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.

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.