SPEAKERS
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 | 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. |
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:Enhancing Road Safety Through Machine Learning: Driver Profiling in Malaysia Abstract: The persistent rise in annual road accidents in Malaysia presents a significant challenge in obtaining reliable pre-crash data within the transportation community. Traditional methodologies for understanding Malaysian drivers' behavior, such as simulators, police crash reports, and surveys, have been criticized for their biases and lack of reliability. To address this gap, this study introduces the first naturalistic driving study in Malaysia. Thirty drivers participated in driving an instrumented vehicle for 750 kilometers, enabling continuous data collection. Utilizing a comprehensive data acquisition system consisting of various sensors, including OBDII, lidar, ultrasonic sensors, IMUs, and GPS, irrelevant data was effectively filtered out. Machine learning techniques were employed to identify safety criteria related to diverse driving metrics such as maximum acceptable speed limits, safe accelerations, decelerations, acceptable distances to vehicles ahead, and safe steering behavior. These thresholds were then used to examine the influence of social and cultural factors on Malaysian driving habits. The results reveal statistically significant disparities among drivers based on gender, age, and cultural background, with notable distinctions observed between weekday and weekend driving behaviors. Furthermore, the study provides several recommendations spanning the public and governmental sectors to mitigate future accidents and enhance traffic safety, drawing on insights derived from machine learning methodologies. |
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2024 4th International Conference on Image Processing and Intelligent Control (IPIC 2024) http://icipic.org/