Quantum Machine Learning – A Crystal Ball into the Future
Dr Ali Al Ataby
Chair, department of electrical engineering and electronics, American University of Ras Al Khaimah
Dr Ali Al Ataby is Chair of the Department of Electrical and Electronics Engineering at the American University of Ras Al Khaimah (AURAK), UAE. He holds a Ph.D. in Electrical Engineering, with a focus on Signal Processing and Machine Learning, from the University of Liverpool, UK, where he also earned a Postgraduate Diploma in Higher Education in Teaching and Learning. With a career spanning academia and industry for nearly three decades across the UAE, the UK, and Iraq, he served at the University of Liverpool as Assistant Professor and then Associate Professor before joining AURAK in 2023. Dr Al-Ataby is a Chartered Engineer (CEng), a Senior Member of IEEE (SMIEEE), and a Senior Fellow of the UK Higher Education Academy – Advance HE (SFHEA). He is the founder and chair of the IEEE International Conference series on Electrical/Electronics, Robotics, Artificial Intelligence, and Informatics (ICERAI), and has served extensively on technical committees of many international conferences. His research spans signal and image processing, machine learning, deep learning, computer vision, robotics, IoT, and biomedical signal processing, with applications in non-destructive testing, driver fatigue prediction, visual augmentation for the visually impaired, and intelligent healthcare, alongside a sustained interest in technology-enhanced education. He has supervised numerous Ph.D. and M.Sc. theses and published widely in leading journals and conferences. His honours include the Sir Alastair Pilkington Award for Teaching Excellence and Innovation and the Faculty of Science and Engineering Learning and Teaching Award at the University of Liverpool.
Abstract
We stand at the frontier where two revolutions meet: quantum computing and artificial intelligence. Quantum Machine Learning (QML) is not merely about accelerating existing algorithms – it is about asking questions that have never been asked before and approaching problems that classical computation considers intractable. While classical AI has been devoted to teaching machines to think like humans, QML opens the door to systems that compute as the universe itself does: probabilistically, in superposition, and through entanglement.
This keynote bridges physics, data science, and engineering to make the foundational ideas of QML accessible to a broad engineering audience. It will introduce the essential concepts of quantum physics and quantum mechanics that underpin the field, including superposition, interference, entanglement, and the qubit as the fundamental unit of quantum information. From there, the talk will move to the architecture of quantum computation and the central question of how classical data is encoded into quantum states, comparing the principal encoding strategies and explaining why data encoding is widely regarded as the bottleneck of practical QML in the present Noisy Intermediate-Scale Quantum (NISQ) era.
Flagship QML models will be examined in depth. The keynote will also survey the current QML ecosystem, and outline emerging applications across drug discovery, finance, cybersecurity, energy systems, and sustainability. The session closes with a forward-looking reflection: ML and AI will be reshaped, and this is the future.
Energy Storage Beyond Lithium-Ion Batteries in the Era of Industry 4.0
Professor Amor M. Abdelkader, FRSC, FIMMM
Professor of Advanced Materials, Bournemouth University, UK
Director of Advanced Materials Research Centre, Université Côte d’Azur, France.
Professor Amor M. Abdelkader is an internationally recognised materials scientist whose research spans advanced materials, nanotechnology, energy storage, graphene and two-dimensional materials, self-healing systems, and sustainable manufacturing technologies. He is Professor of Advanced Materials at Bournemouth University, UK, Director of the Advanced Materials Research Centre, and Chair of Advanced Research at Université Côte d’Azur, France.
He obtained his PhD in Materials Science and Metallurgy from the University of Cambridge in 2011 under the supervision of Professor Derek J. Fray FRS. He subsequently conducted research on self-healing materials at Delft University of Technology before joining the University of Manchester and later the National Graphene Institute, where he worked alongside Professor Sir Kostya Novoselov, Nobel Laureate in Physics and co-discoverer of graphene. He later returned to the University of Cambridge, working with Professor Andrea C. Ferrari at the Cambridge Graphene Centre.
Prof. Abdelkader maintains active international collaborations and academic appointments with leading institutions worldwide, including the University of Cambridge, Delft University of Technology, North Eastern University, and City University of Hong Kong. His research has led to significant advances in energy storage technologies, graphene-enabled devices, materials recycling, electrochemical carbon dioxide sequestration, and sustainable materials processing.
He has authored more than 150 peer-reviewed publications, holds over 30 patents, and has contributed to several major international research initiatives. His work has attracted more than 9000 citations. He is a Fellow of the Royal Society of Chemistry (FRSC) and a Fellow of the Institute of Materials, Minerals and Mining (FIMMM).
Prof. Abdelkader is a sought-after keynote and plenary speaker whose interdisciplinary research bridges fundamental materials science and industrial innovation, with a particular focus on developing advanced materials solutions for the global energy and sustainability challenges of the future.
Abstract
The growing global demand for sustainable energy storage, coupled with concerns over the limited availability and uneven geographical distribution of lithium resources, has accelerated the search for next-generation battery technologies. Among the most promising alternatives are sodium-ion and potassium-ion batteries, which benefit from the abundance, low cost, and widespread availability of their constituent elements. In addition, these systems offer attractive electrochemical properties, including rapid ion transport and the potential for scalable, cost-effective deployment.
Despite these advantages, significant scientific and technological challenges remain. The larger ionic radii and distinct electrochemical behaviour of sodium and potassium ions introduce complex issues related to electrode stability, reaction kinetics, interfacial chemistry, and long-term cycling performance. Addressing these challenges requires the development of advanced electrode materials capable of accommodating large ion insertion and extraction while maintaining high capacity, structural integrity, and cycling durability.
In this talk, I will present our recent advances in designing and engineering novel nanostructured electrode materials for post-lithium energy storage systems. Particular emphasis will be placed on crystal engineering, morphology control, defect and interface design, reaction mechanisms, and scalable synthesis approaches. I will also discuss the integration of these materials into practical full-cell configurations and highlight emerging opportunities for developing high-performance, low-cost, and sustainable energy storage technologies beyond lithium-ion batteries.
Soft Computing for Smart Automation: The Role of Fuzzy Logic, Neural Networks, and Genetic Algorithms in Real-Time Control
Professor Kasim Mousa Alwan Al-Aubidy
Centre for Ecology and Conservation, Environment Mechatronics Engineering Department, Director of AI Research Center, Tishk International University, Erbil, Iraq.
Professor Kasim Al-Aubidy holds a B.Sc. and M.Sc. in Control and Computer Engineering from the University of Technology, Iraq (1979 and 1982, respectively), and a Ph.D. in Real-Time Computing from the University of Liverpool, England (1990). Currently, he serves as a Professor of Intelligent Systems and Director of the Artificial Intelligence Research Center at Tishk International University, Erbil, Iraq. From 1998 to 2025, he held several key leadership roles at Philadelphia University, Jordan, including: Dean of Engineering and Technology, Dean of Information Technology, Dean of Scientific Research and Graduate Studies, and Dean of Quality Assurance and Accreditation at Philadelphia University, Jordan.
His research expertise spans embedded systems, real-time computing, fuzzy logic, neural networks, and genetic algorithms, with applications in robotics, automation, and healthcare systems. Recognized for his contributions, he received the Best Researcher Award from Philadelphia University in 2000.
Prof. Al-Aubidy serves as Editor-in-Chief of two international journals and sits on the editorial boards of multiple scientific journals. He has co-authored four books, contributed five book chapters, and published 123 research papers in indexed journals and international conferences.
Abstract:
In the era of Industry 4.0, intelligent automation has gained paramount significance in contemporary industrial and technical advancements. Conventional control systems, dependent on exact mathematical models, commonly face difficulties in managing uncertainty, nonlinearity, and dynamic real-world conditions. As a result, numerous studies and research attempts have focused on utilizing soft computing technologies to identify suitable automation solutions. Soft computing tools construct powerful models that facilitate intelligent decision-making in complex, nonlinear, and real-time systems. Soft computing methodologies encompass three primary tools: fuzzy logic, neural networks, and genetic algorithms. These tools offer robust, flexible, and learning-oriented solutions for intelligent automation. Compared to conventional hard computing, which requires precise inputs and deterministic outputs, soft computing depends on approximation, adaptability, and heuristic optimization, making it suitable for real-time monitoring and control.
The Fuzzy Logic tool emulates human reasoning, enabling systems to handle uncertain or partial input, thereby making it essential in rule-based control. Biologically inspired neural networks do well in pattern recognition, predictive analytics, and adaptive control, enabling self-improving automation in manufacturing and energy management. Genetic Algorithms allow evolutionary optimization by dynamically adjusting system parameters to achieve optimal efficiency in robotics, logistics, and industrial process control.
The integration of various methodologies results in hybrid intelligent systems, such as neuro-fuzzy controllers and GA-optimized neural networks, which outperform conventional methods in ability to adapt and recover. Soft computing transforms automation in a variety of applications including predictive maintenance in smart factories and real-time defect detection in power grids with its capabilities.This lecture explores the fundamental concepts, the applications, and future directions of soft computing in smart automation will be addressed in this lecture, which also shows how fuzzy logic, neural networks, and genetic algorithms work together to enable smart automation.
Computational Intelligence in Healthcare Technology: Current Landscape, Challenges, and Future Directions for Gen AI
Professor Adel Al-Jumaily
Associate Head of the School of IT & Engineering at MIT Sydney.
Professor Adel Al-Jumaily is a distinguished researcher and educator in the fields of Computational Intelligence and Health Technology. He currently serves as the Associate Head of the School of IT & Engineering at MIT Sydney and holds the esteemed position of Professor of Data Analytics. Renowned for his expertise, he is also a Professor Research Fellow at ENSTA Bretagne, France, and holds adjunct professor positions at the University of Western Australia and Fahad Bin Sultan University.
Dr. Al-Jumaily earned his Ph.D. in Electrical Engineering (AI) and has cultivated a distinguished career spanning over two decades. His research contributions have been instrumental in advancing the fields of applied computational intelligence, humanised computational intelligence technology, health technology, and bio-mechatronic systems. His innovative work leverages the power of machine learning, artificial intelligence, and generative AI tools to develop tailored solutions that address real-world challenges.
Prof. Al-Jumaily’s research has garnered significant recognition, with over 6,200 citations and 14 patents, 13 of which were fully sponsored by industry. He has received two prestigious Higher Degree Research Supervision Completion Awards and has successfully supervised over 20 Ph.D. students to completion, along with more than 30 other higher-degree research students. His exceptional contributions have been acknowledged with 6 best paper awards and 27 research achievement prizes.
Beyond his research accomplishments, Prof. Al-Jumaily has also made substantial contributions to the academic community. He has delivered 32 invited talks at conferences and seminars, served as Program Chair at 33 events, and contributed as a member of 124 technical program committees. Furthermore, he has chaired 20 sessions, demonstrating his leadership and expertise in the field.
Prof. Al-Jumaily’s broad expertise encompasses both research and teaching, with over 20 years of professional experience. He is a dedicated senior member of the IEEE, serving as Co-Vice Chair of the IEEE Computational Intelligence Chapter (NSW), and actively participates in various other professional committees. His contributions have significantly impacted the advancement of computational intelligence and health technology.
Abstract:
Applied computational intelligence holds immense potential to revolutionise healthcare by enabling personalised, adaptive, and anticipatory care. However, several challenges must be addressed before these technologies can be widely adopted.
One of the most significant challenges is real-time processing. Many healthcare applications demand systems capable of processing data in real-time. However, current computational intelligence systems often struggle to keep pace with the volume and velocity of data generated in these environments. Additionally, the limited size of available datasets poses a challenge. Many computational intelligence models require large datasets for training and validation. However, healthcare datasets are often limited due to privacy concerns and the complexities of collecting patient data. Furthermore, computational intelligence systems typically require extensive data pre-processing before they can be used for training or inference. This can be a substantial barrier to adoption in healthcare settings, where time is often critical.
Despite these challenges, a growing body of research is dedicated to addressing these issues. This talk will delve into these challenges and present our work on solutions that enable real-time data processing, effective utilisation of small datasets, and faster data pre-processing. We will also discuss strategies for tackling other challenges that need to be addressed in the future, including Gen AI. These advancements will pave the way for extending the benefits of applied computational intelligence to a broader spectrum of healthcare applications.
Educating for Industry 4.0 through AI, Robotics and Computer Science in Every Classroom
Professor Răzvan Bologa
Department of Computer Science, Bucharest University of Economic Studies.
Professor Răzvan Bologa is a professor at the Department of Computer Science at the Bucharest University of Economic Studies. His research focuses on Industry 4.0, cybersecurity, artificial intelligence, and online learning. He has coordinated several national and international research projects and actively collaborates with start-ups developing AI-based solutions for education and cybersecurity. Prof. Bologa also leads a master’s program in enterprise systems and coordinates South-Eastern Europe’s largest educational robotics initiative for children, promoting early STEM education and digital innovation.
Abstract:
In the age of Industry 4.0, artificial intelligence, robotics, and computer science have become essential skills that define the modern workforce. These fields are no longer the exclusive domain of engineers or technology specialists; rather, they form the foundation of digital literacy for all professions. Integrating these competencies into every level of education is therefore a strategic priority for preparing future generations to thrive in a technology-driven world. Achieving this goal presents a major challenge: not all students are naturally drawn to subjects such as computer science, AI, or robotics. Creating inclusive, engaging, and accessible learning experiences that connect these disciplines to real-world applications is crucial to ensuring that every learner develops the skills needed to participate meaningfully in the digital economy, no matter their background or initial interest.
The Role and Limitations of Artificial Intelligence in Radiology Imaging
Dr Osama Alnuaimi
Elias University Emergency Hospital – Bucharest/Romania.
Dr Osama Alnuaimi is a consultant Radiologist who is currently working at Elias University Emergency Hospital – Bucharest/Romania, and as consultant and resident‘s supervisor at Affidea Hiperdia Bucharest-Romania. He is also a lecturer for postgraduate students in the University of Medicine and Pharmacy “Carol Davila” Bucharest, and a consultant radiologist at the Memorial Private hospital at Enayati medical city. His main areas of expertise are oncology and MSK radiology. He is also a member of the Romanian Board of Radiology. Dr AlNuaimi has 15 years’ experience in radiology in European schools of radiology. He has contributed to 90 national / international / faculty scientific participations in Europe and USA, and is also main author/ co-author of tens of published scientific presentations, posters and papers/articles. He also supervised a doctorate thesis in AI of a student at Department of Imaging, Universiti Putra Malaysia, Serdang, Malaysia.
Abstract:
The integration of Artificial Intelligence (AI) into radiology has become increasingly prominent, with claims that it can streamline workflows and reduce human error. However, AI cannot replace radiologists due to medico-legal responsibilities and clinical judgment needs. This presentation explores the practical applications, benefits, and limitations of AI in radiological imaging. The presentation draws upon real-world clinical experience and literature review. It evaluates AI use in three core imaging stages: image acquisition, comparison, and interpretation. It also discusses AI support in oncology (e.g., RECIST for lesion tracking) and addresses the necessity of radiologist validation in all AI-driven software. AI significantly reduces time for both image acquisition and interpretation. It enhances workflow efficiency, allowing radiologists to handle more cases with improved accuracy. The technology also assists in optimizing MRI protocols (e.g., for claustrophobic patients) and facilitates comparative analysis in treatment monitoring. Nonetheless, AI requires constant human oversight. Radiologists remain essential for validating diagnoses, interpreting context-specific findings, and assuming legal responsibility. While AI shows great promise, its successful implementation requires interdisciplinary collaboration, software validation, and standardized imaging protocols. Ethical concerns and training gaps further emphasize the need for cautious integration. AI should be viewed as an augmentative tool rather than a replacement. AI improves imaging efficiency, supports diagnostic accuracy, and reduces waiting times. However, it cannot replace the radiologist’s expertise. Future integration depends on structured protocols, legal frameworks, and ongoing training.