[hpc-announce] Elsevier book calling for chapters: Novel AI and Data Science Advancements for Sustainability in the Era of COVID-19 due on 30 Nov 2020

[Victor Chang]

Dear colleagues,

We are honored and pleased to have a book with Elsevier, calling for chapters: Novel AI and Data Science Advancements for Sustainability in the Era of COVID-19 due on 30 Nov 2020.

CFP: http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=111380
Submission: https://easychair.org/conferences/?conf=covid19-book2020

We welcome high-quality and unpublished research related to AI and Data Science for COVID-19. Elsevier is a top publisher. We look forward to your submission. Details are available below Many thanks in advance.

Thanks and regards,

Victor

COVID19_Book 2020 : Novel AI and Data Science Advancements for Sustainability in the Era of COVID-19 (Elsevier book)
Call for Book Chapters (submit to https://easychair.org/conferences/?conf=covid19-book2020):

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


Editors:

Prof. Victor Chang (Lead), Teesside University, UK. Email: victorchang.research at gmail.com
Dr. Mohamed Abdel Baset, Zagazig University, Egypt. Email: analyst_mohamed at yahoo.com
Dr. Muthupandi Ramachandran Leeds Beckett University, UK. Email: M.Ramachandran at leedsbeckett.ac.uk
Dr. Nicolas Green, University of Southampton, UK. Email: ng2 at ecs.soton.ac.uk
Dr. Gary Wills, University of Southampton, UK. Email: gbw at ecs.soton.ac.uk

Recommend reading: https://www.sciencedirect.com/science/article/pii/S1568494620305809