Rebwar Khalid | Artificial Intelligence | Editorial Board Member

Published on by Academic Awards

Dr. Rebwar Khalid | Artificial Intelligence | Editorial Board Member

Erbil Polytechnic University | Iraq

Dr. Rebwar Khalid Hamad is an emerging researcher in artificial intelligence with a strong focus on nature-inspired algorithms, metaheuristics, and data-driven optimization systems. His work advances cutting-edge computational models such as the Krill Herd, FOX, Gravitational Search, and GOOSE algorithms, contributing significantly to optimization theory and its real-world engineering and healthcare applications. He has developed impactful frameworks for intelligent problem-solving, integrated AI-based search techniques, and enhanced algorithmic performance through systematic reviews and novel implementations. His publications in high-impact journals highlight his ability to bridge theoretical AI mechanisms with advanced data management and practical optimization challenges. Beyond research, he contributes to academic development through teaching, student supervision, and the design of data management systems. His scholarly portfolio demonstrates strong analytical capabilities, innovation in metaheuristic modeling, and a commitment to advancing the fields of artificial intelligence, data science, and computational optimization.

Profile:  Google Scholar

Featured Publications

Hamad, R. K., & Rashid, T. A. (2023). GOOSE algorithm: A powerful optimization tool for real-world engineering challenges and beyond. Evolving Systems.

Hamad, R. K., & Rashid, T. A. (2023). Current studies and applications of Krill Herd and Gravitational Search Algorithms in healthcare. Artificial Intelligence Review, 56(Suppl 1), 1243–1277.

Hamad, R. K., & Rashid, T. A. (2023). A systematic study of Krill Herd and FOX algorithms. In Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB) (pp. 168–186).

Hamad, R. K., & Rashid, T. A. (2025). A systematic study of GOOSE algorithms. In Multi-objective Optimization Techniques: Variants, Hybrids, Improvements, and Applications.

Hamad, R. K. (2024). GOOSE algorithm: A powerful optimization tool for real-world engineering challenges and beyond [Computer software]. GitHub.

Mehdi Moayed Mohseni | Machine Learning | Best Researcher Award

Published on by Academic Awards

Assist. Prof. Dr. Mehdi Moayed Mohseni | Machine Learning | Best Researcher Award

Islamic Azad University Science and Research Branch | Iran

Assist. Prof. Dr. Mehdi Moayed Mohseni is a distinguished chemical engineer at Islamic Azad University, Tehran, Iran, with expertise in non-Newtonian fluid mechanics, convective heat transfer, viscoelastic fluids, rheology, and exergy analysis. He earned his Ph.D. in Chemical Engineering from Amirkabir University of Technology, Iran, focusing on hydrodynamic and heat transfer modeling and entropy analysis of viscoelastic fluids in centric and eccentric annuli, followed by an M.Sc. on heat transfer of Giesekus viscoelastic fluids and a B.Sc. on biological natural gas sweetening processes. His research integrates analytical and semi-analytical methods (HPM, perturbation, homotopy) with computational fluid dynamics (CFD) and mass/heat transfer studies. Assist. Prof. Dr. Mehdi Moayed Mohseni has authored 17 publications, including studies on thermal and rheological performance of nanofluids, contributing to 229 citations and an h-index of 8. He actively participates in conferences, such as the National Iranian Chemical Engineering Congress, and his work demonstrates a strong commitment to advancing understanding of complex fluid behavior and transport phenomena.

Profile: Scopus | Google Scholar

Featured Publications

Mohseni, M. M., Jouyandeh, M., Sajadi, S. M., Hejna, A., Habibzadeh, S., … (2022). Metal-organic frameworks (MOF) based heat transfer: A comprehensive review. Chemical Engineering Journal, 449, 137700.

Montazeri, N., Salahshoori, I., Feyzishendi, P., Miri, F. S., Mohseni, M. M., … (2023). pH-sensitive adsorption of gastrointestinal drugs (famotidine and pantoprazole) as pharmaceutical pollutants by using the Au-doped@ ZIF-90-glycerol adsorbent: Insights from … Journal of Materials Chemistry A, 11(47), 26127–26151.

Salahshoori, I., Vaziri, A., Jahanmardi, R., Mohseni, M. M., Khonakdar, H. A. (2024). Molecular simulation studies of pharmaceutical pollutant removal (rosuvastatin and simvastatin) using novel modified-MOF nanostructures (UIO-66, UIO-66/chitosan, and UIO-66 …). ACS Applied Materials & Interfaces, 16(20), 26685–26712.

Mohseni, M. M., & Rashidi, F. (2010). Viscoelastic fluid behavior in annulus using Giesekus model. Journal of Non-Newtonian Fluid Mechanics, 165(21-22), 1550–1553.

Bateni, A., Salahshoori, I., Jorabchi, M. N., Mohseni, M. M., Asadabadi, M. R., … (2025). Molecular simulation-based assessing of a novel metal-organic framework modified with alginate and chitosan biopolymers for anionic reactive black 5 and cationic crystal violet … Separation and Purification Technology, 354, 128986.