Prof. Lazim Abdullah
Universiti Malaysia Terengganu, Malaysia

Lazim Abdullah is a Professor of Computational Mathematics at the Faculty of Computer Science and Mathematics, Universiti Malaysia Terengganu. He received his Ph.D (Information Technology) from the Universiti Malaysia Terengganu, in 2004. His research and expertise focus on fuzzy set theory of mathematics, decision making models, applied statistics, and their applications to environment, health sciences and technology management. His research findings have been published in more than 395 publications including refereed journals, conference proceedings, chapters in book, monographs, and textbooks. He has been ranked among the world’s top 2% scientists by Stanford University in the field of artificial intelligence and image processing since 2018. Prof Lazim is a member of the IEEE Computational Intelligence Society, and a member of International Society on Multiple Criteria Decision Making.

Speech Title: "An Integrated Bipolar Fuzzy-DEMATEL for Elucidating Factors Influencing Customers Choice: A Case of Life Insurance Companies"

Abstract: Multi-criteria decision-making (MCDM) methods have gained substantial traction across various scientific disciplines, with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method being particularly prominent. This study advances the DEMATEL framework by incorporating bipolar fuzzy sets to better handle complex, uncertain decision environments. The primary objectives are twofold: (1) to propose an integrated Bipolar Fuzzy-DEMATEL model and (2) to apply the model to identify key factors influencing customer choice in life insurance companies. The model introduces a novel linguistic scale for bipolar fuzzy sets, allowing simultaneous evaluation of positive and negative membership degrees across truth, falsity, and uncertainty dimensions. A sensitivity analysis was also conducted to assess the robustness of the findings. Results indicate that the cause factors influencing customer choice include F1, F2, F7, F8, and F9, while F3, F4, F5, F6, and F10 are classified as effect factors. Among them, ‘F2|: Competitive pricing and clear terms’ emerged as the most influential. The sensitivity analysis confirmed the model’s robustness, showing minimal impact of weight variations on factor rankings. The stability of top-ranked factors under changing conditions highlights the model’s reliability and its practical relevance for strategic decision-making in the insurance sector.

 

Assoc. Prof. Marko Đurasević
University of Zagreb, Croatia

Marko Đurasević is an Associate Professor at the Faculty of Electrical Engineering and Computing (FER), University of Zagreb. His research is centered on evolutionary computation, particularly genetic programming and hyper-heuristics for solving complex scheduling and optimization problems. He earned his Ph.D. in Computer Science from FER in 2018, with a dissertation focused on the automated design of dispatching rules in unrelated machines environments.
Dr. Đurasević has published over 100 scientific papers in international journals and conferences, contributing extensively to the fields of combinatorial optimization, machine learning, and soft computing. He is the principal investigator of two nationally funded projects dealing with optimization of containers in ports and routing of electric vehicles. Furthermore, he also leads a project in collaboration with the company AVL-AST.
His scientific excellence has been recognized with the Annual Award for Young Researchers by the Croatian Parliament in 2023 and several other national institutions. Dr. Đurasević is an active member of IEEE, IEEE CIS, ACM, and ACM SIGEVO, and regularly serves as a reviewer for leading journals in artificial intelligence and operations research.

 

Assoc. Prof. Mahdi Madani
Université Bourgogne Europe, France

Mahdi Madani received his Ph.D. in Electronics Systems from the University of Lorraine on July 12, 2018. He was a temporary research and teaching associate at IUT Auxerre, University of Burgundy, from September 2018 to August 2020, and he was also a temporary researcher at IETR laboratory and teaching associate at IUT Nantes from September 2020 to August 2022. In September 2022, he joined the Université Bourgogne Europe and the CORES team in the IMVA laboratory for the associate professor position. His research interest is information security in new digital networks, algorithm-architecture suitability, FPGA, and SoC implementation of complex algorithms, applying security techniques (confidentiality, integrity, encryption, chaotic systems, etc.) to image, signal, and vision applications, and exploring artificial intelligence packages for data and privacy preserving.

Speech Title: "Secure and Efficient Tele-Radiography Based on the Fusion of a Convolutional Autoencoder and Chaotic Latent Encryption"

Abstract: This work addresses the dual challenges of efficient compression and secure transmission for medical images, particularly in bandwidth-constrained telemedicine scenarios like tele-radiography. We proposed an end-to-end pipeline combining deep learning-based compression with chaos-based encryption. A convolutional autoencoder (CAE), optimized with a Structural Similarity Index Measure (SSIM) loss function and incorporating residual connections and batch normalization, achieves an 8:1 (87.5%) compression ratio on Chest X-ray images while maintaining a high fidelity of 96% SSIM and 36 dB Peak signal-to-noise ratio (PSNR). To secure the compact latent representation generated by the CAE, we introduce a lightweight, chaos-based encryption scheme operating directly on the latent space. This scheme utilizes a logistic map for confusion and secure permutations for diffusion. The experimental results confirm the effectiveness of the compression module in preserving high-frequency details and the encryption scheme’s resistance against statistical attacks, by achieving high entropy (7.92), strong randomness (0.99), correlation (close to 0 in horizontal, vertical, and diagonal directions), and very sensitive to small changes in the key (1 single bit change conduct to a completely different keystream). Our work offers a promising solution for secure and efficient medical image transmission over constrained networks.

 

Assoc. Prof. Maciej Kusy
Rzeszow University of Technology, Poland

Maciej Kusy received his MSc degree in Electrical Engineering from the Rzeszów University of Technology, Poland, in 2000; his PhD in Biocybernetics and Biomedical Engineering from the Warsaw University of Technology, Poland, in 2008; and his DSc in Information and Communication Technology from the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland, in 2019. He is currently an Associate Professor at the Faculty of Electrical and Computer Engineering, Rzeszów University of Technology. His research interests focus on artificial intelligence, particularly machine learning, generative learning, data mining, and video/image processing.

Speech Title: "Task-Focused Label Selection for Improving YOLO Performance in Video Detection"

Abstract: The talk will focus on enhancing urban scene datasets by introducing critical object categories through the use of an open-vocabulary detection model. This innovation enables automatic annotation, eliminating the need for manual labelling and allowing fine-tuning of real-time detection models. To simplify the training process and improve model performance, static or less informative categories are selectively excluded. This targeted approach addresses class imbalance by prioritising task-relevant elements, even when they are underrepresented in the dataset. Through prompt-guided detection and efficient annotation conversion, the model is trained on a reduced label set. Evaluation results demonstrate consistent precision, stable or improved accuracy, and minimal recall drops for certain categories — illustrating the effectiveness of a simplified and focused labelling strategy.

 

Dr. Amir Hajiyavand
University of Birmingham, UK

Dr. Amir Hajiyavand holds both a Master of Engineering (MEng) degree in Mechanical Engineering (biomedical) and a PhD in Robotics from the University of Birmingham, United Kingdom. He is currently an academic at the Institute of Robotics within the School of Engineering at the University of Birmingham.
His research focuses on the design and development of advanced automated systems and robotic solutions, with a particular emphasis on integrating Artificial Intelligence (AI) for applications spanning healthcare and the Circular Economy. Dr Hajiyavand possesses substantial expertise in industrial automation, robotic inspection, and robotic manipulation, including high-precision micromanipulation techniques.
With extensive experience in advancing innovative technologies from lower Technology Readiness Levels (TRLs) through to deployment at high TRLs, he has established active collaborations with a wide range of industrial partners and academic institutions worldwide. His work contributes significantly to the advancement of automation, robotics, and AI-driven systems, aiming to enhance the efficiency, precision, and adaptability of robotic platforms for both industrial and scientific applications.

Speech Title: "Beyond the Scalpel: Automation as Healthcare’s New Cutting Edge — Challenges and Opportunities"

Abstract: Automation is transforming healthcare, fundamentally shifting how care is delivered, managed, and experienced. Robotic-assisted surgery, AI-enabled diagnostics, automated laboratory workflows, and remote patient monitoring now form an integrated continuum of technological interventions that promise enhanced efficiency, accuracy, and scalability within clinical practice. Yet, whilst these developments demonstrate significant opportunities for improved care, their integration also presents complex technical, regulatory, and operational challenges.
This keynote will examine the adoption of automation within healthcare. It will consider the evolution of advanced automated solutions from concept through to deployment, highlighting the barriers encountered along the development pathway. Drawing upon real-world examples and cross-disciplinary research, it will demonstrate how automation can be strategically implemented to increase throughput, reduce error, and enhance patient outcomes. Beyond technical considerations, this talk will address wider implications including data privacy, cybersecurity, workforce transformation, and the critical need for interoperability across systems. It will emphasise that successful adoption depends upon collaboration among engineers, clinicians, computer scientists, and information technology experts.
Finally, the session will situate healthcare automation within the broader context of industrial transformation, underscoring persistent challenges such as regulatory complexity, integration barriers, and the growing imperative for trust and transparency in AI-supported decision-making. The session will provide insights into deploying automation not simply as a tool for operational efficiency, but as a catalyst for systemic improvement in modern healthcare.