Assist. Prof. Dr. Fayzullo Nazarov | Best Researcher Award

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Assist. Prof. Dr. Fayzullo Nazarov | Best Researcher Award

Samarkand State University | Uzbekistan

Dr. Fayzullo Nazarov is a distinguished researcher at Sharof Rashidov Samarkand State University, Uzbekistan, recognized for his contributions to artificial intelligence, data science, and computational optimization. With an h-index of 10, 28 publications, and 217 citations across 94 documents, his research demonstrates significant scholarly impact and growing recognition in the field. Dr. Nazarov earned his advanced degrees in computer science and applied mathematics, where he developed a strong foundation in algorithmic modeling, neural networks, and machine learning applications. His academic and professional experience centers on the intelligent management of data systems, optimization of distribution mechanisms, and neural network ensemble methodologies. Dr. Nazarov’s recent works, including studies on effective distribution determination using neural network ensembles and machine learning-based data storage optimization, highlight his innovative approach to integrating AI with intelligent system design. He actively collaborates with international researchers to advance computational intelligence and smart data technologies. Throughout his career, Dr. Nazarov has received multiple academic recognitions for excellence in research and publication. His dedication to advancing AI-driven optimization techniques positions him as a leading researcher in intelligent systems and computational innovation, with ongoing contributions to the digital transformation of data management and predictive modeling.

Profiles : Google Scholar | Scopus

Featured Publications

khatov, A. R., Nazarov, F. M., & Rashidov, A. (2021). Mechanisms of information reliability in big data and blockchain technologies. In Proceedings of the 2021 International Conference on Information Science and Communications.

Akhatov, A. R., Nazarov, F. M., & Rashidov, A. (2021). Increasing data reliability by using big data parallelization mechanisms. In Proceedings of the 2021 International Conference on Information Science and Communications.

Rashidov, A., Akhatov, A., & Nazarov, F. (2023). The same size distribution of data based on unsupervised clustering algorithms. In The International Conference on Artificial Intelligence and Logistics.

Akhatov, A. R., Sabharwal, M., Nazarov, F. M., & Rashidov, A. (2022). Application of cryptographic methods to blockchain technology to increase data reliability. In 2nd International Conference on Advance Computing and Innovative Technologies in Engineering.

Dagur, A., Shukla, D. K., Makhmadiyarovich, N. F., & Rustamovich, A. A. (2024). Artificial intelligence and information technologies: Proceedings of the 1st International Conference on Artificial Intelligence and Information Technologies (ICAIIT 2023). CRC Press.

Mr. Ulrich Ngnassi Nguelcheu | Best Researcher Award

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Mr. Ulrich Ngnassi Nguelcheu | Best Researcher Award

Researcher at University of Ngaoundéré | Cameroon

Dr. Ulrich Ngnassi Nguelcheu is a researcher at the University of Ngaoundéré, Cameroon, specializing in artificial intelligence applications in renewable energy systems. With 11 publications, 1,302 reads, and 5 citations (h-index: 2), he has made valuable contributions to intelligent control and data-driven optimization for sustainable energy technologies. He holds a doctorate in Artificial Intelligence applied to Renewable Energies, focusing on enhancing the performance and reliability of electromechanical systems through AI-based modeling and simulation. His research experience covers wind energy systems, maintenance optimization, reliability analysis, and composite material development. Among his notable works are studies on the use of artificial neural networks for improved wind turbine control and the optimization of preventive maintenance using genetic algorithms, published in leading journals such as Engineering Applications of Artificial Intelligence. Dr. Nguelcheu actively collaborates with researchers across Cameroon and internationally, emphasizing sustainable and intelligent energy management. His research interests include machine learning for energy systems, renewable energy integration, and smart maintenance strategies. He is dedicated to advancing innovative and eco-efficient technologies that support the global shift toward clean and sustainable energy.

Profiles : Research Gate | Scopus

Featured Publications

Nguelcheu, U. N., Ndjiya, N., Kenmoe Fankem, E. D., Ngnassi Djami, A. B., Guidkaya, G., & Effa, J. Y. (2025). Harnessing artificial neural networks for improved control of wind turbines based on brushless doubly fed induction generator. Engineering Applications of Artificial Intelligence, 154, 110925.

Nguelcheu, U. N., Ndjiya, N., Kenmoe Fankem, E. D., Ngnassi Djami, A. B., Guidkaya, G., & Dountio, T. (2023). Literature review on the control of brushless doubly-fed induction machines. Global Journal of Engineering and Technology Advances, 16(3), 51–69.

Ngnassi Djami, A. B., Nguelcheu, U. N., & Yamigno, S. D. (2023). Formulation, characterization and future potential of composite materials from natural resources: The case of kenaf and date palm fibers. Online Journal of Mechanical Engineering.

Assoc. Prof. Dr. Shuai Zheng | Best Researcher Award

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Assoc. Prof. Dr. Shuai Zheng | Best Researcher Award

Dalian Jiaotong University | China

Dr. Shuai Zheng is an accomplished researcher and Associate Professor at Dalian Jiaotong University, specializing in intelligent transportation infrastructure, geotechnical safety, and computational modeling. He earned his Ph.D. in Civil Engineering and has extensive experience in bridge and tunnel stability, BIM-based digital construction, and AI-driven reliability analysis. His research integrates theoretical modeling with data-driven engineering to improve the safety and resilience of transportation systems. Dr. Zheng has authored 22 peer-reviewed publications with 94 citations and an h-index of 6, demonstrating consistent scholarly impact. His key works include “Research and Application of Reliability Evaluation Model for Water Inrush Risk during Tunnel Construction” (Tunnelling and Underground Space Technology, 2026) and “ZSI-R Method for Evaluating Foundation Pit Stability in Karst Regions” (Geotechnical and Geological Engineering, 2025). He collaborates with over 50 co-authors worldwide, contributing to multidisciplinary studies in digital and sustainable construction. Dr. Zheng has received provincial-level Science and Technology Progress Awards (2020 & 2022) for his innovative contributions to digital infrastructure development. His research advances the understanding of smart, resilient, and sustainable engineering systems, positioning him as a promising leader in next-generation intelligent construction technologies.

Profiles : Orcid | Scopus

Featured Publications

Zhang, Q., Zheng, S., Zhao, J., Liu, X., Du, E., & Yang, Y. (2025). ZSI-R method for evaluating foundation pit stability in karst regions. Geotechnical and Geological Engineering. https://doi.org/10.1007/s10706-025-03358-x

Ma, C., Yang, Y., Wang, X., Zhang, Y., Wang, H., & Zheng, S. (2025). A new DEM calibration method for the adhesion and shear behavior of clay materials based on response surface methodology. Engineering Research Express. https://doi.org/10.1088/2631-8695/ae0411

Jia, B., Yang, Y., Wang, X., Li, L., Zhang, Y., & Zheng, S. (2025). Real-time prediction method of shield tunneling attitude under complex geological conditions. Engineering Research Express. https://doi.org/10.1088/2631-8695/ae0b30

Zhang, H., Dong, S., Li, S., & Zheng, S. (2025). Sensitivity analysis and optimization of urban roundabout road design parameters based on CFD. Eng, 6(7), 156. https://doi.org/10.3390/eng6070156

Jiang, T., Jiang, A., Zheng, S., Xu, M., & Nguyen-Xuan, H. (2021). Prediction of blast-induced ground vibration (BIGV) of metro construction using differential evolution algorithm-optimized Gaussian process (DE-GP). Shock and Vibration, Article 2847180. https://doi.org/10.1155/2021/2847180