|
|
|
|
| LEADER |
05043nam a22006255i 4500 |
| 001 |
978-3-030-55258-9 |
| 003 |
DE-He213 |
| 005 |
20210628135200.0 |
| 007 |
cr nn 008mamaa |
| 008 |
201012s2020 gw | s |||| 0|eng d |
| 020 |
|
|
|a 9783030552589
|9 978-3-030-55258-9
|
| 024 |
7 |
|
|a 10.1007/978-3-030-55258-9
|2 doi
|
| 050 |
|
4 |
|a TA345-345.5
|
| 072 |
|
7 |
|a UN
|2 bicssc
|
| 072 |
|
7 |
|a COM018000
|2 bisacsh
|
| 072 |
|
7 |
|a UN
|2 thema
|
| 072 |
|
7 |
|a TB
|2 thema
|
| 082 |
0 |
4 |
|a 620.00285
|2 23
|
| 245 |
1 |
0 |
|a Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach
|h [electronic resource] /
|c edited by Aboul-Ella Hassanien, Nilanjan Dey, Sally Elghamrawy.
|
| 250 |
|
|
|a 1st ed. 2020.
|
| 264 |
|
1 |
|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2020.
|
| 300 |
|
|
|a XI, 307 p. 169 illus., 130 illus. in color.
|b online resource.
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
| 347 |
|
|
|a text file
|b PDF
|2 rda
|
| 490 |
1 |
|
|a Studies in Big Data,
|x 2197-6503 ;
|v 78
|
| 505 |
0 |
|
|a Coronavirus Spreading Forecasts based on Susceptible-Infectious- Recovered and Linear Regression Model -- Virus Graph and COVID-19 Pandemic: A Graph Theory Approach -- Nonparametric Analysis of Tracking Data in the Context of COVID-19 Pandemic -- Visualization and prediction of trends of Covid-19 pandemic during early outbreak in India using DNN and SVR -- The Detection of COVID-19 in CT Medical Images: A Deep Learning Approach -- COVID-19 Data Analysis and Innovative approach in Prediction of Cases -- Detection of COVID-19 using Chest Radiographs with Intelligent Deployment Architecture -- COVID-19 Diagnostics from the Chest X-Ray Image Using Corner-Based Weber Local Descriptor -- Why are Generative Adversarial Networks Vital for Deep Neural Networks? A Case Study on COVID-19 Chest X-Ray Images -- Artificial intelligence against COVID-19: A meta-analysis of current research -- Insights of Artificial Intelligence to Stop Spread of COVID-19 -- AI based Covid19 analysis-A pragmatic approach -- Artificial Intelligence and Psychosocial Support during the COVID-19 Outbreak -- Role of The Accurate Detection of Core Body Temperature in The Early Detection of Coronavirus -- The effect Coronavirus Pendamic on Education into Electronic Multi-Modal Smart Education -- An H2O’s Deep Learning-inspired model based on Big Data analytics for Coronavirus Disease (COVID-19) Diagnosis -- Coronavirus (COVID-19) Classification using Deep Features Fusion and Ranking Technique -- Stacking Deep Learning for Early COVID-19 Vision Diagnosis.
|
| 520 |
|
|
|a 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. .
|
| 650 |
|
0 |
|a Engineering—Data processing.
|
| 650 |
|
0 |
|a Artificial intelligence.
|
| 650 |
|
0 |
|a Computational intelligence.
|
| 650 |
|
0 |
|a Biomedical engineering.
|
| 650 |
|
0 |
|a Epidemiology.
|
| 650 |
|
0 |
|a Big data.
|
| 650 |
1 |
4 |
|a Data Engineering.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/T11040
|
| 650 |
2 |
4 |
|a Artificial Intelligence.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I21000
|
| 650 |
2 |
4 |
|a Computational Intelligence.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/T11014
|
| 650 |
2 |
4 |
|a Biomedical Engineering and Bioengineering.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/T2700X
|
| 650 |
2 |
4 |
|a Epidemiology.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/H63000
|
| 650 |
2 |
4 |
|a Big Data.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I29120
|
| 700 |
1 |
|
|a Hassanien, Aboul-Ella.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
|
| 700 |
1 |
|
|a Dey, Nilanjan.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
|
| 700 |
1 |
|
|a Elghamrawy, Sally.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
|
| 710 |
2 |
|
|a SpringerLink (Online service)
|
| 773 |
0 |
|
|t Springer Nature eBook
|
| 776 |
0 |
8 |
|i Printed edition:
|z 9783030552572
|
| 776 |
0 |
8 |
|i Printed edition:
|z 9783030552596
|
| 776 |
0 |
8 |
|i Printed edition:
|z 9783030552602
|
| 830 |
|
0 |
|a Studies in Big Data,
|x 2197-6503 ;
|v 78
|
| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-030-55258-9
|
| 912 |
|
|
|a ZDB-2-SCS
|
| 912 |
|
|
|a ZDB-2-SXCS
|
| 950 |
|
|
|a Computer Science (SpringerNature-11645)
|
| 950 |
|
|
|a Computer Science (R0) (SpringerNature-43710)
|
