ICGEC 2010 Keynote Speeches
 |
Professor Ajith Abraham |
| Topic: Nature Inspired Heuristics: Quo Vadis ? | |
Abstract: In this talk, we present the importance of nature inspired meta-heuristic techniques for solving some of the complex global optimization problems. We start with the review of some of the popular algorithms based on Evolution and Swarm Intelligence and then we focus on some of the recent heuristics based on Foraging (Bacterial foraging optimization) and Music (Harmony search). Using Empirical studies, we illustrate the performance of these algorithms for solving global optimization problems. |
|
Biography: Ajith's research and development experience includes nearly 20 years in the Industry and Academia. He works in a multi-disciplinary environment involving machine intelligence, network security, sensor networks, e-commerce, Web intelligence, Web services, computational grids, data mining and applied to various real world problems. He has given more than 40 plenary lectures and conference tutorials in these areas. He has published over 600+ publications and some of the works have also won best paper awards at International conferences and also received several citations. He Co-Chairs the IEEE SMC Technical Committee on Soft Computing. Currently he is also coordinating the activities of the Machine Intelligence Research Labs (MIR Labs), International Scientific Network of Excellence, which has members from over 60 countries. He has a world wide academic experience with formal appointments in Monash University, Australia; Oklahoma State University, USA; Chung-Ang University, Seoul; Jinan University, China; Rovira i Virgili University, Spain; Dalian Maritime University, China; Yonsei University, Seoul and Open University of Catalonia, Spain, National Institute of Applied Sciences (INSA), France and Norwegian University of Science and Technology (NTNU), Norway. For about 2.5 years, he was working under the Institute of Information Technology Advancement (IITA) Professorship Program funded by the South Korean Government. He received Ph.D. degree in Computer Science from Monash University, Australia and a Master of Science degree from Nanyang Technological University, Singapore. He serves the editorial board of several reputed International journals and has also guest edited over 35 special issues on various topics. He is actively involved in the Hybrid Intelligent Systems (HIS); Intelligent Systems Design and Applications (ISDA); Information Assurance and Security (IAS); and Next Generation Web Services Practices (NWeSP) series of International conferences, besides other conferences. He is a Senior Member of IEEE , IEEE Systems Man and Cybernetics Society, IEEE Computer Society, IET (UK), IEAust (Australia) etc. More information at: |
|
 |
Professor Wen-Lian Hsu |
| Topic: Capturing Semantics: Knowledge-Based or Machine Learning Approach? | |
Abstract: IIn natural language processing, many machine learning models, such as maximum entropy, conditional random fields, are based on feature selection and parameter tuning. If the test set is not sufficiently correlated with the training set, the model is less likely to perform well. However, in practice, it looks as though the training set is never large enough to cover most test cases, especially those in user domains. Although adaptation would usually ease the problem a little, current machine learning models do not seem to catch the semantics or the context in free texts, which makes them less portable. After all these years, the following questions remain unanswered: “What kind of knowledge should be extracted from a training set to make the model more general?” “How do we minimize the laborious human annotation task on the training set?” “How do we combine knowledge from heterogeneous sources effectively?” “Which approach is better: knowledge-based or machine learning?” In this talk we shall discuss strategies that attempt to tackle these challenges. |
|
Biography: Wen-Lian Hsu received a B.S. in Mathematics from National Taiwan University, a Ph.D. in operations research from Cornell University. His earlier work was on graph algorithms. He then applied similar techniques to tackle computational problems in biology and natural language. Dr. Hsu is particularly interested in applying natural language processing techniques to the understanding of DNA sequences as well as protein sequences, structures and functions and also to biological literature mining. He received many awards from the National Science Council, including the Distinguished Research Award in 1991, 1994, 1996 and the Appointed Distinguished Research fellow Award in 2005. He received the first K. T. Li Research Breakthrough Award in 1999, the IEEE Fellow in 2006, the Teco Technology Award in 2008, and the Outstanding Research Award of Pan Wen Yuan Foundation. He has been the president of the Artificial Intelligence Society in Taiwan from 2001 to 2002 and is currently the Director of the TIGP Bioinformatics Program in Academia Sinica. |
|
 |
Professor John Roddick |
| Topic: Genetic Algorithms, Pattern Languages and Higher Order Mining | |
Abstract: Genetic Algorithms, Pattern Languages and Higher Order Mining have a lot to offer each other. The computational flexibility of genetic algorithms coupled with the ability to encapsulate best practice through pattern languages and to accommodate the power of higher order data mining offers a powerful combination. This talk to discuss the confluence of these three areas outlining areas of current work and opportunities for new research. |
|
Biography: Professor John Roddick is currently the Dean of the School of Computer Science, Engineering and Mathematics at Flinders University, Adelaide, Australia.
Since the late 1980s Professor Roddick has contributed to the area of conceptual modelling and intelligent databases including the development of techniques for data summarisation, spatio-temporal databases, query languages, evolution and change in data and metadata management, information semantics and, data mining and knowledge discovery. His work has resulted in contributions to the design and development of database architectures, query languages and systems that enable the semantics inherent in data to be more readily understood and manipulated, thus enabling systems to adapt. His research agenda has a particular focus on temporal, complex and large volumes of data, commonly using medical data as the application domain.
Professor Roddick has published widely, including a number of well-cited surveys. He maintains active collaborative links with a number of researchers internationally. He has undertaken commercial research contracts with a number of organisations in his area of expertise including Australia's Defence Science and Technology Organisation, the Royal Australasian College of Surgery, EDS, PriceWaterhouseCoopers and Power-Solutions (with whom he has just completed a $2M START project (currently in beta-testing) in medical data mining). He is the founding series editor of the Conferences in Research and Practice in Information Technology (CRPIT) series and is a Fellow of both the Australian Computer Society and of the Institute of Engineers, Australia.
|
|