KEYNOTE SPEAKER 1
Associate Professor Ts Dr Mazlina Abdul Majid
Faculty of Computer Systems & Software Engineering
University Malaysia Pahang (UMP)
Faculty of Computer Systems & Software Engineering
University Malaysia Pahang (UMP)
I am currently an Associate Professor at Universiti Malaysia Pahang (UMP), Malaysia with more than 15 years’ experience as an academic lecturer at Faculty of Computer Systems & Software Engineering, UMP. I received my PHD in Computer Science from University of Nottingham, UK. I hold various responsibilities in the administrative works including 6 years as the Deputy Dean of Research and Graduate Studies. I am currently a Head of Software Engineering Research Group and an Editor in Chief for International Journal of Computer Systems & Software Engineering. I am one of the academic program committee in UMP and other universities due to my vast experiences in teaching master and undergraduate courses. My research work focusses on Green Sustainability, Simulation Modelling, Software Agent and Software Usability Testing. I have published more than 100 publications in high impact books, journals and conference proceedings. Moreover, I have shown an excellent achievement in research competition by wining gold medals and various awards in local and international exhibitions. In addition, I have obtained 6 copyrights as the principal investigator for my research work. My outstanding performance in academic and research has been recognized locally and internationally.
KEYNOTE TITLE: Modelling Human Traffic Flow using
Agent Based and Discrete Event Simulation Simulation appears to be the preferred choice as a modelling and simulating tool for investigating human behaviour. This is due to diversity of human behaviours is more accurately depicted by using simulation. Throughout the literature, the best-known simulation techniques for modelling and simulating human behaviour are DES and ABS. DES models represent a system based on a series of chronological sequences of events where each event changes the system’s state in discrete time. ABS models comprise several autonomous, responsive and interactive agents which cooperate, coordinate and negotiate among one another to achieve their objectives. DES and ABS can deal with individual elements such as individual behaviour which is located at low abstraction level (greater detail of the problem under investigation). However, it is impossible to model certain human behaviour such as human queuing using purely ABS due to the independent entities inside ABS are decentralised. On the flip side, DES is found suitable to model human queuing and priority sorting due to its event scheduler structure. Therefore, bringing DES inside ABS is significant in modelling the diversity of human behaviours as realistic as possible; such as modelling human traffic flow. Human traffic flow modelling is important for construction or redesigning projects such as shopping centers, airports or railway stations. In addition, simulation analyses can be used by architects in the designing stage or by civil authorities to simulate evacuations for a good design of buildings and pathway projects.
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Associate Professor Ts Dr Mazlina Abdul Majid
Faculty of Computer Systems & Software Engineering University Malaysia Pahang (UMP) |
KEYNOTE SPEAKER 2
Dr. Massudi Mahmuddin
School of Computing, Universiti Utara Malaysia (UUM)
School of Computing, Universiti Utara Malaysia (UUM)
Dr. Mahmuddin obtained his PhD in 2008 in the areas of system engineering, Cardiff University, United Kingdom. He is currently a senior lecturer with the Department of Computer Science, School of Computing, Universiti Utara Malaysia (UUM). During last 18 years of his stay at the school, his teaching, research and development interests have been towards of technical and social aspect of computing, computational intelligent and expert system. Currently he is Dean of Student Development and Alumni for College of Arts and Sciences, UUM. He is also member for Malaysian Statistic Association, Internet Society, chairing for P2A Malaysian Chapter (an association of students mobilities in ASEAN countries), and coordinator for School-UUM Cluster of excellent under Ministry of Education. In UUM, besides serving as Examiner for Master and PhD theses, he is also a Senate member. He is also regularly invited as an external examiner for Master and Ph.D theses from other universities including Universiti Teknologi Malaysia (UTM), Universiti Kebangsaan Malaysia (UKM), Universiti Malaysia Sarawak (Unimas), and Asean e-University (AEU). He also reviewer for many conference and journal including The Security and Communication Networks, Neural Computing and Application, The International Journal of Computer Science and Information Technology for Education, Human Centric Computing and Information Sciences, and Malaysian Journal of Computer Sciences .
KEYNOTE TITLE: Developing a Successful Student: Big Data, Internet of Everything and Artificial Intelligent
Students are the main asset for many countries. Students plays a vital role as part of the continuity of the nation’s wealth and sustainability of the nation. Students must be developed and trained well to prepare them to be successor at least of the current workers. As we already know, each of this human is unique and specialized. Sadly, our current education system at all level, either in primary, secondary or even in tertiary level, unable to identify this and provide a generic training to these students, one system fits all. There are many reasons why this happen, financial burden, lack of man power, difficulties from the bureaucracy (government, local authorities, etc.), are among of the main issue. This is not only wrong but make our education system inconducive for learning process of the student. We would love to propose an integrated computer application that capable of identifying the students’ needs individually and will be able developed that successful characters. In this paper, we humbly tabulate all necessary requirements and the support from the technology. To materialize this idea, we also proposing the usage of the technology namely big data, artificial intelligent, and internet of everything. We hope that with this idea, it can help the management to be able to manage, develop, and flourish the students’ need individually, and no more one system fits all.
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KEYNOTE SPEAKER 2
Dr. Massudi Mahmuddin
School of Computing, Universiti Utara Malaysia (UUM) |
KEYNOTE SPEAKER 3
Dr. Mohd Ridzwan Yaakub
Center for Artificial Intelligence and Technology (CAIT), Faculty of Information Science and Technology,
Universiti Kebangsaan Malaysia (FTSM, UKM)
Center for Artificial Intelligence and Technology (CAIT), Faculty of Information Science and Technology,
Universiti Kebangsaan Malaysia (FTSM, UKM)
Currently a Postgraduate Coordinator and Senior Lecturer at Center for Artificial Intelligence and Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (FTSM, UKM). After finishing his Bachelor Degree in Management Information System (MIS), UKM in 2000, he worked as Software Engineer at Xybase MSC. He had involved in some interesting projects such as Eprocument with Malaysian Government and integrating airport system (back end) at KLIA. In 2003, he obtained his Master Degree in Distributed Computing from Universiti Putra Malaysia (UPM) and had started his career in academic as lecturer in UKM.
A Ph.D. holder from Queensland University of Technology (QUT), Australia in Sentiment Analysis (2015) is also currently the Head Researcher at Sentiment Analysis Lab, CAIT. His expertise is in Sentiment Analysis/Opinion Mining, Feature Selection, Feature Extraction, Ontology , Data Mining, and Social Network Analysis (SNA). In 2016, together with his mentors, Emiritus Professor Dr Abdul Razak Hamdan and Prof Dr Azuraliza Abu Bakar, he has started Master of Data Science program in UKM, which is the first in Malaysia.
In research, he already involved with more than 22 projects which 12 are still active until now. At this moment, he lead three research projects from diffirent funder such as Regional Cluster for Research and Publication (RCRP) and Fundamental Research Grant Scheme (FRGS). His current researches are mostly in Sentiment Analysis and Social Network Analysis. He also has 10 collaboration grants from various organisation in Malaysia.
A Ph.D. holder from Queensland University of Technology (QUT), Australia in Sentiment Analysis (2015) is also currently the Head Researcher at Sentiment Analysis Lab, CAIT. His expertise is in Sentiment Analysis/Opinion Mining, Feature Selection, Feature Extraction, Ontology , Data Mining, and Social Network Analysis (SNA). In 2016, together with his mentors, Emiritus Professor Dr Abdul Razak Hamdan and Prof Dr Azuraliza Abu Bakar, he has started Master of Data Science program in UKM, which is the first in Malaysia.
In research, he already involved with more than 22 projects which 12 are still active until now. At this moment, he lead three research projects from diffirent funder such as Regional Cluster for Research and Publication (RCRP) and Fundamental Research Grant Scheme (FRGS). His current researches are mostly in Sentiment Analysis and Social Network Analysis. He also has 10 collaboration grants from various organisation in Malaysia.
Title: New Online Social Networks (OSNs) Model for Community Detection Based on Minimum Spanning Tree (MST)
Today, with regard to the exponential growth rate of users and their activities on Online Social Networks (OSNs), understanding the characteristics of this network is like a labyrinth puzzle, hence this network should be analyzed accurately rather than past. It analyzes network to help us understand many issues in our society. Huge transaction on this network is an unbeatable opportunity to extract latent relations between people in community. Community detection is one of the most important issues in OSNs.
Community detection methods in static networks try to find a group of similar nodes, so that the nodes in each community have the highest connection to each other than the rest of the network. The existing studies in this domain have 2 main limitations. First, betweeness based and evaluated by modularity metric that assign a same weight to each connection and only consider amounts of connections between all pairs of nodes. In other word, some relationships during the time are passive and friends do not have any interaction with each other. Second, it will be 3 billion OSN’ users by 2020, if each user has 50 friends on average, then 150 billion edges would exist in the network. This means most of the connection will be done with passive users in community. Therefore, this study aims to propose a new Minimum Spanning Tree (MST) structure algorithm to prune the network for detecting communities with regard to user interaction attributes in order to improve the time, space complexity and above all accuracy. To achieve this, we will develop new algorithm for detecting frequency of interaction path between communities, and to propose new algorithms based on MST for community detection process. This research will review current technique on Community detection, and event detection in OSNs. |
KEYNOTE SPEAKER 3
Dr. Mohd Ridzwan Yaakub
Center for Artificial Intelligence and Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (FTSM, UKM) |