Diagnosis and Analysis of COVID-19 using Artificial Intelligence and Machine Learning-Based Techniques

1st Edition - July 17, 2024
Imprint: Academic Press
Editors: Mohammad Sufian Badar, Nima Rezaei, Hassan Imtiyaz, Jawed Ahmed, Afshar Alam
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 5 3 7 4 - 0
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 5 3 7 3 - 3

Diagnosis and Analysis of COVID-19 using Artificial Intelligence and Machine Learning-Based Techniques offers new insights and demonstrates how machine learning (ML), artificia… Read more

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Diagnosis and Analysis of COVID-19 using Artificial Intelligence and Machine Learning-Based Techniques offers new insights and demonstrates how machine learning (ML), artificial intelligence (AI), and (Internet of Things (IoT) can be used to diagnose and fight COVID-19 infection. Sections also discuss the challenges we face in using these technologies. Chapters cover pathogenesis, transmission, diagnosis, and treatment strategies for COVID-19, Artificial Intelligence and Machine Learning, and Blockchain /IoT Blockchain technology, examining how AI can be applied as a tool for detection and containment of the spread of COVID-19, and on the socioeconomic and educational post-pandemic impacts of the disease.

This is a multidisciplinary resource for those engaged in researching COVID-19 and how emerging technologies are being used as tools for detection, transmission and treatment strategies.

Cover image
Title page
Table of Contents
Copyright
Dedication
Contributors
Foreword
Preface
Acknowledgment
Part A. Biology of SARS-CoV-2
Chapter 1. Understanding the molecular basis of pathogenesis of SARS-CoV-2
What you will learn
1. Overview of SARS-CoV-2 pandemic
2. Structure and genome
3. Cell tropism
4. Viral life cycle
5. Molecular aspects of pathogenesis
6. Mechanisms of evasion
7. Currently available therapeutic strategies
8. Summary
Take-home message
List of abbreviations
Chapter 2. Epidemiology, evolution, and phylogeny of coronaviruses
What you will learn?
1. Introduction
2. Overview of the COVID-19 pandemic and its impact
3. Evolution of corona viruses (Fig. 2.5)
4. Phylogeny of coronaviruses
5. Case study
6. Conclusion
Take home message
Chapter 3. Transmission mechanism and clinical manifestations of SARS-CoV-2
What will you learn
1. Background
2. Introduction
3. Pathogenesis and mechanism of transmission
4. Modes of transmission
5. Strategies to combat transmission
6. Clinical manifestation of SARS-CoV-2
7. Postinfection manifestations
8. Clinical manifestation in pediatric population
9. Challenges and future perspectives
10. Conclusion
Abbreviations
Conflict of interest
Author contributions
Consent for publication
Take home message
Chapter 4. Diagnostic approaches in SARS-COV-2 infection (COVID-19)
What you will learn
1. Overview of SARS-COV-2
2. Materials and methodology
3. Diagnosis and complications
4. Limitations of current diagnostic techniques
5. Different COVID-19 diagnostic tests, their advantages, and limitations
6. Alternative testing and future perspectives
7. Conclusion
Take home message
Chapter 5. Emerging therapeutic strategies for COVID-19
What you will learn
1. Introduction
2. Pathophysiology of novel coronavirus
3. Potential targets for the management of COVID-19
4. Emerging therapeutic strategies for combating COVID-19
5. Prognosis in the management of COVID-19
Take home message
Chapter 6. Vaccine development strategies and impact
What you will learn
1. Introduction
2. Immunogen source
3. Nonviral vaccines for addressing current vaccine development challenges
4. Vaccination delivery advancements
5. Case studies
6. Immunoinformatics and artificial intelligence
7. Veterinary vaccines
8. Future prospects and challenges
9. Conclusion
Take-home message
List of abbreviations
Chapter 7. Mutational landscape and emerging variants of SARS-CoV-2
What you will learn
1. Introduction
2. Phylogenetic analysis
3. Sequence data analysis
4. Genome assembly
5. Genetic mutation and their effects
6. Variants of concern
7. Epidemiology of SARS-CoV-2 and its variants of concern
8. Clinical features of COVID-19 and its variants of concern
Take home message
Chapter 8. Clinical management of post-COVID 19 symptoms and consequences
What you will learn
1. Introduction
2. Epidemiology
3. Diagnosis and clinical manifestations of COVID-19
4. Clinical features: Signs and symptoms (Tables 8.4 and 8.5)
5. Post COVID-19 condition, “long COVID”
6. Vaccines used against COVID-19
7. Preparing for the next COVID-19 pandemic
8. Conclusion
Take home message
Part B. Machine learning
Chapter 9. Introduction of artificial intelligence and machine learning
What you will learn
1. Introduction
2. Artificial intelligence
3. Machine learning
4. How are artificial intelligence and machine learning related?
5. Applications
6. Advantages
7. Conclusion
Take home message
Chapter 10. Emerging technologies for coronaviruses (COVID-19)
What you will learn
1. Overview
2. Background
3. Effects of COVID-19
4. Use of technology in combating COVID-19
5. Electronic data capture
6. Artificial intelligence
7. Big Data and Geographic Information System
8. 3D-printing
9. Telemedicine
10. Robotics
11. Conclusion
Take home message
Chapter 11. Diagnosing coronaviruses (COVID-19) using machine learning
What you will learn
1. Introduction
2. COVID-19 datasets
3. Methodologies
3.1. Preprocessing techniques
3.2. Segmentation
3.3. Feature extraction
3.4. Classification
4. Transfer learning with the convolutional neural network
5. Specialized models for COVID-19 diagnosis
6. Discussion and conclusion
Take-home message
Chapter 12. Radiology images in machine learning: Diagnosing and combatting COVID-19
What you will learn
1. Introduction
2. Overview of machine learning techniques used in analyzing radiology images
3. Radiology imaging challenges and limits in COVID-19 diagnosis
4. Interpretability and explainability of machine learning models
5. Techniques for explaining and interpreting machine learning models
6. Techniques for explanation and understanding machine learning models
7. Limitations and challenges in achieving interpretability for COVID-19 diagnosis
8. Interpretability and explainability of machine learning models
9. Importance of interpretability in medical imaging for COVID-19 diagnosis
10. Techniques for explaining and interpreting machine learning models
11. Limitations and challenges in achieving interpretability for COVID-19 diagnosis
12. Advancements and innovations in COVID-19 detection
13. Challenges and future directions
14. Conclusion
Take-home message
Chapter 13. Challenges and constraints of using radiology images to diagnose COVID-19
What you will learn
1. Introduction
2. COVID-19 diagnosis using radiology images
3. A comparison of different radiology images
4. Uses of artificial learning for COVID-19 detection using radiology images
5. Challenges of using radiology imaging for diagnosing COVID-19
6. Conclusion
Take-home message
Part C. Blockchain/IoT
Chapter 14. Artificial Intelligence and Internet of Things: Application in detecting and containing the spread of COVID-19
What you will learn?
1. Introduction
2. A brief on COVID-19
3. Fundamentals of AI and IoT
4. Artificial Intelligence and its role in COVID-19
5. IoT role in COVID-19
6. Applications of AIoT in healthcare with focus on COVID-19
7. Challenges in exploring AI and IoT—COVID-19
8. Future potential
Take-home message
Chapter 15. Checking COVID-19 transmission using IoT
What you will learn
1. Introduction
2. Internet of thing
3. Application of IoT-based devices in COVID-19 pandemic
4. IoT-based approach for early detection of COVID-19
5. IoT in maintaining quarantine and social distancing
6. IOT-based approach for treatment and disease management
7. IoT in post recovery
8. IoT approaches for prevention
9. Advantages and disadvantages
10. Case studies
11. Future directions
12. Conclusion
Take home message
Chapter 16. Interpretation and validation of COVID-19 data obtained from Artificial Intelligence
What you will learn
1. Introduction
2. Overview of various ML-based techniques for CCOVID-19 data
3. Supervised learning techniques for interpretation of COVID-19 data
4. Unsupervised learning techniques for interpretation of COVID-19 data
5. Conclusion
Take home message
Chapter 17. The pandemic: blockchain-based e-voting system, the way forward
1. Introduction
2. Consensus algorithm
3. Use of blockchain in different sectors during the pandemic
4. E-voting security requirements and background
5. E-voting system requirements
6. Information on e-voting
7. Attack on implemented e-voting systems
8. Risks in e-voting systems
9. Blockchain-based e-voting: is blockchain sufficient alone?
10. Results
11. Conclusion and future work
Index

Mohammad Sufian Badar

Dr. M.S Badar, MS, PhD served as a Teaching Faculty in the Department of Bioengineering at the University of California, Riverside, CA, USA. He graduated with an MS degree in Molecular Science and Nanotechnology and Ph.D. in Engineering from Louisiana Tech University Ruston, LA, USA, respectively. Dr. Badar has over 14 years of teaching, research, and industry experience. He has authored a chapter in a book about Machine Learning and Molecular Modeling. He has developed an algorithm for Face Detection, Recognition, and Emotion Recognition. He has developed a device that, by using a Biosensor, can correlate the physiology of the human body with the emotion recognition algorithm, giving us an accurate measurement of stress hormones in the body. His group is developing an ML Model which predicts Covid infection based on the severity of symptoms (mild, moderate, and severe) and if they have had contact with a Covid patient.

Affiliations and expertise

Senior Teaching Faculty, Department of Bioengineering, University of California, Riverside, CA, USA

Nima Rezaei

Professor Nima Rezaei gained his medical degree (MD) from Tehran University of Medical Sciences and subsequently obtained an MSc in Molecular and Genetic Medicine and a PhD in Clinical Immunology and Human Genetics from the University of Sheffield, UK. He also spent a short-term fellowship of Pediatric Clinical Immunology and Bone Marrow Transplantation in the Newcastle General Hospital. Professor Rezaei is now the Full Professor of Immunology and Vice Dean of Research, School of Medicine, Tehran University of Medical Sciences, and the co-founder and Head of the Research Center for Immunodeficiencies. He is also the founding President of the Universal Scientific Education and Research Network (USERN). Professor Rezaei has already been the Director of more than 55 research projects and has designed and participated in several international collaborative projects. Professor Rezaei is an editorial assistant or board member for more than 30 international journals. He has edited more than 35 international books, has presented more than 500 lectures/posters in congresses/meetings, and has published more than 1,000 scientific papers in the international journals.

Affiliations and expertise

Professor, Department of Immunology, School of Medicine; Head, Research Center for Immunodeficiencies, Children’s Medical Center, Tehran University of Medical Sciences; Founding President, Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA),Universal Scientific Education and Research Network (USERN),Tehran, Iran

Hassan Imtiyaz

Dr. Imtiyaz Hassan is another bioinformatician from India who is currently working as an Assistant Professor at a Premier Central University, Jamia Millia Islamia (JMI), New Delhi. He has completed his Ph.D. from All India Institute of Medical Sciences (AIIMS). He was ranked 6 on the list of best Bioinformaticians in India. He was also appointed as a Postdoctoral fellow at Saint Louis University School of Medicine, USA. He has published more than 70 research articles in reputed international journals and has been serving as an Editorial Board Member of Journal of Protein and Proteomics, Journal of Natural Science, Biology and Medicine and many other journals. His thrust area of research is structure-based drug designing aimed to identify novel therapeutic agents. His research group is dedicated to understanding the structure-function relationships and try to develop methods to determine, analyze and exploit the structure of proteins and their complexes. Currently, he is involved in developing potential inhibitors against cancer and diabetes, and search to find anti-obesity agents in humans.

Affiliations and expertise

Assistant Professor, Premier Central University, Jamia Millia Islamia (JMI), New Delhi, India

Jawed Ahmed

Dr. Jawed Ahmed is an Assistant Professor in the Department of Computer Science and Engineering at Hamdard University. His current research interest lies in Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT). He is also a Co-Chair ICT For Digital, Smart, and Sustainable Development International Conference

Affiliations and expertise

Department of Computer Science and Engineering, Hamdard University, Delhi, India

Afshar Alam

Professor Mohammad Afshar Alam completed is presently working as Vice- Chancellor and Professor and Dean of School of Engineering Sciences and Technology at Jamia Hamdard, New Delhi. His research areas include Software Re-engineering, Data Mining, Bio-Informatics, Fuzzy databases, and Sustainable Development. He has authored 10 books, supervised more than 30 Doctoral students and more than 200 Post Graduate research projects and has more than 160 research papers in reputed journals to his credit. He is conferred with many prestigious awards like Bharat Samaj Ratna Award, AMP Award for Excellence in Education, Cooperative Citizen Award, World Environment Day Award, and Spardha Shree Award. He is also the member of various government bodies at both National and International level including University Grants Commission (UGC), All India Council of Technical Education (AICTE), National Assessment and Accreditation Council (NAAC), Department of Science & Technology (DST).

Affiliations and expertise

Vice- Chancellor and Professor and Dean of School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi, India

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Diagnosis and Analysis of COVID-19 using Artificial Intelligence and Machine Learning-Based Techniques

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Mohammad Sufian Badar

Nima Rezaei

Hassan Imtiyaz

Jawed Ahmed

Afshar Alam

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