Intelligent Computing Applications for COVID-19

Intelligent Computing Applications for COVID-19

by Tanzila Saba, Amjad Rehman Khan
Released September 2021
Publisher(s): CRC Press
ISBN: 9781000423631

Read it now on the O’Reilly learning platform with a 10-day free trial.

O’Reilly members get unlimited access to books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.

Start your free trial

Book description

This book provides insight into the recent advances of applications, statistical methods, and mathematical modeling for the healthcare industry.

Table of contents

  1. Cover
  2. Half Title
  3. Series Page
  4. Title Page
  5. Copyright Page
  6. Contents
  7. Preface
  8. Acknowledgments
  9. Notes on the Editors
  10. Contributors
  11. 1 Deep Learning for COVID-19 Infection’s Diagnosis, Prevention, and Treatment
    1. 1.1 Introduction
    2. 1.2 Lung Infections Overview
    3. 1.3 Diagnostics
    4. 1.4 DL Methodology
      1. 1.4.1 Datasets
      2. 1.4.2 Preprocessing
      3. 1.4.3 Segmentation
      4. 1.4.4 Feature Extraction and Selection
      5. 1.4.5 Classification
    5. 1.5 Analysis and Findings
    6. 1.6 Conclusions and Future Challenges
    7. References
  12. 2 Artificial Intelligence in Coronavirus Detection: Recent Findings and Future Perspectives
    1. 2.1 Introduction
      1. 2.1.1 Vaccination
      2. 2.1.2 Datasets
        1. 2.1.2.1 Kaggle
        2. 2.1.2.2 Github Repository
      3. 2.1.3 COVID-19 Detection Through Imaging Technologies
        1. 2.1.3.1 CT Imaging
        2. 2.1.3.2 X-Ray Imaging
        3. 2.1.3.3 MRI
    2. 2.2 Literature Review
      1. 2.2.1 Overview of ML
        1. 2.2.1.1 COVID-19 Detection Through Conventional ML Classifiers
      2. 2.2.2 Overview of DL
        1. 2.2.2.1 COVID-19 Detection Through DL Classifiers
    3. 2.3 Discussion
    4. 2.4 Conclusion
    5. Conflict of Interest
    6. Acknowledgments
    7. References
  13. 3 Solutions of Differential Equations for Prediction of COVID-19 Cases by Homotopy Perturbation Method
    1. 3.1 Introduction
    2. 3.2 How does this disease spread?
    3. 3.3 Education System affected by Covid-19
      1. 3.3.1 Homotopy Perturbation Method (HPM)
    4. 3.4 SIR Model
    5. 3.5 Conclusion
    6. References
  14. 4 Predictive Models of Hospital Readmission Rate Using the Improved AdaBoost in COVID-19
    1. 4.1 Introduction
    2. 4.2 Ensemble Classifiers
    3. 4.3 The Proposed Method
    4. 4.4 Evaluating the Proposed Method
      1. 4.4.1 Databases
      2. 4.4.2 The Results Obtained by Single Classifiers Simulation
      3. 4.4.3 The Results Obtained by Ensemble Classifier with Repeating Classifiers
      4. 4.4.4 The Proposed Combination in AdaBoost for the Improvement of the Results
    5. 4.5 Conclusion
    6. References
  15. 5 Nigerian Medical Laboratory Diagnosis of COVID-19; from Grass to Grace
    1. 5.1 Introduction
    2. 5.2 COVID-19 and Medical Laboratory Testing
    3. 5.3 Methodologies and Techniques for COVID-19 Testing
      1. 5.3.1 Neutralization/Virological Cell Culture Test
      2. 5.3.2 Genome Sequencing
      3. 5.3.3 Immunological Testing
      4. 5.3.4 Biosensors
      5. 5.3.5 Nucleic Acid Testing (NAT)/Amplification Testing
    4. 5.4 Medical Laboratory Testing of COVID-19 Experience in Nigeria
    5. 5.5 Challenges of Medical Laboratory Testing for COVID-19 in Nigeria
    6. 5.6 Post-COVID-19 Medical Laboratory Diagnosis
    7. 5.7 Conclusion
    8. Acknowledgments
    9. References
  16. 6 COVID-19 CT Image Segmentation and Detection: Review
    1. 6.1 Introduction
    2. 6.2 Deep Learning (DL)
      1. 6.2.1 Convolutional Neural Networks (CNNs)
      2. 6.2.2 Segmentation
      3. 6.2.3 Detection
    3. 6.3 Datasets
      1. 6.3.1 Evaluation
    4. 6.4 Discussion
    5. 6.5 Conclusion
    6. Notes
    7. References
  17. 7 Interactive Medical Chatbot for Assisting with COVID-related Queries
    1. 7.1 Introduction
    2. 7.2 Related Work
    3. 7.3 System Framework
      1. 7.3.1 Rasa Framework for Intent Classification
      2. 7.3.2 Know-Corona QA Module
        1. 7.3.2.1 Architecture of Know-Corona QA Module
      3. 7.3.3 Help-Desk Module
    4. 7.4 Methodology
      1. 7.4.1 Know-Corona QA Module
        1. 7.4.1.1 Algorithm of Know-Corona (KC) Module
      2. 7.4.2 Help-Desk
    5. 7.5 Experimental Results
      1. 7.5.1 Know-Corona
      2. 7.5.2 Help-Desk
    6. 7.6 Conclusion
    7. References
  18. 8 COVID-19 Outbreak Prediction After Lockdown, Based on Current Data Analytics
    1. 8.1 Background
    2. 8.2 COVID-19 Classification
    3. 8.3 Transmission and Spread
    4. 8.4 Risk Factors
    5. 8.5 Diagnosis of COVID-19 Infection
    6. 8.6 Prevention of COVID-19
    7. 8.7 Treatments
    8. 8.8 COVID-19 Datasets
    9. 8.9 Analysis & Findings
    10. 8.10 Conclusions and Future Challenges
    11. References
  19. 9 A Deep Learning CNN Model for Genome Sequence Classification
    1. 9.1 Introduction
      1. 9.1.1 DNA and its Structure
    2. 9.2 Literature Survey
    3. 9.3 Proposed Method
      1. 9.3.1 ML Classification Models
      2. 9.3.2 NLP Technique
      3. 9.3.3 DL Model for Classification
        1. 9.3.3.1 Convolutional Layer
        2. 9.3.3.2 Pooling Layer
        3. 9.3.3.3 Fully Connected Layer
    4. 9.4 Results and Discussions
      1. 9.4.1 Input
      2. 9.4.2 Results
    5. 9.5 Conclusion and Future Work
    6. References
  20. 10 The Impact of Lockdown Strategies on COVID-19 Cases with a Confined Sentiment Analysis of COVID-19 Tweets
    1. 10.1 Introduction
    2. 10.2 Analysis of Kingdom of Saudi Arabia (KSA)
      1. 10.2.1 Lockdown Policies
      2. 10.2.2 Understanding the Statistics of Cases
      3. 10.2.3 Validation of the Complete Lockdown Strategy in Saudi Arabia
    3. 10.3 Analysis of the United Kingdom (UK)
      1. 10.3.1 Lockdown Policies
      2. 10.3.2 Understanding the Statistics of Cases
      3. 10.3.3 Validation of Partial Lockdown Strategy in the UK
    4. 10.4 Analysis of Sweden
      1. 10.4.1 Lockdown Policies
      2. 10.4.2 Understanding the Statistics of Cases
      3. 10.4.3 Validation of No Lockdown Strategy in Sweden
    5. 10.5 A Confined Sentiment Analysis of COVID-19 Tweets
      1. 10.5.1 Tools and Languages
      2. 10.5.2 Analysis Approach
        1. 10.5.2.1 Data Pulling
        2. 10.5.2.2 Data Preprocessing and Cleansing
      3. 10.5.3 Results & Discussion
      4. 10.5.4 Limitations & Constraints
    6. 10.6 Conclusion
    7. Abbreviations and Acronyms
    8. Note
    9. References
  21. 11 A Mathematical Model and Forecasting of COVID-19 Outbreak in India
    1. 11.1 Introduction
    2. 11.2 Related Work
    3. 11.3 Mathematical Formulation of COVID-19 Outbreak
      1. 11.3.1 Data Fitting
    4. 11.4 Time Series Modeling
      1. 11.4.1 LSTM Model for India
    5. 11.5 ARIMA Model for World
    6. 11.6 Prophet model for India
    7. 11.7 Conclusion
    8. References
  22. 12 Automatic Lung Infection Segmentation of Covid-19 in CT Scan Images
    1. 12.1 Introduction
    2. 12.2 Lung Anatomy
    3. 12.3 DL in COVID-19
    4. 12.4 History of Transfer Learning (TL)
    5. 12.5 Aim of TL
    6. 12.6 Common Block Diagram of Convolutional Neural Network (CNN)
    7. 12.7 Discussion
    8. 12.8 Evaluation
    9. 12.9 COVID-19 Database
    10. 12.10 Conclusion
    11. References
  23. 13 A Review of Feature Selection Algorithms in Determining the Factors Affecting COVID-19
    1. 13.1 Introduction
    2. 13.2 Feature Selection
    3. 13.3 Review of Feature Selection Algorithms
    4. 13.4 Comparison of Feature Selection Methods
    5. 13.5 Conclusion
    6. Notes
    7. References
  24. 14 Industry 4.0 Technology-based Diagnosis for COVID-19
    1. 14.1 Introduction
    2. 14.2 SARS-CoV-2
    3. 14.3 Classification of SARS-CoV-2
    4. 14.4 Coronaviridae Family, Coronavirus Genome Organization & Structural Proteins
    5. 14.5 Methods to Study Viruses
      1. 14.5.1 Purifying Viruses
      2. 14.5.2 Chemical/Physical Methods of Virus Quantitation
      3. 14.5.3 Detecting Viral Nucleic Acids
    6. 14.6 Consequential Advantages of Industry 4.0 Technologies for COVID-19
    7. 14.7 Technologies of Industry 4.0 which may Help in COVID-19 Outbreaks
    8. 14.8 Industry 4.0-based Techniques Used for Detection of SARS-CoV-2
      1. 14.8.1 Biosensors: Frontiers in Fighting COVID-19
        1. 14.8.1.1 Applications of Biosensors in Combating SARS-CoV-2
      2. 14.8.2 DL or AI
      3. 14.8.3 Cyber-physical Systems
        1. 14.8.3.1 CPS 5C-level Structure
      4. 14.8.4 Internet of Medical Things
        1. 14.8.4.1 Working Process
        2. 14.8.4.2 Applications of IoMT during COVID-19
    9. 14.9 Interoperability and Eminence of Industry 4.0
      1. 14.9.1 Smart Factory and Manufacturing
      2. 14.9.2 Smart Product
      3. 14.9.3 Smart City
    10. 14.10 Future Aspects
    11. Conclusion
    12. References
  25. Index

Product information

  • Title: Intelligent Computing Applications for COVID-19
  • Author(s): Tanzila Saba, Amjad Rehman Khan
  • Release date: September 2021
  • Publisher(s): CRC Press
  • ISBN: 9781000423631