TY - JOUR AU - Venkatasen, Maheshwari AU - Mathivanan, Sandeep Kumar AU - Mani, Prasanna AU - Jayagopal, Prabhu AU - P, Thanapal AU - Sorakaya Somanathan, Manivannan AU - Babu K, Upendra AU - D, Elangovan PY - 2021/06/21 Y2 - 2024/03/29 TI - Effectiveness of Contact Tracing Using KNN for COVID-19 JF - Journal of Mobile Multimedia JA - JMM VL - 17 IS - 4 SE - Enabling AI Technologies Towards Multimedia Data Analytics for Smart Healthcare DO - 10.13052/jmm1550-4646.17415 UR - https://journals.riverpublishers.com/index.php/JMM/article/view/6955 SP - 789-808 AB - <p>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.</p> ER -