INVITED SPEAKER 邀请报告

 

Samir Brahim Belhaouari, Hamad Bin Khalifa University, Qatar

Dr. Samir Brahim Belhaouari is a faculty member in the Division of Information & Computing Technology at Hamad Bin Khalifa University (HBKU), Qatar. He received his Ph.D. in Mathematical Sciences from EPFL, Switzerland, and his MSc in Networks and Telecommunications from INP/ENSEEIHT, France. With international academic experience across Europe, the Middle East, Russia, and Asia, he has developed a strong global research profile.
His work spans mathematics, machine learning, and data science, with notable contributions in classification, GenAI, feature selection, data preprocessing, and optimization. He is the creator of the Multilevel Architecture of Deep Learning (MADL), a framework that enhances neural network training efficiency and performance. He has also developed novel algorithms for time-frequency decomposition, model compression, Green AI, and image/graph representation, as well as an enhanced hashing function inspired by mathematical conjectures.
Dr. Belhaouari has served as principal investigator on major research projects funded by Qatar, Russian, and Malaysia, and his contributions have earned him multiple international awards, including medals at global innovation exhibitions. With over 300 publications, more than 5,000 citations, and an H-index of 36, he continues to influence both theoretical and applied aspects of AI, delivering innovative solutions that bridge mathematics, sustainability, and advanced computing.

Talk Title: Leveraging Mathematics to Address AI and Security Challenges

Abstract: Solving complex computer science problems becomes more manageable by harnessing the power of unsolved mathematical puzzles and inspiration from nature. Mathematics provides the foundation for crafting advanced algorithms in optimization, hashing, data compression, and model refinement, which are essential for tackling a wide range of challenges in artificial intelligence (AI) and cybersecurity. This talk will explore several key projects that illustrate the pivotal role of mathematics in addressing these issues.
Highlights include smart pruning techniques for deep neural networks (Green LLMs, CNNs, etc.), Green AI from Deep to shallow learning, the Walking Algorithm for Longitudinal Key Signatures (WALKS), and optimizing dimensionality reduction and visualization using enhanced clustering and optimal transport.
We will also discuss a novel time-frequency decomposition, KNNOR—a method for oversampling and downsampling imbalanced datasets—and chaos-based hashing combined with Gaussian Kernel LSH for improved data security and similarity search. Other topics include feature selection for high-dimensional data, extending the Komlós Conjecture for categorical variable encoding, and various deep learning innovations, such as architectural designs, fine-tuning methods, specialized loss functions, and activation functions. Additionally, we’ll cover anomaly detection, clustering techniques, and a dynamic Markov Chain coupled with reinforcement learning for optimization and feature selection. Applications of these techniques in biomedical and bioinformatics domains will be examined, along with the use of number theory in security, particularly in hashing and RSA encryption. Through these projects, we demonstrate how mathematical insights lead to cutting-edge solutions in AI and cybersecurity

 

Anand Nayyar, Duy Tan University, Vietnam

Dr. Anand Nayyar received Ph.D (Computer Science) from Desh Bhagat University in 2017 in the area of Wireless Sensor Networks, Swarm Intelligence and Network Simulation. He is currently working in School of Computer Science-Duy Tan University, Da Nang, Vietnam as Professor, Scientist, Vice-Chairman (Research) and Director- IoT and Intelligent Systems Lab. A Certified Professional with 250+ Professional certificates. Published more than 250+ Research Papers in various High-Quality ISI-SCI/SCIE/SSCI Impact Factor Journals cum Scopus/ESCI indexed Journals, 100+ Papers in International Conferences indexed with Springer, IEEE and ACM Digital Library, 80+ Book Chapters with Citations: 21000+, H-Index: 75 and I-Index: 300. He has 18 Australian Patents, 16 German Patents, 4 Japanese Patents, 44 Indian Design cum Utility Patents, 1 USA Patent, 3 Indian Copyrights and 2 Canadian Copyrights to his credit. Awarded 55 Awards for Teaching and Research. He is listed in Top 2% Scientists as per Stanford University (2019, 2020, 2021, 2022, 2023, 2024). He is Listed on Research.com (No:2 in Viet Nam; D-INDEX: 46). He is acting as Managing Editor of IGI-Global, USA Journal titled “IJKSS”- Scopus Q3 Indexed.

 

Abhishek Kumar, Chandigarh University Punjab, India

Dr. Abhishek Kumar, Senior Member of IEEE, is an Assistant Director and Professor in the Computer Science & Engineering Department at Chandigarh University, Punjab, India.and Senior researcher in Ingeniot lab UCLM Spain.With over 14 years of teaching experience, he has published 200+ peer-reviewed papers and successfully supervised 6 Ph.D. scholars, , along with 48+ M.Tech projects. He holds postdoctoral research at Universidad de Castilla-La Mancha, Spain. His research interests span artificial intelligence, renewable energy systems, image processing, and data mining. An award-winning researcher, Dr. Kumar has received several accolades, including the Sir C.V. Raman National Award (2018), and holds a patent. An accomplished author and editor, he has authored seven books and edited 97 volumes with reputed publishers like IET, Elsevier, Wiley, Springer, and De Gruyter. Dr. Kumar also serves as Series Editor for book series.

Talk Title: Multi-Agent Generative AI for Renewable Integration and Grid Operations

Abstract: Rapid renewable penetration is increasing variability and operational complexity across power systems, while electrification and rising AI-driven loads intensify the need for reliability, flexibility, and resilience. This paper proposes an AI-centric framework that unifies (i) foundation-model and generative-AI assistants for operator decision support and automated workflow orchestration, (ii) real-time digital twins to mirror grid and renewable-asset states for what-if analysis and predictive maintenance, and (iii) reinforcement-learning control for adaptive dispatch, microgrid energy management, and storage coordination under uncertainty. We synthesize recent advances in renewable forecasting and control—highlighting how modern AI can improve solar/wind prediction accuracy and enable multi-objective optimization balancing cost, emissions, and resilience. Finally, we discuss deployment considerations—data governance, cybersecurity, and model validation—emphasizing safe integration of AI into critical infrastructure and measurable pathways to accelerate the renewable energy transformation at scale.

 

 

S.Anne Susan Georgena, Sri Ramakrishna Institute of Technology, India

Bio: Dr. Anne Susan Georgena is a Senior Grade Assistant Professor at Sri Ramakrishna Institute of Technology (India) and a current Researcher at the Institute of Engineering Mathematics, Universiti Malaysia Perlis. With over 12 years of university teaching experience and international academic engagement across India, Malaysia, the UAE and Indonesia, she has a strong interdisciplinary research profile.
Her core research focuses on fluid dynamics, magnetohydrodynamics, computational fluid dynamics, and heat and mass transfer, with specialized studies on unsteady hydromagnetic flows over stretching/shrinking surfaces. She also explores the mathematical foundations and applications for AI, data science and intelligent manufacturing, holds multiple patents, and has published book chapters in IntechOpen and CRC Press/Taylor and Francis as well as numerous journal and conference papers.
Dr. Anne has secured funded research projects from Malaysia’s Ministry of Education and India’s UGC, and won international awards including a Gold Award at SIRAC III 2024 (Malaysia) and multiple innovation medals at Universiti Malaysia Perlis. She serves as an Associate Editor of the International Journal of Applied Mathematics & Computational Intelligence, an international committee member for academic conferences, and has acted as session chair and jury member for global academic events. Proficient in MATLAB, ANSYS FLUENT and Python with Cambridge BEC Vantage (B2) English proficiency, she also takes on the role of International Relationship Coordinator to drive institutional academic collaboration and international exchanges.

Talk Title: Modeling and Analysis of Unsteady Hydromagnetic Flows with Radiative and Newtonian Thermal Conditions

Abstract: The modeling of multi-physical transport phenomena involving magnetic fields and thermal effects is essential in advanced engineering and high-temperature industrial systems. This lecture presents a comprehensive analysis of unsteady hydromagnetic (MHD) boundary layer flows over stretching and shrinking surfaces incorporating radiative heat transfer and Newtonian heating conditions. Advanced computational modeling plays a critical role in understanding complex transport phenomena encountered in modern engineering systems. This lecture presents a detailed numerical investigation of unsteady hydromagnetic (MHD) boundary layer flows over stretching and shrinking surfaces incorporating radiative heat transfer and Newtonian heating effects.
The mathematical formulation considers a two-dimensional, viscous, incompressible, electrically conducting fluid subjected to a transverse magnetic field. Thermal radiation is modeled using the Rosseland approximation, while convective surface heating is represented through Newtonian heating conditions. The governing nonlinear partial differential equations are transformed into similarity-based ordinary differential equations and solved using an efficient fourth-order Runge–Kutta shooting algorithm coupled with an iterative boundary correction scheme.
A comprehensive parametric analysis is performed to examine the influence of magnetic interaction, radiation parameter, unsteadiness, suction, Biot number, viscous dissipation, slip effects, and chemical reaction on velocity, temperature, and concentration distributions. The results reveal strong nonlinear coupling between magnetic damping and thermal enhancement mechanisms.
The study demonstrates how advanced numerical techniques and intelligent computational frameworks can be employed to analyze multi-physics transport processes, offering insights relevant to high-temperature materials processing, energy systems, plasma engineering, and thermally optimized industrial designs.