The Organiser:

Faculty of Computer Science and Information Technology, UPM

Universiti Putra Malaysia


Keynote Speakers


Keynote Speaker 1    

Professor Jemal H. Abawajy
Deakin University, Australia

He is currently the Director of the Parallel and Distributing Computing Laboratory. He is a Senior Member of IEEE Computer Society; IEEE Technical Committee on Scalable Computing (TCSC); IEEE Technical Committee on Dependable Computing and Fault Tolerance and IEEE Communication Society.

Prof. Abawajy’s leadership is extensive spanning industrial, academic and professional areas. He has served on the Academic Board, Faculty Board, IEEE Technical Committee on Scalable Computing Performance track coordinator, Research Integrity Advisory Group, Research Committee, Teaching and Learning Committee and Expert of International Standing Grant and external PhD thesis assessor. Prof. Abawajy has delivered more than 50 keynote addresses, invited seminars, and media briefings and has been actively involved in the organization of more than 200 national and international conferences in various capacity including chair, general co-chair, vice-chair, best paper award chair, publication chair, session chair and program committee. He has also served on the editorial-board of numerous international journals and currently serving as associate editor of the International Journal of Big Data Intelligence and International Journal of Parallel, Emergent and Distributed Systems. He has also guest edited many special issues. Prof. Abawajy is actively involved in funded research supervising large number of PhD students, postdoctoral, research assistants and visiting scholar in the area of Cloud Computing, Big Data, Network and System Security, Decision Support System, and E-healthcare. He is the author/co–author of five books, more than 250 papers in conferences, book chapters and journals such as IEEE Transactions on Computers and IEEE Transactions on Fuzzy Systems. He also edited 10 conference volumes. More info at

Keynote Speaker 2    

Dr. Sung-Bae Cho, Professor, Department of Computer Science, Yonsei University, Seoul, Korea.

Dr. Cho received the Ph.D. degree in computer science from KAIST (Korea Advanced Institute of Science and Technology), Korea, in 1993. He was an Invited Researcher of Human Information Processing Research Laboratories at Advanced Telecommunications Research (ATR) Institute, Japan from 1993 to 1995, and a Visiting Scholar at University of New South Wales, Australia in 1998. He was also a Visiting Professor at University of British Columbia, Canada from 2005 to 2006. Since 1995, he has been a Professor in Department of Computer Science, Yonsei University, Korea. Dr. Cho has been serving as an associate editor for several journals including IEEE Transactions on CI and AI on Games (2009-present) and IEEE Transactions on Fuzzy Systems (2013-present). He was also the chair of Games Technical Committee, IEEE CIS (2009-2010), and Student Games-based Competition Subcommittee, IEEE CIS (2011-2012). He is a member of Board of Government (BoG) of Asia Pacific Neural Networks Assembly (APNNA) (2011-present), and a member of three technical committees in IEEE CIS such as Emergent Technologies, Computational Finance and Economics, and Games. His research interests include hybrid intelligent systems, soft computing, evolutionary computation, neural networks, pattern recognition, intelligent man-machine interfaces, and games. He has published over 230 journal papers, and over 680 conference papers.


Title: Prospects and Challenges of Hybrid Deep Learning

Recently, deep learning opens another renaissance of artificial intelligence that is a long dream of human-beings. There are four representative models for deep learning, and two of them can be convolutional neural networks and recurrent neural networks. In this talk, I will give the general idea of both methods, and discuss about the prospects and challenges. Especially, to work out realistic problems, we need a hybrid architecture of several deep learning models. I will also present a generative model via an adversarial process gets a great attention due to the amazing demonstration of performance. It can simultaneously train a generative model to capture the data distribution, and discriminative model to estimate the probability that a sample came from the training data. In this talk, we present a new method of transfer-generative adversarial network (tGAN) with auto-encoders to detect anomaly in malicious software (malware) for computer security. Experiments with the malware dataset from the Kaggle Microsoft malware classification challenge ( show that the tGAN achieves 95.74% average classification accuracy which is higher than accuracy of other state-of-the-art methods and increases the learning stability.













Keynote Speaker 3    


Tengku Shahrizam bin Tengku Abdullah Sulaiman, Cyber Security Architect  of Cisco Systems

Tengku Shahrizam is Cisco Cyber Security architect and business development leader in Malaysia. In his role, he represents Cisco as the main spokesperson for Cyber Security technology, including the area of awareness, consultancy & solution development. He is an active security practitioner with more than 18 years of experience and more than 11 years certified in Cisco Certified Internetwork Expert in Security (CCIE#16734). He is also well known as Cyber Security educator and Industry Advisor in local universities.


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