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. These advancements will pave the way for extending the benefits of applied computational intelligence to a broader
spectrum of healthcare applications.
Prof. 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, Dr. 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.
Dr. 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.
Artificial Intelligence is a leading topic in both academia and industry. However, we do not have decent theoretical understanding of the various models, such as the deep learning models. As a matter of fact, we are at the doorstep of the theoretical breakthrough for AI. In this talk, we firstly present the basic concepts of differential geometry, a powerful tool for the study of high dimension space. Secondly, we discuss some applications of differential geometry for unprecedented AI problems. We hope and believe the talk will shed light on the promising field for interested audience.
Shui Yu is a Professor of School of Computer Science, University of Technology Sydney, Australia. His research interest includes Cybersecurity, Network Science, Big Data, and Mathematical Modelling. He has published seven monographs and edited two books, more than 600 technical papers at different venues, such as IEEE TDSC, TPDS, TC, TIFS, TMC, TKDE, TETC, ToN, and INFOCOM. His current h-index is 80. Professor Yu promoted the research field of networking for big data since 2013, and his research outputs have been widely adopted by industrial systems, such as Amazon cloud security. He is currently serving the editorial boards of IEEE Communications Surveys and Tutorials (Area Editor) and IEEE Internet of Things Journal (Editor). He is a Distinguished Visitor of IEEE Computer Society, and an elected member of Board of Governors of IEEE VTS and IEEE ComSoc, respectively. He is a member of ACM and AAAS, and a Fellow of IEEE.
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This presentation explores the use of machine learning to improve cybersecurity. By combining machine learning with techniques that explain how models work and
protect privacy, we can create stronger cybersecurity solutions. We'll look at a few specific examples:
Machine learning has been used to classify network traffic for a long time. We'll discuss a new method that uses neural networks to accurately identify
harmful traffic on the Tor network. To make the model's decisions clear and trustworthy, we can use techniques that explain how it works.
Also, new large language models for network traffic classification raise questions about transparency. We'll discuss the risks of making wrong conclusions when
these models are used with encrypted traffic.
The rapid rise of generative AI, particularly tools for creating "deepfakes" that are now accessible to the public, is a double-edged sword.
While this technology offers exciting possibilities, it also poses significant challenges. The presentation will conclude by examining the complexities
surrounding emerging voice and video cloning technologies.
Sanjay K. Jha is a Professor and Director of Research and Innovation at the School of Computer Science and Engineering, University of New South Wales (UNSW),
Australia, and leads UNSW in the Cybersecurity Cooperative Research Centre. With over two decades of experience in the field, he has made significant contributions
to the development of robust and secure network systems.
Dr. Jha's research interests span a wide range of cybersecurity topics, including artificial intelligence, generative AI, network security, and the security
of mobile devices and IoT. He has published extensively in top-tier journals and conferences, and his work has been widely cited by his peers.
Beyond his academic pursuits, Dr. Jha has also been actively involved in the cybersecurity industry. He has consulted for major organizations and served
on the editorial boards of several leading IEEE journals. As a dedicated educator, Prof Jha has supervised 32 PhD students and has been a mentor to many
postdoctoral fellows.
The use of Artificial Intelligence (AI) in education offers efficiency to both educators and students. Educators can optimise their time by using prompts to plan their lessons, tailor the course specific assessment tasks, and create personalised learning experiences. Similarly, students can efficiently use their research time and increase their productivity. However, there are significant risks associated with the misuse or over reliance on AI. There are growing concerns that excessive use may make individuals overly dependent on technology impeding their critical thinking and problem-solving skills. This is particularly worrying that AI’s overuse in learning and teaching can result in inequitable outcomes potentially undermining the integrity of academic standards. This talk will examine some of the important considerations about the responsible use of AI in education settings.
Tanveer Zia currently serves as the Professor and Head of Computer Science at the University of Notre Dame, Australia.
His previous role was as the Associate Director and a founding member of the Centre of Excellence in
Cybercrimes and Digital Forensics at the Naif Arab University for Security Sciences in Riyadh, a position he held from February 2021 to February 2024.
Prior to that, Tanveer contributed significantly to academic and institutional leadership during his 12-year tenure at Charles Sturt University Wagga Wagga Campus.
There, he held the positions of Professor of Computing and Associate Head of the School of Computing, Mathematics, and Engineering.
Notably, Tanveer is recognized as a Senior Fellow of the Higher Education Academy (SFHEA) in the UK.
Robotics has made significant progress in cases of structured and constrained environments, e.g., manufacturing. However, it is still in its infancy when it comes to
applications in unstructured and unconstrained situations e.g., social environments. In some respects, such as speed, strength and accuracy, robots have superior capacities compared to
humans but that is not the case for person/object recognition, language, manual dexterity, and social interaction and understanding capabilities.
Developing a computer vision system with Human visual recognition capabilities has been a very big challenge. It has been hindered mainly by: (i) the non-availability of 3D sensors (with the
capabilities of the human eye) which are able to simultaneously capture appearance (colour and texture), surface shapes of objects while in motion, and
(ii) the non-availability of algorithms to process this information in real-time. Recently, several affordable 3D sensors appeared in the market which is resulting in the development of
practical 3D systems. Examples include 3D object and 3D face recognition for biometric applications, as well as the development of home robotic platforms to assist the elderly with mild
cognitive impairment.
The objective of the talk will be to describe few 3D computer vision projects and tools used towards the development of a platform for assistive robotics in messy living environments.
Various systems with applications and their motivations will be described including 3D object recognition, 3D face/ear biometrics, grasping of unknown objects, and systems to estimate the
3D pose of a person.
Mohammed Bennamoun is Winthrop Professor in the Department of Computer Science and Software Engineering at UWA and is a researcher in computer vision,
machine/deep learning, robotics, and signal/speech processing. He has published 4 books (available on Amazon), 1 edited book, 1 Encyclopedia article (by invitation),
14 book chapters, 220+ journal papers, 260+ conference publications, 16 invited & keynote publications. His h-index is 75 and his number of citations is 30,000+ (Google Scholar).
He was awarded 80+ competitive research grants (approx. $40+ million in funding) from the Australian Research Council, and numerous other Government,
UWA and industry Research Grants. He has delivered conference tutorials at major conferences, including IEEE Computer Vision and Pattern Recognition (CVPR 2016),
Interspeech 2014, IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP) and European Conference on Computer Vision (ECCV). He was also
invited to give a Tutorial at an International Summer School on Deep Learning (DeepLearn 2017). He widely collaborated with researchers from different disciplines
within Australia, and internationally (e.g. Germany, France, Finland, USA). He served for two terms (3 years each term) on the Australian Research Council (ARC) College
of Experts, and the ARC ERA 2018 (Excellence in Research for Australia). He is currently Senior Area Editor of the IEEE Signal Processing Letters and Associate Editor of
the IEEE Transactions on Image Processing, and Associate Editor of the IEEE Transactions on Artificial Intelligence.
