Dr. Uma Jothi | Team Building and Team Management | Excellence in Research

Amrita University, India

Dr. J. Uma is an accomplished academic and researcher in the fields of information technology, cloud computing, artificial intelligence, and cybersecurity. She holds a B.Tech in Information Technology, an M.E. in Computer Science and Engineering with distinction, and has submitted her Ph.D. thesis in Information and Communication Engineering at Anna University. With more than twelve years of academic experience, she has served as Assistant Professor in leading engineering institutions, contributing significantly to teaching, curriculum development, and research mentorship. Her research focuses on cloud resource allocation, deep reinforcement learning, intelligent optimization algorithms, blockchain technologies, IoT-based systems, and data security. She has published impactful journal articles in reputed outlets like Transactions on Emerging Telecommunications Technologies and Springer’s Lecture Notes in Networks and Systems, along with several book chapters and conference papers. Her work includes innovations in heuristic optimization, adversarial defenses in deep learning, and smart healthcare IoMT solutions. She is also a published patent holder in IoT-based agriculture monitoring systems and employee training platforms. Dr. Uma has organized major AICTE- and RGNIYD-funded programs, contributing to national-level capacity building in data science, IoT, and smart city technologies. Her career reflects a strong commitment to advancing research, innovation, and academic excellence.

Profiles: ORCID

Featured Publications

Arunasree, P. V., Jothi, U., & Sumathi, S. (2025). GA-PoW: A novel genetic algorithm-based proof of work approach for optimal nonce selection in blockchain network. International Journal of Information Technology. https://doi.org/10.1007/s41870-025-02920-3

Yejnakshari, M. K., Bhargav, Y. S., Radhika, N., Jothi, U., Radhika, G., & Mahaveerakannan, R. (2025). Chimp optimization algorithm-based recurrent neural network for smart health care system in edge computing-based IoMT. Journal of Machine and Computing. https://doi.org/10.53759/7669/jmc202505034

Uma, J., Vivekanandan, P., & Shankar, S. (2022). Optimized intellectual resource scheduling using deep reinforcement Q-learning in cloud computing. Transactions on Emerging Telecommunications Technologies, 33(5), e4463. https://doi.org/10.1002/ett.4463

Mr. Naveen Kumar | Performance Management | Best Scholar Award

Jawaharlal Nehru university, New Delhi | India

Naveen Kumar is an experienced academician in computer engineering, currently serving as an Assistant Professor at the Panipat Institute of Engineering and Technology, Haryana, since 2014. He holds a Master of Technology in Computer Engineering from the University Institute of Engineering and Technology, Kurukshetra University, and a Bachelor of Technology in Computer Engineering from Maharishi Markandeshwar Engineering College, Mullana. His teaching career spans over fifteen years, including previous roles as Lecturer at N.C. College of Engineering and Technology, Panipat. He has qualified both the GATE (2018) and UGC-NET (July and December 2018), reflecting his strong academic foundation. His research interests include genetic algorithms, optimization techniques, digital image processing, data structures, and artificial intelligence. Naveen Kumar has published research papers in reputed journals, focusing on comparative analyses of genetic algorithm operators and their applications in solving the Traveling Salesman Problem. He has actively participated in various national workshops and faculty development programs on topics such as big data, cyber forensics, high-performance computing, and intellectual property rights. Recognized for his commitment to academic excellence, he has contributed to departmental and institutional development activities and accreditation processes. Naveen Kumar continues to inspire students and peers through his dedication to research, teaching innovation, and technical education advancement.

Profile: Google Scholar

Featured Publications

Kumar, N., & Karambir, R. (2012). A comparative analysis of PMX, CX and OX crossover operators for solving traveling salesman problem. International Journal of Latest Research in Science and Technology, 1(2), 98–101.

Kumar, N., & Chaudhary, A. (2024). Surveying cybersecurity vulnerabilities and countermeasures for enhancing UAV security. Computer Networks, 252, 110695.

Kumar, N. (2012). A genetic algorithm approach to study traveling salesman problem. Journal of Global Research in Computer Science, 3(3), 33–37.

Kumar, N., Chaudhary, V., & Dubey, S. K. (2025). Cybersecurity and emerging technologies: Challenges and opportunities. In Cybersecurity preparedness among Indian firms: Opportunities, challenges, and strategies (pp. xx–xx).

Kumar, N., Kumar, G., & Chaudhary, V. (2024). Redefining national security threats in cyberspace: A challenging problem. In Proceedings of the International Seminar on Emerging Threats to National Security: Cyber and Information Warfare (pp. xx–xx).