Retraction Note: Picture fuzzy set-based decision-making approach using Dempster-Shafer theory of evidence and grey relation analysis and its application in COVID-19 medicine selection.

[This retracts the article DOI: 10.1007/s00500-021-05909-9.].

Retraction Note: A fuzzy rough hybrid decision making technique for identifying the infected population of COVID-19.

[This retracts the article DOI: 10.1007/s00500-020-05451-0.].

COVID-19 and Stress of Indian Youth: An Association with Background, On-line Mode of Teaching, Resilience and Hope

Background : COVID-19 pandemic causes serious threats to physical health and triggers wide varieties of psychological problems, including anxiety and depression. Youth exhibit a greater risk of developing psychological distress, especially during epidemics influencing their wellbeing. Objectives : To identify the relevant dimensions of psychological stress, mental health, hope and resilience and to examine the prevalence of stress in Indian youth and its relationship with socio-demographic information, online-mode of teaching, hope and resilience. Method : A cross-sectional online survey obtained information on socio-demographic background, online-mode of teaching, psychological stress, hope and resilience from the Indian youth. A Factor Analysis is also conducted on the recompenses of the Indian youth on psychological stress, mental health, hope and resilience separately to identify the major factors associated with parameters. The sample size in this study was 317, which is more than the required sample size (Tabachnik et al. 2001). Results : About 87% of the Indian youth perceived moderate to a high levels of psychological stress during the current COVID-19 pandemic. Different demographic, sociographic and psychographic segments were found to have high stress levels due to the pandemic, while psychological stress was found to be negatively correlated with resilience as well as hope. The findings identified significant dimensions of the stress caused by the pandemic and also identified the dimensions of mental health, resilience and hope among the study subjects. Conclusion : As stress has a long-term impact on human psychology and can disrupt the lives of people and as the findings suggest that the young population of the country have faced the greatest amount of stress during the pandemic, a greater need for mental health support is required to the young population, especially in post pandemic situations. The integration of online counselling and stress management programs could assist in mitigating the stress of youth involved in distance learning.

Picture fuzzy set-based decision-making approach using Dempster-Shafer theory of evidence and grey relation analysis and its application in COVID-19 medicine selection.

To offer better treatment for a COVID-19 patient, preferable medicine selection has become a challenging task for most of the medical practitioners as there is no such proven information regarding it. This article proposes a decision-making approach for preferable medicine selection using picture fuzzy set (PFS), Dempster-Shafer (D-S) theory of evidence and grey relational analysis (GRA). PFS is an extended version of the intuitionistic fuzzy set, where in addition to membership and non-membership grade, neutral and refusal membership grades are used to solve uncertain real-life problems more efficiently. Hence, we attempt to use it in this article to solve the mentioned problem. Previously, researchers considered the neutral membership grade of the PFS similar to the other two membership values (positive and negative) as applied to the decision-making method. In this study, we explore that neutral membership grade can be associated with probabilistic uncertainty which is measured using D-S theory of evidence and FUSH operation is applied for the aggregation purpose. Then GRA is used to measure the performance among the set of parameters which are in conflict and contradiction with each other. In this process, we propose an alternative group decision-making approach by the evidence of the neutral membership grade which is measured by the D-S theory and the conflict and contradiction among the criteria are managed by GRA. Finally, the proposed approach is demonstrated to solve the COVID-19 medicine selection problem.

A fuzzy rough hybrid decision making technique for identifying the infected population of COVID-19.

Decision theoretic rough set model have been used over many years in most of the application areas. It provides a novel way for knowledge acquisition, especially when dealing with vagueness and uncertainty. Many mathematical modelings have been presented recently to control the pandemic nature of COVID-19 and along with its control model as well. Decision-based treatment recommendation has not yet been found so far in any of the articles. In this paper, we have proposed a novel approach of three-way decision based on linguistic information of a COVID-19 susceptible person. To present this, we have discussed the probabilistic rough fuzzy hybrid model with linguistic information. This model helps us to guess the infected person and decide whom to send for self-isolation, home quarantine and medical treatment in an emergency situation. The significance of the proposed hybrid model has been discussed by presenting a comparative study and reported along with justifications too.

COVID-19 and stress of Indian youth: An association with background, on-line mode of teaching, resilience and hope.

Background: COVID-19 pandemic causes serious threats to physical health and triggers wide varieties of psychological problems, including anxiety and depression. Youth exhibit a greater risk of developing psychological distress, especially during epidemics influencing their wellbeing. Objectives: To identify the relevant dimensions of psychological stress, mental health, hope and resilience and to examine the prevalence of stress in Indian youth and its relationship with socio-demographic information, online-mode of teaching, hope and resilience. Method: A cross-sectional online survey obtained information on socio-demographic background, online-mode of teaching, psychological stress, hope and resilience from the Indian youth. A Factor Analysis is also conducted on the recompenses of the Indian youth on psychological stress, mental health, hope and resilience separately to identify the major factors associated with parameters. The sample size in this study was 317, which is more than the required sample size (Tabachnik et al., 2001). Results: About 87% of the Indian youth perceived moderate to a high levels of psychological stress during the current COVID-19 pandemic. Different demographic, sociographic and psychographic segments were found to have high stress levels due to the pandemic, while psychological stress was found to be negatively correlated with resilience as well as hope. The findings identified significant dimensions of the stress caused by the pandemic and also identified the dimensions of mental health, resilience and hope among the study subjects. Conclusion: As stress has a long-term impact on human psychology and can disrupt the lives of people and as the findings suggest that the young population of the country have faced the greatest amount of stress during the pandemic, a greater need for mental health support is required to the young population, especially in post pandemic situations. The integration of online counselling and stress management programs could assist in mitigating the stress of youth involved in distance learning.

Preferred hospitalization of COVID-19 patients using intuitionistic fuzzy set-based matching approach

Preferable hospitalization of COVID-19 patients has become an urgent and challenging task to save lives amidst the unexpected rising of the 3rd wave, where fuzzy set and matching techniques are considered due to their inherent capability to deal with uncertain suitable pair selection. The matching technique has been widely used to solve decision-making problems due to its capability to determine the suitable pair between the objects of two disjoint sets, whereas fuzzy set is well known to manage uncertain situations. This paper extends the matching technique using fuzzy set and proposes a novel fuzzy matching approach to solve uncertain decision-making problems. We also extend the fuzzy matching approach in the framework of an intuitionistic fuzzy set. A relation between the matching technique and fuzzy set theory is established by developing the preference sequence of the elements. The fuzzy entropy is used to measure the closeness among the elements between two distinct sets. Applicability of the proposed approach is measured by providing an illustrative case study concerned with the preferred hospitalization of the COVID-19 patients. Finally, a comparative study is given to analyze the effectiveness of the proposed approach, where the intuitionistic fuzzy set-based matching approach shows better performance compared to fuzzy and conventional matching based approach. For experimentation purpose, this study uses 9424 patients and 234 hospitals with a total available capacity of 18,024 beds.

A Cross-Sectional Study on Mental Health of School Students during the COVID-19 Pandemic in India

The broad objective of the present study is to assess the levels of anxiety and depression of school students during the COVID-19 lockdown phase and their association with students' background, stress, concerns and social support. In this regard, the present study follows a novel two stage approach. In the first phase, an empirical survey was carried out, based on multivariate statistical analysis, wherein a group of 273 school students participated in the study voluntarily. In the second phase, a novel Picture Fuzzy FFA (PF-FFA) method was applied for understanding the dynamics of facilitating and prohibiting factors for three categories of focus groups (FG), formulated on the basis of attendance in online classes. Findings revealed a significant impact of anxiety and depression on mental health. Further, PF-FFA examinedthe impact of the driving forces that steered children to attend class as contrasted to the the impact of the restricting forces.

A Study on Decision-Making of the Indian Railways Reservation System during COVID-19

The Indian Railways Reservation System (IRRS) is one of the world's busiest reservation systems of railway tickets. Recently, the COVID-19 pandemic situation has severely impacted the Indian Railway's (IR) transportation, which eventually has enforced the IR to alter the passenger reservation system. This research attempts to evaluate and analyse the factors that modify the IRRS. In this research, a rough set-based Data Mining Scaffolding (DMS) has been proposed. Here, the relevant preferential information related to the IRRS is managed by introducing a multi-criteria decision-making (MCDM), where a decision-maker (DM) can make a decision based on several decision rules. The effectiveness of the proposed DMS is explained by gathering realistic data of 26 trains, which run between railway stations of two metro cities of India during the COVID-19 pandemic period. [ FROM AUTHOR] Copyright of Journal of Advanced Transportation is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

An extended fuzzy decision-making framework using hesitant fuzzy sets for the drug selection to treat the mild symptoms of Coronavirus Disease 2019 (COVID-19).

The whole world is presently under threat from Coronavirus Disease 2019 (COVID-19), a new disease spread by a virus of the corona family, called a novel coronavirus. To date, the cases due to this disease are increasing exponentially, but there is no vaccine of COVID-19 available commercially. However, several antiviral therapies are used to treat the mild symptoms of COVID-19 disease. Still, it is quite complicated and uncertain decision to choose the best antiviral therapy to treat the mild symptom of COVID-19. Hesitant Fuzzy Sets (HFSs) are proven effective and valuable structures to express uncertain information in real-world issues. Therefore, here we used the hesitant fuzzy decision-making (DM) method. This study has chosen five methods or medicines to treat the mild symptom of COVID-19. These alternatives have been ranked by seven criteria for choosing an optimal method. The purpose of this study is to develop an innovative Additive Ratio Assessment (ARAS) approach to elucidate the DM problems. Next, a divergence measure based procedure is developed to assess the relative importance of the criteria rationally. To do this, a novel divergence measure is introduced for HFSs. A case study of drug selection for COVID-19 disease is considered to demonstrate the practicability and efficacy of the developed idea in real-life applications. Afterward, the outcome shows that Remdesivir is the best medicine for patients with mild symptoms of the COVID-19. Sensitivity analysis is presented to ensure the permanence of the introduced framework. Moreover, a comprehensive comparison with existing models is discussed to show the advantages of the developed framework. Finally, the results prove that the introduced ARAS approach is more effective and reliable than the existing models.

Neural Network Based Country Wise Risk Prediction of COVID-19

The recent worldwide outbreak of the novel coronavirus (COVID-19) has opened up new challenges to the research community. Artificial intelligence (AI) driven methods can be useful to predict the parameters, risks, and effects of such an epidemic. Such predictions can be helpful to control and prevent the spread of such diseases. The main challenges of applying AI is the small volume of data and the uncertain nature. Here, we propose a shallow long short-term memory (LSTM) based neural network to predict the risk category of a country. We have used a Bayesian optimization framework to optimize and automatically design country-specific networks. The results show that the proposed pipeline outperforms state-of-the-art methods for data of 180 countries and can be a useful tool for such risk categorization. We have also experimented with the trend data and weather data combined for the prediction. The outcome shows that the weather does not have a significant role. The tool can be used to predict long-duration outbreak of such an epidemic such that we can take preventive steps earlier.