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.