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
  • Kumaraswamy Inverted Topp–Leone Distribution with Applications to COVID-19 Data
  • Abstract In this paper, an attempt is made to discover the distribution of COVID-19 spread in different countries such as; Saudi Arabia, Italy, Argentina and Angola by specifying an optimal statistical distribution for analyzing the mortality rate of COVID-19. A new generalization of the recently inverted Topp Leone distribution, called Kumaraswamy inverted Topp–Leone distribution, is proposed by combining the Kumaraswamy-G family and the inverted Topp–Leone distribution. We initially provide a linear representation of its density function. We give some of its structure properties, such as quantile function, median, moments, incomplete moments, Lorenz and Bonferroni curves, entropies measures and stress-strength reliability. Then,… More
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  • COVID-DeepNet: Hybrid Multimodal Deep Learning System for Improving COVID-19 Pneumonia Detection in Chest X-ray Images
  • Abstract Coronavirus (COVID-19) epidemic outbreak has devastating effects on daily lives and healthcare systems worldwide. This newly recognized virus is highly transmissible, and no clinically approved vaccine or antiviral medicine is currently available. Early diagnosis of infected patients through effective screening is needed to control the rapid spread of this virus. Chest radiography imaging is an effective diagnosis tool for COVID-19 virus and follow-up. Here, a novel hybrid multimodal deep learning system for identifying COVID-19 virus in chest X-ray (CX-R) images is developed and termed as the COVID-DeepNet system to aid expert radiologists in rapid and accurate image interpretation. First, Contrast-Limited… More
  •   Views:374       Downloads:175        Download PDF

  • Geospatial Analytics for COVID-19 Active Case Detection
  • Abstract Ever since the COVID-19 pandemic started in Wuhan, China, much research work has been focusing on the clinical aspect of SARS-CoV-2. Researchers have been leveraging on various Artificial Intelligence techniques as an alternative to medical approach in understanding the virus. Limited studies have, however, reported on COVID-19 transmission pattern analysis, and using geography features for prediction of potential outbreak sites. Predicting the next most probable outbreak site is crucial, particularly for optimizing the planning of medical personnel and supply resources. To tackle the challenge, this work proposed distance-based similarity measures to predict the next most probable outbreak site together with… More
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  • Artificial Neural Networks for Prediction of COVID-19 in Saudi Arabia
  • Abstract In this study, we have proposed an artificial neural network (ANN) model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17, 2020. The proposed model is based on the existing data (training data) published in the Saudi Arabia Coronavirus disease (COVID-19) situation—Demographics. The Prey-Predator algorithm is employed for the training. Multilayer perceptron neural network (MLPNN) is used in this study. To improve the performance of MLPNN, we determined the parameters of MLPNN using the prey-predator algorithm (PPA). The proposed model is called the MLPNN–PPA. The performance of the proposed… More
  •   Views:853       Downloads:408        Download PDF

  • Intelligent Decision Support System for COVID-19 Empowered with Deep Learning
  • Abstract The prompt spread of Coronavirus (COVID-19) subsequently adorns a big threat to the people around the globe. The evolving and the perpetually diagnosis of coronavirus has become a critical challenge for the healthcare sector. Drastically increase of COVID-19 has rendered the necessity to detect the people who are more likely to get infected. Lately, the testing kits for COVID-19 are not available to deal it with required proficiency, along with-it countries have been widely hit by the COVID-19 disruption. To keep in view the need of hour asks for an automatic diagnosis system for early detection of COVID-19. It would… More
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  • 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
  •   Views:1084       Downloads:608        Download PDF