Yaxin Bi is a Professor in the School of Computing at Ulster University and a core member of the Artificial Intelligence Research Centre within the School. He has over thirty years of research experience in Artificial Intelligence and its applications. He is a Senior Fellow of the Higher Education Academy, UK Yaxin’s research interests are in the fields of machine learning and ensemble learning in conjunction with the Dempster-Shafer theory of evidence. He has pioneered ensemble approaches for combining multiple classifiers using Dempster’s rule of combination, as well as measuring the diversity impact of evidential classifiers with varying metrics. He has secured more than ￡5 million in research funding from the funding bodies of the European Space Agency, the EU Frameworks and Horizon 2020 programs, Knowledge Transfer Partnership (KTP), Invest NI, and the Royal Society, UK, etc. His research extends to the development of data analytics and decision-making methods under uncertainty to tackle real-world challenges in a wide range of applications. These include anomaly/change detection in satellite data and manufacturing process, energy consumption and generation prediction, crop classification and disease detection, generative simulation and digital twin, sentiment analysis, and sensor fusion for activity/event recognition. Yaxin has over 200 peer-reviewed publications across international journals, edited books, book chapters and conference proceedings. Yaxin held key roles for various international conferences and workshops as a general co-chair and program co-chair. He served as an associate editor for the International Journal of Artificial Intelligence Review (2015-2020). Currently, he serves as an associate editor for the International Journal of Intelligent Systems and as a steering committee member for both the International Conference on Knowledge Science, Engineering, and Management (KSEM) and the Science and Information (SAI) Conference. Personal webpage: https://www.ulster.ac.uk/staff/y-bi?

Speech Title: Deep Learning-based Approaches for Generating Synthetic Electromagnetic Data and Predicting Seismic Anomalies within Satellite and Synthetic Electromagnetic data Speech

Abstract: This talk will present a study on the development of viable deep learning (DL) methods for generating synthetic electromagnetic data and detecting anomalies within synthetic data generated and observed by the SWARM satellites and the Control Source Extremely Low Frequency (CSELF) network in China. The study also investigates the correlation between detected anomalies and earthquakes. These two data sources offer a unique opportunity to explore potential causes and effects embedded in the measured magnetic fields, and how they can be used for generating synthetic data, particularly in relation to earthquake preparation and seismic precursors. We developed a data-driven, non-physics-informed DL approach targeting earthquake-prone areas, treating electromagnetic data as time series prediction and reconstruction tasks. Two models were proposed: one using Long Short-Term Memory (LSTM) networks and another based on Generative Adversarial Networks (GANs). To ensure the quality over the five-year period of observed electromagnetic data for the targeted areas, extensive pre-processing was performed. For predictions, time series data were processed by extending the given look-back time window using an appropriate frequency interpolation. For reconstructions, the original data window was recovered by the interpolating the frequency representation of its down-sampled part. The pre-processing represents initial steps toward generating synthetic electromagnetic data observed. Among the two methods, the LSTM-based approach demonstrated superior performance in long-term predictions. Additionally, using equal input and output window lengths significantly enhanced the models' effectiveness. A rigorous evaluation was conducted using five-year electromagnetic data from both SWARM and CSELF. The results underscore the suitability of deep learning techniques for these tasks and provide a solid foundation for future efforts to detecting anomalies within observed electromagnetic data incorporated by synthetic electromagnetic data.

Guo Baoqing, professor and doctoral supervisor at Beijing Jiaotong University. He is a recipient of the Baosteel Education Award (Outstanding Teacher) and has been honored as a Beijing Municipal Distinguished Young Teaching Master. He serves as the Chief Professor of the National Key Laboratory of Advanced Rail Transit Autonomous Operation and the Chief Scientist of a National Key R&D Program of China. He is also a committee member of the Intelligent Operation and Maintenance Division of the China Mechanical Engineering Society. His research focuses on monitoring, detection, and intelligent perception for rail transit operational environments. He has led over national key R&D projects and National Natural Science Foundation projects, published more than 50 SCI/EI indexed papers, and received 1 Second Prize for Beijing Science & Technology Progress Award and 2 First Prize of Science & Technology Award of China Railway Society.

Prof El Namaki is a graduate of the universities of Brussels (Ph. D, 1977), Erasmus (MA, 1967) and MIT (Executive Program, 1982). Prof El Namaki teaches and consults on strategic thinking, entrepreneurship and international business. He is past founder and Dean of the Maastricht School of Management (MSM), Maastricht, The Netherlands (1984-2002). He is now Dean of the VU School of Management, Switzerland. Prof El Namaki has developed and introduced management degree programs (MBA, EMBA, DBA and Ph. D) at institutions in the Netherlands, China, Egypt, Brazil, Poland, Kazakhstan, Syria, Singapore, Malaysia and Indonesia, among others.

Prof El Namaki taught and consulted globally at as recognized institutions as MSM (Maastricht), Helsinki School of Economics (Helsinki), Sheffield University (Sheffield) Kellogg (Chicago), Jiao Tong University (Shanghai), Beijing University (Beijing), AIT (Bangkok). His latest book publications include "Strategic thinking in the age of artificial intelligence" (2022) and Macmillan's "Neo Strategic Management, Conceptual and Operational Foundations of tomorrow's strategic thinking" (Sept 2023).

Prof. Bin Chen is a graduate of the University of CAS (Ph. D,2005), Sichuan (MA. 2001), Tsinghua (BA, 1992). He is now a doctoral researcher at the Harbin Institute of Technology, Shenzhen. His research interests include computer vision, deep learning and MLLM. He has been a doctoral supervisor in University of Chinese Academy of Sciences since 2006,. In 2010, he was honored as an Outstanding Expert with Prominent Contributions in Sichuan Province. He has received four Science and Technology Progress Awards at the ministerial level (second class) and holds 20 authorized invention patents. In 2025, he published high-quality papers at top international conferences including AAAI, CVPR, and ACMMM.

He received his B.S. degree in Information Engineering in 1997 and his M.S. degree in Computer and Mathematical Sciences in 1999, both from Tohoku University, Sendai, Japan. In 1999, he joined NEC Soft, Ltd.,Tokyo, Japan, where he was mainly engaged in research on face recognition. He received his Ph.D. degree from the Graduate School of Science and Engineering, Waseda University, Tokyo, Japan, in 2007. From 2013 to 2017, he served as an Assistant Professor at Waseda University. He is currently an Associate Professor in the School of Information Science, Meisei University. His research interests include information retrieval, video anomaly detection, pattern recognition, and machine learning. He is involved in the video retrieval evaluation benchmark (TRECVID) sponsored by the National Institute of Standards and Technology (NIST), contributing to the development of video retrieval technology. His submitted systems achieved the highest performance in the TRECVID AVS task in 2016, 2017, 2022, and 2025.