Artificial intelligence computing analysis of fractional order COVID-19 epidemic model

Artificial intelligence plays a very prominent role in many fields, and of late, this term has been gaining much more popularity due to recent advances machine learning. Machine learning is sphere artificial where machines are responsible for doing daily chores believed be intelligent than humans. Furthermore, significant behavioral, social, physical, biological engineering, biomathematical sciences, disciplines. Fractional-order modeling real-world problem powerful tool understanding the dynamics problem. In study, an investigation into fractional-order epidemic model novel coronavirus (COVID-19) presented using computing through Bayesian-regularization backpropagation networks (BRBFNs). The designed BRBFNs exploited predict transmission COVID-19 disease by taking dataset from fractional numerical method based on Grünwald–Letnikov backward finite difference. datasets mathematical Wuhan Karachi metropolitan cities trained with biased unbiased input target values. proposed technique (BRBFNs) implemented estimate integer spread dynamics. Its reliability, effectiveness, validation verified consistently achieved accuracy metrics that depend error histograms, regression studies, mean squared error.