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Special Issue of Information Fusion 2020 : Information Fusion CALL FOR PAPERS for Special Issue: Advance Machine Learning Fusion Approaches for COVID-19

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Link: https://www.journals.elsevier.com/information-fusion/call-for-papers/advance-machine-learning-fusion-approaches-for-covid-19
 
When N/A
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Submission Deadline TBD
Categories    computer   biomedicine
 

Call For Papers

Special Issue on Advance Machine Learning Fusion Approaches for COVID-19

Undoubtedly, irrespective of the latest development in science and technology COVID-19 (Corona virus) is the biggest harmful buzzword throughout the globe. The threat of this virus is so dread that more than 3.12million people have lost their lives within a span of four months. World Health Organization (WHO) declared the virus outbreak a pandemic in the second week of March 2020. The major problem in the identification of COVID-19 is detection and diagnosis due to the nonavaliability of medicine. In this situation, only Reverse Transcription Polymerase Chain Reaction (RT-PCR) method is used for the diagnosis and bas been widely adopted. With the evolvement of COVID-19, the present research community has witnessed many machine learning and deep learning based approaches with incremental dataset over the month. However, the present scenario of COVID needs an effectual research with original clinical data rather a collection of random internet based data(limited for mathematical analysis). With the help of original real time data (under expert doctors and experts) the accurate identification and diagnosis of such pandemic is possible, which may help to provide a major breakthrough for this disease throughout the globe. Efforts for expedited data and results reporting should not be limited to only for the sake of clinical trials, but should include observational studies which may lead to many major developments for further studies on the virus.

The main objective of this special issue is to develop innovative, state-of-the-art fusion of advance machine learning approaches with complex real life problems to protect from this hazardous pandemic. This special issue provides an ideal platform to submit manuscripts that discuss the prospective fusion based original developments of advance machine learning experimented on real/original COVID data and innovative ideas in the diagnosis of COVID-19. This issue encourages real time clinical and epidemiological investigations for COVID-19 with novel methodologies. The Issue will act as a resource of guidance to extend COVID research a step ahead to attract many clinical and epidemiological studies on this outbreak, ensuring a fast turnaround time for high quality research.

Topics appropriate for this special issue include (but are not necessarily limited to):

• Identification and diagnosis of clinical characteristics of novel corona virus based on real data with expert supervision
• Novel machine learning techniques for tracking COVID with the help of original clinical data
• Novel methodologies for effective diagnosis of infection and transmission dynamics of the disease
• Advanced machine learning techniques fusion with clinical medical image analyses of COVID-19
• Advanced machine learning techniques for long term and short term risk prediction of COVID-19 based on clinical data
• Advanced machine learning techniques for evaluation of impact of interventions including pharmaceutical and non-pharmaceutical approaches.
• Advance machine learning based fusion modelling of COVID-19 Spread based on original data
• Advance machine learning, forecasting and inference from pandemic data
• Focused advance deep learning algorithms for infectious disease modelling based on clinical data
• Fusion of machine learning and quantitative social science approaches for epidemiological models
• Fusion of advanced machine learning techniques and real time big data for future challenges of COVID-19

Please prepare your paper along with all the supplementary materials for your submission. The papers submitted to this special issue must be original. Besides that, they must not be published, “under review”, or even be submitted in any other journal, conference, or workshop. Papers will be peer-reviewed by at least three independent reviewers and will be chosen based on contributions including their originality, scientific quality as well as their suitability to this special issue. The journal editors will make the final decision on which papers will be accepted.

Authors must ensure that you carefully read the guide for authors before submitting your papers. The guide for authors and link for online submission is available on the Information Fusion homepage at: https://www.journals.elsevier.com/information-fusion. Please select “SI: AMLFAC” when you reach the “Article Type” step when submitting your papers. For any inquiry or question regarding this special issue, authors may contact directly via email to Weiping Ding at dwp9988@163.com.

Guest Editor(s)

Weiping Ding
Nantong University, China
Email: dwp9988@163.com

Janmenjoy Nayak
Aditya Institute of Technology and Management (AITAM), India
Email: mailforjnayak@gmail.com

Ajith Abraham
Machine Intelligence Research Lab, USA
Email : ajith.abraham@ieee.org

Bighnaraj Naik
Veer Surendra Sai University of Technology, India
Email: mailtobnaik@gmail.com

Danilo Pelusi
University of Teramo, Italy
Email : dpelusi@unite.it

Deadline for Submission: September 1st, 2020
First Round Notification: November 1st, 2020
First Revision: December 1st, 2020
Second Round Notification: January 1st, 2021
Second Revision: February 1st, 2020
Final Notification (expected): March 1st, 2021

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