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Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach

  • Book
  • © 2020

Overview

Editors:
  1. Aboul-Ella Hassanien

    1. Department of Information Technology, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

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    You can also search for this editor in PubMed   Google Scholar

  2. Nilanjan Dey

    1. Department of Computer Science and Engineering, JIS University, Kolkata, India

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    You can also search for this editor in PubMed   Google Scholar

  3. Sally Elghamrawy

    1. Department of Computer Engineering, MISR Higher Institute for Engineering and Technology, Mansoura, Egypt

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    You can also search for this editor in PubMed   Google Scholar

  • Presents big data analytics tools and Artificial Intelligence (AI) techniques, useful for the fight against COVID-19
  • Focuses on the integration between Big Data Analytics tools and Artificial Intelligence technologies in order to help healthcare workers for early and accurately diagnosing COVID-19 cases
  • Exhibits state-of-the-art research challenges, results, architecture, applications, and other achievements related to the area of AI/Big data to COVID-19

Part of the book series: Studies in Big Data (SBD, volume 78)

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Table of contents (18 chapters)

  1. Front Matter

    Pages i-xi
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  2. Forecasting and Visualization

    1. Front Matter

      Pages 1-1
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    2. Coronavirus Spreading Forecasts Based on Susceptible-Infectious-Recovered and Linear Regression Model

      • Neha Tyagi, Naresh Dhull, Meenakshi Sharma, Vishal Jain, Shashank Awasthi
      Pages 3-14

    3. Virus Graph and COVID-19 Pandemic: A Graph Theory Approach

      • H. R. Bhapkar, Parikshit N. Mahalle, Prashant S. Dhotre
      Pages 15-34

    4. Nonparametric Analysis of Tracking Data in the Context of COVID-19 Pandemic

      • D. A. Klyushin
      Pages 35-50

    5. Visualization and Prediction of Trends of Covid-19 Pandemic During Early Outbreak in India Using DNN and SVR

      • Mahua Nandy Pal, Shuvankar Roy, Supriya Kundu, Sasmita Subhadarsinee Choudhury
      Pages 51-70

  3. Diagnosis and Predictions of COVID-19

    1. Front Matter

      Pages 71-71
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    2. The Detection of COVID-19 in CT Medical Images: A Deep Learning Approach

      • Nour Eldeen M. Khalifa, Mohamed Hamed N. Taha, Aboul Ella Hassanien, Sarah Hamed N. Taha
      Pages 73-90

    3. COVID-19 Data Analysis and Innovative Approach in Prediction of Cases

      • Abhijeet Kushwaha, Pallavi Vijay Chavan, Vivek Kumar Singh
      Pages 91-115

    4. Detection of COVID-19 Using Chest Radiographs with Intelligent Deployment Architecture

      • Vedant Bahel, Sofia Pillai
      Pages 117-130

    5. COVID-19 Diagnostics from the Chest X-Ray Image Using Corner-Based Weber Local Descriptor

      • S. N. Mohammed, A. K. Abdul Hassan, H. M. Rada
      Pages 131-145

    6. Why Are Generative Adversarial Networks Vital for Deep Neural Networks? A Case Study on COVID-19 Chest X-Ray Images

      • M. Y. Shams, O. M. Elzeki, Mohamed Abd Elfattah, T. Medhat, Aboul Ella Hassanien
      Pages 147-162

  4. Artificial Intelligence (AI) Against COVID-19

    1. Front Matter

      Pages 163-163
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    2. Artificial Intelligence Against COVID-19: A Meta-analysis of Current Research

      • Khalid Raza
      Pages 165-176

    3. Insights of Artificial Intelligence to Stop Spread of COVID-19

      • Abu Sufian, Dharm Singh Jat, Anuradha Banerjee
      Pages 177-190

    4. AI Based Covid19 Analysis-A Pragmatic Approach

      • Prerana Mukherjee, Sarul Malik
      Pages 191-207

    5. Artificial Intelligence and Psychosocial Support During the COVID-19 Outbreak

      • Poonam Dhaka
      Pages 209-225

    6. Role of the Accurate Detection of Core Body Temperature in the Early Detection of Coronavirus

      • Enas Selem, Sherine M. Abd El-kader
      Pages 227-243

    7. The Effect CoronaVirus Pendamic on Education into Electronic Multi-modal Smart Education

      • Doaa Mohey El-Din, Aboul Ella Hassanein, Ehab E. Hassanien
      Pages 245-259

  5. Deep Learning Against COVID-19

    1. Front Matter

      Pages 261-261
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Keywords

About this book

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. 

Editors and Affiliations

  • Department of Information Technology, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul-Ella Hassanien

  • Department of Computer Science and Engineering, JIS University, Kolkata, India

    Nilanjan Dey

  • Department of Computer Engineering, MISR Higher Institute for Engineering and Technology, Mansoura, Egypt

    Sally Elghamrawy

Bibliographic Information

  • Book Title: Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach

  • Editors: Aboul-Ella Hassanien, Nilanjan Dey, Sally Elghamrawy

  • Series Title: Studies in Big Data

  • DOI: https://doi.org/10.1007/978-3-030-55258-9

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

  • Hardcover ISBN: 978-3-030-55257-2Published: 13 October 2020

  • Softcover ISBN: 978-3-030-55260-2Published: 14 October 2021

  • eBook ISBN: 978-3-030-55258-9Published: 12 October 2020

  • Series ISSN: 2197-6503

  • Series E-ISSN: 2197-6511

  • Edition Number: 1

  • Number of Pages: XI, 307

  • Number of Illustrations: 39 b/w illustrations, 130 illustrations in colour

  • Topics: Data Engineering, Artificial Intelligence, Computational Intelligence, Biomedical Engineering and Bioengineering, Epidemiology, Big Data

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