Prof. Major Singh Goraya | Computer Science | Best Researcher Award

Sant Longowal Institute of Engineering and Technology | India

Dr. Major Singh Goraya is a Professor in the Department of Computer Science and Engineering at Sant Longowal Institute of Engineering and Technology (SLIET), Longowal. He holds a Ph.D. from Punjabi University, complemented by strong academic foundations in computer engineering. His primary research areas include cloud computing, resource management, green computing, fault tolerance, and load balancing. With 27 Scopus-indexed publications, 460 citations across 399 documents, and an h-index of 10, Dr. Goraya has made consistent scholarly contributions to high-performance and sustainable computing. His studies have explored dynamic resource allocation, energy-efficient scheduling frameworks, and deep learning-based optimization techniques. He has supervised several Ph.D. and M.Tech. research scholars and continues to guide emerging researchers in cloud resource efficiency and intelligent computation. His international exposure through conferences in the UK, Canada, and Malaysia reflects his active engagement in global research forums. In addition, he has successfully organized numerous academic workshops, conferences, and training programs. Dr. Goraya’s innovative contributions strengthen the integration of artificial intelligence and cloud technology, promoting scalable and eco-efficient computational solutions that advance modern computer engineering research.

Featured Publications

Goyal, N., Singh, T., & Goraya, M. S. (2025). Deep convolutional neural networks vs. vision transformers for video-based human activity recognition. In Proceedings of Lecture Notes in Computer Science (pp. xx–xx). Springer.

Singh, J., & Goraya, M. S. (2023). An autonomous multi-agent framework using quality of service to prevent service level agreement violations in cloud environment. International Journal of Advanced Computer Science and Applications, 14(3).

Goyal, N., Goraya, M. S., & Singh, T. (2023). An axiomatic analysis for object detection and recognition using deep learning. In Smart Innovation, Systems and Technologies (pp. xx–xx). Springer.

Thakur, A., & Goraya, M. S. (2022). A workload and machine categorization-based resource allocation framework for load balancing and balanced resource utilization in the cloud. International Journal of Grid and High Performance Computing, 14(2).

Hasan, M., Goraya, M. S., & Garg, T. (2022). E-FFTF: An extended framework for flexible fault tolerance in cloud. In Lecture Notes in Networks and Systems (pp. xx–xx). Springer.

Dr. Tejinder Kaur | Computer Science | Best Researcher Award

MM Institute of Computer Technology & Business Management | India

Dr. Tejinder Kaur is an accomplished academic and researcher in Computer Science and Engineering, currently serving as an Associate Professor with extensive teaching and research experience in artificial intelligence, machine learning, big data, and software engineering. She holds a Ph.D. from Thapar University and an M.Tech from Chandigarh University, with postdoctoral research in progress from a reputed public university. Her work has earned significant scholarly recognition with over 54,815 citations, an h-index of 65, and an i10-index of 148, reflecting her impactful contributions to scientific research and innovation. Dr. Kaur has authored and reviewed numerous research papers and book chapters and has guided several postgraduate theses in areas like vehicular networks, routing protocols, and wireless sensor networks. Her research interests span artificial intelligence, IoT, cybersecurity, and advanced computing systems. She has published over 277 papers, filed and been granted multiple national and international patents, and received prestigious awards, including honors from IEEE, Infosys, and SAP. A committed educator and innovator, Dr. Kaur continues to inspire through academic excellence and research leadership. Her outstanding academic record, extensive publication portfolio, and technological innovations highlight her as a dynamic professional dedicated to advancing computing and intelligent systems.

Profiles: Google Scholar | Scopus

Featured Publications

Abbott, B. P., Abbott, R., Abbott, T. D., Abernathy, M. R., Acernese, F., Ackley, K., et al. (2016). Observation of gravitational waves from a binary black hole merger. Physical Review Letters, 116(6), 061102. https://doi.org/10.1103/PhysRevLett.116.061102

Aasi, J., Abbott, B. P., Abbott, R., Abbott, T., Abernathy, M. R., Ackley, K., Adams, C., et al. (2015). Advanced LIGO. Classical and Quantum Gravity, 32(7), 074001. https://doi.org/10.1088/0264-9381/32/7/074001

Abbott, B. P., Abbott, R., Abbott, T. D., Abernathy, M. R., Acernese, F., Ackley, K., et al. (2016). GW151226: Observation of gravitational waves from a 22-solar-mass binary black hole coalescence. Physical Review Letters, 116(24), 241103. https://doi.org/10.1103/PhysRevLett.116.241103

Abbott, R., Abbott, T. D., Acernese, F., Ackley, K., Adams, C., Adhikari, N., et al. (2023). GWTC-3: Compact binary coalescences observed by LIGO and Virgo during the second part of the third observing run. Physical Review X, 13(4), 041039. https://doi.org/10.1103/PhysRevX.13.041039

Abbott, B. P., Abbott, R., Abbott, T. D., Abernathy, M. R., Acernese, F., Ackley, K., et al. (2016). Tests of general relativity with GW150914. Physical Review Letters, 116(22), 221101. https://doi.org/10.1103/PhysRevLett.116.221101

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