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

Published on by Computer Scientists Awards

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 🌐📚.

Professional Profile

Google Scholar

Scopus

🎓 Education Background

Dr. Ben Ismail holds a Ph.D. in Computer Engineering and Computer Science from the University of Louisville, USA (2011) 🇺🇸, where his dissertation focused on image annotation and retrieval using multi-modal feature clustering. He also earned a Master’s in Automatic and Signal Processing and a Bachelor’s in Electrical Engineering from the National School of Engineering of Tunis, Tunisia 🇹🇳. His early academic journey was distinguished by excellence in mathematics, physics, and competitive engineering entrance exams 🧠📘.

🧑‍🏫 Professional Experience

Dr. Ben Ismail currently serves as a Full Professor at King Saud University (2021–present), following roles as Associate Professor (2017–2021) and Assistant Professor (2011–2017). Previously, he worked as a Design & Development Engineer at STMicroelectronics, Tunisia, and as a Graduate Research Assistant at the University of Louisville’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 💼👨‍🏫.

🏆 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 Dean’s Citation Award (2011), and the IEEE Outstanding CECS Student Award (2011) 🥇. 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 🎖️.

🔬 Research Focus

Dr. Ben Ismail’s research interests lie in Artificial Intelligence, Machine Learning, Pattern Recognition, Image Processing, Temporal Data Mining, and Information Fusion 🤖🧠. 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 📊📈.

📌 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 🌟🧑‍🔬.

📚 Top Publications Notes

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

  2. MRI-Based Meningioma Firmness Classification Using an Adversarial Feature Learning Approach
    📅 Published in: Sensors, 2025
    🔍 Cited by: 9 articles
    🧠 Highlights: Enhances brain tumor classification using deep adversarial networks.

  3. RobEns: Robust Ensemble Adversarial Machine Learning Framework for Securing IoT Traffic
    📅 Published in: Sensors, 2024
    🔍 Cited by: 18 articles
    🔐 Highlights: Focuses on adversarial ML methods to enhance IoT network security.

  4. Skin Cancer Recognition Using Unified Deep Convolutional Neural Networks
    📅 Published in: Cancers, 2024
    🔍 Cited by: 25 articles
    🧬 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
    📅 Published in: Computation, 2024
    🔍 Cited by: 14 articles
    💡 Highlights: Compares YOLOv3 to YOLOv7 models for brain scan interpretation.

  6. Toward an Improved Machine Learning-based Intrusion Detection for IoT Traffic
    📅 Published in: Computers, 2023
    🔍 Cited by: 20 articles
    🔒 Highlights: Develops a secure ML framework to prevent intrusions in smart devices.

  7. Simultaneous Deep Learning-based Classification and Regression for Company Bankruptcy Prediction
    📅 Published in: Journal of Business & Economic Management, 2023
    🔍 Cited by: 8 articles
    💼 Highlights: Innovative DL model integrating financial classification with regression.

  8. Novel Dual-Constraints Based Semi-Supervised Deep Clustering Approach
    📅 Published in: Sensors, 2025
    🔍 Cited by: 6 articles
    📊 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
    📅 Published in: Applied Sciences, 2023
    🔍 Cited by: 22 articles
    🔍 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
    📅 Published in: IGI Global Book Chapter, 2025
    🔍 Cited by: 11 articles
    🌐 Highlights: Uses transformer models to detect hate speech and insults online.

 

Dr. Keyong Hu | artificial intelligence | Best Researcher Award

Published on by Computer Scientists Awards

Dr. Keyong Hu | artificial intelligence | Best Researcher Award

Teacher, Hangzhou Normal University, China

Dr. KeYong Hu is an accomplished academic and researcher specializing in artificial intelligence and new energy technology. He earned his Ph.D. from the Zhejiang University of Technology in 2016 and is currently serving as an Associate Professor at Hangzhou Normal University, within the School of Information Science and Technology. Dr. Hu has contributed significantly to the intersection of AI and energy systems, with numerous publications in international journals, showcasing his expertise in predictive modeling and intelligent optimization.

Publication Profile

ORCID

🎓 Education Background

Dr. KeYong Hu completed his doctoral studies at the Zhejiang University of Technology, Hangzhou, China, where he received his Ph.D. in 2016. His academic training laid a strong foundation in computational intelligence and energy-related engineering applications.

💼 Professional Experience

Dr. Hu holds the position of Associate Professor at Hangzhou Normal University, Hangzhou, Zhejiang, China, affiliated with the School of Information Science and Technology. He has been actively involved in teaching, mentoring, and high-impact research since earning his doctorate.

🏆 Awards and Honors

While specific awards are not listed, Dr. Hu’s prolific publishing record in top-tier peer-reviewed journals like Mathematics, Heliyon, Sustainability, and Computers and Electrical Engineering underscores his recognition and influence in the fields of AI and energy optimization.

🔬 Research Focus

Dr. Hu’s research centers on the integration of artificial intelligence with new energy technologies, particularly photovoltaic power forecasting, energy system optimization, and cross-modal data analysis. His innovative use of algorithms such as Copula functions, Transformers, and Dung Beetle Optimization showcases his depth in AI-driven energy analytics.

✅ Conclusion

Dr. KeYong Hu stands out as a forward-thinking researcher contributing impactful work at the intersection of artificial intelligence and sustainable energy. Through his academic leadership and research contributions, he continues to shape the future of intelligent energy systems in China and beyond. 🌍📈

📚 Top Publications 

🔗 Two-Stage Distributionally Robust Optimal Scheduling for Integrated Energy Systems Considering Uncertainties in Renewable Generation and Loads
Journal: Mathematics | Year: 2025
Cited by: Check on Google Scholar

🔗 Short-term Photovoltaic Forecasting Model with Parallel Multi-Channel Optimization Based on Improved Dung Beetle Algorithm
Journal: Heliyon | Year: 2024
Cited by: Check on Google Scholar

🔗 Distributed Regional Photovoltaic Power Prediction Based on Stack Integration Algorithm
Journal: Mathematics | Year: 2024
Cited by: Check on Google Scholar

🔗 Automatic Depression Prediction via Cross-Modal Attention-Based Multi-Modal Fusion in Social Networks
Journal: Computers and Electrical Engineering | Year: 2024
Cited by: Check on Google Scholar

🔗 Short-Term Photovoltaic Power Generation Prediction Based on Copula Function and CNN-CosAttention-Transformer
Journal: Sustainability | Year: 2024
Cited by: Check on Google Scholar

Ulas Bagci | Artificial Intelligence | Outstanding Scientist Award

Published on by Computer Scientists Awards

Assoc. Prof. Dr. Ulas Bagci | Artificial Intelligence | Outstanding Scientist Award

Assoc. Prof., Northwestern University, United States

Dr. Ulas Bagci is a distinguished researcher and tenured Associate Professor at Northwestern University, specializing in Radiology, Electrical and Computer Engineering, and Biomedical Engineering. He is also a courtesy professor at the University of Central Florida’s Center for Research in Computer Vision. As the Director of the Machine and Hybrid Intelligence Lab, Dr. Bagci focuses on the integration of artificial intelligence, deep learning, and medical imaging. His extensive research contributions include over 330 peer-reviewed articles in these domains. Previously, he was a staff scientist and lab co-manager at the National Institutes of Health (NIH), where he played a pivotal role in advancing AI-driven medical imaging applications. Dr. Bagci actively contributes to leading scientific journals, serving as an associate editor for IEEE Transactions on Medical Imaging, Medical Physics, and Medical Image Analysis.

Publication Profile

🎓 Education

Dr. Ulas Bagci holds a Ph.D. in Computer Science from the University of Nottingham (2010), where he conducted pioneering research in medical imaging. He was a Visiting Research Fellow in Radiology at the University of Pennsylvania (2008-2009), further refining his expertise in AI applications for biomedical sciences. He earned his M.Sc. in Electrical and Computer Engineering from Koç University (2005) and his B.Sc. in Electrical and Computer Engineering from Bilkent University (2003).

💼 Experience

Dr. Bagci has built an impressive academic and research career across top institutions. Since 2021, he has been an Associate Professor at Northwestern University, where he leads research in AI-driven medical imaging. Before that, he served as an Assistant Professor in Computer Science at the University of Central Florida (2014-2020), fostering innovation in deep learning for radiology. From 2010 to 2014, he was a Staff Scientist and Lab Manager at the National Institutes of Health (NIH), playing a key role in infectious disease imaging and AI applications in radiology.

🏅 Awards and Honors

Dr. Bagci has received numerous recognitions for his outstanding contributions to artificial intelligence and medical imaging. He has secured multiple NIH grants (R01, U01, R15, R21, R03) as a Principal Investigator and is a steering committee member for the NIH Artificial Intelligence Resource (AIR). Additionally, he has been honored with best paper and reviewer awards in top-tier AI and medical imaging conferences such as MICCAI and IEEE Medical Imaging.

🔬 Research Focus

Dr. Bagci’s research revolves around artificial intelligence, deep learning, radiology, and computer vision. His work has significantly impacted medical imaging applications, including MRI, CT scans, nuclear medicine imaging, and disease diagnosis. He has contributed extensively to federated learning, probabilistic modeling, and AI-powered decision-making in healthcare. His recent studies include advancements in brain tumor segmentation, bias field correction in MRI, and AI-driven road network prediction.

🔚 Conclusion

Dr. Ulas Bagci is a leading expert in AI-powered medical imaging, consistently pushing the boundaries of deep learning, radiology, and computer vision. His impactful contributions in academia and research have earned him global recognition. With a strong presence in prestigious institutions, his pioneering work continues to shape the future of AI in healthcare. 🚀

📚 Publications

Evidential Federated Learning for Skin Lesion Image Classification (2025) – Published in a book chapter DOI: 10.1007/978-3-031-78110-0_23 📖

Paradoxical Response to Neoadjuvant Therapy in Undifferentiated Pleomorphic Sarcoma (2025) – Published in Cancers DOI: 10.3390/cancers17050830 🏥

Foundational Artificial Intelligence Models and Modern Medical Practice (2025) – Published in BJR | Artificial Intelligence DOI: 10.1093/bjrai/ubae018 🧠

A Probabilistic Hadamard U-Net for MRI Bias Field Correction (2024) – Published in arXiv arXiv:2403.05024 🖥️

AI-Powered Road Network Prediction with Fused Low-Resolution Satellite Imagery and GPS Trajectory (2024) – Published in Earth Science Informatics 🌍

Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation (2024) – Presented at the IEEE/CVF Winter Conference on Applications of Computer Vision 🤖

Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation  (2024) – Published in arXiv arXiv:2405.18383 🏥

 

Sikandar Ali | Artificial Intelligence Award | Best Researcher Award

Published on by Computer Scientists Awards

Dr. Sikandar Ali | Artificial Intelligence Award | Best Researcher Award

Postdoc Fellow, Inje University, South Korea

🎓 Sikandar Ali is a passionate AI researcher and educator specializing in Artificial Intelligence applications in healthcare. Currently pursuing a PhD at Inje University, South Korea, he has a strong academic background and extensive research experience in digital pathology, medical imaging, and machine learning. As a team leader of the digital pathology project, he develops innovative AI algorithms for cancer diagnosis while collaborating with a global team of researchers. Sikandar is a recipient of prestigious scholarships, accolades, and recognition for his contributions to AI and healthcare innovation.

Publication Profile

Google Scholar

Education

📘 Sikandar Ali holds a PhD in Artificial Intelligence in Healthcare (CGPA: 4.46/4.5) from Inje University, South Korea, where his thesis focuses on integrating pathology foundation models with weakly supervised learning for gastric and breast cancer diagnosis. He earned an MS in Computer Science from Chungbuk National University, South Korea (GPA: 4.35/4.5), with research on AI-based clinical decision support systems for cardiovascular diseases. His undergraduate degree is a Bachelor of Engineering in Computer Systems Engineering from Mehran University of Engineering and Technology, Pakistan, with a CGPA of 3.5/4.0.

Experience

💻 Sikandar is an experienced researcher and AI specialist. Currently working as an AI Research Assistant at Inje University, he focuses on cutting-edge projects in digital pathology, cancer detection, and medical imaging. Previously, he worked as a Research Assistant at Chungbuk National University, focusing on cardiovascular disease diagnosis using AI. His industry experience includes roles such as Search Expert at PROGOS Tech Company and Software Developer Intern at Hidaya Institute of Science and Technology.

Awards and Honors

🏆 Sikandar has received multiple awards, including the Brain Korean Scholarship, European Accreditation Council for Continuing Medical Education (EACCME) Certificate, and recognition as an outstanding Teaching Assistant at Inje University. He has also earned full travel grants for international conferences, extra allowances for R&D industry projects, and certificates for reviewing research papers in leading journals. Additionally, he is a Guest Editor at Frontiers in Digital Health.

Research Focus

🔬 Sikandar’s research focuses on developing AI algorithms for medical imaging, with expertise in weakly supervised learning, self-supervised learning, and digital pathology. His projects include designing AI systems for cancer detection, COVID-19 prediction, and IPF severity classification. He also works on object detection applications using YOLO models and wearable sensor-based activity detection for pets. His commitment to explainability and interpretability in AI models ensures their practical utility in healthcare.

Conclusion

🌟 Sikandar Ali is a dedicated AI researcher driving innovation in healthcare through artificial intelligence. With his strong educational foundation, diverse research experience, and impactful contributions, he aims to bridge the gap between AI and medicine, making healthcare more efficient and accessible.

Publications

Detection of COVID-19 in X-ray Images Using DCSCNN
Sensors 2022, IF: 3.4

A Soft Voting Ensemble-Based Model for IPF Severity Prediction
Life 2021, IF: 3.2

Metaverse in Healthcare Integrated with Explainable AI and Blockchain
Sensors 2023, IF: 3.4

Weakly Supervised Learning for Gastric Cancer Classification Using WSIs
Springer 2023

Classifying Gastric Cancer Stages with Deep Semantic and Texture Features
ICACT 2024

Computer Vision-Based Military Tank Recognition Using YOLO Framework
ICAISC 2023

Activity Detection for Dog Well-being Using Wearable Sensors
IEEE Access 2022

Cat Activity Monitoring Using Wearable Sensors
IEEE Sensors Journal 2023, IF: 4.3

Deep Learning for Algae Species Detection Using Microscopic Images
Water 2022, IF: 2.9

Comprehensive Review on Multiple Instance Learning
Electronics 2023

Hybrid Model for Face Shape Classification Using Ensemble Methods
Springer 2021

Cervical Spine Fracture Detection Using Two-Stage Deep Learning
IEEE Access 2024