Muhammad Irfan Khan | Deep Learning | Best Researcher Award

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Mr. Muhammad Irfan Khan | Deep Learning | Best Researcher Award

University of Electronic Science and Technology of China | China

Muhammad Irfan Khan is a dedicated ML Security Engineer, researcher, and academic professional specializing in artificial intelligence, cybersecurity, and image processing, currently pursuing his M.S. in Information and Communication Engineering at the University of Electronic Science and Technology of China (UESTC), Chengdu. He has worked as a Machine Learning & Security Engineer at Victoriam.ai Solution, USA, where he developed threat detection models and optimized real-time security frameworks, and as a Research Intern at LinkDoc Technology, contributing to medical image segmentation advancements. At Namal University, Pakistan, he gained substantial experience as a Research Assistant, Teaching Assistant, and Lab Engineer, supporting AI/ML research, supervising projects, and co-authoring multiple peer-reviewed publications. His research contributions include journal articles such as “Genetic Algorithm Based Hybrid Deep Learning Framework for Stability Prediction of ABO3 Perovskites in Solar Cell Applications” (Energies, 2025), “Forecasting Fluctuations in Cryptocurrency Trading Volume Using a Hybrid LSTM-DQN Reinforcement Learning” (Digital Finance Journal, 2025), “Machine Learning-Powered Malware Detection in Encrypted IoT Traffic” (IEEE Journal of IoT, 2024), and “Decoding Emotions: U-Net-Driven Pattern Recognition for fMRI Analysis” (IEEE Transactions on Medical Imaging, 2025), along with conference proceedings in ICICT and IBCAST. He has served as a reviewer for international journals and conferences, including Computational Economics (Springer), Scientific Reports (Nature), and AAAI-26. His technical strengths span deep learning, reinforcement learning, cybersecurity, computer vision, and data-driven optimization, while also excelling in leadership and collaborative research. Despite his growing recognition, his current Scopus/Google Scholar profile records 2 documents reflecting his early yet impactful stage in research.

Profile: Scopus | LinkedIn

Featured Publication

Wali, S., Khan, M. I., & Zulfiqar, N. (2025). Forecasting fluctuations in cryptocurrency trading volume using a hybrid LSTM–DQN reinforcement learning. Digital Finance Journal.

 

Mr. Pingjie Ou | artificial intelligence | Best Researcher Award

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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. 🧠💡

Professional Profile

Scopus

🎓 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. 📘🏫

💼 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. 🔬📊

🏅 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. 🏆📈

🔍 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. 🚗☁️📶

🧾 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. 🚀📚

📚 Publication Top Note

  1. PDRL-CM: An efficient cooperative caching management method for vehicular networks based on deep reinforcement learning
    📅 Published Year: 2025
    📖 Journal: Ad Hoc Networks
    🔗 10.1016/j.adhoc.2025.103888

 

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

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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.

 

Prof. Chen Juan | Deep learning | Best Researcher Award

Published on by Computer Scientists Awards

Prof. Chen Juan | Deep learning | Best Researcher Award

Shanghai University, China

Dr. Juan Chen is a distinguished researcher and educator in the field of big data analytics, autonomous driving, and computer vision, currently serving as a faculty member at SILC Business School, Shanghai University since 2009. With over two decades of academic and research experience, she specializes in developing cutting-edge AI models, especially for transportation and e-commerce applications. Her expertise in deep learning and intelligent transportation systems has earned her recognition in core academic journals and scientific communities.

Publication Profile

ORCID

🎓 Education Background

Dr. Chen obtained her Ph.D. in Control Science and Engineering from Tongji University, China in 2008. She previously completed her Master’s degree at the School of Automation, Xi’an Jiaotong University in 2003, and earned her Bachelor’s degree in Energy and Power Engineering from Shanghai University of Technology in 1996. Her robust academic background laid the foundation for her interdisciplinary work across AI, engineering, and data science.

🏫 Professional Experience

Dr. Chen began her academic career as a lecturer at the School of Electronic and Information Engineering, Northern University for Nationalities from 1996 to 1998 and returned to the same school from 2001 to 2002. Since 2009, she has been actively contributing to teaching and research at SILC Business School, Shanghai University. Her teaching portfolio includes essential courses such as Python Program Design, Fundamentals of Data Analysis, and Deep Learning Practice in Computer Vision, which bridge theory with real-world AI practices.

🏆 Awards and Honors

Dr. Chen has consistently published in prestigious journals indexed in SCI and ESCI, such as the International Journal of Distributed Sensor Networks, IET Intelligent Transport Systems, and Algorithms. Her research achievements, including core journal recognition by Peking University, reflect her impactful contributions to intelligent systems and optimization in traffic networks.

🔬 Research Focus

Dr. Chen’s research is centered on big data analysis applied to transportation and e-commerce, autonomous vehicle control, computer vision, and deep learning. She has developed advanced models such as graph convolutional networks and spatiotemporal LSTM to address challenges in vehicle trajectory prediction, traffic congestion, and signal optimization. Her work integrates reinforcement learning, fuzzy logic, and multi-objective optimization to improve real-world systems’ efficiency and sustainability.

🔚 Conclusion

With an unwavering commitment to advancing AI applications in intelligent transportation, Dr. Juan Chen exemplifies interdisciplinary excellence. Her blend of academic rigor, research innovation, and practical teaching continues to inspire the next generation of engineers and data scientists. 🚗💡📊

📚 Top Publications :

Urban expressway on-ramp control based on improved NSGA-Ⅱ algorithm of reinforcement learning
Journal of Shanghai University (Natural Science Edition), 2023
Cited by: Search in Google Scholar

Vehicle Trajectory Prediction Based on Local Dynamic Graph Spatiotemporal-LSTM Model
World Electric Vehicle Journal, 2024
Cited by: Search in Google Scholar

KGCN-LSTM: A graph convolutional network considering knowledge fusion of point of interest for vehicle trajectory prediction
IET Intelligent Transport Systems, 2023
Cited by: Search in Google Scholar

Connected and automated vehicle control at unsignalized intersection based on deep reinforcement learning in vehicle-to-infrastructure environment
International Journal of Distributed Sensor Networks, 2022
Cited by: Search in Google Scholar

Multi-class expressway traffic control for reducing congestion and emissions based on fuzzy NSGA
Journal of Shanghai University (Natural Science Edition), 2021
Cited by: Search in Google Scholar

Freeway Traffic Congestion Reduction and Environment Regulation via Model Predictive Control
Algorithms, 2019
Cited by: Google Scholar

Traffic congestion prediction based on GPS trajectory data
International Journal of Distributed Sensor Networks, 2019
Cited by: Search in Google Scholar