COVID-19 AI STRUCTURAL MODEL TO MONITOR THE MULTIPLICATIVE NATURE OF COVID-19 INFECTIONS
AbstractIn the present COVID-19 situation, it poses a danger to a person’s life because of organ infection and other health problems. It is mandatory to research work to find a better COVID-19 infection diagnosis method through scans and contact tracing through the AI method. In this, a novel AI structural model is intended to identify the infection features in the respective regions of human being availability, which makes the infection monitoring easier to identify an infected and non-infected human being from the population identified. The method used for monitoring the multiplicative nature of Coronavirus infections is through contact feature tracing and infection confirmation status and confirms the Coronavirus cases from scans and feature analysis to include real-time contact tracking from the same region and distant regions, providing an efficient method to track the infection spread. The anticipated model is used to forecast coronavirus transmission using feature forecasting data. The performance assessment is compared based on the outcomes of the suggested model and shows an enhanced COVID-19 diagnostic model.
Article Information
43
2491-2500
1094 KB
278
English
IJPSR
V. Kakulapati * and A. Jayanthiladevi
Institute of Computer Science and Information Science, Srinivas University, Mangaluru, Karnataka, India.
vldms@yahoo.com
12 January 2023
27 March 2023
18 April 2023
10.13040/IJPSR.0975-8232.14(5).2491-00
01 May 2023
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