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.