时间：2018-12-15 08:00 至 2018-12-17 18:00
会议时间：2018-12-15 08:00至 2018-12-17 18:00结束
所有的录用论文将由 Science Publishing Group出版，录用文章出版于如下期刊：
Journal of Electrical and Electronic Engineering (JEEE)
ISSN Print: 2329-1613 ISSN Online: 2329-1605
Automation, Control and Intelligent Systems (ACIS)
ISSN Print: 2328-5583 ISSN Online: 2328-5591
International Journal of Mechanical Engineering and Applications (IJMEA)
ISSN Print: 2330-023X ISSN Online: 2330-0248
International Journal of Intelligent Information Systems (IJIIS)
ISSN Print: 2328-7675 ISSN Online: 2328-7683
Science Discovery (SD)
ISSN Print: 2331-0642 ISSN Online: 2331-0650
(以上合作期刊可被中知网及部分国外的检索机构检索，如WorldCat, Electronic Journals Library, Zeitschriftendatenbank, EZB, Academickeys, ResearchBib, Polish Scholarly Bibliography, Wissenschaftszentrum Berlin等)
Dr. Mohd Afizi Mohd Shukran, Associate Professor
Faculty of Science and Defence Technolog, Department of Computer Science, Universiti Pertahanan Nasional Malaysia, Malaysia
Biography: Dr. Mohd Afizi Mohd Shukran ,currently an Associate Professor in Department of Computer Science in Universiti Pertahanan Nasional Malaysia (UPNM). He has several research experiences including 60 published journals and over 40 proceedings. Also, he has several computer science professional certifications such as MCSE, MCSA and ENSA. About education backgrounds, he obtained the bachelor degree in Information System from Melbourne University, Australia. Then he got his Master of Information Technology and Doctor of Philosophy (PhD) in Sydney University, Australia.
Topic: Swarm Intelligence in Computer Network
Abstract: Data classification involves solving problems by analyzing data already present in databases. Due to the explosive growth of both business and scientific databases, extracting efficient classification rules from such databases has become an important task. This is because classification technique is an important form of knowledge extraction and can help to make key decisions. Nevertheless, classification technique can be improved by integrating the latest technology, namely, Swarm Intelligence. This study proposes two types of classification techniques: Artificial Bee Colony, and Intelligent Dynamic Swarm, which are both based on Swarm Intelligence. This is because Swarm Intelligence has the capability to adapt well in changing environments and is immensely flexible and robust. The first swarm based classifier involves using the advantages of Artificial Bee Colony as an optimization tool to do the data classification. This proposed Artificial Bee Colony based classifier has been implemented to the Anomaly based Network Intrusion Detection System. To our knowledge, it is the first time that the Artificial Bee Colony technique has been applied to solve the network intrusion detection problem. Another swarm based classifier that has been proposed in this study is a novel Intelligent Dynamic Swarm, which is based on Particle Swarm Optimization. Unlike a conventional Particle Swarm Optimization algorithm, this novel algorithm can directly cope with discrete variables. In addition, Intelligent Dynamic Swarm can successfully avoid premature convergence, which is considered a serious drawback of traditional Particle Swarm Optimization. These two proposed new swarm based data classification algorithms have been evaluated using the UCI data set, KDD-99 datasets developed by MIT Lincoln Labs, and the pre-processed image data. The experimental results showed that both the Anomaly based Network Intrusion Detection System and Intelligent Dynamic Swarm are robust and able to achieve high classification accuracy in a changing environment within the data instances. Therefore, both proposed classifiers can provide a promising direction for solving complex problems that may not be solved by traditional approaches.
Dr. Lazim Abdullah, Professor
School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, Malaysia
Biography: Dr. Lazim Abdullah is a professor of computational mathematics at the School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu. He holds a B.Sc (Hons) in Mathematics from the University of Malaya, Kuala Lumpur in June 1984 and the M.Ed in Mathematics Education from University Sains Malaysia, Penang in 1999. He received his Ph.D. in Information Technology Management from the Universiti Malaysia Terengganu in 2004. His research focuses on the mathematical theory of fuzzy sets, decision making methods and its applications to social ecology, environmental sciences, health sciences, socio-economics, and technology management and manufacturing engineering. His research findings have been published in over three hundred publications, including refereed journals, conference proceedings, chapters in books, monographs and research books. Currently, he is a member of editorial boards of several international journals related to computing and applied mathematics. He is also a regular reviewer for international impact factor journals, member of scientific committees of several symposia and conferences at national and international levels. Dr Abdullah is an associate member of IEEE Computational Intelligence Society, and a member of the International Society on Multiple Criteria Decision Making.
Topic: The Use of Intuitionistic Fuzzy DEMATEL in Developing Cause and Effect Criteria in Sub-Contractors Selection
Abstract: Subcontractors usually help general contractors to overcome problems that related to the need for special expertise, limitation in finances and shortage in resources. However, selecting a good sub-contractor is not a trivial task as many criteria need to be wisely categorized. The purpose of this paper is to develop groups of causes and effect criteria of sub-contractors’ selection using Intuitionistic Fuzzy DEMATEL method. A group of experts’ opinions were sought to provide linguistic evaluations regarding the degree of influence between criteria in sub-contractors selection. Matlab software was used to assist in developing causal diagram where groups of cause and effect criteria in sub-contractors selection are identified. The results show that four criteria are grouped as cause criteria while six criteria are grouped as effect criteria. The result also suggests that the criteria ‘experience’ is the main cause that influences the selection of subcontractors. The categorization of cause and effect criteria would be a great significance for the practical implementation of the sub-contractors selection.
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