LSTM and RNN to Predict COVID Cases: Lethality’s and Tests in GCC Nations and India
| Autor: | A. Razia Sulthana, A K Jaithunbi, A. Arokiaraj Jovith |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
education.field_of_study
2019-20 coronavirus outbreak Population count Coronavirus disease 2019 (COVID-19) business.industry Computer science Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Mean squared prediction error Deep learning Population Recurrent neural network Statistics Artificial intelligence Safety Risk Reliability and Quality business education |
| Zdroj: | International Journal of Performability Engineering. 17:299 |
| ISSN: | 0973-1318 |
| DOI: | 10.23940/ijpe.21.03.p5.299306 |
| Popis: | The spread of COVID across world countries is better handled by applying learning algorithms Machine learning and deep learning algorithms can be applied to analyze the effects of COVID in multidimensional ways This paper brings a detailed study of the COVID cases, deaths and tests across five of the GCC countries and India The proposed method analyzes the COVID count against the population density of each of the countries An analysis of the raw count would only give a false impression, whereas a population-based comparison gives the exact measure of the effect of COVID As India is a densely populated country, the number of precautionary steps taken by the country against the population count needs to be measured for accurate prediction Recurrent Neural Network and Long Short-Term memory are used to predict the future cases, deaths and tests of India A time span of 20 days is used in the prediction In the sense that ith day to (i+20)th day values are taken to predict the (i+21)th day values The accuracy of the LSTM model designed with multiple hidden layers is notable and the prediction error is minimal © 2021 Totem Publishers Ltd All rights reserved |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|