Karim El Moutaouakil | Neural network | Research Excellence Award

Prof. Dr. Karim El Moutaouakil | Neural network | Research Excellence Award

Université Sidi Mohamed Ben Abdellah | Morocco

Prof. Dr. Karim El Moutaouakil is an active researcher in artificial intelligence, computational intelligence, and applied optimization, with a strong emphasis on machine learning for data-driven decision systems. His work spans deep learning architectures (LSTM, CNNs, HRNNs), metaheuristic and fractional optimization algorithms, fuzzy systems, and their applications in financial forecasting, big data analytics, healthcare prediction, energy systems, and smart decision support. He has authored 98 peer-reviewed publications, receiving 650 citations with an h-index of 15, reflecting consistent scholarly impact. His research demonstrates methodological innovation, notably the integration of Ito-based optimizers, genetic algorithms, fuzzy logic, and hybrid AI frameworks to enhance model accuracy and robustness. With extensive international collaboration involving 90+ co-authors, his work contributes to interdisciplinary knowledge exchange. The societal relevance of his research is evident in applications addressing economic forecasting, personalized tourism, medical risk prediction, and energy optimization, supporting data-informed policy and sustainable technological development at a global level.

Citation Metrics (Scopus)

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Top 5 Featured Publications

Davoud Shahgholian-Ghahfarokhi | Neural Network | Best Researcher Award

Dr. Davoud Shahgholian-Ghahfarokhi | Neural Network | Best Researcher Award

Senior Researcher | Tarbiat Modares University | Iran

Dr. Davoud Shahgholian-Ghahfarokhi is a researcher affiliated with Tarbiat Modares University whose work spans advanced structural engineering, materials mechanics, and computational modeling, with a focus on improving the performance, durability, and safety of modern engineering systems. With 20 peer-reviewed publications, 15 h-index and over 792 citations, his research demonstrates sustained scholarly influence and recognition within the global engineering community. His expertise encompasses mechanics-based structural design, vibration analysis, composite and sandwich structures, offshore pipeline integrity, auxetic and graded materials, and artificial-intelligence-assisted engineering assessment. Across his body of work, he has contributed analytical, numerical, and hybrid computational frameworks for understanding complex structural behaviors under dynamic and environmental loads. Notable contributions include the formulation of vibration models for sandwich folded plates with auxetic honeycomb cores and FG-GPLRC coatings, advancing next-generation lightweight, high-performance structures. Additionally, his research on corrosion-induced degradation of offshore pipelines—combining code-based methods, finite-element simulations, and neural-network prediction—provides industry-relevant tools for failure assessment and risk mitigation. Dr. Shahgholian-Ghahfarokhi has collaborated with at least 25 co-authors, reflecting a strong record of interdisciplinary and international engagement. His work supports structural reliability, materials innovation, and infrastructure resilience, offering direct societal benefits in safety-critical sectors such as offshore energy, transportation, and advanced manufacturing. Collectively, his research contributes to a more robust scientific understanding of complex structural systems while fostering emerging engineering solutions that balance performance, sustainability, and safety.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Shahgholian-Ghahfarokhi, D., Safarpour, M., & Rahimi, A. (2021). Torsional buckling analyses of functionally graded porous nanocomposite cylindrical shells reinforced with graphene platelets (GPLs). Mechanics Based Design of Structures and Machines, 49(1), 81–102.
Cited by: 119

2. Shahgholian, D., Safarpour, M., Rahimi, A. R., & Alibeigloo, A. (2020). Buckling analyses of functionally graded graphene-reinforced porous cylindrical shell using the Rayleigh–Ritz method. Acta Mechanica, 231(5), 1887–1902.
Cited by: 102

3. Shahgholian-Ghahfarokhi, D., & Rahimi, G. (2018). Buckling load prediction of grid-stiffened composite cylindrical shells using the vibration correlation technique. Composites Science and Technology, 167, 470–481.
Cited by: 81

4. Khodadadi, A., Liaghat, G., Taherzadeh-Fard, A., … (2021). Impact characteristics of soft composites using shear thickening fluid and natural rubber – A review of current status. Composite Structures, 271, Article 114092.
Cited by: 80

5. Ghahfarokhi, D. S., & Rahimi, G. (2018). An analytical approach for global buckling of composite sandwich cylindrical shells with lattice cores. International Journal of Solids and Structures, 146, 69–79.
Cited by: 77

Dr. Shahgholian-Ghahfarokhi’s research advances the scientific foundations and practical tools needed to design safer, lighter, and more resilient engineering structures. By integrating novel materials, computational intelligence, and rigorous mechanics, his work contributes to global innovation in sustainable infrastructure, industrial reliability, and engineering risk reduction.