Covid-19 Ai Structural Model to Monitor the Multiplicative Nature of Covid-19 Infections
International Journal of Pharmaceutical Sciences and Research
; 14(5):2451-2500, 2023.
Article
in English
| EMBASE | ID: covidwho-2323953
ABSTRACT
In 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.Copyright All © 2023 are reserved by International Journal of Pharmaceutical Sciences and Research.
Full text:
Available
Collection:
Databases of international organizations
Database:
EMBASE
Language:
English
Journal:
International Journal of Pharmaceutical Sciences and Research
Year:
2023
Document Type:
Article
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