Assoc. Prof. Dr. Mümine KAYA KELEŞ | Women Researcher Award

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Assoc. Prof. Dr. Mümine KAYA KELEŞ | Women Researcher Award

Adana Alparslan Türkeş Science and Technology University | Turkey

Mümine Kaya Keleş is a researcher at Adana Alparslan Türkeş Bilim ve Teknoloji Üniversitesi, where she specializes in Veri Madenciliği (Data Mining), Uzaktan Eğitim (Distance Education), and Metin/Doküman İşleme (Text/Document Processing), with additional work in plagiarism detection. Her scholarly contributions span data mining applications in health, engineering, and education, including breast cancer prediction, feature selection, machine learning–based performance estimation, and streamflow forecasting. She has also made notable advances in distance education systems, the integration of assessment tools into learning management platforms, and text similarity detection. Her research includes the application of optimization algorithms such as artificial bee colony, binary anarchic society optimization, and binary black widow approaches for feature selection across diverse datasets, as well as studies on data mining impacts on various sectors and open-source tools. She has contributed to projects involving microstrip antenna classification, concrete strength prediction, Alzheimer’s disease diagnosis using volumetric data, and productivity prediction in construction. With verified email affiliation at atu.edu.tr, she continues to develop innovative solutions at the intersection of data mining, machine learning, and educational technologies, maintaining a strong publication record across interdisciplinary applications.

Profile : Google Scholar

Featured Publications

Keleş, M. K. (2019). Breast cancer prediction and detection using data mining classification algorithms: A comparative study. Tehnički vjesnik, 26(1), 149–155.

Umit, K., Esra, S. E., & Mumine, K. K. (2023). Binary Anarchic Society Optimization for Feature Selection. Romanian Journal of Information Science and Technology, 26(3–4), 351–364.

Kaya, M., & Özel, S. A. (2015). Integrating an online compiler and a plagiarism detection tool into the Moodle distance education system for easy assessment of programming assignments. Computer Applications in Engineering Education, 23(3), 363–373.

Keleş, M. K., & Özel, S. A. (2016). A review of distance learning and learning management systems. Virtual Learning.

Kaya, M. (2012). Distance education systems used in universities of Turkey and Northern Cyprus. Procedia – Social and Behavioral Sciences, 31, 676–680.

Assoc. Prof. Dr. Niaz Abdolrahim | Best Researcher Award

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Assoc. Prof. Dr. Niaz Abdolrahim | Best Researcher Award

University of Rochester | United States

Dr. Niaz Abdolrahim is an accomplished materials scientist and Assistant Professor in the Department of Mechanical Engineering at the University of Rochester, where she leads pioneering research in multiscale modeling, nanomechanics, and computational materials science. She earned her Ph.D. in Mechanical Engineering and has since developed a strong research portfolio that integrates atomistic simulations, machine learning, and continuum mechanics to study deformation mechanisms, structural phase transformations, and the design of high-performance nanostructured materials. With over 44 published documents, more than 717 citations, and an h-index of 16, her scholarly contributions have been widely recognized in the fields of materials modeling and nanostructure design. Dr. Abdolrahim has secured multiple NSF-funded projects, including the study of stress-assisted phase transformations and data-driven analysis of lattice dynamics. Her work has been published in prestigious journals such as Acta Materialia, npj Computational Materials, Physical Review B, and ACS Applied Nano Materials. Her research interests encompass nanostructured metals, deformation physics, data-driven materials design, and high-performance alloys. Dr. Abdolrahim’s innovative contributions continue to advance the understanding of mechanical behavior in nanoscale systems and establish her as a leading figure in computational materials science and multiscale simulation.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Mostafa, A., Qian, S., Li, F., Rabkin, E., & Abdolrahim, N. (2026). Bending-induced phase transformations and penta-twinning in molybdenum: From nano to microscale. Acta Materialia, 264, 121646. https://doi.org/10.1016/j.actamat.2025.121646

Karami, S., Kum, T. B., Kirmani, A. R., & Abdolrahim, N. (2025). Proton radiation effects in indium oxide using cascade molecular dynamics simulations. APL Energy, 4(9), 0266752. https://doi.org/10.1063/5.0266752

Alvarez, A., Abdolrahim, N., & Singh, S. (2025). Anomalous elastic softening in ferroelectric hafnia under pressure. Physical Review B, 111(6), 064106. https://doi.org/10.1103/PhysRevB.111.064106

Mostafa, A., Vu, L., Guo, Z., Shargh, A. K., Dey, A., Askari, H., & Abdolrahim, N. (2024). Phase-transformation assisted twinning in molybdenum nanowires. Computational Materials Science, 237, 113273. https://doi.org/10.1016/j.commatsci.2024.113273

Salgado, J. E., Lerman, S., Du, Z., Xu, C., & Abdolrahim, N. (2023). Automated classification of big X-ray diffraction data using deep learning models. npj Computational Materials, 9(1), 214. https://doi.org/10.1038/s41524-023-01164-8

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.

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