Keynote Speakers and Plenary Speakers and Invited Speakers

2018 International Conference on Intelligent Information Technology (ICIIT 2018) 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 2018 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
Hang Seng Management College and Emeritus Professor, 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. He is now working as the Chair Professor and Head of Department of Computing at Hang Seng Management College and is in-charge of a Hong Kong RGC-funded project on Deep Learning.

Speech Title:"Deep Learning and its Recent Developments"

Abstract: There has been a resurgence of research into Artificial Intelligence and its applications, especially Machine Learning in the context of Big Data and Cloud Computing. Over the past few years, Google, Amazon, Baidu, Facebook and many major internet companies have invested significantly in Machine Learning technology, especially the so-called “Deep Learning” using very-large-scale multi-layer neural networks, in order to enhance their services and products, for example, speech/ image recognition, searching, data analytics, robotics, self-driving vehicles, Go-playing, etc.
This talk explains how computers learn and the recent breakthroughs in Deep Learning which affect our lives in many aspects. We shall briefly mention the role of our newly established Deep Learning Research and Application Centre and potential research areas which might benefit from the new technology. The history of the development of machine translation will be reviewed, followed by the key breakthroughs in using a single large neural network for statistical machine translation and our recent developments in translating business documents..

Plenary Speaker

Prof. Akinori Ito
Tohoku University, Japan

Akinori Ito was born in Yamagata, Japan on 1963. He received B.E., M.E., and Ph.D. degrees from Tohoku University, Sendai, Japan, on 1986, 1988 and 1991, respectively. Since 1991, he has worked with Research Center for Information Sciences and Education Center for Information Processing, Tohoku University. He was with the Faculty of Engineering, Yamagata University, from 1995 to 2002. From 1998 to 1999, he worked with the College of Engineering, Boston University, MA, USA, as a Visiting Scholar. He is now a Professor of the Graduate School of Engineering, Tohoku University. He is engaged in speech signal processing, human machine communication, music signal processing and speech-based language learning system. He is a member of the Acoustical Society of Japan, the Information Processing Society of Japan, IEICE, and the IEEE. He was a vice-president of the Acoustical Society of Japan from 2013 to 2014, a chair of IEEE Signal Processing Society Sendai Chapter from 2013 to 2016, and the editor-in-chief of the Acoustical Science and Technology from 2015 to 2016.

Speech Title:"Human-Machine Meta-Communication"

Abstract: "Meta-communication" is defined as "communication about communication". Humans are not only making communication each other but also making meta-communication, which means that a uman who wants to talk to others monitors whether the partner is ready to talk or not, how he/she is interested in the talk, how is the mental situation of the partner, etc. This kind of "meta-communication" is absolutely needed for artificial agents who talk with humans, but only limited approaches have been investigated for this purpose. In this talk, several attempts to establish the "meta-communication" between humans and machines will be introduced.

Plenary Speaker

Prof. Sung-Nien Yu
National Chung Cheng University, Taiwan

Prof. Sung-Nien Yu received both his B.S. and M.S. degrees in Electrical Engineering from the National Taiwan University, Taipei, Taiwan, in 1987 and 1991, respectively. He received his Ph.D. degree in Biomedical Engineering from the Case Western Reserve University, Ohio, USA, in 1996. After graduation, he entered the Department of Physical Therapy at Chang Gung University, Tao-Yuan County, Taiwan and served as an assistant professor from 1996 to 1999. After that, he joined the Department of Electrical Engineering at National Chung Cheng University, Chia-Yi County, Taiwan in 1999 and is currently a professor of the department and the director of the Biomedical Signal Processing and System Design Laboratory. He is a member of the IEEE Engineering in Medicine and Biology Society and a permanent member of the Taiwanese Society of Biomedical Engineering. His research interests include biomedical signal processing, biomedical image processing, and the application of pattern recognition and machine learning technologies to biomedical problems.

Speech Title:"Wavelet Decomposition and Higher Order Statistics for Electrocardiogram-Based Arrhythmia Recognition"

Abstract: Arrhythmias are disorders of the rhythmic beating of the heart. Serious arrhythmias usually lead to heart diseases, stroke, or even sudden death. The electrocardiogram (ECG) is a low-cost, convenient, and non-invasive method to detect the electro-activity changes of the heart. Thus, ECG is usually used in the hospital as a routine and crucial means for the diagnosis of heart diseases by differentiating the pattern changes of different arrhythmias. In order to build an effective computer-aided-diagnosis (CAD) system for heart diseases, our laboratory has been exploring the use of wavelet decomposition and higher order statistics (HOS) to recognize different types of arrhythmias based on ECG. The discrete wavelet transform (DWT) decomposes a signal into subband components. Features extracted from these components can efficiently characterize the original signal in different frequency subbands. On the other hand, the HOS has been demonstrated to effectively suppress the influence of noises. Thus, the integration of the two techniques provides an opportunity not only to extract features that may otherwise been hidden in the original signal but also to reduce the influence of noises at the same time. In this talk, I will explain the method of integrating DWT and HOS for ECG-based arrhythmia recognition. The advantages of this approach, in terms of the recognition rates and noise-tolerance capability when compared with other methods, will be discussed. I will also describe our recent work implementing this system on a smartphone for mobile health (mHealth) applications. Technologies will be addressed about how to transfer the algorithm onto a smartphone to achieve effective and real-time arrhythmia recognition.

Plenary Speaker

Assoc. Prof. Rana E. Ahmed,
American University of Sharjah, UAE

Rana E. Ahmed received his PhD degree in Electrical Engineering from Duke University, NC, USA, in 1991. He has worked as a faculty member at Lakehead University, Canada, and at King Saud University, Saudi Arabia. He was a visiting faculty member at the University of Ottawa during academic year 2007-08 while on sabbatical leave. He also worked at Research In Motion (RIM) (now BlackBerry) and SpaceBridge in Canada in the areas of software testing, ATM switch testing and software quality assurance. He is currently working as an Associate Professor at the Dept. of Computer Science and Engineering, American University of Sharjah, United Arab Emirates. His current research interests include wireless networks, fault-tolerant computing, datacenter architectures, software engineering, and architectures for signal processing applications. He has published 60 research papers in international journals and conferences, and has authored three book chapters.

Invited Speaker

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:"Large Sparse Complex Model and Advance Visualization of Magnetic Nanoparticle for Drug Delivery, Drug Release and Effects of Abnormal Cell Treatment on HPC Platform"

Abstract: Mathematical modeling and simulation are the important tools to predict and visual the nanocarriers movement for delivering anti-cancer drugs to therapeutic sites. It is because the experiment and implementation involve a nano-scale level and very complex process of drug delivery systems along circulation in the blood flow. In additional, the location of the targeted and effected cell is extremely high sensitivity such as brain, breast, prostate and cervical cancers. The technology of magnetic nanoparticle for drug delivery avoids the movement disorders and highlights the size-controlled preparation of drug release at the effected cancer cell. Thus, a large sparse complex model and advance visualization of the nanopartical drug delivery and drug release are the alternative strategies to observe and monitor a long-term drug delivery, adsorption applicability and controlled the release of drugs under simulated digestion conditions of the nanocarriers. This paper focuses on two mathematical models, discretization based on finite element method (FEM), finite difference method (FDM), simulation of parallel algorithms, implementation of high performance computing (HPC) system and analyze using numerical results and parallel performance indicators (PPI). The HPC platform consists a distributed memory architecture integrated with a number of processors to support a large sparse data simulation. The significance of nanodrugs design, delivery patterns and effects of abnormal cell can be observed using the integration of the two mathematical models. The visualization of modeling, simulation effect of abnormal cell and the validation analysis are considered as the outcome of this research. Graph visualization and table form support the numerical results, PPI and discussions. This research provides the accurate prediction for preparing, characterizing and classifying the magnetic nanoparticle to the immunotherapy, nanotechnology and biomedical practitioners.