Assoc. Prof. Dr AZIZI ABDULLAH | Computer Vision | Best Paper Award

Researcher, Universiti Kebangsaan Malaysia, Malaysia

Azizi Abdullah is an esteemed academic and researcher, currently serving as an Associate Professor at Universiti Kebangsaan Malaysia. He holds a Ph.D. in Computer Vision from Utrecht University, The Netherlands. With over two decades of experience in the fields of machine learning, computer vision, and robotics, Dr. Abdullah is recognized for his contributions to medical applications, particularly breast cancer classification and object recognition. He has authored several influential research papers and is an expert in Simultaneous Localization and Mapping (SLAM). Dr. Abdullah is passionate about advancing AI and deep learning techniques, with a focus on applications in autonomous vehicles and medical image analysis.

PROFESSIONAL PROFILE

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STRENGTHS FOR THE AWARD

1. Strong Academic Foundation: Azizi Abdullah holds a Ph.D. in Computer Vision from Utrecht University, The Netherlands, along with a Master’s in Software Engineering and a Bachelor’s in Computer Science. This strong academic background provides a solid foundation for his research endeavors.
2. Proven Research Impact: Abdullah’s publications reflect significant contributions in computer vision, machine learning, and robotics, with a focus on real-world applications in areas such as medical diagnostics (e.g., breast cancer classification) and autonomous systems (e.g., autonomous vehicle systems and SLAM). The high citation count for his work highlights the widespread impact of his research in the academic community.
3. Diverse Research Interests: His research spans several cutting-edge areas, including deep learning, AI, object recognition, and autonomous mobile robotics. This multidisciplinary approach is critical for advancing knowledge in these fields.
4. Leadership and Experience: Having held academic positions from Lecturer to Associate Professor at Universiti Kebangsaan Malaysia, Abdullah has demonstrated leadership in both research and teaching, further underlining his ability to shape the future of the field.

AREAS FOR IMPROVEMENT

1. Expansion of Collaborative Research: While Abdullah has published extensively, fostering collaborations with more international researchers could further broaden the scope and impact of his work.
2. Interdisciplinary Applications: Although Abdullah’s research touches on multiple domains, additional focus on interdisciplinary applications, particularly in industries outside academia (e.g., healthcare, manufacturing), could maximize the practical application of his work.

EDUCATION

Dr. Azizi Abdullah earned his Ph.D. in Computer Vision from Utrecht University in 2010. He completed his Master of Software Engineering (MSE) at Universiti Malaya in 1999 and his Bachelor of Science in Computer Science from Universiti Kebangsaan Malaysia in 1996. His academic journey reflects his deep commitment to expanding his expertise in software engineering, artificial intelligence, and computer vision, which are central to his groundbreaking work in machine learning and robotics. His diverse academic background has laid the foundation for his successful career in both research and teaching.

EXPERIENCE

Dr. Abdullah’s academic career spans over two decades, beginning as a Research Assistant at Universiti Kebangsaan Malaysia in 1997. He served as a Tutor and Lecturer before being promoted to Senior Lecturer in 2010. His expertise and leadership earned him the title of Associate Professor in 2015. Throughout his career, Dr. Abdullah has made significant contributions to teaching and research, guiding numerous students in software engineering, computer vision, and AI. He has also played an active role in various academic and research initiatives, further enhancing the global impact of his work.

AWARDS AND HONORS

Dr. Abdullah’s exceptional research contributions have earned him recognition in the fields of computer vision, machine learning, and AI. His work on improving neural network performance and breast cancer classification using deep learning has received widespread acclaim. His research on object categorization and autonomous vehicles has been influential in both academic and industrial sectors. In addition to his numerous citations, Dr. Abdullah’s expertise continues to be acknowledged with awards for his outstanding contributions to technological advancements and the scientific community.

RESEARCH FOCUS

Dr. Abdullah’s research is primarily focused on the intersection of computer vision, machine learning, and robotics. His current work revolves around deep learning models, particularly their application in medical image analysis, such as breast cancer detection. He also explores object categorization and recognition using machine learning techniques. Another key area of his research is autonomous mobile robots, specifically Simultaneous Localization and Mapping (SLAM), which is integral to the development of autonomous systems. His interdisciplinary approach combines cutting-edge AI algorithms with practical applications in medical and robotics fields.

PUBLICATION TOP NOTES

• Deep CNN model based on VGG16 for breast cancer classification 🏥
• A linear model based on Kalman filter for improving neural network classification performance 🤖
• Support vector machine approach to real-time inspection of biscuits on moving conveyor belt 🍪
• Absolute cosine-based SVM-RFE feature selection method for prostate histopathological grading 🧬
• Detection of leukemia in human blood sample based on microscopic images 🩸
• Vision-based autonomous vehicle systems based on deep learning: A systematic literature review 🚗
• Machine vision for crack inspection of biscuits featuring pyramid detection scheme 🍪
• An ensemble of deep support vector machines for image categorization 🖼️
• Spatial pyramids and two-layer stacking SVM classifiers for image categorization 🖼️
• Fixed partitioning and salient points with MPEG-7 cluster correlograms for image categorization 🖼️

CONCLUSION

Azizi Abdullah is a highly deserving candidate for the “Best Researcher Award.” His strong academic qualifications, broad research interests, and impactful contributions to computer vision, AI, and robotics make him a standout figure in his field. While there is room to enhance the interdisciplinary reach and foster more international collaborations, his record of achievement in both theory and application positions him as an influential researcher poised to continue making significant advancements in his areas of expertise.