Nirmal Varghese Babu | Artificial Intelligence | Best Researcher Award 

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Dr. Nirmal Varghese Babu | Artificial Intelligence | Best Researcher Award 

Dr. Nirmal Varghese Babu | Karunya Institute of Technology and Sciences | India

Author Profiles

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Early Academic Pursuits

Dr. Nirmal Varghese Babu began his academic journey with a strong inclination toward computer science and technology. He completed his B.Tech in Information Technology from Karunya Institute of Technology and Sciences, Coimbatore, in 2017 with a CGPA of 8.0. His passion for research and innovation in computing led him to pursue an M.Tech in Computer Science & Engineering from Amal Jyothi College of Engineering, Kanjirappally (2017–2019), graduating with an impressive CGPA of 8.72. Currently, he is pursuing a Ph.D. in Computer Science & Engineering from Karunya Institute of Technology and Sciences, expected to be completed in 2025. His academic excellence was rooted in his formative years at Mathews Mar Athanasius Residential School, Chengannur, where he built a strong foundation in analytical and computational thinking.

Professional Endeavors

Dr. Nirmal Varghese Babu is currently serving as an Assistant Professor at the School of Computer Science and Technology, Karunya Institute of Technology and Science, Coimbatore (since July 26, 2022). His professional endeavors include teaching, research, and mentoring in various areas of computer science and Artificial Intelligence. He has delivered lectures on Artificial Intelligence: Principles and Techniques, Cloud Computing for Data Analytics, AI for Games, AI for Food Processing Engineering, AI for Biotechnology, and MLOps. Alongside teaching, he mentors undergraduate students, coordinates final-year projects, and supervises academic research initiatives. His teaching methodology emphasizes experiential learning, guiding students to bridge theoretical knowledge with real-world technological applications.

Contributions and Research Focus

Dr. Nirmal’s research contributions revolve around Artificial Intelligence, machine learning, data analytics, and real-time systems. His M.Tech project, Multiclass Sentiment Analysis of Social Media Data using Neural Networks, explored advanced deep learning algorithms like CNN and RNN for classifying sentiment across social media platforms, specifically Twitter. This study integrated text and emoticon data for multiclass classification using one-hot encoding and neural networks. His earlier project, Real-Time Traffic Incident Detection using Social Media Data, demonstrated innovative use of Natural Language Processing to detect and analyze traffic incidents using Twitter data, integrating AI for real-time decision-making. His work exemplifies how Artificial Intelligence can transform data into actionable insights for societal and industrial benefit.

Impact and Influence

Dr. Nirmal Varghese Babu’s impact as an educator and researcher extends across academia and applied technology. At Karunya Institute, he plays a vital role in shaping the next generation of AI-driven engineers and data scientists. As a mentor and coordinator, he has successfully guided numerous B.Tech projects, fostering innovation in the domains of Artificial Intelligence, MLOps, and machine learning. His pedagogical style emphasizes research-based learning, promoting creative problem-solving and real-world application of AI. Through his leadership in academic project coordination and curriculum development, he has significantly influenced the integration of AI-based methodologies into modern engineering education.

Academic Cites

Dr. Nirmal’s academic contributions are recognized through his published works, research projects, and student-guided studies. His projects on sentiment analysis and traffic incident detection have been well-cited and appreciated within the AI and data analytics community. The relevance of his research is reflected in growing academic references to his work in areas such as neural networks, data mining, and sentiment classification. His scholarly achievements continue to inspire students and researchers pursuing advanced studies in Artificial Intelligence and computational learning.

Legacy and Future Contributions

Looking ahead, Dr. Nirmal Varghese Babu aims to expand his research in Artificial Intelligence, focusing on its integration with real-time analytics, smart systems, and cognitive computing. His future contributions are expected to advance the use of AI in multidisciplinary fields such as biotechnology, healthcare, and environmental systems. As an educator, his legacy lies in his ability to inspire and mentor young researchers, promoting a culture of innovation and ethical AI development. His ongoing research and academic leadership will undoubtedly continue to shape the evolution of AI-driven solutions and their transformative potential across industries.

Artificial Intelligence

Dr. Nirmal Varghese Babu’s expertise in Artificial Intelligence is evident through his teaching, research, and innovation in deep learning, neural networks, and data analytics. His projects and mentorship highlight the transformative role of Artificial Intelligence in addressing real-world challenges. The continued advancement of Artificial Intelligence under his guidance promises to create meaningful impact in both academic and applied technological domains.

Featured Publications

Babu, N. V., & Kanaga, E. G. M. (2022). Sentiment analysis in social media data for depression detection using artificial intelligence: A review. SN Computer Science, 3(1), 1–15. https://doi.org/10.1007/s42979-021-00921-2

Babu, D. E. G. M. K. N. V. (2022). Sentiment analysis in social media data for depression detection using artificial intelligence: A review. SN Computer Science, 3, 350.

Babu, N. V., & Rawther, F. A. (2021). Multiclass sentiment analysis in text and emoticons of Twitter data: A review. Proceedings of the Second International Conference on Networks and Advances in Computational Technologies (NetACT).

Prince, S. C., & Babu, N. V. (2024). Advancing multiclass emotion recognition with CNN-RNN architecture and illuminating module for real-time precision using facial expressions. Proceedings of the 2024 International Conference on Advances in Modern Age Technologies for Sustainable Development (AMATS).

Babu, N. V., Kanaga, E. G. M., Kattappuram, J. T., & Benny, R. V. (2023). AI-based EEG analysis for depression detection: A critical evaluation of current approaches and future directions. Proceedings of the 2023 International Conference on Computational Intelligence and Sustainable Technologies (CIST).

Babu, D. E. G. M. K. N. V. (2022). Depression analysis using electroencephalography signals and machine learning algorithms. Proceedings of the Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT).

Adityasai, B., & Babu, N. V. (2024). Advancing Alzheimer’s diagnosis through transfer learning with deep MRI analysis. Proceedings of the 2024 International Conference on Advances in Modern Age Technologies for Sustainable Development (AMATS).

Babu, N. V., & Kanaga, E. G. M. (2023). Multiclass text emotion recognition in social media data. In Machine Intelligence Techniques for Data Analysis and Signal Processing (pp. 123–135). Springer.

Rawther, F. A., & Babu, N. V. (2019). User behavior analysis on social media data using sentiment analysis or opinion mining. International Research Journal of Engineering and Technology (IRJET), 6(6), 3081–3085.

Konstantinos Diamantaras | Machine Learning | Best Researcher Award 

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Prof. Konstantinos Diamantaras | Machine Learning | Best Researcher Award 

Prof. Konstantinos Diamantaras | International Hellenic University | Greece

Prof. Konstantinos Diamantaras is a Professor at the International Hellenic University, Department of Information & Electronic Engineering, and Vice Rector since 2023, holding a Beng from NTUA, Greece, and an MSc and PhD in Electrical Engineering from Princeton University. His research focuses on machine learning, signal processing, and augmented/virtual reality, with over 230 scientific publications and 79 journal articles indexed in SCI and Scopus, accumulating more than 7,300 citations on Google Scholar (h-index 30) and 3,027 citations on Scopus (h-index 23). He has authored four books, including Principal Component Neural Networks (1996) and Artificial Neural Networks (2007), and received the IEEE Best Paper Award in 1997 for Adaptive Principal Component Extraction (APEX). He leads multiple EU- and university-funded projects, such as Kids Radio Europe, METACHEF, Digital4all, and AI-based food recognition. His collaborations include Prof. S. Y. Kung (Princeton), Prof. Athina Petropulu (Rutgers), Prof. Tomas McKelvey (Chalmers), and partnerships with Alzheimer Hellas and the University of Alicante on NLP applications. He serves on editorial boards of Journal of Signal Processing Systems and Applied Sciences, contributing to advancements in deep learning, pattern recognition, biomedical informatics, adaptive signal processing, and educational technology. His work spans practical AI applications in health, digital learning, and immersive experiences, influencing both academic research and societal impact. He is an active IEEE member and IEEE Signal Processing Society participant, advancing knowledge in neural networks, computational intelligence, and multilingual natural language generation.

Profiles: Scopus | Orcid | Google Scholar | Staff Page

Featured Publications

Diamantaras, K. I., & Kung, S. Y. (1996). Principal component neural networks: Theory and applications. In Adaptive and learning systems for signal processing, communications, and control (p. 1694). Springer.

Vafeiadis, T., Diamantaras, K. I., Sarigiannidis, G., & Chatzisavvas, K. C. (2015). A comparison of machine learning techniques for customer churn prediction. Simulation Modelling Practice and Theory, 55, 1–9

Giatsoglou, M., Vozalis, M. G., Diamantaras, K., Vakali, A., & Sarigiannidis, G. (2017). Sentiment analysis leveraging emotions and word embeddings. Expert Systems with Applications, 69, 214–224.

Lampropoulos, G., Keramopoulos, E., Diamantaras, K., & Evangelidis, G. (2022). Augmented reality and gamification in education: A systematic literature review of research, applications, and empirical studies. Applied Sciences, 12(13), 6809.

Maglaveras, N., Stamkopoulos, T., Diamantaras, K., Pappas, C., & Strintzis, M. (1998). ECG pattern recognition and classification using non-linear transformations and neural networks: A review. International Journal of Medical Informatics, 52(1–3), 191–208.

Gravanis, G., Vakali, A., Diamantaras, K., & Karadais, P. (2019). Behind the cues: A benchmarking study for fake news detection. Expert Systems with Applications, 124, 292–303.

Kung, S. Y., & Diamantaras, K. I. (1990). A neural network learning algorithm for adaptive principal component extraction (APEX). In ICASSP-90. Acoustics, Speech, and Signal Processing (pp. 256–259).

Kung, S. Y., Diamantaras, K. I., & Taur, J. S. (1994). Adaptive principal component extraction (APEX) and applications. IEEE Transactions on Signal Processing, 42(5), 1202–1217.

Stamkopoulos, T., Diamantaras, K., Maglaveras, N., & Strintzis, M. (1998). ECG analysis using nonlinear PCA neural networks for ischemia detection. IEEE Transactions on Signal Processing, 46(11), 3058–3067.

Jinkai Zheng | Computer Vision | Best Researcher Award 

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Prof. Jinkai Zheng | Computer Vision | Best Researcher Award 

Hangzhou Dianzi University, China

Prof. Jinkai Zheng is a Distinguished Associate Researcher at Hangzhou Dianzi University, Director of the Scientific Research Management Department at the Hangzhou Dianzi University Lishui Research Institute, and an active member of the Multimedia and Biometric Recognition Professional Committees of the China Society of Image and Graphics. His research focuses on artificial intelligence, computer vision, and multimedia analysis, with a particular emphasis on gait recognition and human-centered intelligent analysis. He has published over 40 academic documents, including multiple first-author and corresponding-author papers in top-tier venues such as CVPR, ACM Multimedia, and IEEE Transactions on Multimedia, accumulating more than 900 citations with an h-index of 16 (Google Scholar, 2025). His contributions include the Gait3D dataset, now a widely adopted benchmark by over 300 prestigious institutions worldwide, including Columbia University, University of Pennsylvania, Johns Hopkins University, and NUS. He has received notable accolades, such as the Special Prize of the 2024 Wu Wenjun Artificial Intelligence Science and Technology Progress Award, the Outstanding Paper Award at the 2023 CSIG Youth Scientists Conference, and the Best Paper Award-Honorable Mention at IEEE ISCAS 2021. With four authorized invention patents, long-term service as a reviewer for leading journals and conferences, and significant participation in national R&D projects, Prof. Zheng has become a recognized young leader in advancing AI-driven multimedia understanding.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Zheng, J., Liu, X., Gu, X., Sun, Y., Gan, C., Zhang, J., Liu, W., & Yan, C. (2022). Gait recognition in the wild with multi-hop temporal switch. Proceedings of the 30th ACM International Conference on Multimedia, 6136–6145.

Zheng, J., Liu, X., Wang, S., Wang, L., Yan, C., & Liu, W. (2023). Parsing is all you need for accurate gait recognition in the wild. Proceedings of the 31st ACM International Conference on Multimedia, 116–124.

Zheng, J., Liu, X., Yan, C., Zhang, J., Liu, W., Zhang, X., & Mei, T. (2021). Trand: Transferable neighborhood discovery for unsupervised cross-domain gait recognition. 2021 IEEE International Symposium on Circuits and Systems (ISCAS), 1–5. IEEE.

Zheng, J., Liu, X., Zhang, B., Yan, C., Zhang, J., Liu, W., & Zhang, Y. (2024). It takes two: Accurate gait recognition in the wild via cross-granularity alignment. Proceedings of the 32nd ACM International Conference on Multimedia, 8786–8794.

Yuan, S., Zheng, J., Li, X., Sun, Y., Li, W., Gao, R., Omar, M. H., & Zhang, J. (2025). Noisy label learning for gait recognition in the wild. Electronics, 14(19), 3752.

Zhang, S., Zheng, J., Zhu, S., & Yan, C. (2025). TrackletGait: A robust framework for gait recognition in the wild. arXiv preprint arXiv:2508.02143.

Zheng, J., Liu, X., Liu, W., He, L., Yan, C., & Mei, T. (n.d.). Supplementary material for “Gait recognition in the wild with dense 3D representations and a benchmark.”