Ganesh Gopal Devarajan
25 Papers
Ganesh Gopal Devarajan is an academic researcher. The author has contributed to research in topics: Computer science & Cloud computing. The author has an hindex of 1, co-authored 7 publications.
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Papers
Intelligent AI-based Healthcare Cyber Security System using Multi-Source Transfer Learning Method
Chinmay Chakraborty,Senthil Murugan Nagarajan,Ganesh Gopal Devarajan,M. V. Ramana,R.P. Mohanty +4 more
TL;DR: In this article , the authors proposed a centralized and federated transfer learning (CMTL) for cyber attack detection system for healthcare sector with edge of things (EoT) framework.
AI-Assisted Deep NLP-Based Approach for Prediction of Fake News From Social Media Users
Ganesh Gopal Devarajan,Senthil Murugan Nagarajan,Sardar Irfanullah Amanullah,Ali Kashif Bashir +3 more
TL;DR: Wang et al. as mentioned in this paper proposed a novel artificial intelligence (AI)-assisted fake news detection with deep natural language processing (NLP) model, which is characterized in four layers: publisher layer, social media networking layer, enabled edge layer, and cloud layer.
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Enhancing network lifespan in wireless sensor networks using deep learning based Graph Neural Network
N. Sivakumar,Senthil Murugan Nagarajan,Ganesh Gopal Devarajan,Lokaiah Pullagura,Rajendra Prasad Mahapatra +4 more
TL;DR: In this article , a deep learning-based Graph Neural Network (DL-GNN) was proposed to solve the problem of finding the smallest possible dominant set in a WSN.
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Intelligent Task Scheduling Approach for IoT Integrated Healthcare Cyber Physical Systems
Senthil Murugan Nagarajan,Ganesh Gopal Devarajan,Amin Salih Mohammed,M. V. Ramana,Uttam Ghosh +4 more
TL;DR: In this paper , the authors proposed an IoT-based healthcare cyber-physical system that provides effective resource utilization at fog and cloud levels with minimum execution cost, where homogeneity score-based K-means clustering is used as a feature extraction and selection method for sensor data features.
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DDNSAS: Deep reinforcement learning based deep Q-learning network for smart agriculture system
Ganesh Gopal Devarajan,Senthil Murugan Nagarajan,M. V. Ramana,T. Vignesh,Uttam Ghosh,Waleed S. Alnumay +5 more
TL;DR: In this paper , a two-stage end-to-end DRL based smart agricultural system is presented, where in stage one, an ACO enabled DQN (MACO-DQN) model is proposed to offload task including fire detection, pest detection, crop growth monitoring, irrigation scheduling, soil monitoring, climate monitoring, field monitoring etc.
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