7 Papers
11 Citations
I. Nelson is an academic researcher from Sri Sivasubramaniya Nadar College of Engineering. The author has contributed to research in topics: Computer science & Mesh networking. The author has an hindex of 1, co-authored 2 publications.
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Papers
Biometric Authentication-Based Intrusion Detection Using Artificial Intelligence Internet of Things in Smart City
TL;DR: A novel technique for secure data transmission and detecting an intruder in a biometric authentication system by feature extraction with classification is proposed and results revealed that the proposed method provides better intrusion detection outcomes.
An Efficient AlexNet Deep Learning Architecture for Automatic Diagnosis of Cardio-Vascular Diseases in Healthcare System
TL;DR: The proposed novel system called ABFog is useful to provide best quality of service in various configuration modes or to predict accuracy as needed to assort in different situations and for various users prerequisites.
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Mitigation of co-channel interferences in cognitive multi-carrier code division multiple access system by singular value decomposition techniques
TL;DR: A singular value decomposition based pre-processing approaches to mitigate CCI interferences is developed and the result shows that superior performance obtained with proposed techniques.
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Image Watermarking Based Data Hiding by Discrete Wavelet Transform Quantization Model with Convolutional Generative Adversarial Architectures
TL;DR: In this article , the authors proposed a novel technique in digital watermarking, with data hiding based on segmentation and classification, using deep learning techniques, which achieved an attained bit error rate of 71%, an SSIM of 56, a Normalized Cross-Correlation of 71, a training accuracy of 98, and a validation accuracy of 95%.
Optical bio sensor based cancer cell detection using optimized machine learning model with quantum computing
G. Balamurugan,C. Annadurai,I. Nelson,K. Nirmala Devi,A. S. Oliver,S. Gomathi +5 more
TL;DR: This research proposed novel method in detection of lung cancer utilizing feature selection as well as classification utilizing deep learning architectures with optical bio sensor, exceeding accuracies of previously published research.
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