Mostafa Gamal | Artificial Intelligence | Best Researcher Award

Published on by Popular Scientist

Mr. Mostafa Gamal | Artificial Intelligence | Best Researcher Award

Egyptian Russian University | Egypt

Mr. Mostafa Gamal, is a dedicated researcher and academic specializing in artificial intelligence, machine learning, and natural language processing, with a particular focus on text summarization and semantic graph-based models. His research explores the integration of deep learning, swarm intelligence, and optimization algorithms to enhance automated summarization and intelligent decision-making systems. He has contributed to several high-impact journals, including IEEE Access, Results in Engineering, Discover Cities, and the International Journal of Data Science and Analytics, covering areas such as transformer architectures, reinforcement learning, and graph neural networks. Mr. Gamal’s work advances the field of AI through the development of novel, explainable, and efficient models for NLP applications and autonomous systems. Beyond research, he is actively involved in academic teaching and professional training, fostering AI literacy through programs with the Egyptian Russian University, Huawei Academy, and the Digital Egypt Cubs Initiative. His technical expertise spans TensorFlow, PyTorch, and Keras, alongside proficiency in Python and data analytics frameworks. With a strong foundation in applied AI, he bridges theoretical research with practical implementation, contributing to the development of intelligent systems that address real-world challenges. His scholarly and instructional activities reflect a commitment to advancing artificial intelligence education and applied innovation in computational sciences.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Abdul Salam, M., Gamal, M., Hamed, H. F. A., & Sweidan, S. (2025, December). GRAYSUM: Gray Wolf optimized multi-level semantic graph summarization. Results in Engineering, (2025), 107275.

Abdul Salam, M., Gamal, M., Hamed, H. F. A., & Sweidan, S. (2025, October). Abstractive text summarization using deep learning models: A survey. International Journal of Data Science and Analytics.

Gamal, M., & Ibrahim, O. A. (2025, October 24). Graph neural networks for real-time optimization of autonomous urban transit systems. Discover Cities.

Gamal, M. M., Abdul Salam, M., Sweidan, S., & Hamed, H. F. A. (2025, May 1). ACOSUM: Ant colony optimized multi-level semantic graph summarization. International Journal of Applied Intelligent Computing and Informatics.

Abdul Salam, M., Aldawsari, M., Gamal, M., Hamed, H. F. A., & Sweidan, S. (2024). Improving Arabic text summarization using advanced pre-trained models. Journal of Southwest Jiaotong University, 59(3), Article 5.

Rui Miao | Artificial Intelligence | Best Researcher Award

Published on by Popular Scientist

Assoc. Prof. Dr. Rui Miao |  Artificial Intelligence | Best Researcher Award

Associate Researcher at Zhejiang Lab, China

Dr. Rui Miao is an Associate Researcher at Zhejiang Lab, specializing in artificial intelligence and image processing. He earned his Ph.D. in Engineering from Beihang University in 2022 and began postdoctoral research the same year, focusing on multi-modal cross-domain image enhancement for intelligent navigation systems across air, land, and water. Dr. Miao has led or participated in 7 research projects, published in top-tier journals such as IEEE Transactions on Geoscience and Remote Sensing and Pattern Recognition, and holds 28 patents (published or pending). His work contributes significantly to intelligent visual systems and applied AI.

Profile:

👨‍🎓 Academic Background

Dr. Rui Miao earned his Ph.D. in Engineering from Beihang University in 2022. He is currently an Associate Researcher at Zhejiang Lab, China.

🧠 Research Focus

His work explores cutting-edge areas such as:

  • Multimodal Image Processing

  • AI-based Image Generation & Matching

  • Visual Enhancement for Intelligent Systems

  • Model Inference Acceleration

🧪 Research Contributions

Dr. Miao has contributed to 7 major research projects and published impactful papers in top-tier journals like:

  • IEEE Transactions on Geoscience and Remote Sensing (TGRS)

  • Pattern Recognition (PR)

🔬 Innovations & Patents:

He has filed or published 28 patents, focusing on advanced image enhancement algorithms tailored for cross-domain AI perception systems used in air, land, and water navigation.

📚 Publications & Recognition:

While still early in his academic journey, Rui’s innovative work has already gained visibility in the scientific community, although citation metrics and editorial roles are still forthcoming.

Publication:

“Attention-Guided Progressive Frequency-Decoupled Network for Pan-Sharpening”
IEEE Transactions on Geoscience and Remote Sensing, 2024.
DOI: 10.1109/TGRS.2024.3376730
Authors: Rui Miao, Hang Shi, Fengguang Peng, Siyu Zhang

 

 

Xiang Ma | Computer Science and Artificial Intelligence | Best Researcher Award

Published on by Popular Scientist

Mr. Xiang Ma | Computer Science and Artificial Intelligence | Best Researcher Award

Postgraduate sichuan unviersity China

📖 Xiang Ma is a student at Sichuan University specializing in Electronic Information and Control Engineering. His research focuses on developing innovative solutions for image super-resolution reconstruction in construction site scenarios. By leveraging computer vision, machine learning, and engineering principles, Xiang’s work aims to improve image quality, safety, and monitoring efficiency in real-world construction environments.

Profile

Orcid

Education

🎓 Xiang Ma is pursuing a degree in Electronic Information and Control Engineering at Sichuan University. With a strong academic foundation, he integrates principles of electronic systems, computer vision, and machine learning in his research.

Experience

🔧 Xiang Ma has contributed to three completed and ongoing research projects, including collaborations with CSCEC First Bureau Technology R&D Program and the Sichuan Province Major Special Project on Intelligent Manufacturing and Robotics. His work bridges academic research with industrial applications in safety and automation technologies for construction sites.

Research Interest

🔍 Xiang Ma is passionate about Image Super-Resolution Reconstruction, with a focus on enhancing low-resolution images affected by noise in construction scenarios. His research includes proposing the Lightweight Feature Enhancement Network (LFEN) to improve visual perception, edge detection, and noise immunity using advanced machine learning techniques.

Awards

🏆 Xiang Ma is applying for the Best Researcher Award for his contributions to image processing technologies in construction scenarios. His work has been recognized for its innovative approach to leveraging lightweight network designs for practical applications.

Publications Top Notes: 

📚 Xiang Ma has published three research papers in prestigious journals:

Liu, Y., Ma, X. & Cheng, J. (2024). Lightweight Feature Enhancement Network for Image Super-Resolution Reconstruction at Construction Sites. Arab Journal of Science and Engineering. Published Year: 2024. Cited by: 15 articles.