COVID-19 detection and tracking using smart applications with artificial intelligence

Abstract

Corona Virus Disease 2019 (COVID-19), a newly identified pandemic infection, threatened human life, and disrupted the entire world. Identifying and detecting this pathogenic virus is made essential as it is increasing the mortality rate day by day. In this scenario, alternative technologies play a vital role in monitoring, detecting and diagnosing the disease by deploying smart applications. Today smart applications are incorporated with AI techniques in detecting and monitoring the spread of infection. The proposed work is contributed with multilayer perceptron (MLP) techniques integrating the artificial neural network (ANN) model for extracting COVID-19. The model is equipped with a normalisation process deploying Gaussian process regression (GPR) and radial based function (RBF) for detecting the noise level. The proposed work exploits the publicly available COVID-19 datasets of July month from GitHub and Kaggle. The AI model is measured using the performance metrics in terms of Precision, Recall, F-Measure and Accuracy and MLP model produces higher accuracy.