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.