COVID19_Book 2020 : Novel AI and Data Science Advancements for Sustainability in the Era of COVID-19 (Elsevier book)
Call For Papers
Call for Book Chapters (submit to
The most severe issue that concerns the world during this period is the outbreak of the novel Coronavirus (COVID-19). The rapid spread of the virus around the world poses a real threat to all countries, as a result of that, researchers must pay attention to studying the details of this calamity. COVID-19 symptoms may be similar to other viral chest diseases in some of the symptoms that may cause the doctor's uncertainty in making the correct diagnosis decision due to the novelty of this virus. The recent diagnosis of COVID-19 is based on real-time reverse-transcriptase polymerase chain reaction (RT-PCR), and regarded as the gold standard for confirmation of infection. It has already been widely recognized that deep learning techniques can potentially have a substantial role in streamlining and accelerating the diagnosis of COVID-19 patients. Numerous open dataset enterprises have been set up over the past weeks to aid the researchers in developing and improving methods that could contribute to countering the Corona pandemic. To report the above unique problems in diagnosis of COVID-19, various techniques need to be developed. This book focuses on novel analysis techniques related to COVID-19.
This Elsevier book provides a perfect platform to submit chapters that discuss the prospective developments and innovative ideas in artificial intelligence techniques in the diagnosis of COVID-19.
COVID-19 is a huge challenge to humanity and medical sciences so far as of today, we have been unable to find a medical solution (Vaccine). However, globally we are still managing the use of technology for our work, communications, analytics, and predictions with the use of advancement in data science, communication technologies (5G & Internet), and AI. Therefore, we might be able to continue and live safely with the use of research in advancements in data science, AI, Machine learning, Mobile apps, etc. until we could found a medical solution such as a vaccine.
There are urgent needs globally to understand how to tackle this challenge. In terms of computing and multimedia research, scientists can offer insights, recommendations and new discoveries, which may offer positive impacts and findings related to the causes, cure and analysis of treatment. The recent diagnosis of COVID-19 is based on real-time reverse-transcriptase polymerase chain reaction (RT-PCR) and regarded as the gold standard for confirmation of infection. It has already been widely recognized that advanced AI and Data Science techniques can potentially have a substantial role in streamlining and accelerating the diagnosis of COVID-19 patients, offering high-quality research outputs and accurate predictive modeling. Therefore, this requires pioneering methods such as deep learning, artificial intelligence and computational intelligence since they are highly important. Together with innovative multimedia techniques, innovative AI and Data Science for COVID-19 can provide added values for scientists. In this special issue, we seek high quality and unpublished work based on pioneering AI, Data Science and multimedia techniques and findings.
During this COVID-19, there has been a number of privacy and security issues of personal details and this can be securely managed with the use of smart contracts in intelligent technologies using pioneering AI and Data Science techniques.
This Elsevier book will be useful for readers and researchers to apply techniques, methods, algorithms, and application of AI and Data Science methods/techniques for further advancements of research.
Topics of interests (but not limited to):
• AI-driven medical imaging (including chest X-ray and CT) analysis for COVID-19 detection
• AI-driven histopathology analysis for COVID-19 diagnosis
• Bioinformatics for COVID-19 subtype rational drug design
• Deep learning-based treatment evaluation and outcome prediction
• AI-based care pathways planning for comorbid patients
• Deep Learning for COVID-19 treatment, and prognosis
• Sensor informatics for monitoring COVID-19 infected patients
• Artificial intelligence in COVID-19 drug discovery and development
• Advanced Data Science techniques in COVID-19 analysis
• Knowledge representation in COVID-19 analysis
• Machine learning for COVID-19 tracking and prediction models
• Computer vision in COVID-19-related medical imaging
• Artificial intelligence methods in COVID-19 patient tracking or monitoring
• Security, privacy and Blockchain methods for COVID-19 research
• Evidence-based reasoning and correlation vs. causality analysis for COVID-19
• Social media security and forensics in COVID-19 risk management
• Predictive Analytics in COVID-19 risk profiling
• AI-driven exploration of susceptibility and infection in humans
• Pattern recognition in COVID-19 risk analysis
• Applications of the Internet of Things in COVID-19
• Artificial intelligence methods in hospital management during an epidemic or pandemic
• Real-world solutions and case studies involved in scientific contributions.
Submission deadline: 30 Nov 2020 (or as early as possible)
Notification Due: 31 Jan 2021 (or as early as possible)
Final Version: 31 March 2021
Recommendations for submissions:
- We welcome papers of all types but prefer technical papers, or papers with scientific processes, steps, methodology, results and analysis.
- References should follow the IEEE-like reference format. There are no fixed numbers of references, but around 30 references or so.
- Around 6,000 - 11,000 words on average per chapter (excluding references).
- Demonstrations of the novelty and new findings are important.
Prof. Victor Chang (Lead), Teesside University, UK. Email: email@example.com
Dr. Mohamed Abdel Baset, Zagazig University, Egypt. Email: firstname.lastname@example.org
Dr. Muthupandi Ramachandran Leeds Beckett University, UK. Email: M.Ramachandran@leedsbeckett.ac.uk
Dr. Nicolas Green, University of Southampton, UK. Email: email@example.com
Dr. Gary Wills, University of Southampton, UK. Email: firstname.lastname@example.org
Prof. Victor Chang is currently a Full Professor of Data Science and Information Systems at the School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK, since September 2019. He currently leads Beneficial Artificial Intelligence (BAI) Research Group and co-leads Computational Systems Biology and Data Analytics (CBD) Research Group at Teesside University. He was a Senior Associate Professor, Director of Ph.D. (June 2016- May 2018) and Director of MRes (Sep 2017 - Feb 2019) at International Business School Suzhou (IBSS), Xi’an Jiaotong-Liverpool University (XJTLU), Suzhou, China, between June 2016 and August 2019. He was also a very active and contributing key member at Research Institute of Big Data Analytics (RIBDA), XJTLU. He was an Honorary Associate Professor at University of Liverpool. Previously he was a Senior Lecturer at Leeds Beckett University, UK, between Sep 2012 and May 2016. Within 4 years, he completed Ph.D. (CS, Southampton) and PGCert (Higher Education, Fellow, Greenwich) while working for several projects at the same time. Before becoming an academic, he has achieved 97% on average in 27 IT certifications. He won a European Award on Cloud Migration in 2011, IEEE Outstanding Service Award in 2015, best papers in 2012, 2015 and 2018, the 2016 European special award and Outstanding Young Scientist 2017. He is a visiting scholar/Ph.D. examiner at several universities, an Editor-in-Chief of IJOCI & OJBD journals, former Editor of FGCS, Associate Editor of TII & Information Fusion, founding chair of two international workshops and founding Conference Chair of IoTBDS and COMPLEXIS since Year 2016. He is the founding Conference Chair for FEMIB since Year 2019. He published 3 books as sole authors and the editor of 2 books on Cloud Computing and related technologies. He gave 18 keynotes at international conferences. He 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.
Dr. Mohamed Abdel-Basset received his B.Sc. and M.Sc from Faculty of Computers and Informatics, Zagazig University, Egypt. Received his Ph.D from Faculty of Computers and Informatics, Menoufia University, Egypt. Currently, Mohamed is Associate Professor at Faculty of Computers and Informatics, Zagazig University, Egypt. His 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 an/a 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. (http://www.staffdata.zu.edu.eg/dr-mohamedabdelbasset/home.html)
Dr. Muthu Ramachandran is currently a Principal Lecturer in the School of Built Environment, Engineering, and Computing at Leeds Beckett University in the UK. Muthu has more than thirty years of teaching and research experience both in academia and in industrial research setting. Prior to this, he spent eight years in industrial research at Philips Research Labs and subsequently at Volantis Systems Ltd, Surrey, UK where Muthu has worked on various research projects including software engineering, cloud computing, data science, IoT, and machine learning. Currently, Muthu 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. Muthu has published 15 books, 50 book chapters, 100s of journal articles and conferences. Muthu has also chair and keynote speakers of conferences on SE-CLOUD, IoTBDS, and COMPLEXIS.
Dr. Nicolas Green is a Reader (Associate Professor) in Bioelectronics and Microfluidics at the University of Southampton. His research primarily focusses 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, characterisation, 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. He has published widely, including on the use of real-time biomolecular methods such as the industry standard NASBA and RT-PCR.
Dr. Gary Wills is an Associate Professor of computer science at the University of Southampton, UK. Gary's research project focus on Secure Systems Engineering and applications for industry, medicine, and FinTech. Gary works 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, Cloud Services. Gary 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 journals, and so on.