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

Mr. Sandesh Aryal | Best Researcher Award

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Mr. Sandesh Aryal | Best Researcher Award

National Institute of Technology Rourkela | India

Mr. Sandesh Aryal holds a B.Tech in Electronics and Communication Engineering from the National Institute of Technology Rourkela (2021-2025) and has rapidly emerged as a research innovator in the domain of deep-learning–based biomedical image analysis. His work focuses on the classification of white blood cells via attention-augmented convolutional networks. His standout publication, “AFMNet: Adaptive Feature Modulation Network for Classification of White Blood Cells”, was published in Biocybernetics and Biomedical Engineering, and leverages adaptive spatial–channel feature modulation to achieve state-of-the-art classification performance. (Citation data: the article is indexed on ScienceDirect and ResearchGate.) His research interest spans machine learning, computer vision, biomedical signal processing, and image-based disease diagnostics. During his internship at Nepal Telecom, he gained practical exposure to wireless, transmission and power systems, complementing his core computational skills. His scholarship-supported undergraduate tenure and leadership roles (such as captain of his institute’s volleyball team) reflect a combination of technical excellence and organisational ability. With a proven ability to translate deep learning methods into biomedical applications, Sandesh is poised to contribute significantly to the intersection of AI and healthcare diagnostics.

Profile : Orcid

Featured Publication

Aryal, S., Naik, S. K., Madarapu, S., & Ari, S. (2025). AFMNet: Adaptive feature modulation network for classification of white blood cells. Biocybernetics and Biomedical Engineering.

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.

Assist. Prof. Dr. Shravan Kumar Rudrabhatla | Best Researcher Award

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Assist. Prof. Dr. Shravan Kumar Rudrabhatla | Best Researcher Award

Anurag University | India

Dr. Shravan Kumar Rudrabhatla is an Assistant Professor at Anurag University, Hyderabad, specializing in fluid dynamics and artificial neural networks. He earned his Ph.D. in Applied Mathematics from the National Institute of Technology (NIT), Warangal in 2023 under the supervision of Prof. D. Srinivasacharya, focusing on the artificial neural network treatment of Casson fluid flow over a radially stretching sheet. His research integrates deep learning, computational fluid dynamics, and heat and mass transfer modeling, contributing to the understanding of complex non-Newtonian flows. Dr. Rudrabhatla has authored 6 research articles, accumulated 49 citations from 43 documents, and achieved an h-index of 4, as indexed by Scopus. His recent works include publications in European Journal of Mechanics B/Fluids, Physics of Fluids, Mathematical Models and Computer Simulations, and Journal of Thermal Analysis and Calorimetry. He has participated in numerous faculty development programs, workshops, and GIAN courses focused on machine learning and computational modeling. His academic journey is complemented by strong technical skills in Python, MATLAB, and C++, and a teaching background spanning over a decade. Dr. Rudrabhatla’s work continues to advance the intersection of mathematics, fluid mechanics, and artificial intelligence, contributing significantly to modern computational sciences.

Profiles : Orcid | Google Scholar | Scopus

Featured Publications

Srinivasacharya, D., & Kumar, R. S. (2022). Artificial neural network modeling of the Casson fluid flow over unsteady radially stretching sheet with Soret and Dufour effects. Journal of Thermal Analysis and Calorimetry, 147, 14891–14903. https://doi.org/10.1007/s10973-022-11694-w

Srinivasacharya, D., & Shravan Kumar, R. (2023). Neural network analysis for bioconvection flow of Casson fluid over a vertically extending sheet. International Journal of Applied and Computational Mathematics, 9(5), 80. https://doi.org/10.1007/s40819-023-01556-w

Srinivasacharya, D., & Kumar, R. S. (2023). An artificial neural network solution for the Casson fluid flow past a radially stretching sheet with magnetic and radiation effect. Mathematical Models and Computer Simulations, 15(5), 944–955. https://doi.org/10.1134/S2070048223050101

Nallapu, S., Sneha, G. S., & Kumar, S. R. (2018). Effect of slip on Jeffrey fluid flow through an inclination tube. Journal of Physics: Conference Series, 1000(1), 012041. https://doi.org/10.1088/1742-6596/1000/1/012041

Rudrabhatla, S. K., & Srinivasacharya, D. (2025). Deep learning framework for Casson fluid flow: A PINN approach to heat and mass transfer with chemical reaction and viscous dissipation. European Journal of Mechanics – B/Fluids, 204401. https://doi.org/10.1016/j.euromechflu.2025.204401

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

Dr. Shyamal Mondal | Best Research Article Award

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Dr. Shyamal Mondal | Best Research Article Award

Defence Institute of Advanced Technology | India

Shyamal Mondal is a leading researcher with an h-index of 9, 58 publications, and 293 citations across 235 documents, demonstrating significant contributions in photonics, terahertz technologies, and ultrafast optics. He earned his Ph.D. in Physics and Meteorology from the Indian Institute of Technology Kharagpur and is currently a faculty member at SRM Institute of Science and Technology, Kattankulathur, India. His research focuses on terahertz imaging and antenna design, deep learning for image enhancement, nonlinear optical phenomena, ultrafast fiber lasers, and advanced materials such as carbon nanostructures and MXenes. Dr. Mondal has advanced interdigitated photoconductive antennas, coherent mid-infrared laser sources, and modelocked fiber lasers, integrating theoretical and experimental approaches. He has published in high-impact journals including ACS Applied Optical Materials, Optics Express, and Journal of Applied Physics, and presented his work at international conferences. His contributions have strengthened the fields of terahertz communications, optical nonlinearity, and laser technologies. Dr. Mondal continues to drive innovation, mentoring emerging researchers, and bridging fundamental science with applied photonics solutions, thereby expanding the frontiers of optical and terahertz research.

Profiles : Google Scholar | Orcid | Scopus | Research Gate

Featured Publications

Mondal, S., Jampani, K., Raj, A. R., Roy Chowdhury, D., & Sethi, A. (2025). Implementing W-Net deep learning for terahertz image enhancement and segmentation. Engineering Research Express.

Mondal, S., Raj, A. R., & Saha, S. (2024). Advancements in the use of artificial saturable absorbers for modelocking of 2 µm ultrafast fiber lasers. Annalen der Physik.

Rathinasamy, V., Thipparaju, R. R., Boby, E. N. F., & Mondal, S. (2022). Interdigitated photoconductive antenna for future wireless communications. Microwave and Optical Technology Letters, 64(12), 2189–2196.

Boby, E. N. F., Prajapati, J., Rathinasamy, V., Mukherjee, S., & Mondal, S. (2022). Parametric investigation of interdigitated photoconductive antenna for efficient terahertz applications. Arabian Journal for Science and Engineering, 47(3), 3597–3609.

Mitra, N., Patra, A. K., Singh, S. P., Mondal, S., Datta, P. K., & Varshney, S. K. (2020). Interfacial delamination in glass-fiber/polymer-foam-core sandwich composites using singlemode–multimode–singlemode optical fiber sensors: Identification based on experimental investigation. Journal of Sandwich Structures and Materials.

Mondal, S., Mukherjee, S., Singh, S. P., Rand, S. C., Bhattacharya, S., Das, A. C., & Datta, P. K. (2016). Dynamic gain aperture modelocking in picosecond regime based on cascaded second-order nonlinearity. Optics Express, 24(15), 15274–15285.