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Expecting confirmed and death cases of covid-19 in Iraq by utilizing backpropagation neural network
- Source :
- Bulletin of Electrical Engineering and Informatics. 10:2137-2143
- Publication Year :
- 2021
- Publisher :
- Institute of Advanced Engineering and Science, 2021.
-
Abstract
- The world is currently facing a strong epidemic and pandemic of coronavirus. This motivates establishing our paper, where this virus pushes researchers to study, investigate, observe, analyse and try solving its related issues. In this work, an artificial neural network (ANN) model of backpropagation neural network (BNN) with two hidden layers is proposed for expecting confirmed cases and death cases of coronavirus disease 2019 (covid-19). As a field of study, Iraq country has been considered in this paper. Covid-19 dataset from our world in data (OWID) is used here. Promising result is achieved where a very small error value of 0.0035 is reported in overall the evaluations. This paper may implicate establishing further researches that consider other parameters and other countries over the world. It is worth mentioning that the suggested ANN model may help decision maker people in taking quarantine movements against the strong epidemic and pandemic of covid-19.
- Subjects :
- 2019-20 coronavirus outbreak
Control and Optimization
Coronavirus disease 2019 (COVID-19)
Computer Networks and Communications
Computer science
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
Machine learning
computer.software_genre
Field (computer science)
Pandemic
Computer Science (miscellaneous)
Electrical and Electronic Engineering
Instrumentation
Backpropagation neural network
Artificial neural network
business.industry
Decision maker
Backpropagation
Hardware and Architecture
Control and Systems Engineering
Artificial intelligence
Covid-19
Prediction
business
computer
Information Systems
Subjects
Details
- ISSN :
- 23029285 and 20893191
- Volume :
- 10
- Database :
- OpenAIRE
- Journal :
- Bulletin of Electrical Engineering and Informatics
- Accession number :
- edsair.doi.dedup.....b325f68a82d01dd5aa597fccbd97f4c6