Effectiveness of Contact Tracing Using KNN for COVID-19

Authors

  • Maheshwari Venkatasen School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, TamilNadu, India
  • Sandeep Kumar Mathivanan chool of Information Technology and Engineering, Vellore Institute of Technology, Vellore, TamilNadu, India
  • Prasanna Mani School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, TamilNadu, India https://orcid.org/0000-0002-3107-8017
  • Prabhu Jayagopal School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, TamilNadu, India https://orcid.org/0000-0003-3335-6911
  • Thanapal P School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, TamilNadu, India https://orcid.org/0000-0001-6151-4468
  • Manivannan Sorakaya Somanathan School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, TamilNadu, India
  • Upendra Babu K Computer Science and Engineering, JNN Institute of Engineering, Chennai, TamilNadu, India
  • Elangovan D Computer Science and Engineering, Panimalar Engineering College, Chennai, TamilNadu, India

DOI:

https://doi.org/10.13052/jmm1550-4646.17415

Keywords:

COVID-19, Contact tracing, k- nearest neighbour, World Health Organization (WHO).

Abstract

COVID-19 virus started to outbreak in China in the year January 2020. Contact tracing is an open-minded measure of control that applies to an extensive range of transmissible diseases. It is being used to fight infections like SARS, tuberculosis, smallpox, and many sexually transmitted diseases (STDs). From the moment of the lockdown, there have been a great many talks of applications helping to combat the coronavirus. Technical developers bring a solution to this problem by providing tools that help to contain the coronavirus. This kind of application is helpful, but it lacks in accuracy and privacy concerns. COVID-19 virus, irrespective of causes, solution, treatments, clinical signs, and symptoms is discussed in this paper. The main aim of this paper proposes a contact tracing using k-nearest neighbour, which shows the correct prediction of an affected person of COVID-19 based on the distance and also reduces the transmission of disease. It was tested on the WHO dataset obtained the prediction accuracy of which was carried out on clinical and quarantine data. The evaluation result shows that the contact tracing technique’s accuracy has been improved using the proposed algorithm.

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Author Biographies

Maheshwari Venkatasen, School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, TamilNadu, India

Maheshwari Venkatasen has completed M.S (By Research) in Computer Science from the Vellore institute of technology. Presently she is pursuing a Ph.D. at Vellore Institute of Technology, Vellore, Tamil Nadu. She has published many papers in reputed journals—her current area of research interest in Blockchain security, Software Engineering, Security testing, Machine Learning.

Sandeep Kumar Mathivanan, chool of Information Technology and Engineering, Vellore Institute of Technology, Vellore, TamilNadu, India

Sandeep Kumar Mathivanan received an M.S degree in Software engineering from the Vellore Institute of Technology (VIT), Vellore, India, in 2016, and the M.Tech (By research) degree from VIT in 2020, where he is currently pursuing the Ph.D. degree with the School of Information Technology and Engineering. His current research interests include Machine learning, Big data analytics, Recommender systems, Virtualization, Blockchain. He is the author/co-author of papers in International Journals, Conferences, and book chapters.

Prasanna Mani, School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, TamilNadu, India

Prasanna Mani has completed his M.S. in Computer Science from Anna University. He received his doctorate in Software Engineering from Anna University. Presently he is working as Associate Professor in Vellore Institute of Technology Vellore (Deemed to be University), Vellore, Tamil Nadu. He has published nearly 30 papers in various national and international journals. He is guiding Research scholars in the area of Software testing and is an eminent reviewer of various international journals. He also authored a book for cracking interview questions of C programming. His area of interest includes Software Engineering, Software Testing, and the Internet of Things.

Prabhu Jayagopal, School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, TamilNadu, India

Prabhu Jayagopal received the bachelor’s degree in Information Technology from the University of Madras Under Vellore Engineering College, Vellore, India, in 2004, the master’s degree in Computer science and engineering from Sathyabama University, Chennai, India, in 2007, and the Ph.D. degree in Computer Science and Engineering from Sathyabama University, Chennai, India in 2015. He worked as an Assistant Professor in various Engineering colleges for more than 14 years. Now he is working as an Associate Professor in the School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, from 2009 to date. He has published in National, International journals and conferences. He is also involved in collaborative research projects with various national and international level organizations and research institutions. His research interests are software testing, Machine Learning, Deep Learning, and Big data.

Thanapal P, School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, TamilNadu, India

Thanapal P received his B.E degree in Computer Science and Engineering from Madurai Kamaraj University, Madurai India in 1998, M.E degree in Computer Science and Engineering from Anna University Chennai, India, in 2005, and a Ph.D. degree from Vellore Institute of Technology University Vellore, India. He has published more than 30 research papers in reputed international journals and conferences. His main research interests include cloud computing, mobile cloud computing, wireless network, and IoT.

Manivannan Sorakaya Somanathan, School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, TamilNadu, India

Manivannan Sorakaya Somanathan is working as Associate Professor in the Information Technology Department at VIT University, Vellore. He has completed a Ph.D. in the domain of network and information security in the School of Information Technology and Engineering, VIT University, Vellore. Also, he has B.E and M.E degrees in the area of Computer Science and Engineering at the University of Madras and Anna University, Chennai, respectively. He has 15 years of experience and standard publications in reputed journals.

Upendra Babu K, Computer Science and Engineering, JNN Institute of Engineering, Chennai, TamilNadu, India

Upendra Babu K received his bachelor’s degree in Computer science and Engineering from Karnataka University, Dharwad, Karnataka, under Rural Engineering college, Hulkoti, Gadag, Karnataka in 2000, and master’s degree also in Computer science and Engineering from Dr. M.G.R. University, Chennai, India, in 2011, and is pursuing his Ph.D. degree in Computer Science and Engineering at Manonmaniam Sundaranar University, Tirunelveli, India. He has over 17 years of experience as an Assistant Professor in various Engineering colleges. He is currently pursuing a Ph.D. He has had several articles published in national and international journals. He also participates in collaborative research projects with several national and international organizations and research institutions. Data Mining, Machine Learning, Deep Learning, Big Data, Artificial Intelligence, and Image Processing are among his research interests.

Elangovan D, Computer Science and Engineering, Panimalar Engineering College, Chennai, TamilNadu, India

Elangovan D is working as an Associate Professor in Computer Science and Engineering Department at Panimalar Engineering College, Chennai. He has teaching experience of more than two decades. He has published many papers in international journals and conferences. He has filed few patents also. He actively took part in conducting conferences, FDPs, and seminars.

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Published

2021-06-21

How to Cite

Venkatasen, M., Mathivanan, S. K., Mani, P., Jayagopal, P., P, T., Sorakaya Somanathan, M., Babu K, U., & D, E. (2021). Effectiveness of Contact Tracing Using KNN for COVID-19. Journal of Mobile Multimedia, 17(4), 789–808. https://doi.org/10.13052/jmm1550-4646.17415

Issue

2021: Vol 17 Iss 4

Section

Enabling AI Technologies Towards Multimedia Data Analytics for Smart Healthcare