Article Per Year (5 Year)
p-Index From 2021 - 2026
0.553
P-Index
This Author published in this journals
All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Shridevi S. Krishnakumar
Vellore Institute of Technology
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Detecting face mask using eigenfaces and vanilla neural networks Raghav Sharma; Shridevi S. Krishnakumar; Abishek Seshan; Manan Rajotia
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp911-921

Abstract

Coronavirus has become one of the most deadly pandemics in 2021. Starting in 2019, this virus is now a significant medical issue all over the world. It is spreading extensively because of its modes of transmission. The virus spreads directly, indirectly, or through close contact with infected people. It is proclaimed that people should wear a mask in public areas as a counteraction measure, as it helps in suppressing transmission. A portion of the spaces, where the virus has broadly fanned out, is because of inappropriate wearing of facial cover. In crowded areas, keeping a check on facial masks manually is difficult. To automate this process, an effective and robust face mask detector is required. This paper discusses a hybrid approach using a machine learning technique called eigenfaces, along with vanilla neural networks. The accuracy was compared for three different values of principal components. The test accuracy achieved was 0.87 for 64 components, 0.987 for 512 components, and 0.989 for 1,000 components. Hence, this approach proved to be more promising and efficient than its counters.
Quantum transfer learning for image classification Geetha Subbiah; Shridevi S. Krishnakumar; Nitin Asthana; Prasanalakshmi Balaji; Thavavel Vaiyapuri
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 1: February 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i1.24103

Abstract

Quantum machine learning, an important element of quantum computing, recently has gained research attention around the world. In this paper, we have proposed a quantum machine learning model to classify images using a quantum classifier. We exhibit the results of a comprehensive quantum classifier with transfer learning applied to image datasets in particular. The work uses hybrid transfer learning technique along with the classical pre-trained network and variational quantum circuits as their final layers on a small scale of dataset. The implementation is carried out in a quantum processor of a chosen set of highly informative functions using PennyLane a cross-platform software package for using quantum computers to evaluate the high-resolution image classifier. The performance of the model proved to be more accurate than its counterpart and outperforms all other existing classical models in terms of time and competence.
Co-Authors Abishek Seshan Geetha Subbiah Manan Rajotia Nitin Asthana Prasanalakshmi Balaji Raghav Sharma Thavavel Vaiyapuri