Novel AI and data science advancements for sustainability in the era of COVID-19
Novel AI and Data Science Advancements for Sustainability in the Era of COVID-19 discusses how the role of recent technologies applied to health settings can help fight virus outbreaks. Moreover, it provides guidelines on how governments and institutions should prepare and quickly respond to drastic...
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| Other Authors: | , , |
| Format: | eBook |
| Language: | English |
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London
Academic Press
2022
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| Online Access: | |
| Collection: | O'Reilly - Collection details see MPG.ReNa |
Table of Contents:
- 6.1. International Classification of Diseases (ICD)
- 6.2. Digital Imaging and Communications in Medicine (DICOM)
- 6.3. International Committee on the Taxonomy of Viruses (ICTV)
- 7. Role of core team
- 7.1. Medical research activities
- 7.2. Virtual medical research center
- 8. Overview of viruses
- 8.1. Viruses
- 8.2. Spreading vectors
- 8.3. Human immunodeficiency viruses
- 8.4. Role of immune system
- 8.5. Parts of immune system
- 8.6. Characteristics of immune system
- 8.6.1. White blood cells
- 8.6.2. Antibodies
- 8.6.3. Complement system
- 8.6.4. Lymphatic system
- Intro
- Novel AI and Data Science Advancements for Sustainability in the Era of COVID-19
- Copyright
- Contents
- Contributors
- Chapter 1: Deep learning-based hybrid models for prediction of COVID-19 using chest X-ray
- 1. Introduction
- 2. Related work
- 3. Modeling
- 3.1. PCA-feature ensembles
- 3.2. Optimally weighted majority voting
- 3.3. Feature extraction
- 3.4. Layer modification
- 4. Experimental setup
- 4.1. Baseline models
- 4.1.1. VGG-16 (Simonyan & Zisserman, 2015)
- 4.1.2. ResNet 50 (He et al., 2016)
- 4.1.3. Inception V3 (Szegedy et al., 2015)
- 4.2. Dataset
- 3.2. Big data analytics as a tool for fighting pandemics: A systematic review of literature
- 4. Review of big data analytics, artificial intelligence and nature-inspired computing models towards accurate detection ...
- 5. Significant applications of big data in COVID-19 pandemic
- 6. Research problem
- 7. Research questions
- 8. Objectives
- 9. Methodology
- 9.1. Techniques
- 10. Algorithm
- 11. Conclusion
- 11.1. Big data
- 11.2. Machine learning
- 11.3. COVID-19
- Acknowledgment
- References
- 4.3. Data augmentation
- 4.4. Other preprocessing
- 4.5. Evaluation metrics
- 4.5.1. Accuracy
- 4.5.2. Precision
- 4.5.3. Recall
- 4.5.4. F-1 score
- 4.6. Experimental details
- 5. Results and discussion
- 6. Conclusions
- References
- Chapter 2: Investigation of COVID-19 and scientific analysis big data analytics with the help of machine learning
- 1. Introduction and background
- 2. Literature review
- 3. COVID-19 pandemic in the new era of big data analytics: Methodological innovations and future research directions
- 3.1. Deep learning applications for COVID-19
- Chapter 3: Designing a conceptual model in the artificial intelligence environment for the health care sector
- 1. Introduction
- 2. Background
- 3. Literature review
- 4. Approach suggested for designing a conceptual model
- 5. Selection of concepts in information and communication technology
- 5.1. Artificial intelligence
- 5.2. Role of artificial intelligence
- 5.3. Machine learning
- 5.4. Algorithms
- 5.5. Data warehouse
- 5.6. Virtual reality
- 5.7. Cloud computing
- 6. Databases related to classification of diseases, digital image code, and viruses taxonomy