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• W4281550340 endingPage "6373" @default.
• W4281550340 startingPage "6373" @default.
• W4281550340 abstract "The COVID-19 pandemic raises awareness of how the fatal spreading of infectious disease impacts economic, political, and cultural sectors, which causes social implications. Across the world, strategies aimed at quickly recognizing risk factors have also helped shape public health guidelines and direct resources; however, they are challenging to analyze and predict since those events still happen. This paper intends to invesitgate the association between air pollutants and COVID-19 confirmed cases using Deep Learning. We used Delhi, India, for daily confirmed cases and air pollutant data for the dataset. We used LSTM deep learning for training the combination of COVID-19 Confirmed Case and AQI parameters over the four different lag times of 1, 3, 7, and 14 days. The finding indicates that CO is the most excellent model compared with the others, having on average, 13 RMSE values. This was followed by pressure at 15, PM2.5 at 20, NO2 at 20, and O3 at 22 error rates." @default.
• W4281550340 created "2022-05-27" @default.
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• W4281550340 date "2022-05-24" @default.
• W4281550340 modified "2023-09-25" @default.
• W4281550340 title "In the Seeking of Association between Air Pollutant and COVID-19 Confirmed Cases Using Deep Learning" @default.
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• W4281550340 doi "https://doi.org/10.3390/ijerph19116373" @default.
• W4281550340 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35681961" @default.
• W4281550340 hasPublicationYear "2022" @default.
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