Sara A. Shehab | Artificial Intelligence | Best Researcher Award

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Assoc. Prof. Dr. Sara A. Shehab | Artificial Intelligence | Best Researcher Award

Faculty Of Computer And Artificial Intelligence | Egypt

Assoc. Prof. Dr. Sara A. Shehab  is an Associate Professor in Computer Science at the University of Sadat City, Egypt, with expertise spanning artificial intelligence, bioinformatics, computational biology, quantum computing, and computer security. Her research focuses on developing intelligent algorithms for biological data analysis, optimization, and machine learning applications in medicine and environmental sustainability. She has contributed significantly to the advancement of multiple sequence alignment techniques, parallel and dynamic algorithms, and predictive modeling using machine learning. Her recent work explores deep learning for biomedical image analysis, explainable AI for green energy production, and hybrid optimization approaches for precision classification and prediction tasks. Dr. Shehab has published extensively in peer-reviewed international journals and conferences, collaborating with leading scholars in AI-driven bioinformatics and sustainable computing. She also serves as a reviewer for international journals and conferences, contributing to the academic community through quality evaluation and mentorship. Her professional experience includes leadership in e-learning, digital transformation, and program coordination within higher education, reflecting a strong integration of research, teaching, and institutional development. Through her interdisciplinary approach, she bridges artificial intelligence with biological and environmental sciences, fostering innovation in intelligent systems for healthcare, sustainability, and data-driven decision-making.

Profile: Google Scholar

Featured Publications

Shehab, S. A., Keshk, A., & Mahgoub, H. (2012). Fast dynamic algorithm for sequence alignment based on bioinformatics. International Journal of Computer Applications, 37(7), 54–61.

Ahmed, R. A. E. H., Shehab, S. A., Elzeki, O. M., & Darwish, A. (2024). An explainable AI for green hydrogen production: A deep learning regression model. International Journal of Hydrogen Energy, 83, 1226–1242.

Shehab, A. E. H. S., Mohammed, K. K., & Darwish, A. (2024). Deep learning and feature fusion-based lung sound recognition model to diagnose respiratory diseases. Soft Computing.

Shehab, A. E. H. S., & Darwish, A. (2023). Water quality classification model with small features and class imbalance based on fuzzy rough sets. Environment, Development and Sustainability.

Shehab, S., Shohdy, S., & Keshk, A. E. (2017). PoMSA: An efficient and precise position-based multiple sequence alignment technique. arXiv preprint arXiv:1708.01508.

Mostafa Gamal | Artificial Intelligence | Best Researcher Award

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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.

Pakezhamu Nuradili | Computer Science and Artificial Intelligence | Best Researcher Award

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Dr. Pakezhamu Nuradili| Computer Science and Artificial Intelligence | Best Researcher Award

PhD candidate University of Electronic Science and Technology of China

Pakezhamu Nuradili, a native of China, is a Ph.D. student specializing in Information and Communication Engineering. She is currently enrolled in a joint Ph.D. program between the University of Electronic Science and Technology of China (UESTC) and the University of Trento, Italy. Her expertise spans deep learning-based image processing, semantic segmentation, and thermal infrared imaging. Known for her attention to detail and excellent communication skills in multiple languages, she excels in both technical and interpersonal domains.

Profile

Orcid

Education 🎓

  • High School: Jiangpu Senior High School, Jiangsu Province, China (2010–2013)
  • Bachelor’s Degree: Electronics and Information Engineering, Hebei University of Science and Technology (2013–2017)
  • Master’s Degree: Radio Physics, Yili Normal University, China, focusing on face recognition algorithms (2017–2020)
  • Ph.D.: Information and Communication Engineering, UESTC, with a joint program at the University of Trento, Italy (2021–Present)

Work Experience 💼

  • Teaching:
    • Substitute Teacher, Basic Computer Applications, Silk Road College of Ili (2017–2018)
    • Graduate Assistant, Basic Computer Applications, Yili Normal University (2018–2019)
    • Substitute Teacher, Advanced and Intermediate Mathematics, Ili Vocational and Technical College (2020–2021)
    • Graduate Teaching Assistant, Principles of Remote Sensing, UESTC (2022)
  • Volunteering: Marathon Distance Race Volunteer, Trento, Italy (2024)

Research Interests 🔬

Pakezhamu’s research focuses on:

  • Deep learning-based image processing and semantic segmentation.
  • Thermal infrared and multispectral imaging for UAV applications.
  • Wetland segmentation using advanced models like SegFormer.

Awards 🏆

  • Hebei Provincial Inspiration Scholarship (2016)
  • Outstanding Graduation Design Award, Hebei University of Science and Technology (2017)
  • Graduate Student Scholarship, Yili Normal University (2018)
  • Xinjiang Autonomous Region Postgraduate Scholarship (2019)
  • UESTC Academic Scholarships (2022, 2023, 2024)
  • Outstanding Teaching Assistant Award, UESTC (2022)

Publications Top Notes: 📚

P. Nuradili et al., “UAV Remote-Sensing Image Semantic Segmentation Strategy Based on Thermal Infrared and Multispectral Image Features,” IEEE Journal on Miniaturization for Air and Space Systems, 4(3): 311-319, Sept. 2023. Cited by: 5

Nuradili, P. et al., “Semantic segmentation for UAV low-light scenes based on deep learning and thermal infrared image features,” International Journal of Remote Sensing, 45(12): 4160–4177, 2024. Cited by: 8

Nuradili, P. et al., “Wetland Segmentation Method for UAV Multispectral Remote Sensing Images Based on SegFormer,” IGARSS 2024 IEEE Symposium, 2024. Cited by: 3

Nuradili, P. et al., “Deep Learning Method for Wetland Segmentation in Unmanned Aerial Vehicle Multispectral Imagery,” Remote Sensing, 16(24): 4777, 2024. Cited by: 6

Nuradili, P. et al., “Fire Detection Based on Deep Learning Segmentation Methods,” Journal TBD, 2024 (Under Process).

Wang, Z. et al., “Removing temperature drift and temporal variation in thermal infrared images of a UAV uncooled thermal infrared imager,” ISPRS Journal of Photogrammetry and Remote Sensing, 203: 392-411, 2023. Cited by: 12