AI, Big Data & Multimedia for COVID 2020 : MTAP (Q2): Pioneering AI, Data Science and Multimedia Techniques and Findings for COVID-19
Call For Papers
MTAP Call for Papers: Pioneering AI, Data Science and Multimedia Techniques and Findings for COVID-19 
COVID-19 is the greatest challenge human beings have encountered since World War 2 (WW2). It is highly infectious and has infected several million people worldwide. In early March 2020, the total number of infected cases was still not reaching 100,000. Since then, not only has the number of infections risen rapidly, so has the number of deaths, and the way we live has been greatly impacted, such as the need for social distancing.
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 In this special issue, we seek high quality and unpublished work based on pioneering AI, Data Science and multimedia techniques and findings.
Topics of interest include, but are not limited to:
Pioneering AI and Data Science techniques based medical image analyses of COVID-19
Pioneering AI and Data Science techniques based on COVID-19 diagnostic systems
Pioneering AI and Data Science techniques for lung and infection segmentation
Detection of COVID-19 disease based on Pioneering AI and Data Science features
Pioneering AI and Data Science-based CT assessment
Pioneering AI and Data Science techniques based on CT images
Early prediction of COVID-19 based on advanced Pioneering AI and Data Science methods
Pioneering AI and Data Science techniques for tracking COVID-19
Pioneering AI and Data Science techniques for data mining in COVID-19
Pioneering AI and Data Science techniques for managing COVID-19
Pioneering AI and Data Science techniques for big data analytics in COVID-19
Pioneering AI and Data Science techniques for predicting the long-term risk of COVID-19
Pioneering AI and Data Science Systems to screen Coronavirus diseases
Novel applications by advanced Deep Learning for COVID-19
Prof. Victor Chang (Lead Guest Editor)
Teesside University, UK
Email: email@example.com; V.Chang@tees.ac.uk
Dr. Muthu Ramachandran
Leeds Beckett University, UK
Prof. Víctor Méndez Muñoz
Universitat Oberta de Catalunya, Barcelona, Spain
Submission deadline: 30 November, 2020
Final manuscript due: 31 March, 2021
Tentative publication date: Summer or autumn 2021
Authors should prepare their manuscript according to the Instructions for Authors available from the Multimedia Tools and Applications website. Authors should submit through the online submission site at https://www.editorialmanager.com/mtap/default.aspx and select “SI 1192 - Pioneering AI, Data Science and Multimedia Techniques and Findings for COVID-19” when they reach the “Article Type” step in the submission process. Submitted papers should present original, unpublished work, relevant to one of the topics of the special issue. All submitted papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation, by at least three independent reviewers. It is the policy of the journal that no submission, or substantially overlapping submission, be published or be under review at another journal or conference at any time during the review process.
The special issue will consider papers extending previously published conference papers, provided the journal submission presents a significant contribution beyond the conference paper. Authors must explain in the introduction to the paper the new contribution to the field made by the submission, and the original conference publication should be cited in the text. Note that neither verbatim transfer of large parts of the conference paper nor wholesale reproduction of already published figures is acceptable.