Recognition and Investigation of Covid-19 with Artificial Intelligence and Deep Learning Approaches

Abstract

Machine learning (ML)-based forecasting techniques have demonstrated their use in predicting perioperative outcomes and improving decision-making about future actions. Many application domains that required the detection and prioritization of adverse aspects of a threat have long used machine learning models. To deal with forecasting challenges, a variety of prediction approaches are widely utilized. This investigation illustrates the ability of machine learning models to forecast the number of patients who would be afflicted by COVID-19, which is now regarded as a possible threat to humanity. In this work, four conventional forecasting models were utilized to forecast the dangerous situation: linear regression (LR), least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and exponential smoothing (ES).