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- W3201633891 abstract "Coronavirus is a respiratory disease which is produced by the virus SARS-CoV-2. The symptoms of the coronavirus include flu, sore throat, common cold, cough, fatigue, and shortness of oxygen in the body. COVID-19 is a pandemic that spreads across the world. In this paper, we presented a model to analyze the trend of the coronavirus. We used artificial intelligence analytics including linear regression, support vector machine, auto regressive integrated moving average, and long short-term memory methods on the real data of coronavirus by using Python 3.0 (Jupyter Notebook). The prediction shows cumulated trend prediction of confirmed cases. Linear regression and ARIMA model give the best results with maximum accuracy (98% and 99.87% respectively) and performed better with existing to counter parts. Later, we will develop specialized deep learning models, including multi-perceptron and autoencoders for trend prediction with better accuracy." @default.
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- W3201633891 date "2021-09-20" @default.
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- W3201633891 title "Assessing Spatiotemporal Transmission Dynamics of COVID-19 Outbreak Using AI Analytics" @default.
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- W3201633891 doi "https://doi.org/10.1007/978-981-16-3346-1_67" @default.
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