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In the transition from Industry 4.0 to Industry 5.0, the integration of human-centric considerations into manufacturing processes becomes vital. This study advances beyond conventional Industry 4.0 frameworks by introducing a novel fatigue model that emphasizes ergonomic risk management during the design phase of robotic assembly lines. Utilizing a hybrid model that employs a fuzzy inference system based on ergonomist knowledge, this fuzzy approach manages the imprecise nature of ergonomic optimization more accurately than deterministic models and effectively reflects real-world complexities. By evaluating fatigue at both task and worker levels, this fuzzy inference mechanism addresses technical aspects through feasibility studies and significantly improves ergonomic outcomes. The integration of supportive robots is explored through scenario-based analysis, highlighting productivity enhancements and substantial ergonomic benefits in line with Industry 5.0's focus on enhancing worker well-being and operational resilience. The proposed heuristic algorithm enables decision-makers to identify the most efficient solutions tailored to individual preferences, showcasing flexibility and real-world applicability. Empirical validation, enriched with synthesized ergonomic-oriented instances, confirms the superiority of this approach in creating more sustainable and ergonomically optimized assembly lines. Implementing this approach is predicted to decrease system costs by up to 47%, particularly advantageous during human resources crises, by reducing ergonomic risk, recovery needs, and additional capacity requirements for fatigue mitigation. This study contributes to the discourse on the practical implications of Industry 5.0 and demonstrates its applicability in designing ergonomically optimized assembly lines that prioritize long-term productivity and worker satisfaction. [ABSTRACT FROM AUTHOR]

Leukemia is a type of blood cancer. Treatment for leukemia patients can last for years because the dose of medication given is adjusted to the patient's immune system. The aim of this research is the use of information technology through a combination of certainty factors and the development of a fuzzy expert system (FES) module to determine the therapeutic schedule for administering leukemia drugs. The urgency of this research is to help medical personnel in measuring the dose of leukemia medication to be given to patients so as to increase the cure rate for leukemia patients. The method used is certainty factor and fuzzy logic. The combination of the certainty factor method and the FES module which is carried out using input variables in the form of the severity of the leukemia suffered by the patient is to produce an appropriate therapeutic schedule for administering leukemia drugs. The result of this research is a combination of the factor certainty method and the FES module which has been tested and the accuracy level is 95.17%, the same as recommendations from experts. [ABSTRACT FROM AUTHOR]

In the era of Industry 5.0, there is a significant gap in addressing ergonomic risks and imprecise task times in manufacturing systems. This study aims to fill this gap by extending the ergonomic assembly line balancing problem with worker assignment. It employs a novel two-phase framework combining a constructive heuristic for feasibility with a unique ergonomic assessment method developed through a fuzzy expert system. Validated using 96 synthesized numerical instances, the proposed method addresses the scarcity of fuzzy and ergonomic-oriented data in benchmarks. Then, computational results are thoroughly analyzed to evaluate the method’s performance and identify potential areas for further research. The proposed optimization method provided high-quality solutions with a majority demonstrating low ergonomic risk and high worker safety, contributing to an overall improvement in ergonomic conditions. The integration of fuzzy expert system and advanced optimization techniques yields a robust framework for achieving a safer and more efficient manufacturing environment. [ABSTRACT FROM AUTHOR]

The classification of roadway contexts and speeds is a critical step in the planning, design, and operation of highway infrastructure. In developing countries, road users encounter safety and operational issues due to poorly defined roadway contexts and inappropriately determined target speeds for a highway network. This study developed an expert system for classifying roadway contexts and target speeds of multilane highway segments and applied the classification process to 16,235 km of multilane highways in Thailand's highway network. The proposed methodology used a fuzzy decision mechanism to deal with subjective and imprecise expert judgment (e.g., low, high), many variables, and a complex evaluation process. This study used the Fuzzy Delphi method to identify the possible important factors influencing contexts and speeds and the Fuzzy Inference System method to reason factors to categorize multilane highway segments in Thailand into different classes of roadway contexts (e.g., rural, low-density suburban, high-density suburban, and urban highways) and target speeds (e.g., ≤50 km/h, 50–60 km/h, 60–70 km/h, 70–80 km/h, 80–90 km/h, 90–100 km/h, and 100 km/h). The study was based on data from questionnaire surveys of experts and field investigations of 120 highway segments. The results showed that roadside environments and activities influence the roadway contexts, while the target speeds are sensitive to the roadway characteristics and contexts. These findings support the need for changes in context-adapted highway design and speed management. The proposed expert system provided high accuracy (90.8%) in classifications of both roadway contexts and target speeds. The fuzzy expert system provides a systematic and structural framework for analyzing imprecise data in highway contextual and speed classifications and improving the clarity and accuracy of the evaluation process. The implementation of the fuzzy expert system has the potential to revolutionize the highway classification decision-making problem under uncertainty. [ABSTRACT FROM AUTHOR]

The diagnosis of liver fibrosis (LF) is crucial as it is a deadly and life-threatening disease. Artificial intelligence techniques aid doctors by using the previous data on health and making a diagnostic system, which helps to take decisions about patients' health as experts can. The historical data of a patient's health can have vagueness, inaccurate, and can also have missing values. The fuzzy logic theory can deal with these issues in the dataset. In this paper, a multilayer fuzzy expert system is developed to diagnose LF. The model is created by using multiple layers of the fuzzy logic approach. This system aids in classifying the health of patients into different classes. The proposed method has two layers, i.e., layer 1 and layer 2. The input variables used in layer 1 for diagnosing liver fibrosis are Appetite, Jaundice, Ascites, Age, and Fatigue. Similarly, in layer 2, the input variables are Platelet count, White blood cell count, spleen, SGPT ALT (Serum Glutamic Pyruvic Transaminase Alanine Aminotransferase), SGOT ALT (Serum Glutamicoxalacetic Transaminase Alanine Aminotransferase), Serum bilirubin, and Serum albumin. The output variables for this developed system are no damage, minimal damage, significant damage, severe damage, and cirrhosis. This research work also presents the examination of results based on performance parameters. The proposed system achieves a classification accuracy of 95%. Moreover, other performance parameters such as sensitivity, specificity, and precision are calculated as 97.14%, 92%, and 94.44%, respectively. [ABSTRACT FROM AUTHOR]

This paper proposed a fuzzy expert system for diagnosing diabetes. In the proposed method, at first, the fuzzy rules are generated based on the Pima Indians Diabetes Database (PIDD) and then the fuzzy membership functions are tuned using the Harris Hawks optimization (HHO). The experimental data set, PIDD with the age group from 25-30 is initially processed and the crisp values are converted into fuzzy values in the stage of fuzzification. The improved fuzzy expert system increases the classification accuracy which outperforms several famous methods for diabetes disease diagnosis. The HHO algorithm is applied to tune fuzzy membership functions to determine the best range for fuzzy membership functions and increase the accuracy of fuzzy rule classification. The experimental results in terms of accuracy, sensitivity, and specificity prove that the proposed expert system has a higher ability than other data mining models in diagnosing diabetes.

In this paper, we present a novel approach to task planning based on an intelligent expert system that makes it possible to obtain a conclusion based on linguistically characterized knowledge. The main goal of the proposed task planning system is to arrange and display tasks for the solver in an effective way. Therefore, the system shows the most important tasks first and then the less important ones (in a determined ordering). The solver has a list of tasks arranged according to their importance at each time the task list is displayed. Another goal of the system is to show the effectiveness of all subordinate workers (solvers) for the manager. The expert knowledge contained in the system is characterized by three linguistic descriptions: determination of the task importance, determination of the final task importance, and determination of the efficiency of the task solvers. The system shows the ordered task list in real time. Evaluation of the relative and final importance of the tasks is performed periodically. The system has been implemented as a WEB application and verified on real data set. We also present experimental results of our system. [ABSTRACT FROM AUTHOR]

In early December 2019, a new virus named “2019 novel coronavirus (2019-nCoV)” appeared in Wuhan, China. The disease quickly spread worldwide, resulting in the COVID-19 pandemic. In the current work, we will propose a novel fuzzy soft modal (i.e., fuzzy-soft expert system) for early detection of COVID-19. The main construction of the fuzzy-soft expert system consists of five portions. The exploratory study includes sixty patients (i.e., forty males and twenty females) with symptoms similar to COVID-19 in (Nanjing Chest Hospital, Department of Respiratory, China). The proposed fuzzy-soft expert system depended on five symptoms of COVID-19 (i.e., shortness of breath, sore throat, cough, fever, and age). We will use the algorithm proposed by Kong et al. to detect these patients who may suffer from COVID-19. In this way, the present system is beneficial to help the physician decide if there is any patient who has COVID-19 or not. Finally, we present the comparison between the present system and the fuzzy expert system. [ABSTRACT FROM AUTHOR]

The increasing implementation of renewable energy sources (RES), along with the diversity of energy source types, has additionally imposed significant operational and management problems in distribution networks. These problems are manifested in voltage regulations, system stability and coordination of protection, both in the distribution and transmission networks. For the medium voltage (MV) network, this includes new energy sources and higher amounts of fault currents, invisibility of several faults in the existing protection scheme, reduction of the range of protection devices and reduction of the possibility of detecting small fault currents with existing protection relays. Such changes significantly reduce the possibility of proper distribution system protection, both in subordinate and superior networks. The subject of this paper is the presentation of a new concept of the use of automation in the management and arrangement of power system protection dependent on the scheme and configuration of an active MV network. The goals of this analysis and research are to find and define the necessary architecture in which the scheme and appearance of the MV network should be automatically detected, and based on network topology to establish new settings of protection devices (ground fault, overcurrent and short circuit protection). The contributions of generation units of RES in the MV network must be considered. This paper specifically analyses the problems of power system management with simultaneous harmonization of protection systems both in the transmission and in the radial distribution network, offering optimization algorithms that have the ability of achieving the optimal solution. The implementation of the proposed technique was tested on a radial connection integrated with a microgrid (MG) which has the possibility of two-way power supply. The obtained results indicate that the proposed technique can solve described problems in the coordination of protection system and network management, even with the dynamic character of operation mode of the networks.

The genes data produced by microarray experiments is complex in terms of dimensions and samples. It consumes a lot of computation power and time when it is processed for a disease analysis while working with an expert system. At the same time, data can help doctors identify a patient’s health condition if it is presented in a meaningful way and processed on time. Several methods have been proposed to reduce the dimensions of medical microarray data and optimize its search space with minimal accuracy loss. However, the discretization of continuous gene-values in the process of dimension reduction is failed to preserve the inherent meaning of genes. Also, ensuring high accuracy and interpretability in the reduction process may result in extra processing time, which is unfavorable for time-critical applications. To overcome these issues, in this paper, we propose a dimension reduction method in conjunction with a fuzzy expert system (FES) optimization approach, while keeping an accuracy-interpretability-speedy tradeoff in mind. To accomplish this, we use a fuzzy rough set on ${f}$ -information to identify meaningful genes without changing their original values. We propose a conditionally guided particle swarm optimization for faster knowledge acquisition, where the velocity is adjusted based on a predefined update probability, resulting in a faster search. A big data processing architecture is designed using the Hadoop ecosystem along with a $MapReduce$ -equivalent algorithm of the proposed method for speedy processing, enabling parallel processing on microarray data to reduce dimensions and perform classification through knowledge extraction. The proposed method is thoroughly tested on eleven microarray datasets by considering accuracy-interpretability-speed tradeoff. The results show that the proposed method is effective in identifying disease-causing genes while also understanding the patient’s genetic profile with only a few operations and a small amount of CPU time. Statistical tests are also run to validate the proposed method’s efficacy in comparison to other methods.

Agricultural crop production was affected worldwide due to the variability of weather causing floods or droughts. In climate change impacts, flood becomes the most devastating in deltaic regions due to the inundation of crops within a short period of time. Therefore, the aim of this study was to propose climate-adaptive crops that are suitable for the flood inundation in risk-prone areas of Bangladesh. The research area included two districts adjacent to the Jamuna River in Bangladesh, covering an area of 5489 km2, and these districts were classified as highly to moderately vulnerable due to inundation by flood water during the seasonal monsoon time. In this study, first, an inundation vulnerability map was prepared from the multicriteria analysis by applying a fuzzy expert system in the GIS environment using satellite remote sensing datasets. Among the analyzed area, 42.3% was found to be highly to moderately vulnerable, 42.1% was marginally vulnerable and 15.6% was not vulnerable to inundation. Second, the most vulnerable areas for flooding were identified from the previous major flood events and cropping practices based on the crop calendar. Based on the crop adaptation suitability analysis, two cash crops, sugarcane and jute, were recommended for cultivation during major flooding durations. Finally, a land suitability analysis was conducted through multicriteria analysis applying a fuzzy expert system. According to our analysis, 28.6% of the land was highly suitable, 27.9% was moderately suitable, 19.7% was marginally suitable and 23.6% of the land was not suitable for sugarcane and jute cultivation in the vulnerable areas. The inundation vulnerability and suitability analysis proposed two crops, sugarcane and jute, as potential candidates for climate-adaptive selection in risk-prone areas. [ABSTRACT FROM AUTHOR]

Rheumatoid Arthritis (RA) is a very common autoimmune disease that causes significant morbidity and mortality, and therefore early diagnosis and treatment are important. Early diagnosis of RA and knowing the severity of the disease are very important for the treatment to be applied. The diagnosis of RA usually requires a physical examination, laboratory tests, and a review of the patient's medical history. In this study, the diagnosis of RA was made with two different methods using a fuzzy expert system (FES) and machine learning (ML) techniques, which were designed and implemented with the help of a specialist in the field, and the results were compared. For this purpose, blood counts were taken from 286 people, including 91 men and 195 women from various age groups. In the first method, an FES structure that determines the severity of RA disease has been established from blood count using the laboratory test results of CRP, ESR, RF, and ANA. The FES result that determines RA disease severity, the Anti-CCP level that is used to distinguish RA disease, and the patient's medical history were used to design the Decision Support System (DSS) that diagnoses RA disease. The DSS is web-based and publicly accessible. In the second method, RA disease was diagnosed using kNN, SVM, LR, DT, NB, and MLP algorithms, which are widely used in machine learning. To examine the effect of the patient's history on RA disease diagnosis, two different models were used in machine learning techniques, one with and one without the patient's history. The results of the fuzzy-based DSS were also compared with the diagnoses made by the specialist and the diagnoses made according to the 2010 ACR / EULAR RA classification criteria. The performed DSS has achieved a diagnostic success rate of 94.05% on 286 patients. In the study of machine learning techniques, the highest success rate was achieved with the LR model. While the success rate of the model was 91.25 % with only blood count data, the success rate was 97.90% with the addition of the patient's history. In addition to the high success rate, the results show that the patient's history is important in diagnosing RA disease. [ABSTRACT FROM AUTHOR]

Software plays a vital role in modern society's application systems. Typically, it is assumed that there are finite faults in the software during software development. However, the development of new functions, such as user requests or other sources, can cause the size of test-case pools to increase, necessitating the reduction of the test suite due to time and resource constraints. To address this issue, this study proposes a Fuzzy Expert System (FES)-based method to improve the fault detection effectiveness of reduced test suites. Specifically, the FES approach is integrated into the traditional test suite reduction technique, the GE algorithm. The performance of our proposed modified Fuzzy-GE algorithm is evaluated based on real programs by using objective criteria. The results show that our approach can effectively reduce the sizes of test suites while also improving the effectiveness of fault detection. [ABSTRACT FROM AUTHOR]

Evaluating the priority of any school capital project is still considered unique because of its attachment to the fundamental human right, "the right to education", and the prioritization methodology for school infrastructure projects has yet to be formalized. This paper presents a comprehensive framework and enhances the prioritization process of school infrastructure projects by quantifying project needs. The proposed "school project needs evaluation framework" consists of two parts. The first part describes the input selection process and integration of the fuzzy expert system to evaluate the school project's need as a quantitative term. The fuzzy rule-based system was developed in consultation with domain experts. The second part leverages the fuzzy expert system designed in the first part and quantifies the new school capacity and construction project type. The application steps of the automation solution developed based on the proposed framework are presented using examples. [ABSTRACT FROM AUTHOR]

Due to the limited power of human perception of the outside world and also the power of comprehensive and deep reasoning, he faces uncertainty and uncertainty. Multi-criteria decision-making methods, including methods to increase confidence and reduce uncertainty due to judgment Decision makers and comprehensiveness of information. One of the indicators calculated in the decision matrix in conditions of uncertainty is the minimum regret index. The purpose of this study is to identify options with the least regret in selecting faculty members. Therefore, using the variables obtained from the previous literature and the opinion of experts in the field of study, an expert system was designed to assist in decision-making in attracting faculty members. The results of the analysis indicated that the options of academic record, ethics (communication skills), teaching ability (ability to express and convey content) and decision-making skills are indicators that have the least risk of being considered in the scientific selection of individuals. According to the results of this research and the designed expert system can be used to help decision-making in attracting faculty members of universities by reducing time, increasing the quality and accuracy of decision-making. [ABSTRACT FROM AUTHOR]

Rapid development of new digital technologies and their use, on the one hand, have opened up new opportunities for improving the efficiency of managing technological and business processes. On the other hand, it has led to significant increase in security threats, making businesses and organizations more vulnerable to cybercriminals. Soaring growth in the number of incidents of various types in recent years indicates insufficiency of traditional methods of ensuring information security (hereinafter IS). At present, consideration of the issue of IS from the point of view of risk management makes it possible to take cost-effective and effective enterprise security measures. In this paper, we are considering a project to create a system for assessing information security risks. In this paper escribed scientific novelty and significance of the project, also presented research methods used in the development of intelligent information security risk assessment systems. [ABSTRACT FROM AUTHOR]

This study proposes an online learning model based on fuzzy expert systems to recommend learning contents that are most appropriate for facilitating more efficient learning. The process used two computer applications which are Matlab for fuzzy analysis and the learning management system Moodle platform for online learning environment. Besides, considering the learner themselves by their self-directed learning readiness as the moderating variable in this study. To examine the learning strategies, we used aptitude treatment interaction for the learning model. Statistical analysis is used to examine the effect of this strategy and the students' self-directed learning readiness on the learning outcomes. We found that students who are taught by online learning strategies based on fuzzy expert systems have better learning outcomes than students who are given traditional online learning strategies. Students who have high self-directed learning readiness have higher learning outcomes than students who have low self-directed learning readiness in both online learning strategies. Students who have low self-directed learning readiness are more appropriate to learn by the online learning strategies based on fuzzy expert systems to improve their learning outcomes [ABSTRACT FROM AUTHOR]

Yarn quality is an essential concept that requests the satisfaction of various parameters simultaneously. These parameters are fiber characteristics and yarn properties. Thus, it is necessary to develop model that can encompass all these variables and predict an overall yarn quality index. Since hybrid approaches combining two or more techniques have proved their abilities in processing and predicting accurately various variables over different research areas, this paper reports two hybrid models by combining two different approaches for predicting a new quality index: Back-propagation artificial neural networks (ANN) and fuzzy expert system. The hybrid models are ANN combined with ANN and ANN combined with fuzzy logic. The ANN is used to predict four yarn characteristics namely tenacity, breaking elongation, CVm and hairiness. Then, these four outputs are used to predict a new quality index by means of ANN or fuzzy expert system. Several performance criteria are necessary to evaluate the performance of the established models. They are correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE) and mean relative percent error (MRPE). The obtained results show that these constructed hybrid models are able to predict yarn quality from the chosen input variables with a reasonable degree of accuracy. Moreover, the hybrid model using two ANN systems performs significantly better than the other one. [ABSTRACT FROM AUTHOR]

Background & Purpose: Focusing on the diversity of human capital in organizations and its possible role in the effectiveness of the compensation system, the purpose of this study was to predict the effectiveness of the compensation system for various types of human capital in Police Force of Qazvin Province.Methodology: The present research is descriptive, and it is applied in terms of purpose. The statistical population of the study includes 53 senior, middle, and operational managers of Qazvin Police Force, of which it was possible to access the opinions of 46 of them. The assessment tool was a questionnaire. In order to analyze the data, network analysis methods and fuzzy inference system were used.Findings: According to the results, the effectiveness of the service compensation system varies according to its types of human capital. Priority of the elements of compensation for core human capital included perquisites, rewards, salaries, benefits, welfare services, awards; for idiosyncratic human capital: salaries, benefits, perquisites, rewards, welfare services, awards; for compulsory human capital: salaries, benefits, welfare services, rewards, awards, perquisites; for ancillary human capital: benefits, rewards, salaries, welfare services, awards, perquisites respectively. Also, the evaluation of the model in the police force suggested that the most observed effectiveness belongs to compulsory human capital.Conclusion: The most important achievement of this article is designing an effective compensation system in the Police Force, which will dynamically determine the amount of output based to the amount of inputs. This model can be used to predict the effectiveness of the service compensation system.

The importance of managing the material recourse supply has become apparent in the modern economy as it largely determines the survival of a corporation and its success in the market, which is especially important in a crisis. The procurement has a particular impact on this process. The problem of subjectivity when choosing a supplier is increasing in modern condition, which forces companies to use new tools, such as artificial intelligence system to make management decisions. The article proposes an expert supplier management system as an integral part of the material recourse supply management system for automotive enterprises. The possibility of improving the efficiency of procurement system, in particular supplier management. Based on a fuzzy expert system is considered. The fuzzy knowledge used to build the expert system will allow the company's management to take into account the uncertainty when making decisions about choosing a particular supplier, as well as see a description of the supplier's criteria that cannot be quantified. The use of an expert system becomes especially relevant when difficulties arise in objective decision making and choosing from a variety of alternatives. As a result of the work of the expert system, the top management of the company will receive an objective decision on choosing a supplier.

The aim of this study was to assess the LULC changes over 26 years from 1995 to 2021 to find the most changed land use conditions within the 25 km territory of the main river systems of Bangladesh. In addition, the prediction of vulnerable areas for agricultural land use in terms of inundation by river water was also analyzed. The study area includes river networks distributed through eight administrative divisions (Rangpur, Rajshahi, Mymensingh, Sylhet, Dhaka, Khulna, Barishal and Chittagong) of Bangladesh, covering an area of 64,556 km2. The study was conducted by identifying permanent water bodies from NDWI indices and preparing LULC maps that include the five main land use classes (water body, bare land, vegetation, agricultural land, and urban area) in the Google Earth Engine platform using supervised classification. The LULC maps were then analyzed in the ArcGIS® environment. A vulnerability map for agricultural land use was also prepared using a fuzzy expert-based system applying multicriteria analysis. From the land use land cover map of the study area, it was found that among the five land use classes, water bodies, bare land, vegetation, and urban areas increased in size by 3.65%, 2.18%, 3.31% and 2.55%, respectively, whereas agricultural land use significantly decreased by 11.68%. This decrease in agricultural land use was common for the analyzed area of all administrative divisions. According to the vulnerable area map of the eight divisions, more than 50% of the analyzed area of the Khulna and Dhaka divisions and more than 40% of the analyzed area of the Rajshahi, Mymensingh, Sylhet, Barishal and Chittagong divisions were highly vulnerable to agricultural land use due to the possibility of inundation by water. However, approximately 44% of the analyzed area of the Rangpur division was not vulnerable for agricultural land use. The prepared LULC and vulnerability maps can be helpful for the future land use planning of Bangladesh to meet the increasing demand for food production and livelihoods for increasing populations. [ABSTRACT FROM AUTHOR]

Coronary Artery Disease (CAD) is one of the most important diseases that cause people to die worldwide. Although developments in medicine facilitate the treatment of this disease, there are still some inadequacies in identifying and evaluating risk factors. In this study, various risk factors used in the diagnosis were determined by considering individuals with typical symptoms and complaints related to CAD. In addition, an artificial intelligence system has been developed to determine the CAD risk levels of individuals by using the fuzzy expert system method. The designed system is rule-based, and this rule-based structure was created with the knowledge obtained from medical experts. The system provides self-risk assessment and customized recommendations to reduce individuals' disease risk. In this way, the increase in the number of people who have coronary artery disease can be prevented or delayed. [ABSTRACT FROM AUTHOR]

According to previous research, most studies on the evaluation of sustainable supply chain capabilities are limited to statistical variables and mathematical modeling. The purpose of this study is to propose multiple capacity evaluation models in the sustainable supply chain. The spatial scope of this research includes a survey of 16 companies active in the ceramic & tile industry in Iran. According to fuzzy expert system modeling, four capabilities, including competitiveness, operational, technology, and resilience as input variables and three levels for sustainable supply chain capabilities as output variables were determined. Simulink was used to simulate and integrate the designed fuzzy systems. The evaluation results show that most of the companies studied fall into the level 2 capability. It is recommended that the ceramic tile manufacturing companies evaluate the level of capability and determine the sustainable capabilities of their supply chain capabilities to exploit these variables. Resiliency capabilities have an impact on upgrading the evaluation of multiple capabilities in the sustainable supply chain. Therefore, companies are advised to incorporate flexibility and adaptability into developing domestic and foreign markets and market orientation and not to define future projects as environmental changes.

The purpose of this study is to build a fuzzy expert system for assessing the financial component of the economic security of telecommunications enterprises. The methodological basis of the research is founded on scientific works of domestic and foreign scientists and leading experts in the field of financial analysis and modeling of economic processes, as well as statistical and financial reporting data that are publicly available. To construct an integral indicator of the financial security of an enterprise, a fuzzy conclusion is used. Three financial indicators are used as input variables. The first indicator X1 is the Current Ratio (CR). The second indicator X2 is Equity Ratio (ER). The third indicator is Return on Assets (ROA). The output variable is defined as an indicator of the financial security of an enterprise Y123 (FS). Both the input variables and the output variable are converted to fuzziness by constructing membership functions. The type and parameters of the affiliation function are justified, and the bell-shaped affiliation function is chosen to describe the uncertainty of values that fall under the normal distribution. The quantity of fuzzy sets at every input is considered as z=3 and the quantity of input variables is considered as ω=3. To achieve completeness of the model, the quantity of logic rules is considered as r=33=9. To calculate a degree of market concentration, Mamdani fuzzy conclusion is applied. Defuzzification is engaged to calculate the value of the output variable Y123(FS) for an indicator that determines the degree of financial security of an enterprise and, as a result, the degree of its economic security. To assess the level of the financial security indicator of an enterprise, a fuzzy expert system is constructed. The fuzzy expert system allows you to use various indicators thanks to the fuzzy logic methodology, which takes into account the fuzziness of input variables and output variables as much as possible. For the three telecommunications companies whose core business is wireline communication, ratios are calculated based on financial reports. Financial coefficients are used to determine the integral indicator of financial security of enterprises. This indicator can be characterized by both numerical values and linguistic terms.

Decreasing the impact of educational buildings on the environment is a primary concern of the sustainable building movement. However, limited research has centered on improving educational building sustainability from users' perspectives. This study aims to determine an overall user perspective satisfaction scale (OUPS) for post-secondary educational buildings, determine the factors and sub-factors that affect users' perspectives, and create a building sustainability platform for evaluating user satisfaction. The main goal of the developed model is to offer facility managers the current users' perspectives on post-secondary educational buildings to improve sustainability from users' perspectives. The results revealed a significant influence of thermal comfort (40%) and aesthetics (22%) on users' perspectives. The developed model was validated by experts working in the facility management field and they acknowledged it as having good potential. This work first utilized the Fuzzy Analytic Network Process (FANP) modelling technique to determine weights for each factor and sub-factor and then employed the fuzzy expert system to develop an OUPS. Questionnaire responses and interviews with facility managers and users were used to develop these models for a case study of the M.B. building at Concordia University in Montreal, Quebec. [ABSTRACT FROM AUTHOR]

Landslide susceptibility zonation (LSZ) is a prerequisite for sustainable development and disaster management, especially in mountainous settings. In recent landslide literature, due to the shortcomings of qualitative, statistical, and probabilistic approaches, artificial intelligence (AI) techniques are widely applied for geographic information system–based LSZ. Dharamshala, state capital of Himachal, is one of India’s fastest-growing tourist hotspots in Himalaya where developmental activities are taking place at a rapid pace. Increased pressure of urbanization and re-occurrence of slope instability problems demand systemic landslide hazard evaluation in this area. In this respect, GIS-based LSZ has been attempted for this area using different AI models: fuzzy set procedure (FSP), the fuzzy expert system (FES), and artificial neural network (ANN). To create the landslide susceptibility map (LSM), 9 causative factors and landslide inventory were defined using remote sensing and field data. A total of 12 LSMs were generated, then validated and compared statistically with the help of area under the receiver operating characteristic (ROC) curve and frequency ratio (FR) analysis, and also in terms of spatial distribution quality to depict an accurate map for the study area. The comparative analysis shows that ANN performs better than the other two models, and LSM-ANN-I is the best map for the research area. The northern section, made of metamorphic rocks like slate and phyllite, with a high slope, is more prone to landslides, whereas the southern part, comprised sandstone, shale, on gentle slopes, has minimal landslide susceptibility. [ABSTRACT FROM AUTHOR]

Aiming at the problems of sufficient adaptability and lack of tactical capabilities of unmanned combat air vehille (UCAV) in short-range escape maneuvers in existing research, an escape maneuvering dearon algorithm combining fuzzy expert system and archiving differential evolution algorithm with dual strategy competition (DSC-JADE) is proposed. Through the learning of tactical knowledge, a fuzzy expert system is established, and the escape dedrion process is divided into discrete segments through the receding horizon. According to the dedrion of the fuzzy expert system, the maneuver is obtained. In the fearible regoon corresponding to is control quantity, an improved differential evolution (IDE) algorithm is used to search the optimal control quantity, and the escape maneuver deadon is completed. Under the initial conditions of inferior (UCAV) for rimulation and veriiication, the DSC-JADE algorithm has a stronger search abliity than the differential evolution algorithm and other traditional group inteliigence algorithms; the use of expert systems has better escape decision-making capabllitles than the use of expert systems. [ABSTRACT FROM AUTHOR]

The purpose of this research is to construct fuzzy expert system to estimate an integral degree of market concentration by fuzzy-logical approach. The methodological basis of the study consists of scientific works of domestic and foreign scientists and leading specialists, statistical and analytical materials of state authorities. Fuzzy Inference is introduced for the integrated indicator construction. Two indicators are chosen as input variables. The first indicator, CR, is a level of concentration ratio. The second indicator, HHI, is the Herfindahl-Hirschman index. Output variable, MC indicator, means a degree of market concentration. Both input and output variables are transformed to fuzziness through the construction of membership function. The function type and parameters are substantiated and “bell”-shaped membership function to describe uncertainty of the values falling under normal distribution is chosen. The quantity of fuzzy sets at every input is considered z=3 and the quantity of input variables is considered ω=2. To achieve completeness of the model, the quantity of logic rules is considered r=3²=9. To calculate a degree of market concentration, Mamdani fuzzy conclusion is applied. Defuzzification is engaged to calculate value of the output variable which is MC indicator to mean a degree of market concentration and therefore readiness to implement the innovation strategy for enterprises active in innovation. To estimate a degree of market concentration, the fuzzy expert system given allows for engagement of different indicators due to fuzzy logic methodology which considers fuzziness of the input and output variables. The construction of an integral indicator for assessing the state of economic competition in order to establish the feasibility of identifying operators and providers with a significant market advantage will improve the assessment of the state of economic competition in the market in the telecommunications services market in Ukraine.

In this paper, we present a novel approach to task planning based on an intelligent expert system that makes it possible to obtain a conclusion based on linguistically characterized knowledge. The main goal of the proposed task planning system is to arrange and display tasks for the solver in an effective way. Therefore, the system shows the most important tasks first and then the less important ones (in a determined ordering). The solver has a list of tasks arranged according to their importance at each time the task list is displayed. Another goal of the system is to show the effectiveness of all subordinate workers (solvers) for the manager. The expert knowledge contained in the system is characterized by three linguistic descriptions: determination of the task importance, determination of the final task importance, and determination of the efficiency of the task solvers. The system shows the ordered task list in real time. Evaluation of the relative and final importance of the tasks is performed periodically. The system has been implemented as a WEB application and verified on real data set. We also present experimental results of our system.

Agricultural crop production was affected worldwide due to the variability of weather causing floods or droughts. In climate change impacts, flood becomes the most devastating in deltaic regions due to the inundation of crops within a short period of time. Therefore, the aim of this study was to propose climate-adaptive crops that are suitable for the flood inundation in risk-prone areas of Bangladesh. The research area included two districts adjacent to the Jamuna River in Bangladesh, covering an area of 5489 km2, and these districts were classified as highly to moderately vulnerable due to inundation by flood water during the seasonal monsoon time. In this study, first, an inundation vulnerability map was prepared from the multicriteria analysis by applying a fuzzy expert system in the GIS environment using satellite remote sensing datasets. Among the analyzed area, 42.3% was found to be highly to moderately vulnerable, 42.1% was marginally vulnerable and 15.6% was not vulnerable to inundation. Second, the most vulnerable areas for flooding were identified from the previous major flood events and cropping practices based on the crop calendar. Based on the crop adaptation suitability analysis, two cash crops, sugarcane and jute, were recommended for cultivation during major flooding durations. Finally, a land suitability analysis was conducted through multicriteria analysis applying a fuzzy expert system. According to our analysis, 28.6% of the land was highly suitable, 27.9% was moderately suitable, 19.7% was marginally suitable and 23.6% of the land was not suitable for sugarcane and jute cultivation in the vulnerable areas. The inundation vulnerability and suitability analysis proposed two crops, sugarcane and jute, as potential candidates for climate-adaptive selection in risk-prone areas.

Recent advances in wireless communication technologies are having a great impact on people’s life, especially in the field of healthcare industry that provides a remote monitoring enabling medical staff to follow up the patient and diagnose his disease. LoRa wireless technology is becoming one of the most energetic effective solutions for Wireless Body Area Networks (WBANs) communications. In this work, we propose a LoRa-based low power healthcare WBAN platform called HeaLoRa for an adaptive patient monitoring process. Physicians can remotely monitor patient’s temperature, oxygen saturation level, blood pressure and heart rate to prevent any abnormality. Moreover, data acquisition and transmission are controlled based on a power consumption optimization strategy aiming to reduce redundant data. Based on the Early Warning Score (EWS), the system configuration is dynamically adapted, using a Fuzzy logic controller that makes the decision about the system sleep mode duration and the sent data rate. Furthermore, an analytical model of the energy consumption for acknowledged LoRa transmission is presented in this paper. The graphical abstract of the paper illustrates a medical application of a wireless body area network system where LoRa technology is used to transmit the data to a gateway then the data is transmitted to the doctor using IP based technology. By comparing with a reference system, the simulation results indicate that our system consumes 3 to 10 times less of energy depending on the studied scenario. [ABSTRACT FROM AUTHOR]

Many spectrum-based fault localization (SBFL) techniques have been proposed in order to improve debugging efficiency. These SBFL techniques were designed according to different underlying assumptions and then adopt different fault locator functions to evaluate the likelihood of each statement being faulty, called suspiciousness. So far no single SBFL technique claims that it can outperform all of the others under every scenario. That is, the effectiveness of fault localization may vary considerably by just adopting a single SBFL technique. Due to the aforementioned reasons, this study presents a framework for improving fault localization effectiveness by using a Fuzzy Expert System (FES) to integrate different SBFL techniques. In the presented framework, the outputs of several SBFL techniques will be put into the fuzzification and then transferred to fuzzy input sets. After undergoing the fuzzy inference based on the given fuzzy rules, the fuzzy input sets will be transferred to a fuzzy output set. Finally, the fuzzy set will be transferred to a crisp output (called a weighted suspiciousness value). The code statements will then be ranked according to their weighted suspiciousness values. In other words, no additional instrumentations and analyses on the source code and the test suite are necessary for our approach. Our experiment results indicate that our FES-based framework is effective at combining the SBFL techniques from different equivalent groups and achieves high effectiveness on the nine subject programs. It is also noted that in the literature, most of the approaches that combine multiple SBFL techniques are learning-based and they are suitable for long-term projects with sufficient historical data. Since our approach does not reference historical data for model training, it can be applied to new software projects. Thus, the application scenarios of our approach should be complementary to those of the state-of-the-art learning-based approaches.

Purpose: The aim of this study was to explain the loyalty history of education staff using fuzzy logic.. Method: The research was conducted in terms of applied purpose and in a combined (quantitative-qualitative) way. The statistical population consisted of 1200 staff members, teachers and directors of education in Qazvin province, based on the Cochran's formula, 292 people were selected as the sample size in the fall of 1399. In order to collect data, two methods were used: documentary (library, dissertation, Internet) and field (completion of a researcher-made questionnaire) and in interviews, indicators such as gender, education, age were considered. Formal validity and reliability were obtained by Cronbakh's test. Data were analyzed using inferential factor analysis (confirmatory factor analysis and structural equation modeling) with MICMAC software. PLS software was used. Finally, in order to provide the desired combination of model dimensions in order to optimize employee loyalty, mathematical modeling method was used. Findings: The results of the qualitative section showed that 23 components and 47 indicators in the form of 5 dimensions extracted had a positive effect on the loyalty of education staff. The results of a small part showed that the model had a good fit (GOF = 0.39) and according to the fuzzy modeling method, if a proper relationship is established between the personal, professional and organizational dimensions, employee loyalty will be increased by 58%. Conclusion: Creating an appropriate relationship between the components of personal, job and organizational conditions can predict the needs and values of employees in government organizations and improve their motivation, commitment and dynamism. [ABSTRACT FROM AUTHOR]