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.

Manjunath BR | Machine Learning | Best Researcher Award

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Prof. Dr. Manjunath BR | Machine Learning | Best Researcher Award

Professor | Tecnologico De Monterrey | Mexico

Prof. Dr. Manjunath BR is an accomplished academic leader and finance professional specializing in business analytics, financial modeling, econometrics, fintech, and artificial intelligence applications in finance. With extensive experience across academia and industry, he has contributed significantly to advancing data-driven financial education and research. His expertise spans financial analytics, investment management, corporate restructuring, and data visualization using advanced tools such as EViews, R, Python, Tableau, and Power BI. He has published extensively in ABDC, Scopus, UGC, and peer-reviewed journals, focusing on the intersection of finance, data science, and technology. As a researcher and educator, he integrates predictive analytics and machine learning into financial decision-making, contributing to the understanding of fintech adoption, banking innovations, and risk management. His academic leadership includes curriculum design, faculty development, and corporate collaborations to enhance experiential learning. He has served as a resource person for numerous international workshops and training programs on financial analytics, econometrics, and data visualization, empowering professionals and students with analytical and quantitative skills. Dr. Manjunath has authored and edited several books with leading global publishers, covering transformative areas such as AI in management education, blockchain economics, sustainable investment, and Quality 5.0 paradigms. He has also secured a patent for the application of AI in optimizing HR data management and authored a textbook on machine and deep learning. His professional journey embodies innovation, interdisciplinary scholarship, and a commitment to integrating technology with finance to foster global academic and industry excellence.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Raju, J. K., Manjunath, B. R., & Rehaman, M. (2018). An empirical study on the effect of gross domestic product on inflation: Evidence from Indian data. Academy of Accounting and Financial Studies Journal, 22(6), 1–11.

Raju, J. K., Manjunath, B. R., & Dhakal, M. H. (2015). Impact and challenges of merger and acquisition in Nepalese banking and financial institutions. Journal of Exclusive Management Science, 4(8), 25–33.

Raju, J. K., Manjunath, B. R., & G. M. M. N. (2015). Performance evaluation of Indian equity mutual fund schemes. Journal of Business Management & Social Sciences Research (JBM&SSR).

Manjunath, B. R., & Raju, J. K. (2020). Short-run performance evaluation of under-priced Indian IPOs. Law and Financial Markets Review.

Chaitra, R., Manjunath, B. R., & Rehaman, M. (2019). An analysis of pre and post-merger of Indian banks: An event analysis approach. International Journal for Research in Engineering Application & Management, 4.