Mendeley
TY  _  JOUR
ID  -  2022022334246
TI  -  An Analysis of Covid-19 Pandemic Outbreak on Economy using Neural Network and Random Forest
JO  -  Journal of Information Systems and Telecommunication (JIST) 
JA  -  ES
LA  -  en
SN  -  2322-1437
AU  -  Hasan Md. Nahid 
AU  -  Ahmed Tanvir
AU  -  Ashik Md 
AU  -  Hasan Md. Jahid 
AU  -   Azmin Tahaziba
AU  -  Uddin Jia
AD  -  Woosong University
AD  -  Brac University
AD  -  Brac University
AD  -  Brac University
AD  -  Brac University
AD  -  Brac University
Y1  -  2023
PY  -  2023
VL  -  42
IS  -  11
SP  -  163
EP  -  175
KW  -  Multi-layer perceptron (MLP)
KW  -   Long Short-Term Memory (LSTM)
KW  -   Random Forest
KW  -   Economic Recession
KW  -   Machine learning (ML)
KW  -   Covid-19
DO  -  
N2  -  The pandemic disease outbreaks are causing a significant financial crisis affecting the worldwide economy. Machine learning techniques are urgently required to detect, predict and analyze the economy for early economic planning and growth. Consequently, in this paper, we use machine learning classifiers and regressors to construct an early warning model to tackle economic recession due to the cause of covid-19 pandemic outbreak. A publicly available database created by the National Bureau of Economic Research (NBER) is used to validate the model, which contains information about national revenue, employment rate, and workers' earnings of the USA over 239 days (1 January 2020 to 12 May 2020). Different techniques such as missing value imputation, k-fold cross validation have been used to pre-process the dataset. Machine learning classifiers- Multi-layer Perceptron- Neural Network (MLP-NN) and Random Forest (RF) have been used to predict recession. Additionally, machine learning regressors-Long Short-Term Memory (LSTM) and Random Forest (RF) have been used to detect how much recession a country is facing as a result of positive test cases of covid-19 pandemic. Experimental results demonstrate that the MLP-NN and RF classifiers have exhibited average 88.33% and 85% of recession (where 95%, 81%, 89% and 85%, 81%, 89% for revenue, employment rate and workers earnings, respectively) and average 90.67% and 93.67% of prediction accuracy for LSTM and RF regressors (where 92%, 90%, 90%, and 95%, 93%, 93% respectively).
UR  - rimag.ir/fa/Article/34246
L1  - rimag.ir/fa/Article/Download/34246
ER  -  
