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Comparison of existing works on COVID-19 prediction methodologies with the proposed work.
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Comparison of existing works on COVID-19 prediction methodologies with the proposed work.
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Comparison of existing works on COVID-19 prediction methodologies with the proposed work.
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Comparison of existing works on COVID-19 prediction methodologies with the proposed work.

Journal: Results in Physics

Article Title: Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant?

doi: 10.1016/j.rinp.2021.103817

Figure Lengend Snippet: Comparison of existing works on COVID-19 prediction methodologies with the proposed work.

Article Snippet: Car et al. , Multilayer Perceptron (MLP) artificial neural network (ANN) , 30 days ahead forecast , 22nd January 2020 to 12th March 2020 Infected, recovered and deceased data , Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) and supported by ESRI Living Atlas Team and the Johns Hopkins University Applied Physics Lab (JHU APL) , Higher accuracy for confirmed cases with 0.986R 2 Value , To predict the spread of pandemic world-wide..

Techniques: Comparison, Control, Plasmid Preparation, Infection, Virus