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.

Assoc. Prof. Dr. Ateke Goshvarpour | Best Researcher Award

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Assoc. Prof. Dr. Ateke Goshvarpour | Best Researcher Award

Imam Reza International University | Iran

Ateke Goshvarpour is a distinguished researcher at Imam Reza International University, renowned for her extensive contributions to biomedical signal processing, emotion recognition, and neurophysiological data analysis. With an impressive h-index of 20, over 1,093 citations, and 70 research publications, her work has significantly advanced the integration of computational intelligence and physiological signal modeling. She has co-authored several influential studies on electroencephalography (EEG), electrocardiography (ECG), photoplethysmography (PPG), and galvanic skin response (GSR) for emotion and mental disorder recognition. Dr. Goshvarpour earned her higher education in biomedical engineering and has accumulated years of academic and research experience focusing on nonlinear analysis, chaos theory, and machine learning applications in healthcare. Her recent works explore quantum-inspired models, graph-based EEG analysis, and spectral–spatiotemporal fusion for diagnosing schizophrenia and cognitive disorders. She has been recognized for developing innovative feature fusion techniques that enhance accuracy in automated emotion recognition and neurodiagnostic systems. Her publications in high-impact journals such as Cognitive Neurodynamics, Chaos, Solitons & Fractals, and Biomedical Signal Processing and Control underscore her leadership in the field. Through her pioneering research, she continues to shape the future of computational neuroscience and affective computing, bridging the gap between biomedical engineering and mental health diagnostics.

Profiles : Scopus | Google Scholar

Featured Publications

Goshvarpour, A., Abbasi, A., & Goshvarpour, A. (2017). An accurate emotion recognition system using ECG and GSR signals and matching pursuit method. Biomedical Journal, 40(6), 355–368. https://doi.org/10.1016/j.bj.2017.10.001

Goshvarpour, A., & Goshvarpour, A. (2019). EEG spectral powers and source localization in depressing, sad, and fun music videos focusing on gender differences. Cognitive Neurodynamics, 13(2), 161–173. https://doi.org/10.1007/s11571-018-9510-8

Goshvarpour, A., Abbasi, A., & Goshvarpour, A. (2017). Fusion of heart rate variability and pulse rate variability for emotion recognition using lagged Poincaré plots. Australasian Physical & Engineering Sciences in Medicine, 40(3), 617–629. https://doi.org/10.1007/s13246-017-0560-7

Goshvarpour, A., & Goshvarpour, A. (2020). Schizophrenia diagnosis using innovative EEG feature-level fusion schemes. Physical and Engineering Sciences in Medicine, 43(1), 227–238. https://doi.org/10.1007/s13246-019-00853-8

Goshvarpour, A., & Goshvarpour, A. (2018). Poincaré’s section analysis for PPG-based automatic emotion recognition. Chaos, Solitons & Fractals, 114, 400–407. https://doi.org/10.1016/j.chaos.2018.07.009