Karim El Moutaouakil | Neural network | Research Excellence Award

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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.

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Top 5 Featured Publications

Davoud Shahgholian-Ghahfarokhi | Neural Network | Best Researcher Award

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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.

Amirhossein Ghasemi Abyaneh | Machine Learning | Best Researcher Award

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Mr. Amirhossein Ghasemi Abyaneh | Machine Learning | Best Researcher Award

Researcher | Kharazmi University | Iran

Mr. Amirhossein Ghasemi Abyaneh is an emerging scholar in the field of artificial intelligence applications in sustainable supply chains, affiliated with Kharazmi University, Tehran, Iran. His academic endeavors focus on integrating advanced data analytics, optimization techniques, and machine learning frameworks to enhance decision-making, efficiency, and sustainability across complex supply chain networks. With 3 published research papers and an h-index of 1, Mr. Abyaneh has begun establishing a scholarly footprint that bridges technology-driven innovation with environmental and operational resilience. His work, including the open-access article “An Analytical Review of Artificial Intelligence Applications in Sustainable Supply Chains” (2025, Supply Chain Analytics), provides critical insights into the evolving intersection of AI and sustainability, emphasizing how digital intelligence can optimize resource utilization, reduce carbon footprints, and strengthen circular economy practices. Having received citations from international scholars, he actively contributes to the global academic dialogue on sustainable logistics, smart manufacturing, and responsible innovation. Mr. Abyaneh’s collaborative research network includes seven co-authors from diverse academic and institutional backgrounds, reflecting a strong interdisciplinary approach that combines engineering, data science, and environmental management. His studies aim to foster both theoretical advancement and practical applicability, offering valuable implications for policymakers, corporations, and researchers seeking to transition toward greener, data-driven supply chains. Beyond academic impact, his contributions align with global sustainability goals, promoting knowledge transfer, digital equity, and responsible AI adoption for societal benefit.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Sharbati, A., Movahed, A. B., Abyaneh, A. G., & Rahmanian, F. (2025). Risk assessment of healthcare systems using the FMEA method: Medication management process. Journal of Future Digital Optimization, 1(1), 71–85.
Cited by: 4

2. Abyaneh, A. G., Movahed, A. B., Abyari, A., Nodehfarahani, A., & Khakbazan, M. (2025). Evaluating the RFID technology in Costco Company: A focus on logistics and supply chain management. Applied Innovations in Industrial Management, 5(2), 34–51.
Cited by: 2

3. Movahed, A. B., Abyaneh, A. G., Khakbazan, M., & Movahed, A. B. (2025). Smart economy cybersecurity: AI-driven risk management in digital markets. In Dynamic and Safe Economy in the Age of Smart Technologies (pp. 49–72).
Cited by: 2

4. Abyaneh, A. G., Ghanbari, H., Mohammadi, E., Amirsahami, A., & Khakbazan, M. (2025). An analytical review of artificial intelligence applications in sustainable supply chains. Supply Chain Analytics, 100173.
Cited by: 1

5. Abyaneh, A. G., Khakbazan, M., & Movahed, A. B. (2026). Artificial intelligence in digital marketing: Trends, challenges, and strategic opportunities. In Improving Consumer Engagement in Digital Marketing Through Cognitive AI (pp. 225–260)

Mr. Amirhossein Ghasemi Abyaneh envisions a future where artificial intelligence empowers sustainable industrial transformation, enabling supply chains to become more adaptive, transparent, and environmentally responsible. His research advances the integration of smart analytics and sustainability principles, fostering innovation that supports global climate resilience and ethical technological progress.