AI for Combating COVID-2019

  in Special Issue   Posted on June 3, 2020

Information for the Special Issue

Submission Deadline: Thu 31 Dec 2020
Journal Impact Factor : 4.582
Journal Name : Pattern Recognition
Journal Publisher:
Website for the Special Issue: https://www.journals.elsevier.com/pattern-recognition/call-for-papers/special-submission-stream-on-ai-for-combating-covid-2019
Journal & Submission Website: https://www.journals.elsevier.com/pattern-recognition

Special Issue Call for Papers:

Aim & Scope

Currently the COVID-2019 pandemic poses not only an immense threat to human life across the entire globe, with mounting numbers of fatalities, but it has also had an unprecedented impact on our day-day-today lives and the global economy. The fight against COVID-2019 is one of the greatest challenges faced by mankind. The whole gamut of AI techniques has attracted significant attention as well as a massive investment of time and effort among researchers seeking solutions to the problems posed by COVID-2019. For example, machine learning and deep learning have been successfully applied to the detection of COVID-2019 using medical imaging and this has become an important tool in fight against the disease.

The Pattern Recognition Journal (PRJ) will open a special submission stream to promote and collect together the latest cutting-edge AI-driven research based on using pattern recognition and machine learning methods to combat COVID-2019. The scope of the call for papers is broad and includes all areas where pattern recognition and machine learning are used in novel COVID-2019 related data processing and analysis. Also of interest are submissions on the computer-aided diagnosis and automated COVID-2019 data interpretation. We believe that this call for papers will result in a high-quality collection of original contributions that will advance research on the applications of AI for combating COVID-2019.

The topics to be considered are (but not limited to):

  • Machine learning or deep learning for the detection and diagnosis of COVID-2019 using medical imaging, audio or other novel sensors, or multimodal approaches.
  • New feature extraction and representation algorithms for the detection and diagnosis of COVID-2019.
  • Knowledge discovery and pattern recognition from large-scale COVID-2019 epidemiological data.
  • Proteomics of the COVID-2019 virus, and the quest for a vaccine.
  • Deep Learning/machine learning for survival rate prediction, and the analysis of factors affecting survival rate.
  • Severity assessment and classification of COVID-19.
  • Discriminating COVID-19 from other community-acquired pneumonia or respiratory disease.
     

Submission Guideline

The selection of the papers will be based on their scientific quality, their contribution to the field of pattern recognition and their relevance to the clinical, epidemiological or diagnostic study of COVID-2019. We are especially interested in novel pattern recognition and machine learning algorithms for COVID-19 detection and diagnosis. When submitting your manuscript please select the Article Type “SI: AI COVID-2019”. Please submit your manuscript before the submission deadline.

All papers will undergo the journal’s usual review process and will be reviewed by at least three referees. Please refer to the website http://www.journals.elsevier.com/pattern-recognition/ for detailed instructions on paper submission. Papers should be formatted in a single column, with double spacing and numbered pages, and be between 20 and 35 pages in length.

Papers will be reviewed on submission and published online immediately upon acceptance. They will be collected together in the form of Virtual Special Editions with a commentary from the Guest Editors.

Important Dates
The paper stream will open for the period below
Manuscript submissions opens: June 1, 2020
Manuscript submissions closes: Dec. 31, 2020
Acceptance deadline: March 31, 2020

Guest Editors
Jinshan Tang, Michigan Technological University, USA. E-mail: jinshant@mtu.edu

Dinggang Shen, University of North Carolina at Chapel Hill, USA. E-mail: dgshen@med.unc.edu

SoS Agaian, City University of New York, USA. E-mail: Sos.Agaian@utsa.edu; sos.agaian@csi.cuny.edu

Karen Panetta, Tufts University, USA. E-mail: karen@ece.tufts.edu; karen@computer.org

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