TY  - GEN
N2  - This book includes research articles and expository papers on the applications of artificial intelligence and big data analytics to battle the pandemic. In the context of COVID-19, this book focuses on how big data analytic and artificial intelligence help fight COVID-19. The book is divided into four parts. The first part discusses the forecasting and visualization of the COVID-19 data. The second part describes applications of artificial intelligence in the COVID-19 diagnosis of chest X-Ray imaging. The third part discusses the insights of artificial intelligence to stop spread of COVID-19, while the last part presents deep learning and big data analytics which help fight the COVID-19. .
DO  - 10.1007/978-3-030-55258-9
DO  - doi
AB  - This book includes research articles and expository papers on the applications of artificial intelligence and big data analytics to battle the pandemic. In the context of COVID-19, this book focuses on how big data analytic and artificial intelligence help fight COVID-19. The book is divided into four parts. The first part discusses the forecasting and visualization of the COVID-19 data. The second part describes applications of artificial intelligence in the COVID-19 diagnosis of chest X-Ray imaging. The third part discusses the insights of artificial intelligence to stop spread of COVID-19, while the last part presents deep learning and big data analytics which help fight the COVID-19. .
T1  - Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach /
AU  - Hassanien, Aboul-Ella.
AU  - Dey, Nilanjan.
AU  - Elghamrawy, Sally.
ET  - 1st ed. 2020.
VL  - 78
CN  - TA345-345.5
ID  - 945370
KW  - Engineering
KW  - Artificial intelligence.
KW  - Computational intelligence.
KW  - Biomedical engineering.
KW  - Epidemiology.
KW  - Big data.
SN  - 3030552586
SN  - 9783030552589
TI  - Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach /
LK  - https://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-55258-9
UR  - https://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-55258-9
ER  -