ABSTRACT
By early 2020, COVID-19 has caused a global pandemic which led to an enormous number of challenges worldwide in various sectors. The Philippine government has implemented multiple quarantine guidelines and travel restrictions to ensure the people's health and safety. However, the International Labour Organization projected an initial economic and labor market disruption affecting 11 million workers, or about 25% of the Philippine workforce, due to the pandemic. Therefore, the government, thru the concerned agencies continues to encourage employers to implement alternative work plans such as a work-from-home (WFH) operation in compliance with the established regulations in line with existing laws and policies. In line with the telecommuting concept, various research has already been performed, however, some were regarded inconclusive and require further study. Hence, in this study, a Web application was developed along with an embedded fuzzy model to evaluate the telecommuting capability assessment of employees. The proposed web application with embedded fuzzy model is capable of providing capability assessment using the four main input variables which are also relatively characterized for possible telecommuting cost assessment. © 2022 IEEE.
ABSTRACT
PurposeThe present situation of COVID-19 pandemic has put the health-care systems under tremendous stress and stringent tests for their ability to offer expected quality of health-care services, as it decides the sustainability and growth of health-care service providers. This study aims to deliver a quantitative framework for service quality assessment in the health-care industry by classifying the health-care service quality parameters into four balanced scorecard (BSC) perspectives.Design/methodology/approachTo determine the service quality for the Indian health-care system, decision-making trial and evaluation laboratory and analytical network process are integrated in a fuzzy environment to contemplate the interaction among BSC perspectives and respective performance measures.FindingsThe results indicate "internal processes” perspective assumes the key role within BSC perspectives, while performance measures "nursing staff turnover” and "staff training” play the key roles. The results also signify that "patient satisfaction” is the most vital issue and can be strongly influenced by measures belonging to the "learning and growth” perspective. In "learning and growth” perspective, "staff training” is the most decisive criteria, very highly influencing "patient satisfaction”, highly influencing "profitability,” "change of cost per patient (both in and out patients)” and "outpatient waiting time” while moderately influencing "staff satisfaction,” "bed occupancy” and "nursing staff turnover”. Moreover, "staff training” criteria have a positive influence on "nursing staff turnover.”Originality/valueThe contributions of this study are in two folds in the domain of quantification of service quality for the health-care system. First, it delivers an assessment framework for Indian health-care service quality. Second, it demonstrates an application of the framework for a case situation and validates the proposed framework.
ABSTRACT
Strategic management has applications in many areas of social life. One of the basic steps in the process of strategic management is formulating a strategy by choosing the optimal strategy. Improving the process of selecting the optimal strategy with MCDM methods and theories that treat uncertainty well in this process, as well as the application of other and different selection criteria, is the basic idea and goal of this research. The improvement of the process of the aforementioned selection in the defense system was carried out by applying a hybrid model of multicriteria decision-making based on methods defining interrelationships between ranked criteria (DIBR) and multiattributive ideal-real comparative analysis (MAIRCA) modified by triangular fuzzy numbers–"DIBR–DOMBI–Fuzzy MAIRCA model.” The DIBR method was used to determine the weight coefficients of the criteria, while the selection of the optimal strategy, from the set of offered methods, was carried out by the MAIRCA method. This was done in a fuzzy environment with the aim of better treatment of imprecise information and better translation of quantitative data into qualitative data. In the research, an analysis of the model's sensitivity to changes in weight coefficients was performed. Additionally, a comparison of the obtained results with the results obtained using other multicriteria decision-making methods was conducted, which validated the model and confirmed stable results. In the end, it was concluded that the proposed MCDM methodology can be used for choosing a strategy in the defense system, that the results of the MCDM model are stable and valid, and that the process has been improved by making the choice easier for decision makers and by defining new and more comprehensive criteria for selection.
ABSTRACT
As a result of artificial intelligence research that started in the 1950s, the need for human beings in all sectors and labor markets constantly decreases. The increase in the total cost of the labor force increases the productivity pressure on the labor. For this reason, the workforce participating in production is expected to be more efficient and productive. For this reason, the loss of labor is carefully monitored and tried to be reduced as much as possible. However, with each passing day, labor losses are inevitable due to personnel turnover, work accidents, dismissals, and absenteeism. Humanity is still struggling, mainly due to the contagious covid-19 virus, which has recently affected the world. Since it is a condition that affects human health, its adverse effects have been observed in many areas where people are present. Especially in this period, unpredictable workforce losses have occurred in the production and service sectors since people are mostly the primary workforce. Since there is no plan and measure for such a situation in most risk planning, it also brings labor losses and costs. In this study, In order to examine the relationship between health problems and loss of labor, the amount of lost labor due to employees who could not come to work due to health-related reasons was tried to be estimated by Fuzzy Logic and ANFIS methods. This study examined three-year absenteeism data of employees in a courier company, and twenty-eight reasons for absenteeism were determined. The amount of labor loss was estimated using Fuzzy Logic and ANFIS methods, using five factors that cause absenteeism. Estimated and actual values were statistically compared with MAD MAPE, MSE, and RMSE performance measurement values. With fuzzy logic, the MAD value is 4.76;the MAPE value is 155.7;The MSE value was calculated as 52.7, and the RMSE value as 7.26. In ANFIS, the MAD value is 3.2, the MAPE value of 86.24, MSE of 27.5;The RMSE value was calculated as 5.25. When the results are compared, it has been seen that the ANFIS method obtains closer estimations than the fuzzy logic method.
ABSTRACT
The coronavirus has spread fast resulting in a worldwide pandemic. Early discovery of positive patients is critical in preventing the pandemic from spreading further, leading to the development of diagnostic technologies that provide rapid and reliable responses for COVID-19 detection. Previous research has shown that chest x-rays are an essential tool for the detection and diagnosis of sirivanoroC (COVID-19) patients. A radiological finding known as ground-glass opacity (GGO), which causes color and texture changes, was discovered in the lung of a person with COVID-19 as a consequence of x-ray tests. An automatic method to assist radiologists is required due to the carelessness of radiologists who work a long time and misdiagnosis resulting in the confusion of findings with different diseases, in this study, were described a new technique to help us with the early diagnosis of COVID-19 using x-rays that is based on fuzzy classification. The skewness, kurtosis, and average statistical features of x-rays of patients in two classes, COVID and Normal, are calculated in the suggested method, and the value ranges for both classes are identified. In the building of a fuzzy logic classifier, three statistical characteristics and value ranges are used as membership functions. The suggested solution, which uses a user-friendly interface, allows for quick and accurate COVID vs Normal (binary classification). Experiments show that our method has a lot of promise for radiologists to validate their initial screening and enhance early diagnosis, isolation, and therapy, which helps prevent infection and contain the pandemic. © 2023 Author(s).
ABSTRACT
The COVID-19 pandemic has presented unprecedented challenges to healthcare systems worldwide. One of the key challenges in controlling and managing the pandemic is accurate and rapid diagnosis of COVID-19 cases. Traditional diagnostic methods such as RT-PCR tests are time-consuming and require specialized equipment and trained personnel. Computer-aided diagnosis systems and artificial intelligence (AI) have emerged as promising tools for developing cost-effective and accurate diagnostic approaches. Most studies in this area have focused on diagnosing COVID-19 based on a single modality, such as chest X-rays or cough sounds. However, relying on a single modality may not accurately detect the virus, especially in its early stages. In this research, we propose a non-invasive diagnostic framework consisting of four cascaded layers that work together to accurately detect COVID-19 in patients. The first layer of the framework performs basic diagnostics such as patient temperature, blood oxygen level, and breathing profile, providing initial insights into the patient's condition. The second layer analyzes the coughing profile, while the third layer evaluates chest imaging data such as X-ray and CT scans. Finally, the fourth layer utilizes a fuzzy logic inference system based on the previous three layers to generate a reliable and accurate diagnosis. To evaluate the effectiveness of the proposed framework, we used two datasets: the Cough Dataset and the COVID-19 Radiography Database. The experimental results demonstrate that the proposed framework is effective and trustworthy in terms of accuracy, precision, sensitivity, specificity, F1-score, and balanced accuracy. The audio-based classification achieved an accuracy of 96.55%, while the CXR-based classification achieved an accuracy of 98.55%. The proposed framework has the potential to significantly improve the accuracy and speed of COVID-19 diagnosis, allowing for more effective control and management of the pandemic. Furthermore, the framework's non-invasive nature makes it a more attractive option for patients, reducing the risk of infection and discomfort associated with traditional diagnostic methods.
ABSTRACT
As technologies advance and the population grows, electrical energy became one of the necessities for many peoples. Because the availability of electrical energy is limited, it requires various ways to be used efficiently. Electrical load monitoring usage in Indonesia still require an electrical officer to come to an electric panel location to record electrical usage. During the COVID-19 pandemic, it is not feasible to locally visit an electric panel because of the many restrictions. Remote monitoring using Internet of Things (IoT) can be used to address the problem. Going further, by knowing the electrical load usage, prediction can be done using fuzzy logic as a way to understand how to use electricity efficiently. Thus, a fuzzy logic load forecasting system IoT is developed in this research. Fuzzy variables used in this system are time of day, days of the week, measured loads, and forecasted loads. The research produced a system that predicts electrical load with one hour of accuracy based on the previous week's data. The average prediction error rate of the system is 9.48%. The implemented system is available on a web server and can be accessed via a web browser, either via a computer or cellphone. The system allows users to monitor and predict electrical load usage regardless of time and place. © 2023, The authors.
ABSTRACT
Purpose: The objective of the research is proposed a methodology to prepare a Zero-Based Budget ( ZBB) for Small and Medium-sized Enterprises (SMEs) in Ecuador, applying fuzzy logic. Design/methodology/approach: A quantitative approach is assumed to show findings derived from the work carried out in these Ecuadorian business units, belonging to non-essential sectors such as wood, textiles and footwear. Fuzzy logic, the technique of expertise, and Trapezoidal Fuzzy Numbers ( TpFN) are used to capture true budget levels. Findings: The results recommend that optimal budget levels can be obtained for SMEs in restrictive and health emergency contexts. Originality/value: As a result of COVID-19 pandemic, markets and demand are contracting causing variations in income and demanding greater rationalization at the level of expenditures. For SMEs is essential prepared income and disbursements estimates. Based on the methodology proposed, predictions are made to achieve the objectives of SMEs. Directors will be able to make more successful decisions for the benefit of their companies, to streamline operations, direct the achievement of objectives, rationalize expenses (costs and expenses), and to project better scenarios in the future before carrying out cost-benefit analysis.
ABSTRACT
Early prediction of COVID-19 infected communities (potential hotspots) is essential to limit the spread of virus. Diagnostic testing has limitations in big populations because it cannot deliver information at a fast enough rate to stop the spread in its early phases. Wastewater based epidemiology (WBE) experiments showed promising results for brisk detection of 'SARS CoV-2' RNA in urban wastewater. However, a systematic and targeted approach to track COVID-19 virus in the complex wastewater networks at a community level is lacking. This research combines graph network (GN) theory with fuzzy logic to determine the chances of a specific community being a COVID-19 hotspot in a wastewater network. To detect 'SARS-CoV-2' RNA, GN divides wastewater network into communities and fuzzy logic-based inference system is used to identify targeted communities. For the propose of tracking, 4000 sample cases from Minnesota (USA) were tested based on various contributing factors. With a probability score of greater than 0.8, 42% of cases were likely to be designated as COVID-19 hotspots based on multiple demographic characteristics. The research enhances the conventional WBE approach through two novel aspects, viz. (1) by integrating graph theory with fuzzy logic for quick prediction of potential hotspot along with its likelihood percentage in a wastewater network, and (2) incorporating the uncertainty associated with COVID-19 contributing factors using fuzzy membership functions. The targeted approach allows for rapid testing and implementation of vaccination campaigns in potential hotspots. Consequently, governmental bodies can be well prepared to check future pandemics and variant spreading in a more planned manner. Supplementary Information: The online version contains supplementary material available at 10.1007/s00477-023-02468-3.
ABSTRACT
PurposeThe Covid-19 prediction process is more indispensable to handle the spread and death occurred rate because of Covid-19. However early and precise prediction of Covid-19 is more difficult because of different sizes and resolutions of input image. Thus these challenges and problems experienced by traditional Covid-19 detection methods are considered as major motivation to develop JHBO-based DNFN.Design/methodology/approachThe major contribution of this research is to design an effectual Covid-19 detection model using devised JHBO-based DNFN. Here, the audio signal is considered as input for detecting Covid-19. The Gaussian filter is applied to input signal for removing the noises and then feature extraction is performed. The substantial features, like spectral roll-off, spectral bandwidth, Mel-frequency cepstral coefficients (MFCC), spectral flatness, zero crossing rate, spectral centroid, mean square energy and spectral contract are extracted for further processing. Finally, DNFN is applied for detecting Covid-19 and the deep leaning model is trained by designed JHBO algorithm. Accordingly, the developed JHBO method is newly designed by incorporating Honey Badger optimization Algorithm (HBA) and Jaya algorithm.FindingsThe performance of proposed hybrid optimization-based deep learning algorithm is estimated by means of two performance metrics, namely testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219.Research limitations/implicationsThe JHBO-based DNFN approach is developed for Covid-19 detection. The developed approach can be extended by including other hybrid optimization algorithms as well as other features can be extracted for further improving the detection performance.Practical implicationsThe proposed Covid-19 detection method is useful in various applications, like medical and so on.Originality/valueDeveloped JHBO-enabled DNFN for Covid-19 detection: An effective Covid-19 detection technique is introduced based on hybrid optimization–driven deep learning model. The DNFN is used for detecting Covid-19, which classifies the feature vector as Covid-19 or non-Covid-19. Moreover, the DNFN is trained by devised JHBO approach, which is introduced by combining HBA and Jaya algorithm.
ABSTRACT
This paper's core objective is to introduce a novel notion called hyperbolic fuzzy set (HFS) where, the grades follow the stipulation that the product of optimistic and pessimistic degree must be less than or equal to one (1), rather than their sum not exceeding one (1) as in case of IFSs. The concept of HFS originates from a hyperbola, which provides extreme flexibility to the decision makers in the representation of vague and imprecise information. It is observed that IFSs, Pythagorean fuzzy sets (PFSs), and q-rung orthopair fuzzy sets (Q-ROFSs) often failed to express the uncertain information properly under some specific situations, while HFS tends to overcome such limitations by being applicable under those perplexed situations too. In this paper, we first define some basic operational laws and few desirable properties of HFSs. Second, we define a novel score function, accuracy function, and also establish some of their properties. Third, a novel similarity and distance measure is proposed for HFSs that are capable of distinguishing between different physical objects or alternatives based on the grounds of "similitude degree” and "farness coefficient”, respectively. Later, the advantages of all of these newly defined measures have been showcased by performing a meticulous comparative analysis. Finally, these measures have been successfully applied in various COVID-19 associated problems such as medical decision-making, antivirus face-mask selection, efficient sanitizer selections, and effective medicine selection for COVID-19. The final results obtained with our newly defined measures comply with several other existing methods that we considered and the decision strategy adopted is simple, logical, and efficient. The significant findings of this study are certain to aid the healthcare department and other frontline workers to take necessary measures to reduce the intensity of the coronavirus transmission, so that we can hopefully progress toward the end of this ruthless pandemic.
ABSTRACT
This research is aimed to escalate Adaptive Neuro-Fuzzy Inference System (ANFIS) functioning in order to ensure the veracity of existing time-series modeling. The COVID-19 pandemic has been a global threat for the past three years. Therefore, advanced forecasting of confirmed infection cases is extremely essential to alleviate the crisis brought out by COVID-19. An adaptive neuro-fuzzy inference system-reptile search algorithm (ANFIS-RSA) is developed to effectively anticipate COVID-19 cases. The proposed model integrates a machine-learning model (ANFIS) with a nature-inspired Reptile Search Algorithm (RSA). The RSA technique is used to modulate the parameters in order to improve the ANFIS modeling. Since the performance of the ANFIS model is dependent on optimizing parameters, the statistics of infected cases in China and India were employed through data obtained from WHO reports. To ensure the accuracy of our estimations, corresponding error indicators such as RMSE, RMSRE, MAE, and MAPE were evaluated using the coefficient of determination (R2). The recommended approach employed on the China dataset was compared with other upgraded ANFIS methods to identify the best error metrics, resulting in an R2 value of 0.9775. ANFIS-CEBAS and Flower Pollination Algorithm and Salp Swarm Algorithm (FPASSA-ANFIS) attained values of 0.9645 and 0.9763, respectively. Furthermore, the ANFIS-RSA technique was used on the India dataset to examine its efficiency and acquired the best R2 value (0.98). Consequently, the suggested technique was found to be more beneficial for high-precision forecasting of COVID-19 on time-series data.
ABSTRACT
Owing to the COVID-19 pandemic, most of the academic education has suddenly shifted from traditional teaching methods to advanced technological methods on the internet. Many teachers encountered difficulties in successfully evaluating and monitoring their students. We address these challenges and propose a fuzzy logic-based controller that can assists teachers during classes and support allocation of appropriate resources to students. The purpose of the controller is to provide early warning about students who have performed poorly in the initial part of the course assessment. The controller makes predictions based on 5 input parameters which, by applying statistical tools, have been proven to accurately reflect the students' achievements. The model was tested on a group of 50 students and the results indicate 82% prediction accuracy. There is a possibility for additional improvements related to the built-in parameters, both in terms of their selection and in terms of their number.
ABSTRACT
Islanded microgrids (MGs) are now widely used to electrify rural areas at a lesser cost and with greater efficiency. To maintain system balance and guarantee stability when exposed to various disturbances, MGs should be equipped with efficient controllers. Traditional controllers (like the PI controller) are linear and offer the best performance at a certain operating point, but the performance may degrade when the operating situation changes. To mitigate the drawbacks of fixed parameters controller, the paper suggested a fuzzy PI controller-based model reference adaptive control (FPI-MRAC) optimized by an advanced meta-heuristic optimization technique coronavirus herd immunity optimizer (CHIO) for enhancing the dynamic performance of several interconnected MGs. The proposed controller is non-linear adaptive controller that can improve the system performance over a wide range of operating conditions. The effectiveness of FPI-MRAC is assessed by subjecting the system to various disturbances, such as generation variation, load change, changing in uncertain system parameters and occurrence short circuit faults. Additionally, it investigated how quick reaction supercapacitors can improve the dynamic performance of the system. The acquired results show that, for all applied scenarios, the FPI-MRAC offers a much superior dynamic response than PI controller. Using super-capacitors also improves the system frequency when there are disruptions.
ABSTRACT
Due to advancements in information and communication technology, the Internet of Things has gained popularity in a variety of academic fields. In IoT-based healthcare systems, numerous wearable sensors are employed to collect various data from patients. The healthcare system has been challenged by the increase in the number of people living with chronic and infectious diseases. There are several existing IoT-based healthcare systems and ontology-based methods to judiciously diagnose, and monitor patients with chronic diseases in real-time and for a very long term. This was done to drastically minimize the vast manual labor in healthcare monitoring and recommendation systems. The current monitoring and recommendation systems generally utilised Type-1 Fuzzy Logic (T1FL) or ontology that is unsuitable owing to uncertainty and inconsistency in the processing, and analysis of observed data. Due to the expansion of risk and unpredictable factors in chronic and infectious patients such as diabetes, heart attacks, and COVID-19, these healthcare systems cannot be utilized to collect thorough physiological data about patients. Furthermore, utilizing the current T1FL ontology-based method to extract the ideal membership value of risk factors becomes challenging and problematic, resulting in unsatisfactory outcomes. Therefore, this chapter discusses the applicability of IoT-based enabled Type-2 Fuzzy Logic (T2FL) in the healthcare system, and the challenges and prospects of their applications were also reviewed. The chapter proposes an IoT-based enabled T2FL system for monitoring patients with diabetes by extracting the physiological factors from patients' bodies. The wearable sensors were used to capture the physiological factors of the patients, and the data capture was used for the monitoring of patients. The results from the experiment reveal that the model is very efficient and effective for diabetes patient monitoring, using patient risk factors. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Background: Recovery from coronavirus disease 19 (COVID-19) is a gradual process that depends on the disease severity. Immunologic changes that precede and relate clinical symptoms may predict the course of COVID-19 and final outcome. Our goal was to determine prognostic markers of COVID-19 improvement. Method(s): The study included hospitalized patients from the ages 31-72 with moderate to severe COVID-19. All biomarkers were assessed at three checkpoints starting from the first day of hospitalization (day 0), continuing on day 8, and between 40-50 day. Luminex xMAP technology and the Bio-Plex Pro Human Cytokine 17-plex assay was used for quantitative evaluation cytokines and chemokines in peripheral blood of COVID-19 patients. The comparative study was done in combination with clinical data. Univariate and multivariate analyses of data were delivered. Finally, a fuzzy logic model for decision support was proposed and validated for explored data. Result(s): Macrophage inflammatory protein-1beta (MIP1b) was inversely related to COVID-19 evolution. MIP1b significantly higher on day 8 compared to day 0 (p < 0.0001) correlated with clinical improvement and predicted a successful course of the disease. It was also associated with the significant increase in TNF-alpha (p = 0.03), and decrease in IL-10 (p < 0.0001), and IL-6 (p = 0.01). The increase in MIP1b on day 8 correlated positively with eosinophil and lymphocytes counts and negatively with inflammatory mediators (ferritin, procalcitonin, fibrinogen, CRP). Moderately positive correlation between MIP-1b and TNF-alpha was noted, in parallel. Tested the statistical and machine learning predictors exhibited sensitivity to MIP1b input, improving the ROC curve compared to the classification models trained without MIP1b. Conclusion(s): This finding next to already known indicators such as IL-6, eosinophil and lymphocytes counts, highlight a role of MIP1b as a marker of good prognosis in COVID-19 and provide a novel insight into this as a potential diagnostic and therapeutic target.
ABSTRACT
There are many inevitable disruptive events, such as the COVID-19 pandemic, natural disasters and geopolitical conflicts, during the operation of the container port supply chain (CPSC). These events bring ship delays, port congestion and turnover inefficiency. In order to enhance the resilience of the CPSC, a modified two-stage CPSC system containing a container pretreatment system (CPS) and a container handling system (CHS) is built. A two-dimensional resilience index is designed to measure its affordability and recovery. An adaptive fuzzy double-feedback adjustment (AFDA) strategy is proposed to mitigate the disruptive effects and regulate its dynamicity. The AFDA strategy consists of the first-level fuzzy logic control system and the second-level adaptive fuzzy adjustment system. Simulations show the AFDA strategy outperforms the original system, PID, and two pipelines for improved dynamic response and augmented resilience. This study effectively supports the operations manager in determining the proper control policies and resilience management with respect to indeterminate container waiting delay and allocation delay due to disruptive effects.
ABSTRACT
Alternative energy alternatives to traditional energy sources like coal and fossil fuels include solar PV and wind energy conversion systems. The solar and wind energy conversion system's maximum power may be obtained by activating the converters. There are several MPPT (Maximum Power Point Tracking) regulating methods for solar and wind energy conversion systems. For solar PV energy conversion systems, this study suggests two MPPT controlling techniques: Covid-19 MPPT and FLC-based MPPT. The two MPPT methods that are suggested are put into practise using MATLAB. The first Covid-19 approach that has been developed combines aspects of hill climbing and progressive conductance methods. Calculate the direction of the perturbation for the PV modules' operation using the incremental conductance approach. The method of ascending hills is straightforward and involves fewer variables. When dI/dV equals the incremental conductance, the Maximum Power Point (MPP) is attained using the incremental conductance approach. In the hill climbing approach, the MPP is determined by comparing the power in the present and the past. Both incremental conductance and change of power are taken into account in the proposed Covid-19 MPPT regulating approach to obtain the MPP. With this hybrid approach, solar PV generates the most electricity possible under all conditions of temperature and irradiance. As a result, the planned Covid-19 technique moves forward as intended and swiftly reaches the MPP.Copyright © 2022, Anka Publishers. All rights reserved.
ABSTRACT
In the Industry 5.0 era, companies are leveraging the potential of cutting-edge technologies such as artificial intelligence for more efficient and green human-centric production. In a similar approach, project management would benefit from artificial intelligence in order to achieve project goals by improving project performance, and consequently, reaching higher sustainable success. In this context, this paper examines the role of artificial intelligence in emerging project management through a systematic literature review;the applications of AI techniques in the project management performance domains are presented. The results show that the number of influential publications on artificial intelligence-enabled project management has increased significantly over the last decade. The findings indicate that artificial intelligence, predominantly machine learning, can be considerably useful in the management of construction and IT projects;it is notably encouraging for enhancing the planning, measurement, and uncertainty performance domains by providing promising forecasting and decision-making capabilities.
ABSTRACT
Background: The gravity point of all management systems in the new approach of global worldwide standards includes management and assessment of risks and opportunities. Although the spread of COVID-19 as a global pandemic has threatened the health of the workforce and caused catastrophic human and economic consequences, the occurrence of this global challenge has also created opportunities to pay more attention to the risk assessment of biological harmful agents in the workplace. Therefore, this study was designed and implemented to analyze the risk of COVID-19 based on fuzzy logic. Materials and Methods: This cross-sectional and descriptive-analytical study was conducted in 5 hospitals and health-treatment centers in Qom City, Iran (2019). The study sample included 247 employees of these medical centers. The risk assessment of COVID-19 is based on the rapid COVID-19 hazard analysis (RCHA) technique in which the risk level is calculated based on the three components of disease probability, consequence severity, and health belief level. Also, the data were analyzed using fuzzy logic. Results: The results of the fuzzy analysis of COVID-19 risk in these medical centers showed that the studied subjects were placed in five risk levels, including 10.5, 16.25, 26.75, 38.5, and 56.0. These results revealed that the group of nurses is at the highest risk of COVID-19 compared to the other seven groups working in medical centers. The definite risk of COVID-19 among people in this group was calculated at four levels equal to 16.25, 26.75, 38.5, and 56.0. Conclusion: The results of fuzzy analysis of COVID-19 risk indicated that the three groups of nurses, patient carriers, and ward services have the highest risk, respectively. Therefore, these groups should be prioritized in providing suitable solutions to prevent this disease. © 2022, Negah Institute for Scientific Communication. All rights reserved.