COVID-19 Analysis by Using Machine and Deep Learning

Yadav, Dharminder; Maheshwari, Himani; Chandra, Umesh; Sharma, Avinash

doi:10.1007/978-981-15-8097-0_2

Cited by 4 publications

(4 citation statements)

References 38 publications

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“…Fbprophet utilizes time as a regressor and attempts to fit multiple linear/nonlinear time functions as components. FbProphet will provide the data using a linear model by default, but it may be modified to a nonlinear model (logistics growth) using its parameters [ 24 ]; XGBoost is an implementation of Gradient boosted Decision Trees (GDTs) designed for both high-performance and domination [ 25 ]; Tensor Flow —Tensor Flow is an open-source framework that processes datasets arranged as computational graph nodes. Keras is a Python-based open-source software framework that provides an artificial neural network interface.…”

Section: Methodsmentioning

confidence: 99%

“…Equations ( 2 ), ( 3 ) display the formulas. The last parameter, the week of days, is calculated by importing the library named WEEKOFYEAR [ 24 ] …”

Section: Methodsmentioning

confidence: 99%

“…The time series model is built on a prophet, and it is fast, fully automated, and very exact. The trend, seasonality, and holidays form our time series model, which we break down into three key components: trend, seasonality, and holidays [ 24 ]. They are merged in Eq.…”

Section: Methodsmentioning

confidence: 99%

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Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations’ COVID-19 Pandemic

et al. 2022

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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 R 2 score values.

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Section: Methodsmentioning

confidence: 99%

“…Equations ( 2 ), ( 3 ) display the formulas. The last parameter, the week of days, is calculated by importing the library named WEEKOFYEAR [ 24 ] …”

Section: Methodsmentioning

confidence: 99%

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Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations’ COVID-19 Pandemic

et al. 2022

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“…Sujath et al [25] designed a machine learning forecasting model where linear regression (LR), multilayer perceptron (MLP), and vector autoregression model models were performed on COVID-19 Kaggle data to anticipate case loads in India. Yadav et al [26] employed machine learning models such as support vector machine (SVM), naïve Bayes (NB), LR, decision tree (DT), random forest (RF), prophet algorithm and long short-term memory (LSTM) to predict COVID-19 cases in different countries. Also, Rustam et al [6] used different models such as LR, least absolute shrinkage and selection operator (LASSO), SVM and Exponential Smoothing (ES) to predict harmful factors promoting COVID-19 spread.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Prediction of COVID-19 Cases Using Machine Learning Models

et al. 2021

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The first case in Bangladesh of the novel coronavirus disease (COVID-19) was reported on 8 March 2020, with the number of confirmed cases rapidly rising to over 175,000 by July 2020. In the absence of effective treatment, an essential tool of health policy is the modeling and forecasting of the progress of the pandemic. We, therefore, developed a cloud-based machine learning short-term forecasting model for Bangladesh, in which several regression-based machine learning models were applied to infected case data to estimate the number of COVID-19-infected people over the following seven days. This approach can accurately forecast the number of infected cases daily by training the prior 25 days sample data recorded on our web application. The outcomes of these efforts could aid the development and assessment of prevention strategies and identify factors that most affect the spread of COVID-19 infection in Bangladesh.

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