TY - GEN
T1 - A Computational Approach for Predicting the Termination of COVID-19
AU - Dutta, Prateek
AU - Sarkar, Abhiroop
AU - Ambekar, Yash
AU - Pek, Hui Ting
AU - Juwono, F. H.
AU - Sakarkar, Gopal
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In 2019, there was an epidemic to the human society, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus causes coronavirus disease 2019 (COVID-19). It is an uncertain disease encountered in society for which the technology and human society had not prepared before. COVID-19 first spread over the Wuhan city of China. Since, the past two years of time-span, it has affected the citizen's life culture and expectancy. Now, most of the population are concern about when will be COVID-19 terminate. Basically, this paper aims to analyze the COVID-19 data with features as total confirmed cases, death rate, and vaccination rate around the world-wide region. On analyzing the data, with the help of Machine Learning (ML) algorithms, we estimate the termination of COVID-19. The rapid expansion of the COVID-19 epidemic has compelled the need for technology in this field.
AB - In 2019, there was an epidemic to the human society, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus causes coronavirus disease 2019 (COVID-19). It is an uncertain disease encountered in society for which the technology and human society had not prepared before. COVID-19 first spread over the Wuhan city of China. Since, the past two years of time-span, it has affected the citizen's life culture and expectancy. Now, most of the population are concern about when will be COVID-19 terminate. Basically, this paper aims to analyze the COVID-19 data with features as total confirmed cases, death rate, and vaccination rate around the world-wide region. On analyzing the data, with the help of Machine Learning (ML) algorithms, we estimate the termination of COVID-19. The rapid expansion of the COVID-19 epidemic has compelled the need for technology in this field.
KW - COVID-19
KW - Clustering
KW - Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=85146997611&partnerID=8YFLogxK
U2 - 10.1109/GECOST55694.2022.10010342
DO - 10.1109/GECOST55694.2022.10010342
M3 - Conference Proceeding
AN - SCOPUS:85146997611
T3 - 2022 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2022
SP - 51
EP - 57
BT - 2022 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2022
Y2 - 26 October 2022 through 28 October 2022
ER -