Special Issue "Machine Learning and Computational Methods for COVID-19 Disease Detection and Prediction"

Submission Deadline: 30 July 2020 (closed)
Guest Editors
Dr. Ashutosh Kumar Dubey, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.
Dr. Sreenatha Anavatti, School of Engineering and Information Technology, University of New South Wales (UNSW at Canberra), Australia.
Dr. Vicente García-Díaz, Department of Computer Science, University of Oviedo, Oviedo, Spain.
Dr. Sam Goundar, British University Vietnam/University of Staffordshire, Hung Yen, Vietnam.
Dr. Abhishek Kumar, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.

Summary

COVID-19 is an infectious disease that is spreading between people globally. The virus responsible for causing this disease is named as Coronavirus. The symptoms of the disease vary from mild to moderate respiratory illness. Older people and those with underlying medical conditions like diabetes, cardiovascular disease, respiratory problems, and cancer are more likely to develop serious illness. So far COVID-19 has claimed many lives across the globe.

 

In this line, this issue interested to study and analyzes the role of machine learning and computational methods for early detection, control, prediction, and diagnosis based on data analytic on COVID-19. Specifically, innovative contributions that either solve or advance the understanding of issues related to new technologies and applications in the real world in the direction of detection and prediction are very welcome.


Keywords
Potential topics include, but are not limited to the following:
• COVID-19 Epidemiology
• Machine and deep learning approaches based observation in case of COVID-19
• Computational correlation in pneumonia and COVID-19
• Computational methods for COVID-19 prediction and detection
• Data mining and knowledge discovery in healthcare
• Decision support systems for healthcare and wellbeing
• Optimization for symptoms detection
• Medical expert systems
• Applications of artificial intelligence techniques in in case of COVID-19
• Intelligent computing and platforms
• Big data frameworks and architectures for applied computation
• Visualization and interactive interfaces in case of COVID-19
• Role of machine learning and computational methods in mental stress observations due to lockdown

Published Papers
  • IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19
  • Abstract The prediction of human diseases, particularly COVID-19, is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment. To deal with the prediction and diagnosis of COVID-19, we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System (IoMTSM-HMFIS). The proposed system determines the various factors like fever, cough, complete blood count, respiratory rate, Ct-chest, Erythrocyte sedimentation rate and C-reactive protein, family history, and antibody detection (lgG) that are directly involved in COVID-19. The expert system has two input variables in layer 1, and seven input variables… More
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