TY - JOUR
T1 - Impact analysis of recovery cases due to COVID-19 outbreak using deep learning model
AU - Haque, Ershadul
AU - Hoque, Sami Ul
AU - Paul, Manoranjan
AU - Sarker, Mahidur R.
AU - Suman, Abdullah Al
AU - Huque, Tanvir Ul
PY - 2024/1
Y1 - 2024/1
N2 - The present world is badly affected by novel coronavirus (COVID-19). Using medical kits to identify the coronavirus affected persons are very slow. What happens in the next, nobody knows. The world is facing erratic problem and don’t know what will happen in near future. This paper is trying to make prognosis of the coronavirus recovery cases using LSTM(Long Short Term Memory). This work exploited data of 258 regions, their latitude and longitude and the number of death of 403 days ranging from 22-01-2020 to 27-02-2021. Specifically, advanced deep learning-based algorithms known as the LSTM, play a great effect on extracting highly essential features for time series data (TSD) analysis.There are lots of methods which already use to analyze propagation prediction. The main task of this paper culminates in analyzing the spreading of Coronavirus across worldwide recovery cases using LSTM deep learning-based architectures.
AB - The present world is badly affected by novel coronavirus (COVID-19). Using medical kits to identify the coronavirus affected persons are very slow. What happens in the next, nobody knows. The world is facing erratic problem and don’t know what will happen in near future. This paper is trying to make prognosis of the coronavirus recovery cases using LSTM(Long Short Term Memory). This work exploited data of 258 regions, their latitude and longitude and the number of death of 403 days ranging from 22-01-2020 to 27-02-2021. Specifically, advanced deep learning-based algorithms known as the LSTM, play a great effect on extracting highly essential features for time series data (TSD) analysis.There are lots of methods which already use to analyze propagation prediction. The main task of this paper culminates in analyzing the spreading of Coronavirus across worldwide recovery cases using LSTM deep learning-based architectures.
KW - Covid-19
KW - Impact analysis
KW - LSTM
KW - Prediction of recovery cases
KW - Recovery cases
KW - SARS-CoV2 (Severe acute respiratory syndrome coronavirus 2)
UR - https://rdcu.be/dLe2E
UR - http://www.scopus.com/inward/record.url?scp=85163400461&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85163400461&partnerID=8YFLogxK
U2 - 10.1007/s11042-023-14837-9
DO - 10.1007/s11042-023-14837-9
M3 - Article
AN - SCOPUS:85163400461
SN - 1380-7501
VL - 83
SP - 11169
EP - 11185
JO - Multimedia Tools and Applications
JF - Multimedia Tools and Applications
IS - 4
ER -