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.