Mr. Pingjie Ou | artificial intelligence | Best Researcher Award

Mr. Pingjie Ou | artificial intelligence | Best Researcher Award

Student, Guangxi University, China

Pingjie Ou is a passionate master’s student at Guangxi University, China, specializing in edge computing, cloud computing, and machine learning. With a strong academic foundation and growing research portfolio, he is actively contributing to next-generation computing paradigms. His early contributions in deep reinforcement learning applications for vehicular networks have already gained traction within the academic community. πŸ§ πŸ’‘

Professional Profile

Scopus

πŸŽ“ Education Background

Pingjie Ou is currently pursuing his master’s degree at Guangxi University, one of the prominent institutions in China. His academic focus lies in electrical and computer engineering, with emphasis on distributed computing and artificial intelligence. πŸ“˜πŸ«

πŸ’Ό Professional Experience

Although a student, Pingjie Ou has engaged in substantial research activities under funded projects including The National Natural Science Foundation of China (No. 62162003) and GuikeZY24212059 supported by the Guangxi Province. His active involvement in real-time research scenarios demonstrates promising professional potential. πŸ”¬πŸ“Š

πŸ… Awards and Honors

As an emerging scholar, Pingjie Ou has not yet accumulated major awards but has gained recognition through impactful publications and research citations. His growing citation record and h-index reflect the potential for future accolades. πŸ†πŸ“ˆ

πŸ” Research Focus

His core research interests include edge computing, cloud computing, vehicular networks, and machine learning. He is particularly focused on cooperative caching, resource management, and optimizing network efficiency using artificial intelligence approaches such as deep reinforcement learning. πŸš—β˜οΈπŸ“Ά

🧾 Conclusion

Pingjie Ou is a driven young researcher dedicated to advancing intelligent computing technologies. With strong academic grounding, collaborative research exposure, and early citation impact, he stands as a promising candidate for recognition in the domain of computer science and engineering. His scholarly journey is on a clear upward trajectory. πŸš€πŸ“š

πŸ“š Publication Top Note

  1. PDRL-CM: An efficient cooperative caching management method for vehicular networks based on deep reinforcement learning
    πŸ“… Published Year: 2025
    πŸ“– Journal: Ad Hoc Networks
    πŸ”— 10.1016/j.adhoc.2025.103888

 

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 πŸŒπŸ“š.

Professional Profile

Google Scholar

Scopus

πŸŽ“ Education Background

Dr. Ben Ismail holds a Ph.D. in Computer Engineering and Computer Science from the University of Louisville, USA (2011) πŸ‡ΊπŸ‡Έ, 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 πŸ‡ΉπŸ‡³. His early academic journey was distinguished by excellence in mathematics, physics, and competitive engineering entrance exams πŸ§ πŸ“˜.

πŸ§‘β€πŸ« 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 πŸ’ΌπŸ‘¨β€πŸ«.

πŸ† 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) πŸ₯‡. 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 πŸŽ–οΈ.

πŸ”¬ Research Focus

Dr. Ben Ismail’s research interests lie in Artificial Intelligence, Machine Learning, Pattern Recognition, Image Processing, Temporal Data Mining, and Information Fusion πŸ€–πŸ§ . 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 πŸ“ŠπŸ“ˆ.

πŸ“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 πŸŒŸπŸ§‘β€πŸ”¬.

πŸ“š Top Publications Notes

  1. YOLO-Act: Unified Spatiotemporal Detection of Human Actions Across Multi-Frame Sequences
    πŸ“… Published in: Sensors, 2025
    πŸ” 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
    πŸ“… Published in: Sensors, 2025
    πŸ” 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
    πŸ“… Published in: Sensors, 2024
    πŸ” Cited by: 18 articles
    πŸ” Highlights: Focuses on adversarial ML methods to enhance IoT network security.

  4. Skin Cancer Recognition Using Unified Deep Convolutional Neural Networks
    πŸ“… Published in: Cancers, 2024
    πŸ” 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
    πŸ“… Published in: Computation, 2024
    πŸ” Cited by: 14 articles
    πŸ’‘ Highlights: Compares YOLOv3 to YOLOv7 models for brain scan interpretation.

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

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

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

 

Sikandar Ali | Artificial Intelligence Award | Best Researcher Award

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

Postdoc Fellow, Inje University, South Korea

πŸŽ“ 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

πŸ“˜ 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

πŸ’» 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

πŸ† 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

πŸ”¬ 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