. Innovation in diagnostic features of tumours may play a central role in development of new treatment methods. Thus, the purpose of this study is to evaluate proposed morphologi-cal features to classify breast cancer cells. In this paper, the morphological features were evalu-ated using neural networks. The features were presented to several neural networks architecture to investigate the most suitable neural network type for classifying the features effectively. The performance of the networks was compared based on resulted mean squared error, accuracy, false positive, false negative, sensitivity and specificity. The optimum network for classification of breast cancer cells was found using Hybrid Multilayer Perceptron (HMLP) network. The HMLP network was then employed to investigate the diagnostic capability of the features indi-vidually and in combination. The features were found to have important diagnostic capabilities. Training the network with a larger number of dominant morphological features was found to significantly increase the diagnostic capabilities. A combination of the proposed features gave the highest accuracy of 96%. </p><p>Keywords: Morphological features Breast cancer Fine needle aspirates Neural network Classification. </p><p>1 Introduction </p><p>Breast cancer is the most commonly diagnosed cancer and it is the leading cause of cancer-related death among women. It is estimated that approximately one in 12 women will develop breast cancer in their lifetime. The majority of breast cancers (95%) are sporadic. Only a small proportion, particularly those diagnosed in young women, are due to a highly penetrant autosomal-dominant trait. There has been con-siderable progress in the identification and localization of the morphological features of tumors responsible for hereditary breast cancer. Early detection is the key to recog-nize the stage of the disease in order to implement a proper treatment [6]. </p><p>An alternative diagnostic method to mammography is using fine needle aspiration (FNA) technique. FNA provide a way to examine a small amount of tissue from the </p></li><li><p>2 H.A.M. Sakim et al. </p><p>tumor. Conventionally, FNA smear slides are viewed under the microscope to deter-mine the malignancy. By carefully examining both the characteristics of individual cells and important contextual features for instance the size of cell clumps have been able to diagnose successfully using FNA [7]. Specific morphological patterns or fea-tures are invest...</p></li></ul>