Keynote Speaker


Prof. Dr. Lim Chee Peng


Chee Peng Lim received his Ph.D. degree from Department of Automatic Control and Systems Engineering, the University of Sheffield, UK, in 1997. He has published over 300 technical papers in journals, conference proceedings, and books, edited four books and 15 special issues in journals over the years. Recipient of seven best paper awards in international conferences, his research focuses on design and development of computational intelligence-based autonomous learning systems for pattern recognition, optimisation, condition monitoring, medical prognosis and diagnosis, and decision support.

He collaborates closely with researchers in the international arena, whereby he has received the Australia-India Senior Visiting Fellowship (by Australian Academy of Science), Australia-Japan Emerging Research Leaders Exchange Program (by Australian Academy of Technological Sciences and Engineering), Commonwealth Fellowship (at University of Cambridge), Fulbright Scholarship (at University of California, Berkeley), and Visiting Scientists Program of the US Office of Naval Research Global (at Harvard University and Stanford University). He has been a visiting professor at VIT University, India, Minghsin University of Science and Technology, Taiwan, as well as University of South Australia. Currently, he is Associate Director (Research) at Institute for Intelligent Systems Research and Innovation, Deakin University, Australia.


Computational Data Modelling: Methods and Applications

Computational intelligence is a broad discipline that encompasses a variety of methodologies inspired by human and/or animal intelligence. In this talk, the use of computational intelligence techniques for data modelling is elucidated. Specifically, the architectures for designing data-based intelligent systems that can learn incrementally and perpetually are illustrated. A number of computational intelligence-based models to derive the underlying algorithms of learning systems are exemplified. Three key properties for data analytics and decision support applications, namely online learning, knowledge elicitation, and trust measurement, are explained. Real-world case studies to ascertain the efficacy of the resulting autonomous learning systems in practical environments are demonstrated.