Prof. Abdel-Hamid Soliman IEEE Member Staffordshire University, UK | Brief Introduction: Abdel-Hamid Soliman has over 34 years of experience in the academic and industrial fields. He has a multi-disciplinary academic/research experience in digital signal processing, telecommunications, data acquisition systems, wireless sensor networks (WSN), the Internet of Things (IoT), fiber optics communication, and image/video processing. He is currently working to harness and integrate different technologies toward implementing smart systems to contribute to smart cities and real-life applications. His research interest includes not limited to the national level within the U.K., but are internationally extended to many partner universities in various countries. His research has produced over 50 refereed papers. In addition to his research activities, he is involved in several enterprise projects and consultancy activities for national and international companies. Since 2007, he has been leading and involved in several externally funded projects on national, European, and international levels totaling more than £20M.,His work has been recognized through several awards, such as Lord Stafford award “Impact through Innovation,” for designing and developing a smart monitoring and controlling system for diabetic people. The AWM ICT Excellence awards for “Best Knowledge Transfer Project” category, for designing and developing an electronic bladder diary, and UHNS “Clinical Innovation” Award, for designing and developing an online multimedia-based training system for surgeons. He is an Associate Editor of IEEE Access and a regular reviewer of several respected journals and conferences. |
Prof. Azlan bin Mohd Zain IEEE Member Universiti Teknologi Malaysia, Malaysia | Profile: Azlan Mohd Zain (Member, IEEE) obtained his master’s degree in science (productivity and quality improvement) from Universiti Kebangsaan Malaysia (UKM) and his Ph.D. in computer science from Universiti Teknologi Malaysia (UTM) in 2010. He currently holds the position of Professor in the Faculty of Engineering, School of Computing at UTM. Additionally, he serves as the Director of the UTM Big Data Research Centre. As an academic staff member, he has successfully mentored over 25 postgraduate students and secured funding from more than 20 research grants to support their studies. Professor Zain has authored over 100 research papers. He has been invited as a keynote speaker at more than five international conferences, is involved in numerous committees, and has served on the editorial board of three international journals. Title: The Role of Artificial Intelligence (AI) in Computer Vision Abstract: The topic of artificial intelligence (AI) and computer vision is covered in this sharing session. Artificial Intelligence is a technique that allows machines and computers to perform computer vision tasks intelligently. A subset of artificial intelligence (AI) called machine learning (ML) uses algorithms to provide AI applications. A subset of machine learning (ML) called deep learning (DL) is used to tackle increasingly challenging computer vision tasks. In this session, the significance of machine learning |
Assoc. Prof. Dr. Por Lip Yee University of Malaya, Malaysia | Profile: Lip Yee received his Ph.D. from University of Malaya, Malaysia under the supervision of Prof. Abdullah bin Gani in 2012. Currently, he is an Assoc. Professor at the Department of System and Computer Technology, Faculty of Computer Science and Information Technology, University of Malaya, Malaysia. He is also a senior member of IEEE. Lip Yee and his team were the first few pioneers who received IRPA, E-Science, FRGS, ERGS, PRGS, HIR and IIRG grants. He was the first person who managed to secure 2 E-Science funds with the role of PI in 2008. He was also the first person at the FCSIT who managed to secure the PRGS and ERGS grants. Beside collaborators from Malaysia, Lip Yee also has international collaborators from France, UK New Zealand, Turkey, Thailand and China. He also established his connection with his national and international collaborators with some industrial partners in Malaysia and other countries. Title: Strengthening Graphical Authentication Security: Mitigating Shoulder-Surfing Attacks Abstract: This research explores innovative strategies within graphical authentication to combat vulnerabilities associated with shoulder-surfing attacks. In today's data security landscape, creating robust yet user-friendly authentication mechanisms is of paramount importance. Conventional alphanumeric passwords are vulnerable to brute-force attacks and pose challenges in managing complex combinations. Graphical passwords offer a compelling alternative, leveraging human cognitive abilities to recognize and recall images, patterns, and symbols. Our study investigates the effectiveness of graphical passwords, particularly in thwarting shoulder-surfing threats, where malicious actors seek to obtain login credentials by observing legitimate users, posing significant privacy and security risks. We propose methods to effectively balance security and usability. Additionally, our empirical findings demonstrate that our proposed approach not only enhances security but also accelerates login times compared to traditional methods, highlighting the efficiency of user authentication and its practical applicability. In summary, this research introduces innovative strides in graphical authentication to address shoulder-surfing challenges, streamline the user experience, and prioritize accessibility and inclusivity in modern digital settings. |
