Prof. Dr. Mohamed Maher Ben Ismail | Artificial Intelligence | Best Researcher Award

Prof. Dr. Mohamed Maher Ben Ismail | Artificial Intelligence | Best Researcher Award

Prof. Dr. Mohamed Maher Ben Ismail, King Saud University, Saudi Arabia

Dr. Mohamed Maher Ben Ismail is a distinguished full professor in the Computer Science Department at the College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia . With a prolific academic and research background spanning over two decades, Dr. Ben Ismail is recognized for his contributions in artificial intelligence, image processing, and data mining. His work bridges theory and practical applications in machine learning and statistical modeling, making him a leading voice in his field ๐ŸŒ๐Ÿ“š.

Professional Profile

Google Scholar

Scopus

๐ŸŽ“ Education Background

Dr. Ben Ismail holds a Ph.D. in Computer Engineering and Computer Science from the University of Louisville, USA (2011) ๐Ÿ‡บ๐Ÿ‡ธ, where his dissertation focused on image annotation and retrieval using multi-modal feature clustering. He also earned a Masterโ€™s in Automatic and Signal Processing and a Bachelor’s in Electrical Engineering from the National School of Engineering of Tunis, Tunisia ๐Ÿ‡น๐Ÿ‡ณ. His early academic journey was distinguished by excellence in mathematics, physics, and competitive engineering entrance exams ๐Ÿง ๐Ÿ“˜.

๐Ÿง‘โ€๐Ÿซ Professional Experience

Dr. Ben Ismail currently serves as a Full Professor at King Saud University (2021โ€“present), following roles as Associate Professor (2017โ€“2021) and Assistant Professor (2011โ€“2017). Previously, he worked as a Design & Development Engineer at STMicroelectronics, Tunisia, and as a Graduate Research Assistant at the University of Louisvilleโ€™s Multimedia Research Lab, where he pioneered work on CBIR systems and integrated machine learning approaches. His academic role includes supervising thesis work, lecturing across AI, ML, algorithm design, and image processing ๐Ÿ’ผ๐Ÿ‘จโ€๐Ÿซ.

๐Ÿ† Awards and Honors

Throughout his career, Dr. Ben Ismail has received numerous accolades, including the Best Faculty Member Award (2017) at King Saud University, the Graduate Deanโ€™s Citation Award (2011), and the IEEE Outstanding CECS Student Award (2011) ๐Ÿฅ‡. He is also a member of the Golden Key International Honor Society and received early recognition through his promotion at STMicroelectronics and various graduate assistantships and scholarships ๐ŸŽ–๏ธ.

๐Ÿ”ฌ Research Focus

Dr. Ben Ismailโ€™s research interests lie in Artificial Intelligence, Machine Learning, Pattern Recognition, Image Processing, Temporal Data Mining, and Information Fusion ๐Ÿค–๐Ÿง . His work emphasizes robust statistical modeling and intelligent systems design, often applied to domains like IoT security, brain tumor detection, real estate prediction, and hyperspectral imaging. His prolific publication record in top-tier journals and conferences highlights his continuous contributions to advanced computational techniques and interdisciplinary innovation ๐Ÿ“Š๐Ÿ“ˆ.

๐Ÿ“Œ Conclusion

With a solid educational foundation, impactful research contributions, and extensive teaching experience, Dr. Mohamed Maher Ben Ismail stands as a key figure in advancing AI-driven solutions in academia and industry. His dedication to excellence and innovation marks him as a thought leader and an inspirational academic voice in the global computer science community ๐ŸŒŸ๐Ÿง‘โ€๐Ÿ”ฌ.

๐Ÿ“š Top Publications Notes

  1. YOLO-Act: Unified Spatiotemporal Detection of Human Actions Across Multi-Frame Sequences
    ๐Ÿ“… Published in: Sensors, 2025
    ๐Ÿ” Cited by: 12 articles (as of mid-2025)
    ๐Ÿง  Highlights: Proposes a YOLO-based system for recognizing actions across video frames.

  2. MRI-Based Meningioma Firmness Classification Using an Adversarial Feature Learning Approach
    ๐Ÿ“… Published in: Sensors, 2025
    ๐Ÿ” Cited by: 9 articles
    ๐Ÿง  Highlights: Enhances brain tumor classification using deep adversarial networks.

  3. RobEns: Robust Ensemble Adversarial Machine Learning Framework for Securing IoT Traffic
    ๐Ÿ“… Published in: Sensors, 2024
    ๐Ÿ” Cited by: 18 articles
    ๐Ÿ” Highlights: Focuses on adversarial ML methods to enhance IoT network security.

  4. Skin Cancer Recognition Using Unified Deep Convolutional Neural Networks
    ๐Ÿ“… Published in: Cancers, 2024
    ๐Ÿ” Cited by: 25 articles
    ๐Ÿงฌ Highlights: Applies CNNs to early skin cancer detection using medical images.

  5. A Deep Learning Approach for Brain Tumor Firmness Detection Based on Five YOLO Versions
    ๐Ÿ“… Published in: Computation, 2024
    ๐Ÿ” Cited by: 14 articles
    ๐Ÿ’ก Highlights: Compares YOLOv3 to YOLOv7 models for brain scan interpretation.

  6. Toward an Improved Machine Learning-based Intrusion Detection for IoT Traffic
    ๐Ÿ“… Published in: Computers, 2023
    ๐Ÿ” Cited by: 20 articles
    ๐Ÿ”’ Highlights: Develops a secure ML framework to prevent intrusions in smart devices.

  7. Simultaneous Deep Learning-based Classification and Regression for Company Bankruptcy Prediction
    ๐Ÿ“… Published in: Journal of Business & Economic Management, 2023
    ๐Ÿ” Cited by: 8 articles
    ๐Ÿ’ผ Highlights: Innovative DL model integrating financial classification with regression.

  8. Novel Dual-Constraints Based Semi-Supervised Deep Clustering Approach
    ๐Ÿ“… Published in: Sensors, 2025
    ๐Ÿ” Cited by: 6 articles
    ๐Ÿ“Š Highlights: Enhances clustering accuracy using semi-supervised constraints in DL.

  9. Better Safe than Never: A Survey on Adversarial Machine Learning Applications towards IoT Environment
    ๐Ÿ“… Published in: Applied Sciences, 2023
    ๐Ÿ” Cited by: 22 articles
    ๐Ÿ” Highlights: Comprehensive survey exploring adversarial ML attacks and defense for IoT.

  10. Detecting Insults on Social Network Platforms Using a Deep Learning Transformer-Based Model
    ๐Ÿ“… Published in: IGI Global Book Chapter, 2025
    ๐Ÿ” Cited by: 11 articles
    ๐ŸŒ Highlights: Uses transformer models to detect hate speech and insults online.

 

Assoc. Prof. Dr. Feng Xie | intelligence systems | Best Researcher Award

Assoc. Prof. Dr. Feng Xie | intelligence systems | Best Researcher Award

School of Information Science and Technology / Sanda University, China

Dr. Feng Xie is an accomplished Associate Professor at the School of Information Science and Technology, Sanda University, China . With a career that bridges academia and industry, he has been at the forefront of intelligent transportation systems, urban mobility, and smart city innovations. As a tech entrepreneur and researcher, he has led over 500 consultancy projects globally and holds numerous patents and software copyrights. His expertise spans traffic management, AI applications, IoT, and big data analytics, with significant contributions that have earned him prestigious awards and talent program recognitions.

Publication Profile

ORCID

๐ŸŽ“ Education Background:

Dr. Xie earned his Ph.D. from Nanyang Technological University, Singapore , in 2002 and completed his postdoctoral research at Tongji University, China , in 2005. His academic foundation is rooted in transportation engineering, computer science, and intelligent systems, providing the basis for his interdisciplinary approach to research and technology deployment.

๐Ÿ’ผ Professional Experience:

Currently serving as an Associate Professor at Shanghai Shanda University, Dr. Xie has also been the founder of Shanghai Van-Chance Trans. Technologies (2010โ€“2022), where he led large-scale smart transportation projects across Asia. He worked extensively with government and industry partners, such as Singaporeโ€™s Land Transport Authority and IKEA, and directed projects like the worldโ€™s largest underground parking facility. He has also held leadership roles in cross-border technology associations and has developed systems used in cities like Beijing, Hangzhou, and Wuhan.

๐Ÿ† Awards and Honors:

Dr. Feng Xie has been recognized with several prestigious awards, including the IES Engineering Achievement Award in 2004 for his contributions to Singapore’s i-Transport project and the Shanghai Science Progress Award in 2013. He has also been selected for elite talent programs such as the Shanghai “3310” Overseas High-level Talent Program and Nanjing “321” Leading Technology Entrepreneurship Talent Program. His innovative work has resulted in 5 patents and 9 software copyrights, solidifying his impact in both academic and applied research domains.

๐Ÿง  Research Focus:

Dr. Xieโ€™s research is centered on Intelligent Transportation Systems (ITS), AI-driven traffic management, smart parking, indoor positioning, urban planning, and emerging tech applications in IoT and quantitative finance. His efforts in traffic simulation, traveler behavior modeling, and data-driven urban development have influenced policies and technologies in smart mobility across multiple major cities. He has collaborated with Tongji University, published in Transportation Research Board journals, and contributed to key projects with global relevance.

โœ… Conclusion:

With a unique blend of academic rigor and entrepreneurial innovation, Dr. Feng Xie exemplifies leadership in intelligent systems and sustainable urban technology ๐ŸŒ. His work has profoundly shaped how modern cities approach mobility, data analytics, and smart infrastructure development. He continues to push the boundaries of AI, transportation science, and cross-border collaboration, earning him a rightful nomination for the Best Researcher Award.

๐Ÿ“š Top Publications :

PDCG-Enhanced CNN for Pattern Recognition in Time Series Data
Journal: Elsevier – Expert Systems with Applications
Year: 2022 | Cited by: 38 articles

Modeling Traveler Behavior Using Hybrid RP/SP Data and Path-Size Logit Models
Journal: Transportation Research Record: Journal of the Transportation Research Board
Year: 2012 | Cited by: 65 articles

AI-Based Traffic Incident Management Systems: A Case Study of Singaporeโ€™s i-Transport Project
Journal: IEEE Transactions on Intelligent Transportation Systems
Year: 2014 | Cited by: 79 articles

Urban Traffic Simulation Using GPS Data Fusion and Adaptive Signal Optimization
Journal: Journal of Transportation Engineering, ASCE
Year: 2016 | Cited by: 45 articles

Smart Parking Systems Powered by IoT and AI: A Case Study of Guinness Record Facility
Journal: Sensors (MDPI)
Year: 2020 | Cited by: 54 articles