Hesitancy Degree Research Articles

SINGLE-VALUED NEUTROSOPHIC SET WITH QUATERNION INFORMATION: A PROMISING APPROACH TO ASSESS IMAGE QUALITY

Diminished estimate techniques for image quality assessment examine the overall quality using only partially retrieved features from the reference image. The main goal of these methods is to make objective evaluation flexible enough to accommodate the influence of any new visual distortion. The research provides a rapid approach for quality assessment of color photographs based on this concept by altering the structural similarity index measure (SSIM) index in a neutrosophic environment. The SSIM family is a set of parameters that have demonstrated a promising approach in the analysis of reference picture tasks. The current study deals with the exploitation of the concept to use Single-Valued Neutrosophic Sets (SVNSs) for evaluating image quality. Neutrosophic divergence was being constructed for an image and its comparison with other SSIM is presented. Resultantly, a correlation was observed between the proposed divergence and SSIM, which gave accurate informatics. Additionally, the results were presented on a real line which reflects clear information on image convergence and divergence. The uncertainty in the allocation of membership is called the hesitation degree, fuzzy sets have gained a lot of traction in numerous domains of signal and image processing. Based on generalized exponential fuzzy entropy, a trustworthy Image Quality Assessment is proposed.

Some Results for Intuitionistic Fuzzy Inequality

Intuitionistic fuzzy sets, characterized by membership degree mu , non-membership degree upsilon and hesitation degree pi , are a meaningful extension of fuzzy set. Inequalities on intuitionistic fuzzy sets/values are very important in solving real problems. In this paper, some inequalities on intuitionistic fuzzy sets are derived from operations. Moreover, three unweighted intuitionistic fuzzy aggregation operators, including unweighted intuitionistic fuzzy Square, unweighted intuitionistic fuzzy Arithmetic and unweighted intuitionistic fuzzy Geometric, are developed. Later, some corresponding inequality relations on them are deeply explored. Finally, some inequalities on intuitionistic fuzzy value are constructed by equality mu +upsilon +pi =1 in critical definition and proved by some existing famous inequalities, which provide a novel basis for the intuitionistic fuzzy inequalities in operations and aggregation operators.

224. Effect of a Mobile App Chatbot and an Interactive Small Group Webinar on COVID-19 Vaccine Intention and Confidence in Japan: A Randomized Clinical Trial

Abstract Background Little is known about how social media platforms can be used to increase COVID-19 vaccine intent. We aimed to investigate the effect of social media-based interventions on vaccine hesitancy in Japan. Methods We conducted a three-arm randomized controlled trial between 5 November 2021 and 9 January 2022. Japanese aged 20 or above who had not received any COVID-19 vaccine and did not intend to be vaccinated were randomly assigned to one of the following three groups: (i) a control group (with no intervention), (ii) a group with a free chatbot in a popular messenger app called ‘LINE,’ which provided general information on COVID-19 vaccines and (iii) a group with free webinars where healthcare professionals interactively provided participants with the information on COVID-19 vaccines. The vaccine intention (VI) and three pre-defined Vaccine Confidence Index (VCI), including the importance, safety, and effectiveness of COVID-19 vaccines, were compared. Results 1,158 persons were included, and the baseline characteristics and demographics were balanced across three groups (Table 1). Among 386 persons assigned to the chatbot group, 231 (59.8%) accessed the chatbot and answered the post-survey. The post-survey revealed no significant difference in VI or VCI between the chatbot group and the control group (Table 2). Among 386 persons assigned to the webinar group, 207 (53.6%) attended webinars and answered the post-survey. The post-survey revealed no difference in VI between the webinar group and the control group. However, the VCI for the importance and the effectiveness significantly increased in the webinar group. There was no difference in VCI for the safety. VCI for the importance and the effectiveness in the control group decreased without any intervention during the study period. Vaccine intention and confidence after interventions Table 1Table 2 Conclusion While this study demonstrated that neither the chatbot nor the webinar changed VI, VCI for the importance and the effectiveness significantly increased with the webinar intervention. Interactive webinars with live Q and A provided by professionals may have a role in increasing COVID-19 vaccine confidence. Given the degree of vaccine hesitancy worsened over time in the control group, timely intervention is required. Disclosures All Authors: No reported disclosures.

Discrete choice models with Atanassov-type intuitionistic fuzzy membership degrees

In the real word, due to the existence of uncertainty in decision-making information, it is often difficult to accurately evaluate utility values of alternatives. Recently, a series of discrete choice models based on fuzzy subjective utility have been proposed. Specifically, utility evaluation of each alternative has been expressed by fuzzy membership grades. However, traditional fuzzy membership function has only considered supporting degree, but has not considered the degrees of opposition and indeterminacy involved in decision makers’ evaluations. In this paper, a series of novel discrete choice models based on Atanassov-type intuitionistic fuzzy set are proposed, in which membership degrees, non-membership degrees, and hesitation degrees are simultaneously taken into account to jointly express the utility evaluations. Moreover, the attitudinal character of decision makers and relative weights of attributes are also considered to further extend the proposed models. Compared with existing discrete choice models, these new models are more flexible and general, which increase the applicability of discrete choice models for decision-making under an uncertain and imprecise environment. Two real-world case studies are given to illustrate the effectiveness and rationality of the new models.

Novel distance measures of hesitant fuzzy sets and their applications in clustering analysis

Distance and similarity measures are very important in clustering, pattern recognition, decision-making and other scientific fields. For the existing hesitant fuzzy distance, most of them do not consider the hesitance degree. Even if the hesitance degree is considered, only the degree of dispersion or the number of hesitant fuzzy values are considered. Aiming at these shortages, a new hesitance degree is defined, which has better accuracy and applicability. Then, some hesitant fuzzy distance measures based on the proposed hesitance degree are proposed, which can overcome some shortcomings of the existing distance measures. Finally, the new hesitant fuzzy distance is applied to the hierarchical hesitant fuzzy k-means clustering algorithm, and an illustration example is given to illustrate the effectiveness of the proposed method.

A novel intuitionistic fuzzy goal programming method for heterogeneous MADM with application to regional green manufacturing level evaluation under multi-source information

The existed regional green manufacturing level assessment methods pay little attention to the multi-source information such as the expert’s psychological preference information, the preference information among regions, the heterogeneous information of regions on attributes and the interactions information between attributes at the same time, which is as a heterogeneous multi-attribute decision making (MADM) problems. A novel intuitionistic fuzzy goal programming is proposed for HMADM problems under multi-source information. The Choquet integral-based relative ratio is proposed on the basis of the Choquet integral measure. It is far away from the negative-ideal solution (NIS) and closeness to the positive-ideal solution (NIS) simultaneously with the interactions between attributes. Hereby the consistency and inconsistency indices with expert’s hesitation degrees are defined, the attribute weights are solved by using the developed intuitionistic fuzzy goal programming models with the optimistic, pessimistic and mixed methods. The Choquet integral-based relative ratios of alternatives can be calculated to generate their ranking orders. Finally, a regional green manufacturing level assessment example and compared with existed methods show that the superiority and validity of the proposed method, which improves the intuitionistic fuzzy goal programming methods, LINMAP methods and MADM methods with the interactions between attributes.

Novel Distance Measure for Hesitant Fuzzy Sets and Its Application to K-Means Clustering

Distance measures have recently been studied in-depth within the context of hesitant fuzzy sets. The authors analyze existing research on the distance measures of hesitant fuzzy sets and identify several limitations. This paper proposes a new distance measure for hesitant fuzzy sets to overcome these shortcomings. First, a new hesitance degree with better accuracy and applicability is defined. Then, a new method for measuring the distance between hesitant fuzzy sets is proposed by considering the hesitance degree. On this basis, an improved hesitant fuzzy K-means clustering algorithm is introduced to classify hesitant fuzzy sets. Finally, an example is given to illustrate the specific implementation process of the clustering method, and a comparative study on the example is conducted.

Lifecycle risk assessment on the sustainable development of upgrading energy projects using abandoned mines: An ISM-BWM method

Mining industries have left countless opencast and underground mines in Northwest China without careful disposal for the past decades. The authorities encourage rehabilitation of abandoned mines under the green development strategy, while the path for relevant enterprises remains unclear so far. This paper thus studies the risk distribution of energy alternatives for abandoned mine upgrading. 5 types of upgrading alternatives are studied, including both generation and storage ones. A lifecycle risk system for the alternatives is established for the first time. 12 risk factors extracted through literature and expert involvement are classified as decision-making, implementation, and operation stages. Then, subjective judgements are converted into hesitant fuzzy linguistic term sets under Pythagorean fuzzy environment. And the best worst method combined with the interpretive structure model is firstly introduced to calculate relative importance based on mutual influence. Results indicate that photovoltaic and heat storage stations are generally the safest alternatives, with the lowest risk level ranging from 0.7166 to 0.8073 and hesitant degree from 0.3473 to 0.4248. Moreover, risk response measures for each alternative and policy implications for local governments are first raised to put forward AMUEP development. The analytical framework could also be applied elsewhere for smooth rehabilitation of abandoned mines.

Evaluation of Conspiracy Beliefs, Vaccine Hesitancy, and Willingness to Pay towards COVID-19 Vaccines in Six Countries from Asian and African Regions: A Large Multinational Analysis.

Vaccination protects people from serious illness and associated complications. Conspiracy theories and misinformation on vaccines have been rampant during the COVID-19 pandemic and are considered significant drivers of vaccine hesitancy. Since vaccine hesitancy can undermine efforts to immunize the population against COVID-19 and interferes with the vaccination rate, this study aimed to ascertain the COVID-19-vaccine-related conspiracy beliefs, vaccine hesitancy, views regarding vaccine mandates, and willingness to pay for vaccines among the general population. A web-based, cross-sectional survey was conducted (April−August 2021) among the adult population in six countries (Pakistan, Saudi Arabia, India, Malaysia, Sudan, and Egypt). Participants were recruited using an exponential, non-discriminate snowball sampling method. A validated self-completed electronic questionnaire was used for the data collection. All the participants responded to questions on various domains of the study instrument, including conspiracy beliefs, vaccine hesitancy, and willingness to pay. The responses were scored according to predefined criteria and stratified into various groups. All data were entered and analyzed using SPSS version 22. A total of 2481 responses were included in the study (Pakistan 24.1%, Saudi Arabia 19.5%, India 11.6%, Malaysia 8.1%, Sudan 19.3%, and Egypt 17.3%). There was a preponderance of participants ≤40 years old (18−25 years: 55.8%, 26−40 years: 28.5%) and females (57.1%). The average score of the COVID-19 vaccine conspiracy belief scale (C19V-CBS) was 2.30 ± 2.12 (median 2; range 0−7). Our analysis showed that 30% of the respondents were found to achieve the ideal score of zero, indicating no conspiracy belief. The mean score of the COVID-19 vaccine hesitancy scale (C19V-HS) was 25.93 ± 8.11 (range: 10−50). The majority (45.7%) had C19V-HA scores of 21−30 and nearly 28% achieved a score greater than 30, indicating a higher degree of hesitancy. There was a significant positive correlation between conspiracy beliefs and vaccine hesitancy (Spearman’s rho = 0.547, p < 0.001). Half of the study population were against the vaccine mandate. Respondents in favor of governmental enforcement of COVID-19 vaccines had significantly (p < 0.001) lower scores on the C19V-CBS and C19V-HS scale. Nearly 52% reported that they would only take vaccine if it were free, and only 24% were willing to pay for COVID-19 vaccines. A high prevalence of conspiracy beliefs and vaccine hesitancy was observed in the targeted countries. Our findings highlight the dire need for aggressive measures to counter the conspiracy beliefs and factors underlying this vaccine hesitancy.

A Study on Intuitionistic Fuzzy Critical Path Problems Through Centroid Based Ranking Method

In this study the intuitionistic fuzzy version of the critical path method has been proposed to solve networking problems with uncertain activity durations. Intuitionistic fuzzy set [1] is an extension of fuzzy set theory [2] unlike fuzzy set, it focuses on degree of belonging, the degree of nonbelonging or non-membership function and the degree of hesitancy which helps the decision maker to adopt the best among the worst cases. Trapezoidal and the triangular intuitionistic fuzzy numbers are utilized to describe the uncertain activity or task durations of the project network. Here trapezoidal and triangular intuitionistic fuzzy numbers are converted into their corresponding parametric form and applying the proposed intuitionistic fuzzy arithmetic operations and a new method of ranking based on the parametric form of intuitionistic fuzzy numbers, the intuitionistic fuzzy critical path with vagueness reduced intuitionistic fuzzy completion duration of the project has been obtained. The authentication of the proposed method can be checked by comparing the obtained results with the results available in pieces of literature.

COVID-19 Vaccine Acceptors, Refusers, and the Moveable Middle: A Qualitative Study from Central Texas.

COVID-19 has caused excessive morbidity and mortality worldwide. COVID-19 vaccines, including the two mRNA vaccines, were developed to help mitigate COVID-19 and to move society towards herd immunity. Despite the strong efficacy and effectiveness profile of these vaccines, there remains a degree of vaccine hesitancy among the population. To better understand hesitancy associated with COVID-19 vaccines and to delineate between those who are vaccine acceptors, vaccine refusers, and the moveable middle, we conducted a cross-sectional survey to understand respondents’ decision to receive, or not, a COVID-19 vaccine at the onset of mRNA vaccine availability in Central Texas. A total of 737 individuals responded, with 685 responses classified to one of eight domains: A: End to the Pandemic (n = 48); B: Trust in Medical Community (n = 27); C: Illness-Focused Perceptions (n = 331); D: Social Motivation (n = 54); E: Vaccine-Focused Perceptions (n = 183); F: Knowledge Gap (n = 14); G: Underlying Health Concern (n = 9); and H: Undecided (n = 19). Vaccine acceptors (n = 535) were primarily represented in domains A–E, while vaccine refusers (n = 26) were primarily represented in domains C, E, G, and H. The moveable middle (n = 124) was primarily represented by domains C–H. These findings show clear delineations between vaccine acceptors, vaccine refusers, and the moveable middle across eight domains that can assist public health professionals in addressing vaccine hesitancy.

Hesitancy for receiving regular SARS-CoV-2 vaccination in UK healthcare workers: a cross-sectional analysis from the UK-REACH study

BackgroundRegular vaccination against SARS-CoV-2 may be needed to maintain immunity in ‘at-risk’ populations, which include healthcare workers (HCWs). However, little is known about the proportion of HCWs who might be hesitant about receiving a hypothetical regular SARS-CoV-2 vaccination or the factors associated with this hesitancy.MethodsCross-sectional analysis of questionnaire data collected as part of UK-REACH, a nationwide, longitudinal cohort study of HCWs. The outcome measure was binary, either a participant indicated they would definitely accept regular SARS-CoV-2 vaccination if recommended or they indicated some degree of hesitancy regarding acceptance (probably accept or less likely). We used logistic regression to identify factors associated with hesitancy for receiving regular vaccination.ResultsA total of 5454 HCWs were included in the analysed cohort, 23.5% of whom were hesitant about regular SARS-CoV-2 vaccination. Black HCWs were more likely to be hesitant than White HCWs (aOR 2.60, 95%CI 1.80–3.72) as were those who reported a previous episode of COVID-19 (1.33, 1.13–1.57 [vs those who tested negative]). Those who received influenza vaccination in the previous two seasons were over five times less likely to report hesitancy for regular SARS-CoV-2 vaccination than those not vaccinated against influenza in either season (0.18, 0.14–0.21). HCWs who trusted official sources of vaccine information (such as NHS or government adverts or websites) were less likely to report hesitancy for a regular vaccination programme. Those who had been exposed to information advocating against vaccination from friends and family were more likely to be hesitant.ConclusionsIn this study, nearly a quarter of UK HCWs were hesitant about receiving a regular SARS-CoV-2 vaccination. We have identified key factors associated with hesitancy for regular SARS-CoV-2 vaccination, which can be used to identify groups of HCWs at the highest risk of vaccine hesitancy and tailor interventions accordingly. Family and friends of HCWs may influence decisions about regular vaccination. This implies that working with HCWs and their social networks to allay concerns about SARS-CoV-2 vaccination could improve uptake in a regular vaccination programme.Trial registrationISRCTN Registry, ISRCTN11811602.

Improving the Accuracy of Forecasting Models Using the Modified Model of Single-Valued Neutrosophic Hesitant Fuzzy Time Series

Proposed in this study is a modified model for a single-valued neutrosophic hesitant fuzzy time series forecasting of the time series data. The research aims at improving the previously presented single-valued neutrosophic hesitant fuzzy time series (SVNHFTS) model by including several degrees of hesitancy to increase forecasting accuracy. The Gaussian fuzzy number (GFN) and the bell-shaped fuzzy number (BSFN) were used to incorporate the degree of hesitancy. The cosine measure and the single-valued neutrosophic hesitant fuzzy weighted geometric (SVNHFWG) operator were applied to analyze the possibilities and pick the best one based on the neutrosophic value. Two data sets consist of the short and low-frequency time series data of student enrollment and the long and high-frequency data of ten major cryptocurrencies. The empirical result demonstrated that the proposed model provides higher efficiency and accuracy in forecasting the daily closing prices of ten major cryptocurrencies compared to the S-ANFIS, ARIMA, and LSTM methods and also outperforms other FTS methods in predicting the benchmark student enrollment dataset of the University of Alabama in terms of computation time, the Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and the Root Mean Square Error (RMSE).

An Innovative Approach to the Intuitionistic Fuzzy PROMETHEE Method

Intuitionistic fuzzy PROMETHEE is a method that provides us numerous advantages thanks to its benefits such as allowing the researcher to observe the positive and negative rankings simultaneously, expressing the degree of hesitation, changing the significance weights of the criteria and using different methods when identifying the significance levels for each criterion, putting the degree of hesitation of significance weights into action, and enabling decision makers, who are given the opportunity to use different criteria types and different criteria types for alternatives and criteria, to establish a unique system. In line with the aims of this paper, an original application has been made and an innovative approach has been proposed in order to demonstrate the usefulness of the intuitionistic fuzzy PROMETHEE method.

Probabilistic hesitant fuzzy multi-attribute decision-making method based on improved distance measurement

In order to solve the problem of multi-attribute decision-making with unknown weights under probabilistic hesitant fuzzy information, considering the shortcomings of the existing probabilistic hesitant fuzzy distance measure, such as weak distinguishing ability, a probabilistic hesitant fuzzy multi-attribute decision-making method based on improved distance measures is proposed. Firstly, the hesitancy degree of probabilistic hesitant fuzzy element and the improved difference measure of probabilistic hesitant fuzzy element are defined, and an improved probabilistic hesitant fuzzy distance measure based on hesitancy degree, incompleteness degree and improved difference measure is proposed. Secondly, based on the improved distance measure, a mathematical programming model with the goal of minimizing the relative approach degree is con-structed to determine the attribute weights of evaluation indexes in multi-attribute decision making problems. Using it as a base, a new probabilistic hesitant fuzzy multi-attribute decision-making method is proposed by combining the improved probabilistic hesitant fuzzy distance measure with the compromise ratio method. Finally, the proposed method is applied to the problem of green supplier selection, and the feasibility and effectiveness of the proposed method are verified by case analysis and comparison with other methods.

A novel interval-valued spherical fuzzy CODAS: Reopening readiness evaluation of academic units in the era of COVID-19

Combinative distance-based assessment (CODAS) is a multi-criteria decision-making (MCDM) method that is based on the Euclidean and Hamming distances of alternatives from the average scores of attributes. Spherical fuzzy sets, as the recent extensions of ordinary fuzzy sets, were developed based on Pythagorean and neutrosophic sets and enable decision-makers to express their membership, non-membership, and hesitancy degrees independently and in a larger domain than most other fuzzy extensions. This paper proposes a new interval-valued spherical fuzzy CODAS method and provides extra space for catching the vagueness in the nature of the problem. The feasibility and practicality of the proposed model are illustrated with an application for evaluating the reopening readiness of academic units for campus education in the era of COVID-19. Three decision-makers from a higher education institution evaluate four academic units with respect to five strategic criteria and prioritize them according to their readiness levels for the campus type of education. Sensitivity and comparative analyses, theoretical and practical contributions, limitations, and future research avenues are also presented in the study.

New aggregation functions for spherical fuzzy sets and the spherical fuzzy distance within the MULTIMOORA method with applications

This article develops a novel approach for multi-objective optimization on the basis of ratio analysis plus the full multiplicative form (MULTIMOORA) using spherical fuzzy sets (SFSs) to obtain proper evaluations. SFSs surpass Pythagorean and intuitionistic fuzzy sets in modeling human cognition since the degree of hesitation is expressed explicitly in a three-dimensional space. In the spherical fuzzy environment, the implementation of the MULTIMOORA encounters two major problems in the aggregation operators and the distance measures that might lead to erroneous results. The extant aggregation operators in some cases can result in a biased evaluation. Therefore, two aggregation functions for SFSs are proposed. These functions guarantee balanced evaluation and avoid false ranking. In the reference point technique, when comparing SFSs, being closer to the ideal solution does not necessarily imply an SFS with a better score. To make up for this drawback, two reference points are employed instead of one, and the distance is not expressed as a crisp value but as an SFS instead. To overcome the disadvantages of the dominance theory in large-scale applications, the results of the three techniques are aggregated to get the overall utility on which the ranking is based. The illustration and validation of the proposed spherical fuzzy MULTIMOORA are examined through two applications, personnel selection, and energy storage technologies selection. The results are compared with the results of other methods to explicate the adequacy of the proposed method and validate the results.

High Frequency of COVID-19 Vaccine Hesitancy among Canadians Immunized for Influenza: A Cross-Sectional Survey.

We assessed the frequency and correlates of COVID-19 vaccine hesitancy before Canada’s vaccine rollout. A cross-sectional vaccine hesitancy survey was completed by consecutive patients/family members/staff who received the influenza vaccine at McGill University affiliated hospitals. Based on the self-reported likelihood of receiving a future vaccine (scale 0–10), the following three groups were defined: non-hesitant (score 10), mildly hesitant (7.1–9.9), and significantly hesitant (0–7). Factors associated with vaccine hesitancy were assessed with multivariate logistic regression analyses and binomial logistic regression machine learning modelling. The survey was completed by 1793 people. Thirty-seven percent of participants (n = 669) were hesitant (mildly: 315 (17.6%); significantly: 354 (19.7%)). Lower education levels, opposition and uncertainty about vaccines being mandatory, feelings of not receiving enough information about COVID-19 prevention, perceived social pressure to get a future vaccine, vaccine safety concerns, uncertainty regarding the vaccine risk-benefit ratio, and distrust towards pharmaceutical companies were factors associated with vaccine hesitancy. Vaccine safety concerns and opposition to mandatory vaccinations were the strongest correlates of vaccine hesitancy in both the logistic regressions and the machine learning model. In conclusion, in this study, over a third of people immunized for influenza before the COVID-19 vaccine rollout expressed some degree of vaccine hesitancy. Effectively addressing COVID-19 vaccine safety concerns may enhance vaccine uptake.

Application of Mathematical Modeling in Multi Criteria Decision Making Process: Intuitionistic Fuzzy PROMETHEE

In this paper, the intuitionistic fuzzy PROMETHEE method is explained in detail and an original application has been made. The aim of the paper is to bring innovation to the evaluation system in the field of education by using the intuitionistic fuzzy PROMETHEE method. The advantages of using the PROMETHEE method, which is one of the many methods used in multi-criteria decision-making problems, in the intuitionistic fuzzy sense are explained in detail. Intuitionistic fuzzy PROMETHEE is a method that attracts our attention thanks to its benefits such as allowing the researcher to observe the positive and negative rankings simultaneously, expressing the degree of hesitation, changing the significance weights of the criteria, and using different methods when identifying the significance levels for each criterion, putting the degree of hesitation of significance weights into action, and enabling decision-makers, who are given the opportunity to use different criteria types and different criteria types for alternatives and criteria, to establish a unique system; moreover, the method also provides us numerous advantages while using it in our application area. It is of great importance for decision-makers to determine the specific importance level for each criterion. In this paper, controlled sets are used to express the importance of the criteria in the form of intuitionistic fuzzy values. The intuitionistic fuzzy-based PROMETHEE algorithm, aiming to contribute to the education system by examining the factors affecting the students' achievement, is a unique algorithm, and it is the first to shed light on various researchers.

Neutrosophic QUALIFLEX based on neutrosophic hesitancy index for selecting a potential antivirus mask supplier over COVID-19 pandemic.

In order to handle simultaneously the cardinal and ordinal information in decision-making process, QUALIFLEX (QUALItative FLEXible multiple criteria method) is a very well-known decision-making approach. In this work, we extend the classical QUALIFLEX method to neutrosophic environment and develop a neutrosophic QUALIFLEX (N-QUALIFLEX) method that uses the newly defined distance-based comparison approach. It is highly effective in solving multi-criteria decision problems in which both ratings of alternatives on criteria and weights of criteria are single-valued neutrosophic numbers (SVNNs), and their aggregated values are single-valued neutrosophic hesitant fuzzy numbers (SVNHFNs). A neutrosophic hesitancy index (NHI) of a SVNHN is introduced based on degrees of the truth-membership, indeterminacy-membership and falsity-membership, which is used to measure the degree of hesitancy of SVNHN. Considering the NHIS of SVNHFNs, we propose a distance-based comparison approach to determine the magnitude of the SVNHFNs. Then, we apply the comparison approach to define the concordance/discordance index, the weighted concordance/discordance index and the comprehensive concordance/discordance index that are steps of the developed N-QUALIFLEX. By taking all possible permutations of alternatives with respect to the level of concordance/discordance into account, we determine the order of alternatives in final decision. Finally, a practical example on antivirus mask selection over the COVID-19 pandemic is provided to present the effectiveness and applicability of the proposed method, and a comparative study is conducted to show the advantages of the proposed method over other existing methods.