Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations’ COVID-19 Pandemic
TL;DR: In this article , the authors investigated and predicted the future environmental circumstances of a COVID-19 to minimize its effects using artificial intelligence techniques using machine learning, deep learning, and time series models.
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Abstract: In the paper, the authors investigated and predicted the future environmental circumstances of a COVID-19 to minimize its effects using artificial intelligence techniques. The experimental investigation of COVID-19 instances has been performed in ten countries, including India, the United States, Russia, Argentina, Brazil, Colombia, Italy, Turkey, Germany, and France using machine learning, deep learning, and time series models. The confirmed, deceased, and recovered datasets from January 22, 2020, to May 29, 2021, of Novel COVID-19 cases were considered from the Kaggle COVID dataset repository. The country-wise Exploratory Data Analysis visually represents the active, recovered, closed, and death cases from March 2020 to May 2021. The data are pre-processed and scaled using a MinMax scaler to extract and normalize the features to obtain an accurate prediction rate. The proposed methodology employs Random Forest Regressor, Decision Tree Regressor, K Nearest Regressor, Lasso Regression, Linear Regression, Bayesian Regression, Theilsen Regression, Kernel Ridge Regressor, RANSAC Regressor, XG Boost, Elastic Net Regressor, Facebook Prophet Model, Holt Model, Stacked Long Short-Term Memory, and Stacked Gated Recurrent Units to predict active COVID-19 confirmed, death, and recovered cases. Out of different machine learning, deep learning, and time series models, Random Forest Regressor, Facebook Prophet, and Stacked LSTM outperformed to predict the best results for COVID-19 instances with the lowest root-mean-square and highest R2 score values.
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A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)
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TL;DR: The results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis.
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Time Series Forecasting of COVID-19 transmission in Canada Using LSTM Networks.
TL;DR: The Long short-term memory (LSTM) networks, a deep learning approach to forecast the future COVID-19 cases are presented, which predicted the possible ending point of this outbreak will be around June 2020 and compared transmission rates of Canada with Italy and USA.
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Shuai Wang,Bo Kang,Jinlu Ma,Xianjun Zeng,Mingming Xiao,Jia Guo,Mengjiao Cai,Jingyi Yang,Yaodong Li,Xiangfei Meng,Bo Xu,Bo Xu +11 more
TL;DR: In this article, a deep learning algorithm was used to detect the presence of COVID-19 in CT images during the 2015-2016 influenza season, achieving an accuracy of 89.5% with a specificity of 0.88 and sensitivity of 0.87.