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 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.

• Discusses AI advancements in predictive and decision modeling and how to design mobile apps to track contagion spread
• Presents the smart contract concept in blockchain and cryptography technology to guarantee security and privacy of people's data once their information has been used to fight the pandemic
• Encompasses guidelines for emergency preparedness, planning, recovery and continuity management of communities to support people in emergencies like a virus outbreak

1. Deep Learning Based Hybrid Models for Prediction of COVID-19 using Chest X-Ray

2. Investigation of COVID-19 and Scientific Analysis big data analytics with the help of machine learning

3. Designing a Conceptual Model in the Artificial Intelligence Environment for the Healthcare Sector

4. Augmented Reality, Virtual Reality and New Age Technologies Demand Escalates Amid COVID-19

5. Using Interpretable Machine Learning Identify Factors Contributing to Covid-19 Cases in the Us

6. Cloud-based Data Pipeline Orchestration Platform for COVID-19 Evidence-based Analytics

7. Threat Model and Security Analysis of Video Conferencing Systems as a Communication Paradigm During COVID-19 Pandemic

8. Role of Artificial intelligence in fast-track drug discovery and vaccine development for COVID-19

9. The economic impact of covid-19 and the role of AI

10. An optimized CNN based automated COVID-19 lung infection identification technique from C.T. images

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.
Mohamed Abdel-Baset, PhD, current research interests are Optimization, Operations Research, Data Mining, Computational Intelligence, Applied Statistics, Decision support systems, Robust Optimization, Engineering Optimization, Multi-objective Optimization, Swarm Intelligence, Evolutionary Algorithms, and Artificial Neural Networks. He is working on the application of multi-objective and robust meta-heuristic optimization techniques. He is also Editor/reviewer in different international journals and conferences. He holds the program chair in many conferences in the fields of decision-making analysis, big data, optimization, complexity and internet of things, as well as editorial collaboration in some journals of high impact.
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.
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.
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.