Prof. Xinmin Zhang
Zhejiang University, China
Speech Title: Reinforcement Learning-based Industrial Process Fault Diagnosis
Abstract: With the upgrading of industrial process systems and the development of monitoring tech­nology, the scale of data collected in industrial fields is increasing, and the data types are di­versified; more complex data characteristics and process characteristics are emerging. Meanwhile, the innovation of science and the big data processing, analysis, and model­ing capabilities based on artificial intelligence technology provide new ideas and directions for data­-driven industrial process modeling research. Focusing on the actual task requirements of industrial process modeling and fault classification, this talk focuses on crucial and difficult prob­lems such as semi­supervised and imbalanced data characteristics, nonlinearity, and dynamics in process characteristics, and proposes several industrial process modeling and fault diagnosis methods based on re­inforcement learning.
Short Bio: Xinmin Zhang is a Professor with the State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, China. He received the Ph.D. degree in System Science from Kyoto University, Japan, in 2019. From April 2019 to December 2019, he was a Postdoctoral Research Fellow in the Department of Systems Science, Kyoto University, Japan. From 2020 to 2023, he was an Associate Professor at the College of Control Science and Engineering, Zhejiang University, China. He has published more than 60 technical papers in peer-refereed journals and prestigious conference proceedings, including IEEE Transactions on Neural Networks and Learning Systems、IEEE Transactions on Artificial Intelligence、IEEE Transactions on Industrial Informatics、Engineering Applications of Artificial Intelligence、Science China Information Sciences. He has received several research projects, including Scientific and Technological Innovation 2030 - "New Generation Artificial Intelligence", National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province, and University-enterprise cooperation projects. He has been invited as a speaker and TPC member for several international conferences. His research interests include Industrial Artificial Intelligence, Industrial Big Data, Fault Diagnosis, Virtual Sensing Technology/Soft-sensor, Machine Learning, Reinforcement Learning, and Deep Learning with applications to industrial processes.

Assoc. Prof. Noor Afiza Mat Razali
National Defence University of Malaysia, Malaysia
Speech Title: Artificial Intelligence for Smart Cities: Sentiment Analysis for National Security
Abstract: Smart Cities is characterised as a city that incorporates Information and Communication Technology (ICT) to model the physical and behavioural aspects of city elements and lifestyle into the digital environment that susceptible to various types of malicious attacks. The attacks motivation ranges from opportunistic monetary gains to acts of terrorism. In Smart Cities environment, text-based online communication on various digital platforms has become a powerful medium for expressing opinions, emotions, and ideas, driven by widespread smartphone usage and high internet penetration. The online expression can capture people's emotional states or sentiments that contributing to a new risk associated with social engineering. Thus, monitoring online sentiments or sentiment analysis, is crucial for detecting any excessive emotions triggered by citizens, which can lead to unintended consequences and pose threats to national security. Traditional security monitoring methods often struggle to keep pace with this rapid change. Thus, NLP techniques are proposed to be utilised in analysing conversations and communications for detecting sentiments based on social engineering to detect indicative of manipulation or deception.
Short Bio: Ts. Dr. Noor Afiza Mat Razali is an Associate Professor at the Defence Science and Technology Faculty at the National Defence University of Malaysia (NDUM). She holds a bachelor's degree in Computer and Information Engineering, as well as a master's degree in Computer Science and a PhD of Science in Computer Science with a research area in Cybersecurity from universities in Japan. She has deep roots in IT and digitalization, having worked 10 years in the IT industry in multiple international corporations focusing on IT solution implementations, operation support, and project management locally and internationally before venturing into academic and research career. Ts. Dr. Noor Afiza’s vast swathe of research and expertise covers Cybersecurity, Artificial Intelligence (AI), Internet of Things (IoT), Big Data Analytics and Blockchain Technology focusing on the domains of National Security, Smart City, Economy and Finance, Human-Computer Interaction and Disaster Management. She is a recipient of various grants for research in Cybersecurity, AI and IoT and published academic publications in her research areas. With regard of her expertise, Ts. Dr. Noor Afiza Mat Razali was appointed to various appointment for Board of Directors, consultation, panel of expert from the public and private sector. Ts. Dr. Afiza also contributes to the well-being of Malaysia’s international relations through assisting in governmental policy discussions, organising national and international level conferences and seminars focusing on economic, education and human resource development between Malaysia and Japan.

Assoc. Prof. Lei Chen
Shandong University, China
Speech Title: Deep Neural Networks for Spatio-temporal Action Localization
Abstract: With the increasing popularity of surveillance devices, there is a growing demand for intelligent action recognition. Spatio-temporal action localization has gradually become an important application of deep learning in computer vision. Compared with the usual sense of action recognition, spatio-temporal action localization task can anchor all kinds of behaviours and individuals from time and space. Traditional spatio-temporal action location algorithms have the problem of insufficient fusion ability for spatio-temporal feature extraction and incomplete application of information at various levels. How to extract spatio-temporal action features more efficiently for detection under the condition of ensuring real-time inference speed, the solution of these problems will greatly promote the development and application of spatio-temporal behavioural localization algorithms. To address the problem, we design a multidimensional path aggregation network for spatio-temporal action location, which aggregates the features of multiple paths and fuses the corresponding hierarchical features to obtain spatio-temporal behavioural features. The experimental results demonstrate better performance compared with other algorithms.
Short Bio: Lei Chen received the B.Sc. and M.Sc. degrees in electrical engineering from Shandong University, Jinan, China, and the Ph.D. degree in electrical and computer engineering from University of Ottawa, Ontario, Canada. He is currently an Associate Professor with the School of Information Science and Engineering, Shandong University, China. His research interests include image processing and computer vision, visual quality assessment and pattern recognition, machine learning and artificial intelligence. He was the principal investigator of projects granted from the National Natural Science Foundation of China, National Natural Science Foundation of Shandong Province, China Postdoctoral Science Foundation, etc. He has published more than 30 papers on top international journals and conferences in recent years including IEEE TIP, Signal Process., ICME, etc. He was awarded the Future Plan for Young Scholars of Shandong University. He served for the ICIGP 2021, ICIGP 2022, IoTCIT 2022, and MLCCIM 2022 as Technical Co-Chair or Publicity Co-Chair.

Dr. Tarak Nandy
UCSI University, Kuala Lumpur, Malaysia
Speech Title: Recent Trends in Security and Privacy in Vehicular Communication
Abstract: Vehicular communication systems, critical to intelligent transportation and autonomous driving, face significant security and privacy challenges. Recent trends focus on countering threats like spoofing, eavesdropping, and denial-of-service attacks through advanced cryptographic protocols, authentication schemes, and intrusion detection systems. Blockchain technology is emerging as a promising solution for enhancing trustworthiness, while privacy-preserving techniques such as pseudonymization and differential privacy are being developed to protect user data. The integration of machine learning and edge computing offers adaptive, real-time threat detection. However, challenges remain in standardizing protocols, balancing privacy with data utility, and ensuring scalable solutions. Continued innovation is essential for the secure deployment of connected and autonomous vehicles.
Short Bio: Dr. Tarak Nandy is a distinguished researcher and academic in the field of cybersecurity and vehicular communication. He holds a Ph.D. in Computer Science from the University of Malaya, a leading institution and has over two decades of experience in the industry and academia. Dr. Nandy's research focuses on the security and privacy of intelligent transportation systems, with a particular interest in authentication, cryptographic protocols, and machine learning applications for vehicular networks. He has published numerous papers in prestigious journals and conferences and has been a keynote speaker at several international events. Dr. Nandy is currently a lecturer at UCSI University, Kuala Lumpur, Malaysia, a renowned university, where he serves as the Head of the Department of Computer Science, along with the Head of Learning Excellence and leads a research team dedicated to advancing the security of next-generation vehicular communication systems.

Mr. Ahmet Tugrul Bayrak
Ata Technology Platforms, Istanbul, Turkey
Speech Title: The Evolution of Natural Language Processing and Generative AI Applications
Abstract: Over the past decade, revolutionary advancements in Natural Language Processing (NLP) have set the stage for transformative changes across multiple industries, from healthcare to finance, enhancing machine understanding and content generation capabilities. As universities, companies and governments look to leverage these technologies, the impact on productivity and information accessibility has been profound. Key innovations like word embeddings, semantic parsing, and attention mechanisms have not only transformed NLP systems but also paved the way for more intuitive human-machine interactions. Additionally, Generative AI has begun a new era, particularly with models such as BERT and GPT. However, using generative AI also introduces other challenges, such as ethical issues and security concerns. Ethically, there is the risk of biases and creating deceptive content, while security risks include the potential misuse of AI tools for fraud or misinformation, emphasizing the need for strict regulatory and security measures. As we move forward, it will be crucial to manage these technologies wisely, ensuring they enhance social benefits while reducing associated risks.
Short Bio: Ahmet Tugrul Bayrak is the Data Science Manager at Ata Technology Platforms in Turkey. He holds a Bachelor of Science degree in Mathematical Engineering from Yildiz Technical University and another in Computer Engineering from The Technological University of the Shannon. Additionally, he attended Uppsala University for a Master's degree in Computer Science. Throughout his career, Mr. Bayrak has engaged extensively as a software engineer and data scientist. He has participated in numerous projects with a particular focus on Natural Language Processing and Recommender Systems. In addition to his contributions to industry practices, Mr. Bayrak is also actively engaged in academia, with his academic portfolio including many published papers.

Assoc. Prof. Dr. Teh Sin Yin
Universiti Sains Malaysia, Malaysia
Speech Title: Leveraging Artificial Intelligence for Digital Transformation
Abstract: In our rapidly evolving digital age, data is generated at an unprecedented rate. According to Forbes, about 90% of the world's data created in just the past two years. Digital transformation leverages this wealth of information, enabling businesses to unlock new strengths and opportunities. This transformation, driven by technologies such as artificial intelligence, voice recognition, robotics, and machine learning, is set to revolutionize business operations in the coming years. Enhanced customer experiences will be achieved through advancements in big data analytics, Internet of Things (IoT), autonomous vehicles, and blockchain. Today, businesses have access to a powerful array of digital technologies that can be strategically deployed to create customer-centric systems and unlock significant value. Companies that recognize this evolving landscape and wisely invest in process changes and supporting digital technologies stand to gain immense benefits.
Short Bio: TEH Sin Yin (Ph.D., CMILT, MLogM) is an Associate Professor of Operations and Business Analytics in the School of Management, Universiti Sains Malaysia. She has strong academic background in Statistics (USM), Executive Program (Applied Business Analytics) and Executive Program (AI: Implications for Business Strategy) awarded by Massachusetts Institute of Technology (MIT). She was a fellow and member of the United Nations System Staff College (UNSSC) and Data-Pop Alliance, New York. She was also research fellow at the City University of Hong Kong, University Tunku Abdul Rahman (UTAR) and AK Shipping.
Dr. Teh is a certified HRDF trainer, TRIZ trainer and Tableau Specialist. She has conducted TRIZ, Tableau, business analytics seminars and workshops for practitioners and researchers from MNCs, SMEs and government agencies. She was involved and had successfully completed research projects on ICT with the Penang state government; “a mobile learning system using machine learning and cloud” with Motorola Solutions Malaysia; “an integration of FMEA and TRIZ” and “Data Driven End-to-End Process FMEA Improvement” with Sanmina-SCI Systems.
Dr. Teh has published more than 100 papers in international journals and proceedings including excellent journals of ISI Q1. She has attended many conferences and held the position of keynote speakers and session chairs. Her research interests are business analytics, data mining, statistical process/quality control, operations management, robust statistics and TRIZ theory of inventive problem solving.