Prof. Dr. Rajeev Tiwari | Next generation communication networks | Editorial Member
Prof. Dr. Rajeev Tiwari | University of Petroluem and Energy Studies | India
Prof. Dr. Rajeev Tiwari i has established a strong academic presence in artificial intelligence, deep learning, next-generation networks, fogedge computing, healthcare informatics, and smart agriculture, supported by 2327 citations, an h-index of 25, and an i10-index of 50. Their contributions span intelligent systems for agriculture, medical diagnostics, cybersecurity, vehicular analytics, and IoT-integrated environments. Notable works include hybrid CNN frameworks for leaf disease recognition, ensemble-based lung cancer diagnosis, Alzheimer’s disease detection models, and deep learning architectures for traffic density estimation and fake review identification. They have also advanced efficient routing in wireless sensor networks, content-centric networking optimization, and IoT-enabled automation in irrigation, parking, and toll management. Their research demonstrates a consistent focus on practical, high-impact solutions that bridge machine learning with real-world applications, contributing significantly to modern AI-driven systems across healthcare, agriculture, smart cities, and cloud-edge ecosystems.
Profile: Google Scholar
Featured Publications:
Kaur, P., Harnal, S., Tiwari, R., Upadhyay, S., Bhatia, S., Mashat, A., & Alabdali, A. M. (2022). Recognition of leaf disease using hybrid convolutional neural network by applying feature reduction. Sensors, 22(2), 575.
Mittal, U., Chawla, P., & Tiwari, R. (2023). EnsembleNet: A hybrid approach for vehicle detection and estimation of traffic density based on faster R-CNN and YOLO models. Neural Computing and Applications, 35(6), 4755–4774.
Bathla, G., Singh, P., Singh, R. K., Cambria, E., & Tiwari, R. (2022). Intelligent fake reviews detection based on aspect extraction and analysis using deep learning. Neural Computing and Applications, 34(22), 20213–20229.
Verma, P., Tiwari, R., Hong, W. C., Upadhyay, S., & Yeh, Y. H. (2022). FETCH: A deep learning-based fog computing and IoT integrated environment for healthcare monitoring and diagnosis. IEEE Access, 10, 12548–12563.
Mishra, D., Khan, A., Tiwari, R., & Upadhay, S. (2018). Automated irrigation system—IoT based approach. 2018 3rd International Conference on Internet of Things: Smart Innovation, (pages not provided).