Dates Countdown

Keynote Speakers


Prof. Hui-Ming Wee
College of Electrical Engineering & Computer Science, Chung Yuan Christian University, Taiwan

Speech Title: Introduction to Industry 4.0 and Logistic Networks Innovation
Abstract: Due to the advancing Internet of Thing (IoT) & Cyber-Physical Systems (CPS), the era of radically different competition is here! It is predicted in the year 2025, all of things will be connected together as a single network. In this talk, we introduce Industry 4.0 and show how it influence Logistic innovation. Today’s advanced analytical tools can extract meaning from all data. Manufacturers and retailers collect data along the supply chains. New technologies grow exponentially. They are changing the world trade. And the introduction of Industry 4.0 will definitely shape the future of logistics. Based on cyber physical production system, orders will be able to steer themselves independently through the entire value chain.
Biography: Prof. Hui-Ming Wee is an Adjunct Chair Professor in the Department of Industrial and Systems Engineering, Chung Yuan Christian University, Taiwan. He has received his B.S. degree (honors) in Electrical and Electronics Engineering from Strathclyde University (UK), M. Eng. from Asian Institute of Technology (AIT), and Ph.D. in Industrial Engineering from Cleveland State University, Ohio (USA). He has received Excellent Research Award from the Ministry of Science & Technology, Lifetime Achievement Award from the Association of Inventory Academicians and Practitioners, 2018, World Class Professor Award from the Ministry of Research, Technology and Higher Education (Indonesia), 2019, Distinguished Educator Award from the International Society of Industrial Engineering and Operations Management, 2018 and the Medal for Distinguished Industrial Engineer Award from the Chinese Society for Industrial Engineers. He has authored/co-authored more than 500 papers in international peer-reviewed journals, conferences, books and book chapters. His papers have been cited in Google 11,199 times with an H-index of 57. In 2020, he is listed as top 2% scientists in Operations Research by the research team led by John Ioannidis, a distinguished professor at Stanford University. He has trained 36 PhDs and 150 Master students globally, and is the Editor-in-Chief for the Journal of Ubiquitous Computing and Communication Technologies, Guest Editor for Journal of Cloud Computing, on ‘Cloud Information Technologies in Education’ and International Journal of Lean Six Sigma, on “How Lean Six Sigma Improve Organizational Resilience post COVID-19.


Prof. Christophe Claramunt
Naval Academy Research Institute & Arts & Métiers Institute of Technology, France

Speech Title: Big Maritime Data: Research Advances and Challenges
Abstract: The large maritime datasets currently available offer many opportunities for developping data manipulation infrastructures whose objective will be to develop real-time monitoring, predictive and planning systems. These expected developments are crucial for a better understanding of the ocean environment which is under increasing economical pressure and then the object of many environmental threats. The objective of this talkis to review current research challenges and trends tied to the integration, management, analysis, and visualization of objects moving at sea as well as a few suggestions for a successful development of maritime forecasting and decision-support systems.
Biography: Prof. Christophe Claramunt is a professor in computer science and head of research at the Naval Academy Research Institute in North West France. He has been before a senior lecturer at the Nottingham Trent University in United Kingdom and a senior scientist at the Swiss Federal Institute of Technology in Lausanne, Switzerand. His research is oriented towards theoretical and pluri-disciplinary aspects of geographical information science and their applications to urban, maritime and environmental systems. Amongst many subjects of interest, his research include spatio-temporal models, semantic and cognitive-based models, and web and location-based services. He has published more than 200 papers in refereed journals and conferences and serves in the editorial boards of several international GIS journals and major GIS conferences including recently acting as an associate editor of the International Journal of Geographical Information Science. Over the past few years he has been regularly involved in national, European (H2020) and internationally funded research projects. Amongst other affiliations, he is currently a Shanghai 1000 talent at the Shanghai Maritime University, and acting research fellow at the Laboratory for Geographical Information Science at the Chinese University of Hong Kong, Research Center for Social Informatics Kwansei University in Japan, and the Centre for Planning Studies at the Laval University in Canada.


Prof. Cheng Siong Chin
Newcastle University in Singapore, Singapore

Speech Title: Intelligent Audio Systems
Abstract: Intelligent Systems heavily rely on context-awareness. Context-awareness is the ability of a system or system component to gather information about its environment at any given time and adapt behaviors accordingly. Being able to detect and classify sounds and events from the surroundings is an essential aspect of context-awareness. An audio-based context recognition system using ensemble classifiers with wavelet feature extraction will be presented. The signal from the environment provides complementary information about context compared to other modalities.
Biography: Prof. Cheng Siong Chin received the B.Eng. degree in Mechanical and Production Engineering from Nanyang Technological University (NTU) in 2000, the M.Sc. degree in Advanced Control and Systems Engineering from The University of Manchester (formerly called UMIST) in 2001. He has published over 100 publications, 4 authored books, and 3 US Patents. His research interests include the design and simulation of complex systems for an uncertain environment. He is a Fellow of the Higher-Education Academy, a Fellow of IMarEST and a Senior Member of IEEE. He received the Best Paper Award for Virtual Reality of Autonomous Marine Vehicle in 10th International Conference on Modelling, Identification, and Control sponsored by IEEE in 2018 and the Best Application Paper on Dynamic Positioning for Vessel in 11th International Conference on Modelling, Identification, and Control, 2019. He also received DCASE2019 Judges' Award (most innovative and original) for Sound Event Detection in Domestic Environments in IEEE AASP Challenge on DCASE2019. He was awarded the Outstanding Contributions in Reviewing for Future Generation Computer Systems, Elsevier in 2018. He is the Associate Editor of IEEE Access, and Electronics (Sections: Systems & Control Engineering and Artificial Intelligence Circuits and Systems).


Prof. Stefano Zedda
Department of Business and Economics, University of Cagliari, Italy

Speech Title: Modeling and Simulation of Banking Systems
Abstract: The Global financial crisis which started in 2008 has shown the need for models and methods to assess the risk of bank defaults, of financial contagion effects, and to prevent new financial crises. While the lack of data limits the use of the traditional econometric approach, simulation models can deal with this problem, by representing the mechanisms trough which contagion starts and propagates, and simulating the possible resulting scenarios in terms of losses and probability to occur. In this aim, and in cooperation with the European Commission, I developed the Systemic Model for Banking Originated Losses (SYMBOL), which also allows for testing the effects of new banking regulation. As a result, the European Commission adopted this approach and included the SYMBOL model as a standard tool for the impact assessment of banking regulation reform proposals, which is used to assess the effects of variations in minimum capital requirements, for Deposits Guarantee Schemes dimensioning, to quantify the possible effects of financial crises on public finances stability, and more. In this lecture, I’ll present the modeling approach, the SYMBOL model, and will describe how modeling and simulation resulted to be fundamental for the proper reforming of the European banking regulation and supervision.
Biography: Prof. Stefano Zedda is associate professor in banking at the University of Cagliari. His research is mainly focused on quantitative analyses for banking and finance, and in particular on banking systems modeling and simulation. In 2008 he started and developed the mathematical modeling and software implementation of the Systemic Model for Banking Originated Losses (SYMBOL), further developed during his activity at the European Commission (2010-2012), that subsequently adopted it as a standard tool for testing banking regulation. His studies were published in international leading journals such as The Journal of Banking and Finance, Journal of Financial Services Research, Computational Economics, Sustainability, in a book on Banking Systems Simulation published by Wiley, and presented in many international conferences in Europe, Asia, America and Australia. He also presented his studies and analyses as invited speaker, among the others, at the European Commission, and at the Treasury Department of the USA.


Prof. T. Velmurugan
Dwaraka Doss Goverdhan Doss Vaishnav College, India

Speech Title: Semantic Relation Extraction in Biomedical Text Data Using Transfer Learning Techniques
Abstract: Relation Extraction (RE) is one of the most important research areas in the field of biomedical text. Early, most of the biomedical RE is based on Machine Learning (ML) approach. Recent progress of biomedical RE models was made possible by the advancements of deep learning (DL) techniques used in natural language processing. Although DL is better than ML in overall comparisons, it often requires an annotated corpus to train a new model. As well, most of them are mainly evaluated on annotated database and have not yet been broadly executed on unannotated databases. From a deep learning perspective, the RE classification problem can be solved through transfer learning. Transfer learning is a subclass of traditional machine learning approach, which transfers their knowledge from previously learnt domains to newer domains and tasks.
Biography: Dr. T. Velmurugan is working as an Associate Professor in the PG and Research Department of Computer Science and Applications, Dwaraka Doss Goverdhan Doss Vaishnav College, Chennai-600106, India. Also, he is the Advisor and Head, Department of Computer Applications (BCA). He holds a Ph.D. degree in Computer Science from the University of Madras. He has 27 years of teaching experience. He guided more than 300 M.Phil., Research Scholars. Also, he guided 16 Ph.D. scholars and currently guiding 7 Ph.D. scholars. He has published more than 110 articles in SCOPUS and SCI indexed journals. He elected and served as a Senate Member from Academic Council, University of Madras. Also, he served as a nominated Senate Member in the Middle East University, Dubai, UAE for a period of three years. He has a lot of administrative experiences. He served as advisory board member to many academic institutions in and around Tamil Nadu, India. He was an invited speaker and keynote speaker for many international conferences around the world. He is a member in Board of studies for many autonomous institutions and Universities. Also, he organized international Conferences and workshops. In addition, he was a resource person for various national workshops entitled "Scientific Research Article Writing and Journal Publications" and many of the recent topics in Computer Science. He is an Editorial Board Member of 6 International Journals. His area of specialization includes Data Mining, Artificial Intelligence, Machine Learning, Network Security, Big Data Analytics, Data Science and etc.

Important Dates

  • Submission Deadline
    December 10, 2021
  • Notification Day
    About 10 days after submission
  • Authors' Registration
    December 17, 2021
  • Conference Date
    November 14-15, 2021

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