Speaker on ICMAI 2019

 

Prof. En-Bing Lin, Central Michigan University, USA

Dr. En-Bing Lin is Chair and Professor of Mathematics at Central Michigan University, USA. He has taught and visited at several institutions including Massachusetts Institute of Technology, University of Wisconsin-Milwaukee, University of California, Riverside, University of Toledo, UCLA, and University of Illinois at Chicago. He received his Ph. D. in Mathematics from Johns Hopkins University. His research interests include Data Analysis, Image Processing, Applied and Computational Mathematics, Wavelet Analysis and Applications, and Mathematical Physics. He has supervised a number of graduate and undergraduate students. Dr. Lin serves on the editorial boards of several mathematics journals and several academic committees of regional and national associations. He has organized several special sessions at regional IEEE conference and American Mathematical Society national and regional meetings.

Speech Title: Generalized Fuzzy Rough Approximations in Analyzing Large Scale Information Systems

Abstract: We begin with a brief overview of current trends of big data analytics to process information systems.  As a powerful artificial intelligence tool, rough set approach is of fundamental importance. In this talk, we represent a data set of an information system as a table and show how we pass from classical rough set theory to variable precision generalized rough set theory (VPGRS). We recall the connection between the concepts of VPGRS-model and neighborhood systems through binary relations. We provide characterizations of lower and upper approximations for VPGRS-model by using minimal neighborhood systems. Fuzzy rough set theory extends rough set theory via a general approach to the fuzzification of rough sets. We develop a generalized fuzzy rough approximation by incorporating VPGRS with fuzzy rough sets and show how to determine the discernibility threshold for a reflexive relational decision system in the variable precision generalized fuzzy rough set model. As applications, we propose some parallel distributed computations to analyze the systems.

 

Assoc. Prof. Qing Tan,
School of Computing and Information Systems, Athabasca University, Canada

Dr. Qing Tan is an associate professor in School of Computing and Information Systems at Athabasca University, Canada. He was born and raised in Chengdu. He left his beloved hometown in 1977 to study Aviation Automation at the Northwest Polytechnic University. He earned his PhD in Cybernetics Engineering for Robotics from the Norwegian Institute of Technology (NTNU - Norwegian University of Science and Technology) in 1993. As a foreign senior research fellow, he did the research on Telepresence Robot for the human acts simulation program at the Japan Atomic Energy Research Institute in 1994. He did his post-doctorial fellowship at University of Alberta in 1996. He joined Athabasca University in 2007 with extensive IT industrial working experiences in Canada. Dr. Tan is teaching and developing both undergraduate and graduate courses including Mobile Computing, Computer Networking, E-Commerce, Enterprise Modeling, Cloud Computing, and Big Data Analytics. Dr. Tan’s research interests and engagements include Location-Based Technologies, Mobile Computing, Adaptive Mobile Learning, Telepresence Robot, Cloud Computing, Internet of Things, Big Data Analytics, Cyber-Physical Systems, and Computer Network and Cyber Security. Dr. Tan received several Canadian national and provincial research grants. He has published many research papers on International journals and conferences. He also sits on many international journal editor boards and various conference committees.

Speech Title: Optimization Algorithms for Cloud Computing
Abstract: Cloud Computing as an effective and novel paradigm of computing technology has been rapidly advanced and widely adopted. To meet the tremendous demand for cloud services, Cloud Service Providers (CSPs) strive to gain their market share and maximize their profit through enhancing cloud performance and optimizing their cloud services. Many optimization algorithms have been studied, developed, and applied for cloud service optimization in different cloud deployment models. This speech will explore optimization algorithms and applications for Cloud Computing with a focus on Swarm Intelligence.