Gangai Selvi | Econometrics and Statistical Economics | Women in Statistics Award

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Dr. Gangai Selvi | Econometrics and Statistical Economics | Women in Statistics Award

Tamil Nadu Agricultural University | India

Dr. R. Gangai Selvi, M.Sc., M.Phil., Ph.D., is a distinguished Professor of Statistics in the Department of Physical Sciences and Information Technology at the Agricultural Engineering College and Research Institute, Tamil Nadu Agricultural University (TNAU), Coimbatore, known for her exemplary contributions to teaching, research, and academic development. She holds a Ph.D. in Econometrics from TNAU, an M.Phil. and M.Sc. in Statistics, and a B.Sc. from PSG College of Arts and Science, along with a Diploma in Computer Applications and certifications in programming, reflecting a strong foundation in both statistical theory and computational methods. Dr. Gangai Selvi’s areas of specialization encompass Econometrics, Spatial Econometrics, Applied Statistics, Design of Experiments, Data Analytics, Biostatistics, and Statistical Methods, complemented by her proficiency in statistical software including R, SAS, SPSS, SYSTAT, and STATA. She has developed innovative ICT-based learning tools, most notably the “TNAU Statistics Quiz” Android Mobile App and its corresponding software version, both officially recognized with copyrights for their contribution to student learning and competitive exam preparation, including ICAR and other agricultural examinations. In addition, she has mentored numerous students in technology-enhanced education initiatives, guided mobile app development projects, and created educational YouTube content to support interactive learning. Her teaching portfolio spans undergraduate, postgraduate, and doctoral courses, covering a wide range of topics such as Applied Statistics, Statistical Methods, Design of Experiments, Statistical Inference, Sampling Techniques, Econometrics, Biostatistics, Multivariate Analysis, Regression Analysis, and Statistical Quality Control, reflecting her commitment to cultivating analytical and research skills among students. Her research experience includes serving as Principal Investigator and Co-Principal Investigator on major projects, focusing on areas such as big data analytics, crop yield prediction, agricultural variability, and spatial econometric modeling, with outcomes contributing significantly to the field of agricultural statistics. Dr. Gangai Selvi’s scholarly work, combined with her innovative approach to education and technology integration, establishes her as a respected academic, educator, and thought leader in statistical and econometric research.

Profiles: Google Scholar | Orcid

Featured Publications

Kiran Sree Pokkuluri | Machine Learning and Statistics | Excellence in Research Award

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Prof. Dr. Kiran Sree Pokkuluri | Machine Learning and Statistics | Excellence in Research Award

Shri Vishnu Engineering College For Women | India

Prof. Dr. Kiran Sree Pokkuluri is a distinguished academician, researcher, and innovator in the field of Artificial Intelligence and Machine Learning with an illustrious career of academic and research excellence. Currently serving as Professor and Head of the Department of Computer Science and Engineering at Shri Vishnu Engineering College for Women, he has significantly contributed to advancing computational intelligence and data-driven innovation in academia and industry. He holds a Ph.D. in Artificial Intelligence from JNTU-Hyderabad and has an impressive scholarly record with over 100 research publications in reputed SCI and Scopus-indexed journals, a citation count exceeding 653, an h-index above 13, and an Documents exceeding 152, reflecting the global impact of his research. His research areas include Deep Learning, Healthcare Analytics, Bioinformatics, IoT Power Optimization, Big Data Analytics, and Cloud Computing. Dr. Sree has authored six textbooks with ISBNs on Artificial Intelligence, Machine Learning, and Deep Learning, and has filed and published six patents in the domains of AI and intelligent systems. His innovations such as the Hybrid Deep Neural ZF Network (HDNZF-Net) have set new benchmarks in real-time speech enhancement for speech-impaired individuals and IoT optimization. He has completed five major funded projects and collaborated with premier institutions including Stanford University through the UIF program, fostering cross-disciplinary innovation. A recognized thought leader, Dr. Sree serves as Editor-in-Chief, editorial board member, and reviewer for multiple international journals. His remarkable achievements have earned him prestigious recognitions like the Bharat Excellence Award and Rashtriya Ratan Award, and he has been featured in Marquis Who’s Who in the World. As Global Vice President of the World Statistical Data Analysis Research Association (WSA) and a member of professional bodies such as IEEE, ISTE, CSI, and IAENG, Dr. Kiran Sree continues to inspire excellence in AI-driven research, education, and technological innovation.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

Venkatachalam, B., Pokkuluri, K. S., Suguna Kumar, S., Dhandapani, A., & Bhonsle, M. (2025). Adaptive fuzzy heuristic algorithm for dynamic data mining in IoT integrated big data environments. Journal of Fuzzy Extension and Applications, 6(3), 615–636.

Pokkuluri, K. S., Sarkar, P., Birchha, V., Mathariya, S. K., Veeramachaneni, V., & others. (2025). Intelligent reasonable optimization for virtual machine provisioning in hybrid cloud using fuzzy AHP and cost-effective autoscaling. SN Computer Science, 6(7), 1–15.

Sivanuja, M., Raju, P. J. R. S., Prasad, M., RR, P. B. V., Kumar, K. S., & Pokkuluri, K. S,. (2025). A novel ensemble-based deep learning framework combining CNN and transfer learning models for enhanced wildfire detection. In Proceedings of the 2025 International Conference on Computational Robotics, Testing and Applications.

Alzubi, J. A., Pokkuluri, K. S., Arunachalam, R., Shukla, S. K., Venugopal, S., & others. (2025). A generative adversarial network-based accurate masked face recognition model using dual scale adaptive efficient attention network. Scientific Reports, 15(1), 17594.

Pokkuluri, K. S., Chandanan, A. K., Mishra, A. K., Jyothi, D., Lavanya, M. S. S. L., & others. (2025). Deep learning-enhanced intrusion detection and privacy preservation for IIoT networks. In Proceedings of the 2025 4th International Conference on Distributed Computing and Electrical Systems.

Seyed Abolfazl Hosseini | Statistical Modeling and Simulation | Best Researcher Award

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Dr. Seyed Abolfazl Hosseini | Statistical Modeling and Simulation | Best Researcher Award

Dr. Seyed Abolfazl Hosseini | Islamic Azad University | Iran

Dr. Seyed Abolfazl Hosseini is an accomplished electrical engineer and academic whose work seamlessly integrates communications systems, signal processing, machine learning, and remote sensing. He earned his Ph.D. in Communications Systems Engineering from Tarbiat Modares University, following an M.Sc. from K. N. Toosi University and a B.Sc. in Control Engineering from Sharif University of Technology. Over his academic career, he has held leadership roles including Dean of the Electrical & Electronics Research Centre, head of the Communications Engineering Department, and overseen more than 35 M.Sc. theses and 5 Ph.D. dissertations. According to his publication record encompasses more than 5 documents, and his works have been cited over 18 times, with an h-index of 3. He has published in top journals on topics such as MIMO-UFMC system optimization, hyperspectral image classification, blind watermarking, and nonparametric density estimation. Beyond research, he has directed industry projects in IoT, AI, surveillance, and power systems, and contributed to drafting technical standards for electricity markets. Dr. Hosseini is proficient in MATLAB, Python, and advanced mathematics including stochastic processes, linear algebra, fractal theory, and graph theory. He continues to blend theory with practice, driving innovation and teaching the next generation of engineers.

Profiles: Scopus | Orcid | ResearchGate

Featured Publications

Hassan Abdollahpour, H., Hosseini, S. A., Raeisi, N., & Azam, F. 3D geometry modeling method for MIMO communication systems using correlation coefficients. Journal of Computer Networks and Communications.

Aghamiri, H. R., Hosseini, S. A., Green, J. R., & Oommen, B. J. Nonparametric probability density function estimation using the Padé approximation. Algorithms.

Asgharnia, M., Hosseini, S. A., Shahzadi, A., Ghazi-Maghrebi, S., & Shaghaghi Kandovan, R. Optimization framework for user clustering, beamforming design and power allocation in MIMO-UFMC systems. IEEE Access.

Khalili, F., Razzazi, F., & Hosseini, S. A. Registration of remote sensing images by the combination of complex nonlinear diffusion and phase congruency attributes. Journal of the Indian Society of Remote Sensing.

Hosseini, S. A., et al. A simple method to prepare and characterize optical fork-shaped diffraction gratings for generation of orbital angular momentum beams. Journal of Optics.