A Deep Learning technology based covid-19 prediction

Abstract

In the middle of the disease's fast expansion, Coronavirus Disease Detection 2019 (COVID-19) is one of the most pressing worldwide issues. COVID19 has been identified in approximately 1,6 million confirmed cases, according to recent figures, and the illness has spread to several nations throughout the globe. This research looks at the global distribution of COVID-19. With the use of a real-world dataset, we discovered COVID19 patients using an artificial intelligence approach based on a deep coevolutionary neural network (CN N). To identify such patients, our technologies scan chest X-rays. Our results show that this research is effective in diagnosing COVID-19 since X-rays are rapid and affordable. According to empirical data from 1,000 X-ray pictures of actual patients, our suggested approach is effective for COVID 19 identification and achieves an F-measurement range of 95-99%. PropHet (PA), ARIMA, long-term memory (LTM), and the LSM were also used to predict the number of COVID-19 confirmations, recoveries, and deaths in the next seven days….. With an average accuracy of 94.80% in Australia and 88.43% in Jordan, the results of the projections are good in both countries. COVID-19 has been shown to be notably impacted by its spread in coastal regions, with a substantially larger number of cases than in non-coastal areas.