A deep learning algorithm for modeling and forecasting of COVID-19 in five worst affected states of India

In this paper, deep learning is employed to propose an Artificial Neural Network (ANN) based online incremental learning technique for developing an adaptive and non-intrusive analytical model of Covid-19 pandemic to analyze the temporal dynamics of the disease spread. The model is able to intellige...

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Main Authors: Junaid Farooq, Mohammad Abid Bazaz
Format: Article
Language:English
Published: Elsevier 2021-02-01
Series:Alexandria Engineering Journal
Subjects:
ANN
Online Access:http://www.sciencedirect.com/science/article/pii/S1110016820304907
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spelling doaj-012b063e3bcc41b1b737f9d49f67b3662021-06-02T15:22:34ZengElsevierAlexandria Engineering Journal1110-01682021-02-01601587596A deep learning algorithm for modeling and forecasting of COVID-19 in five worst affected states of IndiaJunaid Farooq0Mohammad Abid Bazaz1Corresponding author.; Department of Electrical Engineering, National Institute of Technology Srinagar, IndiaDepartment of Electrical Engineering, National Institute of Technology Srinagar, IndiaIn this paper, deep learning is employed to propose an Artificial Neural Network (ANN) based online incremental learning technique for developing an adaptive and non-intrusive analytical model of Covid-19 pandemic to analyze the temporal dynamics of the disease spread. The model is able to intelligently adapt to new ground realities in real-time eliminating the need to retrain the model from scratch every time a new data set is received from the continuously evolving training data. The model is validated with the historical data and a forecast of the disease spread for 30-days is given in the five worst affected states of India.http://www.sciencedirect.com/science/article/pii/S1110016820304907Covid-19Incremental learningANNForecasting
collection DOAJ
language English
format Article
sources DOAJ
author Junaid Farooq
Mohammad Abid Bazaz
spellingShingle Junaid Farooq
Mohammad Abid Bazaz
A deep learning algorithm for modeling and forecasting of COVID-19 in five worst affected states of India
Alexandria Engineering Journal
Covid-19
Incremental learning
ANN
Forecasting
author_facet Junaid Farooq
Mohammad Abid Bazaz
author_sort Junaid Farooq
title A deep learning algorithm for modeling and forecasting of COVID-19 in five worst affected states of India
title_short A deep learning algorithm for modeling and forecasting of COVID-19 in five worst affected states of India
title_full A deep learning algorithm for modeling and forecasting of COVID-19 in five worst affected states of India
title_fullStr A deep learning algorithm for modeling and forecasting of COVID-19 in five worst affected states of India
title_full_unstemmed A deep learning algorithm for modeling and forecasting of COVID-19 in five worst affected states of India
title_sort deep learning algorithm for modeling and forecasting of covid-19 in five worst affected states of india
publisher Elsevier
series Alexandria Engineering Journal
issn 1110-0168
publishDate 2021-02-01
description In this paper, deep learning is employed to propose an Artificial Neural Network (ANN) based online incremental learning technique for developing an adaptive and non-intrusive analytical model of Covid-19 pandemic to analyze the temporal dynamics of the disease spread. The model is able to intelligently adapt to new ground realities in real-time eliminating the need to retrain the model from scratch every time a new data set is received from the continuously evolving training data. The model is validated with the historical data and a forecast of the disease spread for 30-days is given in the five worst affected states of India.
topic Covid-19
Incremental learning
ANN
Forecasting
url http://www.sciencedirect.com/science/article/pii/S1110016820304907
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