A Review of the Machine Learning Algorithms for Covid-19 Case Analysis DOI Open Access
Shrikant Tiwari, Prasenjit Chanak, Sanjay Kumar Singh

и другие.

IEEE Transactions on Artificial Intelligence, Год журнала: 2022, Номер 4(1), С. 44 - 59

Опубликована: Янв. 11, 2022

The purpose of this article is to see how machine learning (ML) algorithms and applications are used in the COVID-19 inquiry for other purposes. available traditional methods international epidemic prediction, researchers authorities have given more attention simple statistical epidemiological methodologies. inadequacy absence medical testing diagnosing identifying a solution one key challenges preventing spread COVID-19. A few statistical-based improvements being strengthened answer challenge, resulting partial resolution up certain level. ML advocated wide range intelligence-based approaches, frameworks, equipment cope with issues industry. application inventive structure, such as handling relevant outbreak difficulties, has been investigated article. major goal 1) Examining impact data type nature, well obstacles processing 2) Better grasp importance intelligent approaches like pandemic. 3) development improved types prognosis. 4) effectiveness influence various strategies 5) To target on potential diagnosis order motivate academics innovate expand their knowledge research into additional COVID-19-affected industries.

Язык: Английский

Using a genetic algorithm to fit parameters of a COVID-19 SEIR model for US states DOI Open Access
Peter Yarsky

Mathematics and Computers in Simulation, Год журнала: 2021, Номер 185, С. 687 - 695

Опубликована: Фев. 15, 2021

Язык: Английский

Процитировано

64
Deep learning model for forecasting COVID-19 outbreak in Egypt DOI Creative Commons
Mohamed Marzouk, Nehal Elshaboury, Amr Abdellatif

и другие.

Process Safety and Environmental Protection, Год журнала: 2021, Номер 153, С. 363 - 375

Опубликована: Июль 24, 2021

The World Health Organization has declared COVID-19 as a global pandemic in early 2020. A comprehensive understanding of the epidemiological characteristics this virus is crucial to limit its spreading. Therefore, research applies artificial intelligence-based models predict prevalence outbreak Egypt. These are long short-term memory network (LSTM), convolutional neural network, and multilayer perceptron network. They trained validated using dataset records from 14 February 2020 15 August results evaluated determination coefficient root mean square error. LSTM model exhibits best performance forecasting cumulative infections for one week month ahead. Finally, with optimal parameter values applied forecast spread epidemic ahead data 30 June 2021. total size infections, recoveries, deaths estimated be 285,939, 234,747, 17,251 cases on 31 July This study could assist decision-makers developing monitoring policies confront disease.

Язык: Английский

Процитировано

63
Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review DOI Creative Commons
Soudeh Ghafouri‐Fard, Hossein Mohammad‐Rahimi, Parisa Motie

и другие.

Heliyon, Год журнала: 2021, Номер 7(10), С. e08143 - e08143

Опубликована: Окт. 1, 2021

COVID-19 has produced a global pandemic affecting all over of the world. Prediction rate spread and modeling its course have critical impact on both health system policy makers. Indeed, making depends judgments formed by prediction models to propose new strategies measure efficiency imposed policies. Based nonlinear complex nature this disorder difficulties in estimation virus transmission features using traditional epidemic models, artificial intelligence methods been applied for spread. importance machine deep learning approaches spreading trend, present study, we review studies which used these predict number cases COVID-19. Adaptive neuro-fuzzy inference system, long short-term memory, recurrent neural network multilayer perceptron are among mostly regard. We compared performance several Root means squared error (RMSE), mean absolute (MAE), R

Язык: Английский

Процитировано

61
Temporal deep learning architecture for prediction of COVID-19 cases in India DOI Open Access
Hanuman Verma, Saurav Mandal, Akshansh Gupta

и другие.

Expert Systems with Applications, Год журнала: 2022, Номер 195, С. 116611 - 116611

Опубликована: Фев. 5, 2022

Язык: Английский

Процитировано

55
A Review of the Machine Learning Algorithms for Covid-19 Case Analysis DOI Open Access
Shrikant Tiwari, Prasenjit Chanak, Sanjay Kumar Singh

и другие.

IEEE Transactions on Artificial Intelligence, Год журнала: 2022, Номер 4(1), С. 44 - 59

Опубликована: Янв. 11, 2022

The purpose of this article is to see how machine learning (ML) algorithms and applications are used in the COVID-19 inquiry for other purposes. available traditional methods international epidemic prediction, researchers authorities have given more attention simple statistical epidemiological methodologies. inadequacy absence medical testing diagnosing identifying a solution one key challenges preventing spread COVID-19. A few statistical-based improvements being strengthened answer challenge, resulting partial resolution up certain level. ML advocated wide range intelligence-based approaches, frameworks, equipment cope with issues industry. application inventive structure, such as handling relevant outbreak difficulties, has been investigated article. major goal 1) Examining impact data type nature, well obstacles processing 2) Better grasp importance intelligent approaches like pandemic. 3) development improved types prognosis. 4) effectiveness influence various strategies 5) To target on potential diagnosis order motivate academics innovate expand their knowledge research into additional COVID-19-affected industries.

Язык: Английский

Процитировано

54