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.
ABSTRACT
In the Artificial intelligence (AI) field, intelligent social awareness is a quantifiable analysis that interacts with humans socially with other infected or non-infected COVID-19 (CoV19) humans. However, less importance is given in this direction. Clinically, there is a need for a social-awareness automated model design to quantify the self-awareness of infected patients and develop a social learning system. In this research paper, a new model of self-aware internal learning coronavirus 19 (SIntL-CoV19) model technique is presented with quantification measures to represent model requirements as an individual self-aware automated detection. Through this model, a human can communicate with the social environment and other humans with an accurate CoV19 infection diagnosis. SIntL-CoV19 model framework for implementation of self-aware architecture with this model is proposed making the diagnosis process compared with the existing architecture. The proposed model achieves improved accuracy Feature Classifier, which outperforms other learning algorithms for CoV19 and normal scans. The data from the investigation show that the proposed SIntL-CoV19 model method might be more effective than other methods. © 2023 Peniero Tupas et al.