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Image Search Results


Comparison of existing works on COVID-19 prediction methodologies with the proposed work.

Journal: Results in Physics

Article Title: Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant?

doi: 10.1016/j.rinp.2021.103817

Figure Lengend Snippet: Comparison of existing works on COVID-19 prediction methodologies with the proposed work.

Article Snippet: Car et al. , Multilayer Perceptron (MLP) artificial neural network (ANN) , 30 days ahead forecast , 22nd January 2020 to 12th March 2020 Infected, recovered and deceased data , Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) and supported by ESRI Living Atlas Team and the Johns Hopkins University Applied Physics Lab (JHU APL) , Higher accuracy for confirmed cases with 0.986R 2 Value , To predict the spread of pandemic world-wide..

Techniques: Comparison, Control, Plasmid Preparation, Infection, Virus

Supervised and un-supervised machine learning for analyzing the COVID-19 disease that included articles with the related details of the Dataset, author name, country of publication, year of publication, the used method in the study, and their results

Journal: Applied Nanoscience

Article Title: Coronavirus disease (COVID-19) cases analysis using machine-learning applications

doi: 10.1007/s13204-021-01868-7

Figure Lengend Snippet: Supervised and un-supervised machine learning for analyzing the COVID-19 disease that included articles with the related details of the Dataset, author name, country of publication, year of publication, the used method in the study, and their results

Article Snippet: 14 , Abolfazl et al. ( ) , 2020 , USA , US Centers for Disease and Control and Johns Hopkins University. Database of 57 candidate , Supervised learning , Classification Artificial Neural Networks (ANN) , Results showed that the presented model (logistic regression) shown that these factors and variables describe the presence/absence of the hotspot of the COVID-19 incidence which was clarified by Getis-Ord Gi ( p < 0.05) in a geographic information system. As a result, the findings provided valuable insights for public health decision makers in categorizing the effect of the potential risk factors associated with COVID-19 incidence level.

Techniques: Diagnostic Assay, Modification, Software, Cell Counting, Biomarker Discovery, Control

Comparative study of existing works on COVID-19 prediction methodologies.

Journal: Frontiers in Artificial Intelligence

Article Title: COVID-19 and beyond: leveraging artificial intelligence for enhanced outbreak control

doi: 10.3389/frai.2023.1266560

Figure Lengend Snippet: Comparative study of existing works on COVID-19 prediction methodologies.

Article Snippet: Car et al. ( ) , Multilayer perceptron (MLP) artificial neural network (ANN) , 30 days ahead , JHU CSSE, Environmental Systems Research Institute (ESRI) Living Atlas Team, JHU Applied Physics Laboratory (APL) , Higher level of accuracy for reported cases.

Techniques: Control, Plasmid Preparation