Artificial neural networks for prediction of COVID‐19 in India by using backpropagation

Abstract

The COVID‐19 pandemic has affected thousands of people around the world. In this study, we used artificial neural network (ANN) models to forecast the COVID‐19 outbreak for policymakers based on 1st January to 31st October 2021 of positive cases in India. In the confirmed cases of COVID‐19 in India, it's critical to use an estimating model with a high degree of accuracy to get a clear understanding of the situation. Two explicit mathematical prediction models were used in this work to anticipate the COVID‐19 epidemic in India. A Boltzmann Function‐based model and Beesham's prediction model are among these methods and also estimated using the advanced ANN‐BP models. The COVID‐19 information was partitioned into two sections: training and testing. The former was utilized for training the ANN‐BP models, and the latter was used to test them. The information examination uncovers critical day‐by‐day affirmed case changes, yet additionally unmistakable scopes of absolute affirmed cases revealed across the time span considered. The ANN‐BP model that takes into consideration the preceding 14‐days outperforms the others based on the archived results. In forecasting the COVID‐19 pandemic, this comparison provides the maximum incubation period, in India. Mean square error, and mean absolute percent error have been treated as the forecast model performs more accurately and gets good results. In view of the findings, the ANN‐BP model that considers the past 14‐days for the forecast is proposed to predict everyday affirmed cases, especially in India that have encountered the main pinnacle of the COVID‐19 outbreak. This work has not just demonstrated the relevance of the ANN‐BP techniques for the expectation of the COVID‐19 outbreak yet additionally showed that considering the incubation time of COVID‐19 in forecast models might produce more accurate assessments.

Counting the uncounted : estimating the unaccounted COVID-19 infections in India

“…These methods are useful when the pandemic datasets are very large and have relatively less complex and interlinked parameters. In contrast to this ANN-based methods can handle extremely complex interlinked data, requiring large number of underlying differential equations [37,44,50]. While various ANN-based methods can be applied to many different stages of the modelling, the PINN approach is used to optimise the unknown interlinked parameters which are used in the underlying model.…”

Finite‐element‐based machine‐learning algorithm for studying gyrotactic‐nanofluid flow via stretching surface

“…The phenomenon reported by Masuda et al as the defining property of nanofluids is thermal conductivity augmentation 9 . Numerous studies appeared to show the beginning of the motile bacteria in nanofluid flow 10 . In contrast to how microbes spread, the nanoparticles are not self‐propelled; instead, Brownian motion and thermophoresis cause their migration.…”

“…9 Numerous studies appeared to show the beginning of the motile bacteria in nanofluid flow. 10 In contrast to how microbes spread, the nanoparticles are not self-propelled; instead, Brownian motion and thermophoresis cause their migration. The less dense microbes sink into the lighter fluid, where they swim to the surface.…”

“…So, it is crucial to use Machine Learning-based numerical computation paradigms due to its validity and effectiveness. Some authors already applied these stochastic AI-based numerical techniques in different fields such as COVID-19 modeling, 10,29,30 nanofluid model, 31 HIV infection model, 32 nonlinear corneal shape model, 33 astrophysics, 34 and finance. 35 The Levenberg-Marquardt scheme is utilized in the recent study by M. Shoaib 36 to investigate the twofold diffusion of nanofluid.…”

A hybrid machine learning algorithm for studying magnetized nanofluid flow containing gyrotactic microorganisms via a vertically inclined stretching surface

“…However, machine learning-based numerical computing paradigms are significant because of their effectiveness, worthiness, and applicability in a variety of sectors. Such stochastic numerical techniques have already been used by certain writers in a variety of fields, including plasma physics, 28 astrophysics, 29 thermodynamics, 30 nanofluid model, 31,32 COVID-19 models, [33][34][35] HIV infection model, 36 and nonlinear corneal shape model. 37 These motivating elements encourage academics to use a consistent and accurate machine learning-based numerical computational paradigm for the numerical analysis of various fluid flow models.…”