Novel AI and Data Science Advancements for Sustainability in the Era of COVID-19

1st Edition - April 5, 2022
Editors: Victor Chang, Mohamed Abdel-Basset, Muthu Ramachandran, Nicolas Green, Gary Wills
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 0 0 5 4 - 6
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 0 3 7 8 - 3

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 out… Read more

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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 situations using technology to support their communities in order to maintain life and functional as efficiently as possible. The book discusses topics such as AI-driven histopathology analysis for COVID-19 diagnosis, bioinformatics for subtype rational drug design, deep learning-based treatment evaluation and outcome prediction, sensor informatics for monitoring infected patients, and machine learning for tracking and prediction models.

In addition, the book presents AI solutions for hospital management during an epidemic or pandemic, along with real-world solutions and case studies of successful measures to support different types of communities. This is a valuable source for medical informaticians, bioinformaticians, clinicians and other healthcare workers and researchers who are interested in learning more on how recently developed technologies can help us fight and minimize the effects of global pandemics.

Cover image
Title page
Table of Contents
Copyright
Contributors
Chapter 1: Deep learning-based hybrid models for prediction of COVID-19 using chest X-ray
Abstract
1: Introduction
2: Related work
3: Modeling
4: Experimental setup
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
Abstract
Acknowledgment
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
4: Review of big data analytics, artificial intelligence and nature-inspired computing models towards accurate detection of COVID-19 pandemic cases and contact tracing
5: Significant applications of big data in COVID-19 pandemic
6: Research problem
7: Research questions
8: Objectives
9: Methodology
10: Algorithm
11: Conclusion
References
Chapter 3: Designing a conceptual model in the artificial intelligence environment for the health care sector
Abstract
1: Introduction
2: Background
3: Literature review
4: Approach suggested for designing a conceptual model
5: Selection of concepts in information and communication technology
6: Databases related to classification of diseases, digital image code, and viruses taxonomy
7: Role of core team
8: Overview of viruses
9: Covid-19
10: Case illustration based on the healthcare sector in India
11: Machine learning approach for analyzing the medical data
12: Indian environment
13: Academic research approach
14: Developing a drug
15: Discussion
16: Conclusion
References
Chapter 4: Augmented reality, virtual reality and new age technologies demand escalates amid COVID-19
Abstract
1: Introduction
2: Updates ways to minimize infections
3: Background and literature review
4: Demand escalation of AR/VR due to COVID-19 pandemic
5: Conclusion
References
Chapter 5: Using interpretable machine learning identify factors contributing to COVID-19 cases in the United States
Abstract
1: Introduction
2: Related work
3: Proposed approach
4: Experiment and results
5: Conclusion and future work
References
Chapter 6: Cloud-based data pipeline orchestration platform for COVID-19 evidence-based analytics
Abstract
1: Challenges in COVID-19 data handling
2: Background and related works
3: OnTimeEvidence architecture and component implementation
4: OnTimeEvidence COVID-19 case study
5: Conclusion—What we have learnt?
References
Chapter 7: Threat model and security analysis of video conferencing systems as a communication paradigm during the COVID-19 pandemic
Abstract
Acknowledgments
1: Introduction
2: Background
3: Assets of video conferencing tools
4: Point of entry
5: Attack model
6: Threats and vulnerabilities
7: Mitigation strategies
8: Conclusion and future work
References
Chapter 8: Role of artificial intelligence in fast-track drug discovery and vaccine development for COVID-19
Abstract
1: Introduction
2: Artificial intelligence in COVID-19
3: Artificial intelligence in drug discovery
4: Chemical structure input for data processing
5: Artificial intelligence in repositioning approaches
6: Artificial intelligence for accelerating computer modeling
7: Artificial intelligence: De novo design of novel small molecules
8: Artificial intelligence in protein structure prediction
9: Artificial intelligence in vaccine development
10: Summary
11: Conclusions and future directions
References
Chapter 9: The economic impact of covid-19 and the role of AI
Abstract
1: Introduction
2: Economic impact of Covid-19
3: Artificial intelligence (AI) as a solace to help mankind out
4: Future scope of AI
5: Conclusion
References
Chapter 10: An optimized CNN based automated COVID-19 lung infection identification technique from C.T. images
Abstract
1: Introduction
2: Literature review
3: Proposed system
4: Simulation results and discussion
5: Conclusion
References
Index

Victor Chang

Victor Chang, PhD, is a Professor of Business Analytics at the Department of Operations and Information Management, Aston Business School, Aston University, UK. He will be involved in leading a New Research Centre. He has been a Full Professor of Data Science and Information Systems and Research Group leader at Teesside University. He was previously a Senior Associate Professor, Director of PhD and Director of MRes at International Business School Suzhou (IBSS), Xi’an Jiaotong-Liverpool University, China. He was also a very active and contributing key member at Research Institute of Big Data Analytics, XJTLU, and an Honorary Associate Professor at University of Liverpool. Before becoming an academic, he has achieved 97% on average in 27 IT certifications. He is Editor-in-Chief of IJOCI & OJBD journals, Associate Editor of IEEE TII, Information Fusion, and JGIM, Scientific Report, IJBSR and IDD journals. He is a founding and Conference Chair of IoTBDS, COMPLEXIS, FEMIB and IIoTBDSC conferences. He authored 5 books and edited 2 and is widely regarded as one of the most active and influential young scientist and expert in IoT/Data Science/Cloud/security/AI/IS, as he has experience to develop 10 different services for multiple disciplines.

Affiliations and expertise

Professor of Business Analytics, Department of Operations and Information Management, Aston Business School, Aston University, Birmingham, United Kingdom

Mohamed Abdel-Basset

Dr. Mohamed Abdel-Basset is Associate Professor and Head of the Department of Computer Science, within the Faculty of Computers and Informatics, at Zagazig University, Egypt. He received his B.Sc., M.Sc and Ph.D in operations research at the Faculty of Computers and Informatics, Zagazig University. Dr. Abdel-Basset’s research interests are in Optimization, Operations Research, Data Mining, Computational Intelligence, Applied Statistics, Decision Support Systems, Robust Optimization, Engineering Optimization, Multiobjective Optimization, Swarm Intelligence, Evolutionary Algorithms, and Artificial Neural Networks. He is currently working on the application of multi-objective and robust meta-heuristic optimization techniques. Dr. Abdel-Basset is an Editor or Reviewer for several international journals and conferences, and has published more than 100 articles in international journals and conference proceedings.

Affiliations and expertise

Associate Professor, Faculty of Computers and Informatics, Zagazig University, Egypt

Muthu Ramachandran

Muthu Ramachandran, PhD, has more than thirty years of teaching and research experience both in academia and industrial research setting. Prior to this, he spent eight years in industrial research at Philips Research Labs and subsequently at Volantis Systems Ltd, where he has worked on various research projects including software engineering, cloud computing, data science, IoT, and machine learning. Currently, Dr. Ramachandran is leading research in the areas of Cloud Software Engineering, Big Data Software Engineering, IoT Software Engineering, Software Security Engineering, SOA, Cloud Computing, and in the main areas of Software Engineering on RE, CBSE, software architecture, reuse, quality and testing. He has published 15 books, 50 book chapters, 100s of journal articles and conferences. He has also been chair and keynote speaker of conferences on SE-CLOUD, IoTBDS, and COMPLEXIS.

Affiliations and expertise

Educational and Research Consultant (CEO), Integrated Cloud Solutions and Public Intelligence, Leeds, UK, Principal Lecturer, Integrated Cloud Solutions and Public Intelligence, Leeds, UK

Nicolas Green

Nicolas G Green, PhD, is an Associate Professor in the School of Electronics and Computer Science at the University of Southampton, with research primarily focusing on design and development of technology and systems for Lab-on-a-Chip and Point of Care applications in medicine and environmental science. He is an expert on electrical and optical techniques for the detection, measurement, characterization, classification and separation of biological cells, bacteria, viruses and biomolecules. He is also developing strategies for the application of machine learning for assisting medical experts and practitioners in diagnoses.

Affiliations and expertise

Bioelectronics and Microfluidity, Electronics and Computer Science, University of Southampton, UK

Gary Wills

Gary Wills, PhD, research project focuses on Secure Systems Engineering and applications for industry, medicine, and FinTech. Dr. Wills’s work cross-discipline with colleagues from industry and academia. His research can be grouped under a number of themes: Machine learning, Internet of things, Blockchain, Security, Computational Finance, Data Protection, and Cloud Services. He has published widely on these topics, in books, book chapters, official reports, journal articles and conferences paper. Gary has co-edited a number of special issues, and regularly reviews articles for international journals.

Affiliations and expertise

Cyber Physical Research Group, Electronics and Computer Science, University of Southampton, UK