Special Issue "Soft Computing Technologies for COVID 19 Assessment, Analysis and Control"

Submission Deadline: 30 November 2020 (closed)
Guest Editors
Dr. Dharma P. Agrawal, University of Cincinnati, USA
Dr. B. B. Gupta, National Institute of Technology, India
Dr. Arcangelo Castiglione, University of Salerno, Italy

Summary

This global COVID 19 crisis is getting worse with each day and governments are finding it really difficult in minimize the unprecedented impacts to human lives. Several attempts have been made to understand the factors associated with the assessment and control of COVID 19. Despite ongoing efforts to understand the pandemic’s characteristics, still, there lacks a general consensus among the research community about a comprehensive method for COVID 19 evolution, assessment, analysis, and control. Understanding the complexities of COVID 19 crisis and its impacts under the influence of global and local factors requires investigation of new techniques, methodologies and approaches.

 

The objective of this special issue is to encourage researchers to share their research outputs for preparing better solutions for tackling the pandemic. Contributions from various engineering, scientific, economic and social fields that exploit data analytics, machine learning, data mining, remote sensing, experimental studies are welcomed.


Keywords
• Analysis of the hosting and transmission modes of COVID-19
• Methods for investigating the role of environmental factors in governing the infection potential
• Prediction and forecast of COVID 19 evolution and spread
• Impact assessment and risk analysis in various sectors such as health, environment, economy and social wellbeing
• Investigation of the fate of COVID 19 in possible climatic and socio-economic scenarios
• Strategies for post-lockdown survival

Published Papers
  • Decision Making Based on Fuzzy Soft Sets and Its Application in COVID-19
  • Abstract Real-world applications are now dealing with a huge amount of data, especially in the area of high-dimensional features. Trait reduction is one of the major steps in decision making problems. It refers to the determination of a minimum subset of attributes which preserves the final decision based on the entire set of attributes. Unfortunately, most of the current features are irrelevant or redundant, which makes these systems unreliable and imprecise. This paper proposes a new paradigm based on fuzzy soft relationship and level fuzzy soft relationship, called Union - Intersection decision making method. Using these new principles, the decision-making strategy… More
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  • Economic Shocks of Covid-19: Can Big Data Analytics Help Connect the Dots
  • Abstract Since the beginning of the Covid-19 pandemic, big data analytics (BDA) remains a signatory medium in the battle against it. Governments and policymakers alike are yet to leverage on this scalable technology in an attempt to curb the economic effects of Covid-19. The primary objective of this study is to leverage on BDA to identify economic shocks, and propose a strategic solution for economic recovery in ASEAN member states (AMS). The findings of this study suggest that BDA techniques, frameworks, and architectures are effective tools in predicting and tracking economic shocks, as well as in designing and implementing an effective… More
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