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- W3204200313 abstract "Novel coronavirus has become a pandemic worldwide since December, 2019. All ongoing plans have been propounded due to COVID-19. Areas with large number of infected and death patients have been taken under lockdown. Continuous locking down areas have created a huge impact on economy especially for the under developed and developing countries. But most of the countries are locking down their areas without any assumption. In this situation, this paper proposes a model to determine which area of a country should be taken under lockdown immediately. The proposed work uses dataset containing the information of different region of the world with their infected, death, recovered patients, and area of the region, population and successfully presence of lockdown. Finally, Machine Learning algorithms have been applied to determine the area to lockdown with the highest accuracy of 95.185%." @default.
- W3204200313 created "2021-10-11" @default.
- W3204200313 creator A5048155806 @default.
- W3204200313 date "2021-01-01" @default.
- W3204200313 modified "2023-09-23" @default.
- W3204200313 title "Noble Machine Learning Approaches for Lock Downing Area during Coronavirus (COVID-19) Pandemic Waves" @default.
- W3204200313 doi "https://doi.org/10.36648/2349-3917.21.9.107" @default.
- W3204200313 hasPublicationYear "2021" @default.
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