Keynote Speakers and Plenary Speakers and Invited Speakers

2019 4th International Conference on Intelligent Information Technology (ICIIT 2019) aims to gather professors, researchers, scholars and industrial pioneers all over the world, ICIIT is the premier forum for the presentation and exchange of past experiences and new advances and research results in the field of theoretical and industrial experience. The conference welcomes contributions which promote the exchange of ideas and rational discourse between educators and researchers all over the world. We aim to building an idea-trading platform for the purpose of encouraging researcher participating in this event. ICIIT 2019 is welcome qualified persons to delivery a speech in the related fields. If you are interested, please send a brief CV with photo to the conference email box:

Keynote Speaker

Prof. Francis Y. L. Chin
Emeritus Professor, The University of Hong Kong, Hong Kong

Professor Chin received his B.A.Sc. degree from the University of Toronto in 1972, and his M.S., M.A. and Ph.D. degrees from Princeton University in 1974, 1975, and 1976, respectively. Prior to joining The University of Hong Kong (HKU) in 1985, he had taught at the University of Maryland, Baltimore County; the University of California, San Diego; the University of Alberta; the Chinese University of Hong Kong; and the University of Texas at Dallas. Professor Chin was the Chair of the Department of Computer Science at HKU and was the founding Head of the Department from its establishment until December 31, 1999. From 2002 until July 31, 2006, he had served as the Associate Dean of the Graduate School. From 2007 to his retirement from HKU in 2015, Prof Chin had served as an Associate Dean of the Faculty of Engineering. Professor Chin is an IEEE Fellow and his research interests include design and analysis of algorithms, machine learning, and bioinformatics including Motif-finding (Motif discovery) and De Novo genome assembly (IDBA). Professor Chin is now an Emeritus Professor of The University of Hong Kong.

Speech Title: "Metagenomic Binning of Next-Generation Sequence (NGS) Reads"

Abstract: Next-generation sequencing (NGS) technologies allow the sequencing of microbial communities directly from the environment without prior culturing. The output of environmental DNA sequencing consists of many reads from genomes of different unknown species, making the clustering together reads from the same (or similar) species (also known as binning) a crucial step. Metagenomic binning remains an important topic in Metagenomic analysis. The difficulties of the unsupervised binning methods for NGS reads are due to the following factors: (1) the lack of reference genomes; (2) uneven abundance ratio of species (especially with some extremely low-abundance species); (3) short NGS reads; and (4) a large number of species. In this talk, the possible approaches for handling these difficulties will be discussed.

Prof. Mohd Zaid Bin Abdullah
Universiti Sains Malaysia, Malaysia

Mohd Z. Abdullah graduated from Universiti Sains Malaysia (USM) with a B. App. Sc. degree in Electronic in 1986 before joining Hitachi Semiconductor as a test engineer. In 1989, he commenced an M.Sc. in Instrument Design and Application at University of Manchester Institute of Science and Technology, UK. He remained in Manchester conducting research in Electrical Impedance Tomography at the same university, and received his Ph.D. degree in 1993. He joined USM in the same year as a lecturer. His research interests include microwave tomography, digital imaging, and ultra wide band sensing. He has published numerous research articles in international journals and conference proceedings. One of his papers was awarded The Senior Moulton medal for the best article published by the Institute of Chemical Engineering in 2002. He is also a recipient of many prestigious international fellowship awards such as the Association of the Commonwealth Universities (UK), the Japanese Society Promotion of Science (Japan), the Royal Society (UK) and the Engineering Physical Sciences Research Council (UK). Presently he is a Professor and Director of the Collaborative Microelectronic Design Excellence Centre (CEDEC), Universiti Sains Malaysia. Professor Mohd Zaid Abdullah is a Chartered Engineer and Fellow of the Institute of Engineering and Technology (IET), UK.

Speech Title: "Tomgraphic Imaging with Ultra-Wide Band (UWB) Sensors"

Abstract: One fundamental weakness of microwave imaging is resolution. Good resolution demands a small wavelength and therefore high frequency. Higher frequencies, in the other hand, are attenuated more rapidly, and the adequate depth of penetration dictates a low frequency, no higher than 5 MHz. This is the main problem that plagues almost all microwave systems. The second weakness is small field of view resulting from the non-availability at this time of an efficient miniturised type microwave sensor with large bandwidth. This problem is particularly chronic in organ sensing where at this point and except for specialised area, this type of application requires the size of antenna to be relatively small compared to the field of view. The third drawback is related to the inherent multiple scaterring effect. This requires very complicated image reconstruction algorithm and advanced signal processing technique. Currently, most algorithms are based on the beam-forming methods such as the Delay and Sum (DAS) or its variants. The appealing features of this method lies in its simplicity and computational efficiency. However, it only produces approximate solution since the field data is lost due to the linearisation of the inversion procedures. All these problems put the microwave system at a disadvantage. The advent of ultra wide band (UWB) technology and high frequency dielectric resonator antenna (DRA) stimulated new interest in this field as its potential for new applications was recognised. This keynote addresses the development of UWB research at USM, focusing on two potential applications – breast cancer detection and through-the-wall-imaging.

Plenary Speaker

Prof. Chuan-Ming Liu
National Taipei University of Technology, Taiwan

Dr. Chuan-Ming Liu is a professor in the Department of Computer Science and Information Engineering(CSIE), National Taipei University of Technology(Taipei Tech), TAIWAN, where he was the Department Chair from 2013-2017. Currently, he is pointed to be the Head of the Extension Education Center at the same school. Dr. Liu received his Ph. D. in Computer Science from Purdue University in 2002 and joined the CSIE Department in Taipei Tech in the spring of 2003. In the summers of 2010 and 2011, he had held visiting appointments at Auburn University and Beijing Institute of Technology, respectively. He has services in many journals, conferences and societies as well as published more than 80 papers in many prestigious journals and international conferences. He was the co-recipients of ICUFN 2015 Excellent Paper Award, ICS 2016 Outstanding Paper Award, MC 2017 Best Poster Award, and WOCC 2018 Best Paper Award. His current research interests include big data management and processing, uncertain data management, data science, spatial data processing, data streams, ad-hoc and sensor networks, location based services.

Speech Title: "Recent Topics on Data Management"

Abstract: In these days, many applications of IoT, Big Data, and Cloud computing have been proposed and discussed. One of the common important issues in these applications is data management and processing. The data in the emerging environments, such as the sensed data, may not be accurate and are referred to as uncertain data. The uncertainty complicates the computing or processing on the data and is inherent in many applications. In addition, the data produced or generated are dynamic and continuous. For example, the monitored data on the product lines in a factory are generated and collected ceaselessly. One of the objectives in such a system is to derive the feedbacks from the data streams instantly. Therefore, the data we now face and manipulate have the properties of velocity, veracity, and volume. The conventional approaches for query processing therefore need to be examined, adapted, or re-designed if necessary. In this talk, the recent issues about managing the data with such properties are introduced and the processes of some interesting query types are presented as well, including probabilistic nearest neighbor query, probabilistic skyline query, and probabilistic top-k dominating query.

Assoc. Prof. Norma Binti Alias
Universiti Teknologi Malaysia, Malaysia

Norma Alias is an Associate Professor at Department of Mathematical Sciences, UTM Johor Malaysia and Research Fellow at Centre for Sustainable Nanomaterials, Ibnu Sina Institute for Scientific and Industrial Research, UTM Johor Bahru (CSNano). She received her Ph.D. in Industrial Computing and Parallel Computing at the Universiti Kebangsaan Malaysia in 2004. She has experience in industrial computing, numerical computation, scientific computing, high performance computing on distributed parallel computer systems, grid computing and software development. Currently, she is a Research Fellow at CSNano. There are 9 innovations and invention medals received, published more than 200 publications, 4 Intellectual property declarations, 2 patent disclosures, 2 product commercialization. She has completed 20 research grants and handling ongoing task as project leader and principal researcher for 16 number of research grants with more than RM 2,000,000 budget. Thus, the three parallel computer systems laboratories have been developed and connected with LAN and MYREN network at the Ibnu Sina Institute, Center of Excellence, UTM. Principle researcher and leader for GRID Computing lab, mathematical parallel software and multicore computing laboratories. The research plan is to contribute in grid technology and middleware combining the worldwide cluster of distributed computer systems for solving the 4iR grand challenge and big data applications.

Speech Title: "The Impact of Big Data Model and Simulation for Valuable Concepts of Property Rights in Numerical Perspective"

Abstract: Recently, big data have received greater attention in diverse research in transdiciplinary field. Protecting Property Rights (PR) in a big data model and simulation give impact to the level of innovation and tie key performance indicators to real-world results. The challenges of PR protection for big data lifecycle highlight the database directive, advantages, and eligible for the protection and granted. To ensure a high scientific standard outcome, a big data simulation constructed by a complex system of mathematical modeling, large scale discretization of the model and huge data simulation. Therefore, there are many potential valuable intellectual properties of PRs to be claimed during the process of big data simulation supported by the multiprogramming system on high speed synchronization processors and pertaining to many indicators for performance evaluation. This paper proposed 5 potentially valuable idea’s impact from the transformation process of small to big data simulation in numerical perspective. The categorization of the valuable ideas and concepts will consider a copyright and patent granted. Copyrighting and patenting the parallel procedure, parallel code and high performance user interface may be eligible to be patented. As a conclusion, this paper has the ability to claim 5 potential PRs protection by enhancing the legal conceptual framework, technology fields and its impact in determining the real solution, high quality resolution and accurate solution of big data visualization.