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V. Nagaraj

Researcher at Knowledge Institute of Technology

Publications -  25
Citations -  182

V. Nagaraj is an academic researcher from Knowledge Institute of Technology. The author has contributed to research in topics: Steganography & Computer science. The author has an hindex of 5, co-authored 20 publications receiving 128 citations. Previous affiliations of V. Nagaraj include University of California, Santa Barbara & Pondicherry Engineering College.

Papers
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Journal ArticleDOI

Color Image Steganography based on Pixel Value Modification Method Using Modulus Function

TL;DR: The experimental outputs validate that good visual perception of stego image with more secret data embedding capacity of stEGo image can be achieved by the proposed PVM method.
Proceedings ArticleDOI

Optimal Planning of Workplace Electric Vehicle Charging Infrastructure with Smart Charging Opportunities

TL;DR: The results highlight the importance of considering revenues from demand charge reduction to incentivize investment in additional infrastructure needed to tap into the smart charging potential of EVs, at least under current energy market prices.
Proceedings Article

A modulo based LSB steganography method

TL;DR: In all the existing schemes detection of a secret message in a cover image can be easily detected from the histogram analysis and statistical analysis, therefore developing new LSB steganography algorithms against statistical and histograms analysis is the prime requirement.
Journal ArticleDOI

Knowledge and practice pattern of non-allopathic indigenous medical practitioners regarding tuberculosis in a rural area of India.

TL;DR: Considerable proportions of TB patients seek non-allopathic indigenous medical practitioners for cure of TB, while the knowledge and practice of IMPs is inadequate.
Journal ArticleDOI

Speech emotion recognition based on machine learning tactics and algorithms

TL;DR: This manuscript provides a general outline of strategies for machine learning, pre-processing, feature extraction techniques and determine the accuracy of suitable classifiers in Speech Emotion Recognition (SER).