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This study contributes to the complex adaptive system theory by offering a valid hierarchical model to evaluate the theory's important features related to resilience. The garment industry in Bangladesh encountered disruption in the supply chain during the COVID-19 pandemic and the supply chain competencies played a vital role in overcoming the crisis. Limited studies are built on a solid theoretical foundation and considered supply chain competencies in assessing supply chain resilience. This study aims to develop a multi-criteria hierarchical measurement structure by considering the supply chain competencies to evaluate supply chain resilience. Fuzzy Delphi method and Fuzzy importance and performance analysis approach were applied for the study purpose. Findings reveal health and safety management, information management system, business intelligence, innovation capabilities management, technological innovation, and artificial intelligence as critical criteria, and data, information, and computing, technological innovation and adaptation are critical aspects that require improvement. [ FROM AUTHOR] Copyright of Journal of Industrial & Production Engineering is the property of Taylor & Francis Ltd 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.)
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Intuitionistic fuzzy set (IFS) theory can be applied for multi-aspect systems due to its capability to address uncertainty and incomplete information in terms of membership and non-membership degrees. Unfortunately, classical Γ-structures cannot handle fuzzy and imprecise information in real problems. In fact, there is no rigorous base to practically express the effectiveness of multi-attribute systems in IFS environment. Here, we develop a generalized IFS with the notion of Γ-module called intuitionistic fuzzy Γ-submodule (IFΓM) to establish a novel "Global electronic (e)-Commerce (GeC) Theory”. To simplify the analysis of parameters, (α,β)-cut representation is proposed in terms of comprehensive distribution of fuzzy number for the classification of components. On the other hand, Cartesian product is implemented to correspond the elements. Substantial properties of IFΓM including (α,β)-cut, Cartesian product and t-intuitionistic fuzzy Γ-submodule (t-IFΓM) are characterized with illustrative examples to extend the framework of IFΓM, where (α,β)-cut and support t-IFΓM are verified to be Γ-submodules based on the properties of IFΓM. Through Γ-module homomorphism, image and inverse image, the parametric connections between (α,β)-cuts are systematically investigated. In addition, a mathematical relationship between the Cartesian product and (α,β)-cut is determined. The overlapping intersection of a collection of t-IFΓM is proved to be t-IFΓM, and the image and inverse image are preserved under Γ-module homomorphism. As global e-trades are increasingly expanding after the recent coronavirus disease 2019 (COVID-19) hit, with the growth of 26.7-trillion dollars, businesses are required to transform their traditional functional natures to online (or blended) strategies for cost efficiency and self-survival in the present competitive environment. Therefore, compared to recent studies on IFS in the context of Γ-structures, the main contribution of this study is to provide a theoretical basis for the establishment of a new GeC Theory through the developed IFΓM method and Γ-module M which targets the purchasing rate of customers through e-commerce companies. In the end, the performance of the proposed method in terms of upper and lower cut, t-intuitionistic fuzzy set, support and IFΓM model, is analyzed in the developed GeC Theory. The proposed GeC Theory is validated using real datasets of e-commerce mega companies, i.e., Amazon, Alibaba, eBay, Shopify. They are characterized based on the amount of online shopping by samples (individuals). Compared to the existing methods, the GeC approach is an effective IFS-based method for complex systems with uncertainty. © 2023 Elsevier Ltd
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With the introduction of the new retail model and the explosion of COVID-19, more and more community residents are using fresh food e-commerce companies to buy the fresh produce they need on a daily basis. In this paper, three fresh produce e-commerce companies with a high market share were selected as research subjects and their company financial reports were used as raw data, and then the intra-city distribution capability of fresh food e-commerce companies was studied based on the raw data. Firstly, the weights of the primary and secondary indicators were calculated using the hierarchical analysis and entropy methods respectively, and the weights were fused. After that, a fuzzy synthetic evaluation of each of the three fresh food e-commerce companies was conducted, which in turn quantified the evaluation results. Finally, the quantified evaluation results are compared and appropriate recommendations are given for each fresh food e-commerce company. © 2022 IEEE.
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Reports an error in "An approach to MCGDM based on multi-granulation Pythagorean fuzzy rough set over two universes and its application to medical decision problem" by Bingzhen Sun, Sirong Tong, Weimin Ma, Ting Wang and Chao Jiang (Artificial Intelligence Review, 2022[Mar], Vol 55[3], 1887-1913). In the original article, the third and fourth author's affiliation were published incorrectly and the correct affiliations are given in this correction. (The following abstract of the original article appeared in record 2021-74641-001). Exploring efficiency approaches to solve the problems of decision making under uncertainty is a mainstream direction. This article explores the rough approximation of the uncertainty information with Pythagorean fuzzy information on multi-granularity space over two universes combined with grey relational analysis. Based on grey relational analysis, we present a new approach to calculate the relative degree or the attribute weight with Pythagorean fuzzy set and give a new descriptions for membership degree and non-membership. Then, this paper proposes a multi-granulation rough sets combined with Pythagorean fuzzy set, including optimistic multi-granulation Pythagorean fuzzy rough set, pessimistic multi-granulation Pythagorean fuzzy rough set and variable precision Pythagorean fuzzy rough set. Several basic properties for the established models are investigated in detail. Meanwhile, we present an approach to solving the multiple-criteria group decision making problems with fuzzy information based on the proposed model. Eventually, a case study of psychological evaluation of health care workers in COVID-19 show the principle of the established model and is utilized to verify the availability. The main contributions have three aspects. The first contribution of an approach of calculating the attribute weight is presented based on Grey Relational Analysis and gives a new perspective for the Pythagorean fuzzy set. Then, this paper proposes a mutli-granulation rough set model with Pythagorean fuzzy set over two universes. Finally, we apply the proposed model to solving the psychological evaluation problems. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
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To compete with the speedy revolution of high technological innovation and restarted economy for the post-COVID-19 period in China, governments and organizations should be active in attracting high-tech talent to enhance independent and indigenous R&D capability. Talent agglomeration effectiveness is the strongest endogenous force pushing competitiveness for regional economy and industrial development. Due to the complexity of high-tech talent agglomeration, there are still considerable gaps to evaluate the incentive factors. This study evaluates the influential indicator system by using a hybrid fuzzy set theory extended Analytic Hierarchy Process (AHP) approach for proximity to reality from individual, organizational and environmental dimensions. The statistical analysis is adopted to verify the results of fuzzy AHP analysis. This research explores the founding that individual incentives are more important than environmental factors, and environmental incentives are more influential than organizational incentives. Job satisfaction, welfare system, and geographical location are the highest ranking factors. High-tech start-ups should give priority to combine geographical location with political support to reserve site selection or firm relocation for a great effectiveness of high-tech talent agglomeration. © 2022 Elsevier Ltd
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The aim of the research is to propose a procedure for the construction of a synthetic measure of subjective household poverty. The proposed procedure takes into account the aggregation of factors describing the past, present, and future, making it easier to discern the issue of the sense of deprivation of needs. To this end, methods based on the fuzzy set theory were used. The fuzzy TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method was applied to the construction of the synthetic measure of subjective household poverty. The procedure also uses fuzzy hierarchical analysis to calculate the weight system of variables. The proposed procedure was used to assess the level of subjective household poverty in Poland one year after the start of the COVID-19 pandemic. The research was based on data collected using the CAWI (Computer-Assisted Web Interview) method in April 2021. The use of the fuzzy approach for the assessment of subjective poverty makes it possible to define its level more precisely than with the standard measurement. The proposed synthetic measure of subjective poverty is an attempt to explain poverty from the perspective of the poor. The quantitative measurement of subjective poverty at the micro (household) level is an important tool for assessing anti-poverty policy. Moreover, the subjective poverty measure can also be used as a measure of poverty sensitivity and can be the basis for formulating policies and strategies for reducing poverty. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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This article is the first step to formulate such higher dimensional mathematical structures in the extended fuzzy set theory that includes time as a fundamental source of variation. To deal with such higher dimensional information, some modern data processing structures had to be built. Classical matrices (connecting equations and variables through rows and columns) are a limited approach to organizing higher dimensional data, composed of scattered information in numerous forms and vague appearances that differ on time levels. To extend the approach of organizing and classifying the higher dimensional information in terms of specific time levels, this unique plithogenic crisp time-leveled hypersoft-matrix (PCTLHS matrix) model is introduced. This hypersoft matrix has multiple parallel layers that describe parallel universes/realities/information on some specific time levels as a combined view of events. Furthermore, a specific kind of view of the matrix is described as a top view. According to this view, i-level cuts, sublevel cuts, and sub-sublevel cuts are introduced. These level cuts sort the clusters of information initially, subject-wise then attribute-wise, and finally time-wise. These level cuts are such matrix layers that focus on one required piece of information while allowing the variation of others, which is like viewing higher dimensional images in lower dimensions as a single layer of the PCTLHS matrix. In addition, some local aggregation operators are designed to unify i-level cuts. These local operators serve the purpose of unifying the material bodies of the universe. This means that all elements of the universe are fused and represented as a single body of matter, reflecting multiple attributes on different time planes. This is how the concept of a unified global matter (something like dark matter) is visualized. Finally, to describe the model in detail, a numerical example is constructed to organize and classify the states of patients with COVID-19.
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The concept of occupational risk assessment is related to the analysis and prioritization of the hazards arising in a production or service facility and the risks associated with these hazards;risk assessment considers occupational health and safety (OHS). Elimination or reduction to an acceptable level of analyzed risks, which is a systematic and proactive process, is then put into action. Although fuzzy logic-related decision models related to the assessment of these risks have been developed and applied a lot in the literature, there is an opportunity to develop novel occupational risk assessment models depending on the development of new fuzzy logic extensions. The 3,4-quasirung fuzzy set (3,4-QFS) is a new type of fuzzy set theory emerged as an extension of the Pythagorean fuzzy sets and Fermatean fuzzy sets. In this approach, the sum of the cube of the degree of membership and the fourth power of the degree of non-membership must be less than or equal to 1. Since this new approach has a wider space, it can express uncertain information in a more flexible and exhaustive way. This makes this type of fuzzy set applicable in addressing many problems in multi-criteria decision making (MCDM). In this study, an occupational risk assessment approach based on 3,4-quasirung fuzzy MCDM is presented. Within the scope of the study, the hazards pertaining to the flight and ground training, training management, administrative and facilities in a flight school were assessed and prioritized. The results of existing studies were tested, and we considered both Pythagorean and Fermatean fuzzy aggregation operators. In addition, by an innovative sensitivity analysis, the effect of major changes in the weight of each risk parameter on the final priority score and ranking of the hazards was evaluated. The outcomes of this study are beneficial for OHS decision-makers by highlighting the most prioritized hazards causing serious occupational accidents in flights schools as part of aviation industry. The approach can also be suggested and adapted for production and service science environments where their occupational health & safety are highly required.
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Entities in public sector supply chains (SCs) often operate independently despite having interdependent objectives. Such a fragmented operational design poses several problems magnified by the presence of necessary public health measures fueled by COVID-19. This work contributes to the domain literature by introducing an overarching framework for synthesizing strategies in public sector SCs. The underlying component is the translation of information from the upstream to the downstream entities of the SCs, which is carried out by a Kano-enhanced quality function deployment. The proposed framework introduces intuitionistic fuzzy (IF) decision maps with the aid of the full consistency method to incorporate inherent interrelationships among strategies in the translation agenda. Under an IF environment that better captures judgment uncertainties, an actual case study of a multi-level public sector SC motivated by a government-funded project under the COVID-19 pandemic is demonstrated in this work. Findings of the case suggest that the government prioritizes meeting all project objectives. This requirement is reflected in the downstream SC. The project planning entity focuses on creating an overarching plan of operations, material request entity on complying with government procurement protocols, and maintaining public health and safety in operations for the procurement entity. Results show the effective synthesis of strategies across the SC, ensuring SC integration and collaboration. The case study demonstrates that maintaining public health and safety is a significant component of post-COVID-19 public sector SCs. Several practical insights on the synthesis of public sector SC strategies are also provided in this work.
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According to the latest ranking of TOP 1, 000 World Banks 2020, the leading position of China's four major banks have increased over their US counterparts: Industrial and Commercial Bank of China (ICBC) topped the list, followed by China Construction Bank (CCB) (No. 2), Agricultural Bank of China (ABOC) (No. 3) and Bank of China (BOC) (No. 4). Due to the influence of COVID-19, banks will come under more pressure as the result of the fiercer competition between them. Although the influence of Chinese banks in the world has increased, their comprehensive strength still needs to be improved. However, at present, there are relatively few academic evaluations on the comprehensive strength of Chinese banks, and the evaluation system is not perfect enough, and the evaluation indicators are not plentiful enough. Therefore, this paper analyses China's four major state-owned banks' data from 2015 to 2019 and establishe a fuzzy comprehensive evaluation model based on the analytic hierarchy process. The final conclusion that is drawn by this paper is about the ranking of these four banks' comprehensive competitiveness: the strongest is Industrial and Commercial Bank of China, followed by China Construction Bank and Bank of China, and the weakest one is Agricultural Bank of China. The evaluation systemof this paper verifies the rationality of the international ranking and has important reference significance for banks to understand their own status and enhance their comprehensive competitiveness. © 2021 IEEE.
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Мета. Урахування фактору випадковосл сощальних процесш при прогнозуванш попиту на електричну енерпю для зменшення похибки. Методика. Апарат математично! статистики, методш лшшного програмування, теорп нечггких множин i методiв експертного оцшювання, теорй' шкал, Байесовський п1дх1д до моделей прогнозування, комп'ютерне моделювання. Результаты. Проаналiзована динамiка споживання електрично! енергп за рiзнi перiоди часу, встановлено вплив фактору пандемп на процес формування попиту на електричну енерпю. Розроблена вербально-числова шкала для комплексного оцшювання впливу на попит на електричну енерпю такого складного сощального явища, як пандемш. Сформована модель прогнозування попиту на електричну енерпю з використанням Байесовського подходу та ощнки експерта, що дозволила використати ретроспективш данi споживання електрично! енергп та врахувати невизначенiсть соцiального фактору впливу пандемп. Наукова новизна. Набула подальшого розвитку модель прогнозування попиту на електричну енерпю, яка, на вщмшу в1д iнших, ураховуе фактор випадковостi соцiальних процеив i вербально-числову шкалу, що дозволяе зменшити похибку прогнозування споживання електрично! енергп. Практична значимтсть. Результата дослщження кориснi для пщприемств, що спецiалiзуються на генерацй', передачi й розподшу електрично! енергп споживачам. Представленi результата надають можливють зменшити похибку прогнозування попиту на електричну енерпю при врахуванш фактору випадковосл сощальних процешв.Alternate :Purpose. Taking into account the factor of randomness of social processes when forecasting the demand for electric energy to reduce the error. Methodology. Apparatus of mathematical statistics, linear programming methods, fuzzy set theory and expert assessment methods, scale theory, Bayesian approach to forecasting models, computer modeling. Findings. The dynamics of consumption of electric energy for different periods of time is analyzed, the influence of the pandemic factor on the process of formation of demand for electric energy is established. A verbal-numerical scale has been developed for a comprehensive assessment of the impact on the demand for electric energy of such a complex social phenomenon as a pandemic. A model for forecasting the demand for electrical energy was formed using the Bayesian approach and an expert's assessment, which made it possible to use retrospective data on electrical energy consumption and take into account the uncertainty of the social factor influencing the pandemic. Originality. The model for forecasting the demand for electrical energy has been further developed, which, unlike others, takes into account the factor of randomness of social processes and a verbal-numerical scale, which makes it possible to reduce the error in predicting the consumption of electrical energy. Practic l value. The research results are useful for enterprises specializing in the generation, transmission and distribution of electrical energy to consumers. The presented results make it possible to reduce the error in forecasting the demand for electric energy, taking into account the factor of randomness of social processes.
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Because there are many types of accommodation facilities in Jiuzhen Mountain Tourist Resort in Wuhan, the evaluation indexes that affect the accommodation output benefit in Jiuzhen Mountain Tourist Resort, including quality grade, brand effect, government policy, market supply and demand relationship, business subject and consumer habits, should be comprehensively considered. In this paper, the fuzzy comprehensive evaluation method is used to evaluate the outgoing benefits of accommodation products in Jiuzhen Mountain Tourist Resort. The results show that the hotel products within the geographical area have high output efficiency, but there is still the problem of uneven development, so the hotel resources should be optimally allocated, and the service and management should be improved to boost the overall efficiency of the tourism resort accommodation industry. © 2021 ACM.
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Against the Covid-19 background, vaccine safety has aroused the wild attention of all social areas. However, the factors that cause vaccine safety risks are complicated and meanwhile, data is difficult to obtain, making it a challenge for analyzing vaccine safety risks quantitatively. This paper concretises the issue of vaccine system safety by creatively proposing an analytical framework for the problem of uncertainty. First, the paper focuses on the whole process of vaccine safety, analyses risk factors affecting vaccine safety in development, approval, production, transportation, and supervision of vaccines in order to build a vaccine risk assessment system. The proposed framework is then used to construct a Bayesian network early warning system for vaccine risk. To address the difficulty of obtaining data, the probability of safety risks occurring throughout the process is calculated by combining expert knowledge and fuzzy set theory to obtain uncertainty data. In response to structural complexity, a comprehensive framework is constructed using fault trees and Bayesian networks to capture the correlation between risk factors. This analytical framework can provide guidance to governments and vaccine-related companies in their decision-making to prevent vaccine safety issues. Finally, sensitivity analysis revealed a high probability of vaccine risk in the transport process. © 2021 IEEE.
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Reply by the current authors to the comments made by Baruch Fischhoff (see record 2022-15515-002), Stephen B. Broomell and Gretchen B. Chapman (see record 2022-15515-003), Kathleen Hall Jamieson (see record 2022-15515-004), Dietram A. Scheufele et al. (see record 2022-15515-005), Christopher R. Wolfe (see record 2022-15515-006) and Valerie A. Thompson et al. (see record 2022-15515-007) on the original article (see record 2022-15515-001). Imagine gathering together the most thoughtful scholars spanning the behavioral sciences to address the conceptual frontier as it pertains to human behavior and COVID-19, including risk communication, prevention, and vaccination. Imagine that this group had vast experience in understanding the mechanisms underlying behavior and in applying this understanding in policy and practice. Collectively, they summarize key concepts that can be applied in programs to combat COVID-19 and provide a blueprint for future research. A theme of these articles is that integrative interdisciplinary work is required to address this massive public health problem. Many highlight how fuzzy-trace theory (FTT) accomplishes this goal by weaving together cognitive, social, emotional, and neuroscientific constructs to explain multiply determined decisions that involve risk, applying falsifiable models. Others encourage looking beyond cognition, and they raise questions about the efficacy of current behavioral theories, including FTT, and whether FTT should be combined with dual-process approaches to achieve greater explanatory and predictive power. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
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Comments on an article by Valerie F. Reyna et al. (see record 2022-15515-001). Even though "knowledge deficit models" have long been proven to be at odds with the best available science, as illustrated again by Reyna et al., they continue to underlie many efforts, especially among science, technology, engineering, and mathematics (STEM) scientists, to communicate with the public. In fact, many of the concerns about a potential misinformation pandemic replicate the same flawed thinking that characterized knowledge deficit models: If citizens only had the correct information, they would make better decisions. When one fails to acknowledge these kinds of intersecting contextual factors as we engage in decision making about misinformation interventions, they also fail to heed the warnings such as those from Reyna et al. about the resonance of pre-existing tropes on social media environments that make the scientific community an easy target for politically-motivated attacks. In fact, as Reyna et al. argue, people can be incorrect or even ignorant about specific details of a topic but can nonetheless usefully understand its "gist," or its bigger-picture meaning. Crucially, these perceptions of the gist influence decisions more than the specific details. In other words, when studies of misinformation interventions-such as prebunking, debunking, or inoculation-operationalize being "misinformed" primarily in terms of one (or even a handful) of individuated evidence-incongruent beliefs, they are probably missing the forest for the trees. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
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Comments on an article by Valerie F. Reyna et al. (see record 2022-15515-001). Reyna and colleagues often argue that the processing of gist tends to be both fast and unconscious (the hallmarks of cognitive autonomy), whereas the processing of verbatim traces is slower and conscious. Their argument about the speed of processing dovetails with the assumption that gist representations are often simple and that reasoners are assumed to use the simplest gist available to them. Given a reasonable ancillary assumption that the processes that act on gist traces are simpler than those that act on verbatim ones, the primary assumption about the relative speed of gist and verbatim processes seems justified. The assumptions made about processing gist and verbatim traces are closely analogous to the DPT assumption about the relative speed of autonomous and WM-dependent reasoning: Like Type 1 processing, gist-based processing tends to be faster and is thus likely to be completed faster than the slower and more deliberate verbatim processing. There is a potentially fruitful alignment of the assumption made by FTT and DPT. The argument is grounded in the principle that theories of representation and processing are incomplete without each other. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
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Comments on an article by Valerie F. Reyna et al. (see record 2022-15515-001). From public health experts to fact-checkers, the communities responsible for protecting the integrity of health knowledge could maximize the memorability and effectiveness of their messaging about viruses, vaccines, and COVID-19 by applying Reyna et al.'s Fuzzy Trace Theory-grounded insights to their work. When Valerie Reyna and her colleagues argue that "reasoning prefers to operate on simple gist, as opposed to exact details" or show that gist, not numbers, is what is remembered, they are referring to "mental representations capturing the bottom-line meaning of information" or experience. The location of the gist and the ability to observe and shape it is different in a study that found that "Facebook sharing of vaccine-related news articles about the Disneyland measles outbreak was predicted most strongly by whether an article incorporated a bottom-line gist". There, the referent is a takeaway explicitly located in a text. An example of a gist was "Parents who don't vaccinate their children due to concerns about side effects can put others at risk-even those who have been vaccinated". (PsycInfo Database Record (c) 2022 APA, all rights reserved)
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Comments on an article by Valerie F. Reyna et al. (see record 2022-15515-001). The diverse applications of fuzzy trace theory demonstrate the power of the approach and the virtuosity of its practitioners. As Reyna et al. note, expectancy-value theories address some elements of fuzzy trace theory, making expectancy-value results potentially useful. However, fuzzy trace applications also require knowledge of other areas of psychology (e.g., self-control, stigma, identity) and the subject matter (e.g., pandemic disease, addiction, concussions). As a result, these applications provide unique opportunities for intra- and interdisciplinary collaboration. These applications require the wisdom needed to use existing research, without making claims beyond what the evidence can support. As a result, fuzzy trace researchers typically avoid sweeping generalizations about what (all) people are like or what (all) people do. Recognizing the diversity of decisions and decision makers, these researchers make conditional assessments of human capability. The success of any design process depends on the skill of the designers. Fuzzy trace interventions succeed by attracting researchers who believe that collaboration with other disciplines can enrich their own and who are more committed to problems than to disciplines. Fuzzy trace research is also faithful to the people being studied. It recognizes the complexity and diversity of their aspirations and circumstances. It listens to them when creating its interventions and research instruments. It evaluates their performance relative to the demands of the decisions that they face. It disciplines its speculations with evidence. Its studies find, rather than show, behavior patterns. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
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Ongoing global Covid-19 pandemic is not only health crisis but the economic challenge. The future of society depends on how successfully the authorities find a balance between imposition of stringent restrictions and economic development. Tax policies play a role in reducing losses caused by the Covid-19 lockdowns. All countries are taking tax measures to mitigate the impact of the effects of Covid-19 pandemic on society. While the Covid-19 pandemic has not yet been defeated, it is too early to draw conclusions about which tax measures against the effects of Covid-19 are efficient. On the other hand, correct trajectory of economic recovery can be missed if not to analyze the other countries experience. The object of this study is tax measures in the European countries against the effects of Covid-19. The subject of the study is the fuzzy set theory to assess the efficiency of tax measures in the European countries against the effects of Covid-19. The aim of the study is to find out which European countries have been more succeeded in tax measures implementing and type of their immediate crisis response. The analysis is carried out in 29 European countries. The result of the study allows to state that the number of tax measures against the effects of Covid-19 does not affect their efficiency and the most popular type of immediate crisis response has been the business cash-flow enhances. © 2022, Politechnika Lubelska. All rights reserved.
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The COVID-19, Coronavirus Disease 2019, emerged as a hazardous disease that led to many causalities across the world. Early detection of COVID-19 in patients and proper treatment along with awareness can help to contain COVID-19. Proposed Fuzzy Cloud-Based (FCB) COVID-19 Diagnosis Assistant aims to identify the patients as confirmed, suspects, or suspicious of COVID-19. It categorized the patients into four categories as mild, moderate, severe, or critical. As patients register themselves online on the FCB COVID-19 DA in real-time, it creates the database for the same. This database helps to improve diagnostic accuracy as it contains the latest updates from real-world cases data. A team of doctors, experts, consultants are integrated with the FCB COVID-19 DA for better consultation and prevention. The ultimate aim of this proposed theory of FCB COVID-19 DA is to take control of COVID-19 pandemic and de-accelerate its rate of transmission among the society.