Nilesh Kumar Jadav, Ph. D
Marwadi University, Nirma University, Marwadi Education Foundation's Group of Institutions
About
Nilesh Kumar Jadav is an Associate Professor & Head in the Department of Computer Engineering- AI, Marwadi University, India. He received his Ph. D. from Nirma University, India, in 2024. He has authored/coauthored publications (including papers in SCI-indexed journals and IEEE ComSoc-sponsored international conferences). Some of his research findings are published in top-cited journals and conferences, such as IEEE Transactions on Industrial Informatics, IEEE Transactions on Network and Service Management, IEEE Open Journal of Vehicular Technology, Digital Communications and Networks (Elsevier), Computers and Electrical Engineering (Elsevier), IEEE INFOCOM, IEEE ICC, and IJCS. His research interests include artificial intelligence, network security, 5G communication networks, and blockchain technology.
Employment
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Marwadi University Associate Professor & Head2024 - Present
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Nirma University Full Time PhD Scholar2021 - 2025
Education
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Marwadi Education Foundation's Group of Institutions ME
Projects & Funding
Projects & funding information is unavailable.
Publications (65)
- Convolutional neural network and unmanned aerial vehicle‐based public safety framework for human life protection Save
- AI‐driven network softwarization scheme for efficient message exchange in IoT environment beyond 5G Save
- FL-ORA: Optimized and Decentralized Resource Allocation Scheme for D2D Communication Save
- Whale Optimization-Based Access Control Scheme in D2D Communication Underlaying Cellular Networks Save
- Blockchain and Onion Routing-Based Secure Data Management Framework for Healthcare Informatics Save
- Blockchain-Orchestrated Intelligent Water Treatment Plant Profiling Framework to Enhance Human Life Expectancy Save
- Fuzzy-Enhanced Secure Messaging Framework for Smart Healthcare System Save
- GreenLand: A Secure Land Registration Scheme for Blockchain and AI-Enabled Agriculture Industry 5.0 Save
- Classification of Potentially Hazardous Asteroids Using Supervised Quantum Machine Learning Save
- Traffic Sign Classification for Autonomous Vehicles Using Split and Federated Learning Underlying 5G Save