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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]

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]

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]

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]

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]

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]

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.

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]

Introduction Corn harvest losses are imposed by several factors, the most important of which is harvesting-time. Since the harvesting-time is coincident with the rainy season, it is necessary to appropriately estimate the corn harvest time to avoid harvesting losses and losing the next cultivation. Accordingly, in the current research, the effect of harvesting-time on corn losses during the month and the day has been into consideration. An expert fuzzy system was designed to predict the best harvest time as it operates based on the losses amounts which are measured in processing and collection units into the combine, and losses due to the humidity percentage. Materials and Methods In this paper, corn harvest losses in a John Deere Combine, Model 1165, was studied in a different climatic circumstance in Moghan region. Moreover, a split plot experiment in a completely randomized block design was conducted with three replications. The losses data were collected from the processing and collection units of the combine harvester on the November 5th, 8th and 11th, 2017, in three different daily times of 8-10, 11-13 and 14-16 with three replications. The Mamdani fuzzy inference system with singleton fuzzifire and center average defuzzifire‎ was used to develop a fuzzy expert system. In the designed expert system, the losses percentage in the processing and collection units and ‎the humidity percentage were considered as system inputs and optimal harvesting time was used as the system output. "Low, Very low, high and very ‎high" and "‏Best, Suitable, Unfit, and Worst" were four groups of linguistic variables for input and output parameters, respectively. These variables follow the triangular and trapezoidal membership functions. The number of 64 fuzzy rules were considered and introduced into the fuzzy system by experts, experienced farmers, and combiners. Furthermore, the same field data (measured data) were applied to evaluate the designed system, so that the predicted value was accounted as the system output. Results and Discussion Analysis of variance showed that there was a significant difference between the harvesting dates at the 0.05 probability level and significant difference between the harvesting times of a day at the 0.01 probability level. It can be concluded that the harvest dates and harvest times of a day were very effective in the number of corn losses, but the interaction effects were not significant. The results appeared that the lowest losses were 10.05% on November 8th, 2017, at 14-16 p.m., and the highest losses were 12.88% on November 11th, 2017, at 8-10 a.m.‎ The amount of losses was increased due to the higher air humidity and lower temperature. In the fuzzy simulation model, the suitable harvesting-time can be predicted based on the losses quantities in the processing and collection units and the humidity percentage. The results showed that the predicted values for harvesting-times, by a designed fuzzy system, were completely matched with measured values in this study. The coefficient of determination (R2) was 0.980 between measured and predicted harvesting times. This coefficient demonstrated that the developed fuzzy logic system was suitable for prediction of harvesting time in the studied area. Conclusion The experimental observations in the field and data analysis showed that in the corn harvesting in the Moghan region, the humidity level, date, and harvesting-time were the most effective factors in the harvesting losses. In this paper, based on measured data from a small farm and implementation of the expert fuzzy system, the most suitable harvest date was set on November 8th at 14-16 p.m, at 21-24°C and relative humidity of 44%-53% to have 10.5% losses which has been confirmed by the lowest losses observed in the corn plan (10%). Moreover, the high value of the determination coefficient demonstrates a high correlation between measured and predicted data.

Possessing appropriate human resources is undoubtedly the most important organizational competitive advantage in today's state of flux governing the world. That’s why organizational success is reliant on human resource development which is viable in various companies and countries merely through identifying and understanding the capabilities and competencies of the enormous potential of human resources. Competency plays a key role in IT professionals’ performance as well as their success and efficiency. Thus, the present study aimed at designing a model for assessing the IT professionals’ competency based on the fuzzy expert system approach. This exploratory two-phase mixed-methods (qualitative-quantitative) research commenced with a qualitative analysis by reviewing the literature and interviewing experts to design the assessment model which was further subject to quantitative scrutiny in the second phase. The research sample comprised 15 hospital IT professionals who completed the questionnaire that was employed as the data collection instrument to glean the research data that were further analyzed through Excel, SPSS and Matlab software programs. The results of the qualitative and quantitative analyses pointed out four main indicators that were next prioritized with regard to their role in the degree of professionals’ competency using the process of fuzzy hierarchical analysis. The if-then database was completed and Mamdani Fuzzy Inference System was used to determine the exact competency level of IT professionals which was found to be high with an accurately equal to 0.712. Validation of the model verified compatibility of the results with the views of experts in the field.

New applications of a fuzzy expert system (FES) for direct control of traffic lights in the areas of locks and one-way sections on inland waterways are presented. FES implements the knowledge of experts in the field of water transport on a controller. FES-SL (application in ship locks) was created for a single-chamber two-way ship lock. The system decides when the lock process will be performed depending on the current requests and queues. FES-1WS (application in one-way sections) was created for a canal network with sections where one-way vessel traffic is allowed due to the width limitation. The system is applicable for different traffic densities (from 10 to 90 vessels per day) and different lengths of one-way sections (from 2km to 24km).

Pada penelitian ini akan dirancang sebuah solar electric sprayer (SES) yang hemat energi. Rancangan ini terdiri dari solar panel (PV), rangkaian maximum power point tracking (MPPT), dan sebuah konverter. Untuk memaksimalkan power dari solar panel, rancangan ini dilengkapi dengan MPPT yang mampu bekerja pada kondisi terbayang-bayang sebagian (partial shaded) apabila tempat yang digunakan tidak memungkinkan untuk memperoleh radiasi matahari yang sempurna. Algoritma yang digunakan pada pembuatan MPPT ini menggunakan fuzzy expert system (FES). Dari hasil pengujian didapatkan daya optimal PV hanya sekitar 75% dengan effisiensi konverter 80% hingga 89% tanpa terjadi saturasi, namun terdapat penurunan effisiensi sekitar 42% apabila dutycycle dibawah 40%. Dengan demikian prototype mesin semprot listrik bertenaga surya sebagai pengganti mesin semprot manual yang sudah tersedia dapat diterapkan.

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.

Electric power systems in most developing countries, especially Ethiopia, are fed by radial distribution systems (RDSs), which serve as the final link between the high voltage transmission system and the consumers. In this paper, a fuzzy logic optimization method was presented for the efficient location of voltage regulators (VR) and capacitors in distribution systems (DSs). An investigation of Gondar power distribution system was performed specifically at the Gondar feeder in Gondar, Ethiopia which has 60 nodes with a total capacity of 3.413 MW. The Gondar distribution system (DS) is classified into different zones for the purpose of proper operation and supply of electricity. The minimum bus voltage is 0.8515 p.u., while the active and reactive power losses of the feeder are 377.75kW and 222.51 KVAR respectively. Voltages and power loss (PL) indices of the DS nodes were modeled using fuzzy membership functions. A fuzzy expert system (FES) containing a set of heuristic rules was used to determine the voltage regulator (VR) and capacitor placement suitability index. The load flow analysis of the system was simulated using NEPLAN MATLAB software before and after recompense. The effectiveness of the results obtained from both the voltage regulator and capacitor placement optimality using the proposed method are presented and compared.

This paper presents a novel method to discriminate between the magnetizing inrush and external and internal fault currents in power transformers. Fault type identification and faulted phase selection are also possible by the proposed algorithm. The proposed method has two main parts. First, by means of Stransform, which is the most accurate method in the field of signal processing, some useful features are extracted from the input signal. Then, the extracted features are converted to some numerical indices. In the second part, an effective decision maker is needed to classify the input signal. One of the best methods, which have been used for decision-making applications is fuzzy logic. So, the numerical indices are used as inputs for the fuzzy system. The output of the fuzzy system not only can reveal whether the input signal is the magnetizing inrush, external or internal fault, but it can also identify the fault type when there is an internal fault. Finally, the faulted phases can be identified with a supplemental algorithm. To generate the test signals, a three-phase transformer is modeled in PSCAD/EMTDC. Testing the proposed algorithm by different simulated data shows the robustness of the proposed method in the transformer differential protection. [ABSTRACT FROM AUTHOR]

In the field of solar industries, extracting maximum power from solar photovoltaic systems under partial shading conditions has gained significant attention in recent years. One of the most efficient concepts brought out to extract the power output of the existing solar photovoltaic system is the reconfigurable solar photovoltaic system. Numerous static and dynamic reconfiguration techniques are mentioned in the literature. This work proposed a Modified Sudoku reconfiguration based on static techniques and compared to the most common existing configuration i.e., Total-Cross-Tied and Sudoku photovoltaic array configurations. In addition, an Artificial Intelligence-based machine learning model (Fuzzy Expert System) is implemented for the prediction of suitable configurations for solar photovoltaic arrays under partial shading conditions. The performance of the FES model is evaluated by comparing the predicted results and the results obtained from the simulations on 5 × 4, 6 × 4, 6 × 6 and 9 × 9 PV arrays. Results demonstrated that the implemented FES model generated accurate results and 0% MAPE in all 33 sample cases for predicting the best suitable configuration. This realistic, simple, and cost-effective fuzzy model can be utilized to replace existing estimation systems that employ the use of complex technological analysis and simulation models. Thus, this approach can be very effectively used in the solar industry for selecting the configuration for installing solar panels. • An efficient reconfiguration technique for extracting maximum power. • Cost-effective with simple circuitry. • AI based Fuzzy Expert System for configuration prediction. • Fast learning, simple to implement, and user friendly. • Useful for small and large solar farms under variable shading conditions. [ABSTRACT FROM AUTHOR]

This paper analyses the management process of the vessel traffic control on one-way section on navigable canal with the adaptive time-sequential filter (traffic lights). One-way section on canal significantly decreases waterway capacity and requests special attention in control and regulation of the vessel traffic. The vessel traffic is a stochastic variable, and the vessel traffic control needs to be flexible and adaptive in order to achieve the required traffic flow with minimal delays. On the one-way section, two independent variable vessel flows from opposite directions are encountered, and fixed (predefined) signal plans lead to an increase in vessel delays. An appropriate solution is development of a Fuzzy Control System (FCS) for the vessel traffic control. A control algorithm is designed according to a set of linguistic rules that describes input parameters for the control strategy. The estimated and approximate input parameters are implemented in the algorithm as fuzzy sets. The final result of the developed algorithm is the traffic light scheme (duration of green light for certain direction). The presented control system can be used as an adaptive automatic control system for the vessel traffic control processes on navigable canals or on critical sections of other waterways.

Several models have been developed to facilitate decision-making in disaster management, especially in relation to emergency resource allocations. These models are required in order for search and rescue personnel to operate efficiently. However, in Indonesia, in general, technology has not been used to help make decisions during the response phase; rather, these decisions are still made subjectively. This paper presents a decision-making model that helps search and rescue teams determine the number of personnel to deploy. Therefore, it streamlines the allocation of personnel in a search area, and it determines the number of personnel that are needed based on the area, population density, equipment, and the number of high buildings. Then, those variables are processed using a fuzzy expert system and a decision tree. The data and knowledge acquired as a reference were obtained from disaster management experts as well as experienced practitioners in the field of Search and Rescue.

Regional flood frequency studies are initialized by the delineation of the homogeneous catchments. This study was based on "Region of Influence" concept, aiming to find the similar catchments in the south of Caspian Sea. The methodology utilized the Particle Swarm Optimization Algorithm, PSO, to optimize the fuzzy system over a dataset of catchment properties. The main catchment variables in relation to flood were determined by the principle component analysis method and employed as the inputs in the fuzzy system. Catchments grouping was performed over these fuzzy input variables by the iterative process. The optimum similar groups were obtained by PSO, and the heterogeneous L-moment index was used as the termination criterion for the optimization process. A total of 61 hydrometric stations located in the study area were selected and their relevant catchmentschr('39') physical, climatic and hydrologic properties in relation to flood were studied. Principle Component Analysis by Variomax Rotation Factor over the catchments datasets tended to four out of 16 physical variables, including area, mean elevation, Gravelious Factor and Form Factor, as the main parameters in terms of homogeneity with 84 percent of accumulative variance. These variables, as well as mean annual rainfall, were used as the input data to define the fuzzy system. PSO algorithm was then employed to optimize the developed fuzzy system. The developed algorithm tended to yield the best result in the 9th iteration with 26 and 22 for the minimum average and the optimum values of cost function, respectively. The topology of the resulting algorithm included inertia weight, local and acceleration rates, the number of generations and population size, with the values of 0.7298, 1.4962, 1.4962, 10 and 5, respectively. This study tended to a total of 61 regions of influence, proportional to the relevant 61 sites. According to the geographical location of the catchments in the region, it could be concluded that the geographical proximity doesnchr('39')t necessarily involve homogeneity. The obtained results indicated the efficient potential of PSO-FES in the delineation of the homogenous catchments in the study area.

Peptic ulcer disease causes abdominal discomfort and pain. Helicobacter pylori bacteria, infection and long-term use of anti-inflammatory drugs are the most common causes of peptic ulcers. Untreated ulcers can lead to other more serious health complications. Gastric cancer is the second commonest cause of death from malignant disease. Speed performance is always quietly essential in detecting and treating peptic ulcer. Combining artificial intelligence with medical knowledge provides a faster and more accurate diagnosis. The main purpose of this study is to design a novel, inexpensive, reliable and quick Fuzzy Expert System for diagnosis of peptic ulcer disease. A data set of 101 Male adult Wistar rats with a weight range of 200–250 g were obtained from the Pasteur institute. Peptic ulcer induced by Indomethacin (50 mg/kg, 2 ml). A computational approach based on a Fuzzy Inference System (FIS) is suggested in this study for the evaluation of peptic ulcer. The Fuzzy Inference System was produced with Fuzzy C-Means and tuned using the Adaptive Neuro-Fuzzy Inference System model (ANFIS). In order to compare the two methods, the performance of the FIS was evaluated with a ROC curve that prepares the FCM accuracy of 90% and ANFIS accuracy of 85%. In conclusion, the Fuzzy Expert System can potentially increase the accuracy and efficiency of medical practices for peptic ulcer diseases to move towards more precision medicine and treatment. This applicable Fuzzy system may impact hospitals and health systems in improving efficiency and productivity, while reducing the cost of care. It can also be considered in medicine to reduce manual tasks and human error.

The concept of eHealth literacy refers to the ability of a person to access electronic health information, evaluate the information, and apply the resulting knowledge to address or solve a health problem. Considering the uncertainties and the subjective nature of e-health literacy, determining the levels of students' e-health literacy is a complex problem. The aim of this research is to develop and implement a fuzzy expert system to determine the level of eHealth literacy anytime and anywhere without accessing the experts personally. Thus, after studying the different methods of measuring people's literacy level, the Digital Health Literacy Instrument was chosen for developing the system. Its reliability and validity were evaluated based on the experts' judgment and by asking for the participation of 50 university students. The implementation of the fuzzy expert system showed that the proposed system succeeded in 88% of analyzed cases. Moreover, to decrease the number of rules systematically to help with expert fatigue while responding to surveys, the fuzzy expert system was modified based on rough set theory, which caused a reduction in the number of rules from 432 to 200. The comparison between the two fuzzy expert systems demonstrated that no significant difference was detected and the modified system. [ABSTRACT FROM AUTHOR]

Peptic ulcer disease causes abdominal discomfort and pain. Helicobacter pylori bacteria, infection and long-term use of anti-inflammatory drugs are the most common causes of peptic ulcers. Untreated ulcers can lead to other more serious health complications. Gastric cancer is the second commonest cause of death from malignant disease. Speed performance is always quietly essential in detecting and treating peptic ulcer. Combining artificial intelligence with medical knowledge provides a faster and more accurate diagnosis. The main purpose of this study is to design a novel, inexpensive, reliable and quick Fuzzy Expert System for diagnosis of peptic ulcer disease. A data set of 101 Male adult Wistar rats with a weight range of 200–250 g were obtained from the Pasteur institute. Peptic ulcer induced by Indomethacin (50 mg/kg, 2 ml). A computational approach based on a Fuzzy Inference System (FIS) is suggested in this study for the evaluation of peptic ulcer. The Fuzzy Inference System was produced with Fuzzy C-Means and tuned using the Adaptive Neuro-Fuzzy Inference System model (ANFIS). In order to compare the two methods, the performance of the FIS was evaluated with a ROC curve that prepares the FCM accuracy of 90% and ANFIS accuracy of 85%. In conclusion, the Fuzzy Expert System can potentially increase the accuracy and efficiency of medical practices for peptic ulcer diseases to move towards more precision medicine and treatment. This applicable Fuzzy system may impact hospitals and health systems in improving efficiency and productivity, while reducing the cost of care. It can also be considered in medicine to reduce manual tasks and human error. [ABSTRACT FROM AUTHOR]

Steam generation, in terms of Marine boiler operation, involves a large number of potential failures and hazards, which impose high risks, causing deadly and costly accidents resulting in human casualties, machinery damage, ship operational disruption, and other financial losses. In this paper, in order to enhance the operational safety concepts onboard and for proactive contribution in the improvement of machinery system reliability, the potential failure modes are summarized by reviewing the past accident data and the reports, for the identification of the worst-case scenarios during boiler operations. For risk quantification and prioritization based on collected data from experts’ elicitations, a fuzzy set approach is developed using a Rule-based fuzzy logic system, modelled and tested using different types of membership functions for the calculation of the corresponding risk values, to assess their potential failure impacts. It is expected that the risk analysis method used in this study is applicable, and the results derived from this study can be used to help in the eradication of the potential failures, ultimately contributing to ship safety and system reliability.

Millions of people encounter coronary artery disease annually, and this disease constitutes a large number of patients. Although considerable progress has been made in medical science, but the early detection of this disease is still a challenging issue. The aim of this paper is to describe developing a fuzzy expert system that will help to detect CAD at an early stage. Rules were extracted with the aid of doctors and fuzzy approach was taken to cope with the present uncertainty in the medical domain. The results indicate a high level of correct detection of normal and abnormal groups of people. The suggested methodology can help the doctors in medical diagnosis

The article discusses the aerodynamic properties of the Guimbal Cabri G2 helicopter. The main considerations concern the helicopters ascending to the altitude under initial conditions, which were assumed in the project and how the change of the defuzzification method affects the work of the fuzzy controller. The authors analyzed the following: the aircraft, the crew and the payload. The authors assessed the altitude and the minimum time of the helicopter climb. In designing and the work simulation process, the authors used Matlab and Simulink software. A comparison of the influence of selected defuzzification methods on the work of the fuzzy controller was made.

Recently, classification systems have received significant attention among researchers due to the important characteristics and behaviors of analysis required in real-time databases. Among the various classification-based methods suitable for real-time databases, fuzzy rule-based classification is effectively used by different researchers in various fields. An important issue in the design of fuzzy rule-based classification is the automatic generation of fuzzy if-then rules and the membership functions. The literature presents different techniques for automatic fuzzy design. Among the different techniques available in the literature, choosing the type, the number of membership functions, and defining parameters of membership function are still challenging tasks. In order to handle these challenges in the fuzzy rule-based classification system, this paper proposes a brain genetic fuzzy system (BGFS) for data classification by newly devising the exponential genetic brain storm optimization. Here, membership functions are optimally devised using exponential genetic brain storm optimization algorithm and rules are derived using the exponential brain storm optimization algorithm. The designed membership function and fuzzy rules are then effectively utilized for data classification. The proposed BGFS is analyzed with four datasets, using sensitivity, specificity, and accuracy. The outcome ensures that the proposed BGFS obtained the maximum accuracy of 88.8%, which is high as compared with the existing adaptive genetic fuzzy system.

This article aims at designing a Fuzzy Expert System that helps select the appropriate industrial investment and prioritize the projects based on credible criteria and international standards in fuzzy environment to reduce the risk of adverse selection where possible. The main objectives of the research issue are optimizing decisions, increasing productivity and reducing investment risk that can ultimately lead to development.This article describes how to design and use fuzzy expert system and indicated the role of technical and economic criteria in selection of industrial projects. In this study, MATLAB (fuzzy inference module system) software was used to analyze the data, fuzzy inputs and outputs, establishment of rules and, ultimately, expert system output and their defuzzification, and the graphical interface. The existence of such a model in addition to helping to improve decision-making will help the development of industry and IndustrialadvancementIn order to validate this study, a case study was done and the comparison of the output of the proposed system with the experts’ opinions approved the validity of our system. The system helps to prioritize and select industrial projects and provides recommendations in more detailed.

A pacemaker is an embedded device that is sited in the chest to regulate irregular heartbeats known as arrhythmias. Since such devices are directed by software, a software failure may cause a serious hazard such as patient death. Runtime monitoring and adaptation of the device software behavior offers a solution for preventing device hazards. We have already obtained some experiences from monitoring medical devices like: 1) insulin pump using fuzzy Petri net and 2) cardiac pacemaker using colored Petri-net (CPN) and hierarchical fuzzy CPN (HFCPN). However, these studies did not present an adaptation method for software runtime faults. This paper, extending our previous work, presents an automatic runtime method for continuous verification of the behavior of the implanted pacemaker using a software agent. The autonomy and the intelligence characteristics of the software agent are used to control the behavior of the cardiac pacemaker software by drawing inferences from a knowledge base where HFCPN is used by the agent as the inference engine. Compared to a flat inference engine, the HFCPN is able to cover the concurrent states initiated by input fuzzy values and improve the running time for finding a suitable rule by up to 92%. In addition, the intelligent software agent checks the runtime operation accuracy of the pacemaker software in vital and unexpected situations, and redirects the software decision if it finds an unacceptable value. To demonstrate the HFCPN’s behavior and decision-making in different situations, three different scenarios are presented. [ABSTRACT FROM AUTHOR]

This paper describes a novel approach in the area of evaluating suitable job applicants for various job positions, and specifies typical areas of requirement and their usage. Requirements for this decision-support system are defined in order to be used in middle-size companies. Suitable tools chosen were fuzzy expert systems, primarily the inference system Takagi-Sugeno type, which were then supplied with implementation of methods of variant multi-criteria analysis. The resulting system is a variable tool with the possibility to simply set the importance of individual selection criteria so that it can be used in various situations, primarily in repeated selection procedures for similar job positions. A strong emphasis is devoted to the explanatory module, which enables the results of the expert system to be used easily. Verification of the system on real data in cooperation with a collaborating company has proved that the system is easily usable.

Background: The migraine headache is a kind of most populated headache which its prevalence rate is so high. The first step for starting the treatment is the recognition stage. In addition, the fuzzy logic has good power for describing enigmatic and imprecise aspects; so, this tool could be used for the system modeling. This research aimed to recognize the migraine via using fuzzy logic and systems. Methods: A fuzzy expert system for diagnosis of migraine via Learning from Examples (LFE) algorithm was presented. Mamdani model was used in fuzzy inference engine using Max-Min as Or-And operators and Centroid method was used as defuzzification technique. Findings: Using the data of 148 patients, the migraine diagnostic system was trained by LFE algorithm and in average, 80 pieces of If-Then rules were produced for fuzzy system. The accuracy, precision, sensitivity, and specificity of the system were 97%, 80%, 70%, and 94%, respectively. Using the migraine diagnostic system by human experts, it was proved that the system had the ability of correct recognition by the rate of 81%. Conclusion: As the linguistic rules may be incomplete when human expert express their knowledge and according to importance of early diagnosis and favorable results, the LFE training algorithm is more effective than human experts system for recognition of migraine headache.

This paper analyses the management process of the vessel traffic control on one-way section on navigable canal with the adaptive time-sequential filter (traffic lights). One-way section on canal significantly decreases waterway capacity and requests special attention in control and regulation of the vessel traffic. The vessel traffic is a stochastic variable, and the vessel traffic control needs to be flexible and adaptive in order to achieve the required traffic flow with minimal delays. On the one-way section, two independent variable vessel flows from opposite directions are encountered, and fixed (predefined) signal plans lead to an increase in vessel delays. An appropriate solution is development of a Fuzzy Control System (FCS) for the vessel traffic control. A control algorithm is designed according to a set of linguistic rules that describes input parameters for the control strategy. The estimated and approximate input parameters are implemented in the algorithm as fuzzy sets. The final result of the developed algorithm is the traffic light scheme (duration of green light for certain direction). The presented control system can be used as an adaptive automatic control system for the vessel traffic control processes on navigable canals or on critical sections of other waterways. [ABSTRACT FROM AUTHOR]

Abstract In this study, fuzzy expert system (FES) in Monte Carlo (MC) method, which is used for estimating fuzzy linear regression model (FLRM) parameters, is applied to determine the parameter intervals, for the first time in the literature. MC method in estimating FLRM parameters is a new field of study that is very useful and time saving. However a major problem might occur in determining the parameter intervals from which the regression model parameters are supposed to come. If the intervals are calculated too large, FLRM error will be very large. Accordingly, the actual model parameters will not be obtained if the intervals are calculated too narrow. This drawback has not been addressed in the literature before and only optimization methods have been applied to achieve the best interval values. In this article, the FES is used for the first time in order to solve the problem in parameter estimation process for the FLRM in the field of statistics. For this purpose, the difference between the fuzzy observation value and fuzzy estimation value's support set (W) is taken into account. The most appropriate intervals calculated for the parameters are those that make W as small as possible. Thus, FES is designed to determine the best intervals for the model parameters. The system knowledge base is composed of 7 fuzzy rules. As a result, it is deduced that the FLRM parameter estimates obtained from the MC method using FES are very close to the real values. The real impact of this paper will be in showing the applicability of FESs in order to solve problems that we encounter in the field of statistics by the help of linguistic expressions. Moreover, these outcomes will be useful for enriching the studies that have already focused on FLRMs and will encourage researchers to use FES to solve problems in statistics. To sum up, this study demonstrates that FESs which is used in technological devices and makes our lives easier can also be used in solving problems that we confront in the field of statistics efficiently with using linguistic expressions like human inference system. Highlights • In this study, the problem encountered in the field of statistics has been solved by using the fuzzy expert system (FES). • The FES is used for the first time in order to solve the problem in parameter estimation process for the fuzzy linear regression model in the field of statistics. • The originality of this work lies in the use of FES in fuzzy linear regression analysis and applied in this study for the first time in the literature. [ABSTRACT FROM AUTHOR]

This study aims at developing a new approach to predict and determine the quality of rotor-spun yarn in terms of fibre characteristics as well as critical yarn properties. Hybrid modeling by combining two or more techniques has been demonstrated to give better performance than that of several single approaches over many research areas. Hence, in this study a hybrid model by combining two soft computing approaches, namely artificial neural network (ANN) and fuzzy expert system, has been developed. The ANN is used to predict three yarn characteristics, namely tenacity, breaking elongation and CVm. Then these three outputs are used to predict the new quality index by means of the fuzzy expert system. The accuracy of predicted model has been estimated using statistical performance criteria, such as correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE) and mean relative per cent error (MRPE). The results show the ability of model to predict the rotor-spun yarn quality and according to the analytical findings, the hybrid model gives accurate result. [ABSTRACT FROM AUTHOR]

Regional flood frequency studies are initialized by the delineation of the homogeneous catchments. This study was based on "Region of Influence" concept, aiming to find the similar catchments in the south of Caspian Sea. The methodology utilized the Particle Swarm Optimization Algorithm, PSO, to optimize the fuzzy system over a dataset of catchment properties. The main catchment variables in relation to flood were determined by the principle component analysis method and employed as the inputs in the fuzzy system. Catchments grouping was performed over these fuzzy input variables by the iterative process. The optimum similar groups were obtained by PSO, and the heterogeneous Lmoment index was used as the termination criterion for the optimization process. A total of 61 hydrometric stations located in the study area were selected and their relevant catchments' physical, climatic and hydrologic properties in relation to flood were studied. Principle Component Analysis by Variomax Rotation Factor over the catchments datasets tended to four out of 16 physical variables, including area, mean elevation, Gravelious Factor and Form Factor, as the main parameters in terms of homogeneity with 84 percent of accumulative variance. These variables, as well as mean annual rainfall, were used as the input data to define the fuzzy system. PSO algorithm was then employed to optimize the developed fuzzy system. The developed algorithm tended to yield the best result in the 9th iteration with 26 and 22 for the minimum average and the optimum values of cost function, respectively. The topology of the resulting algorithm included inertia weight, local and acceleration rates, the number of generations and population size, with the values of 0.7298, 1.4962, 1.4962, 10 and 5, respectively. This study tended to a total of 61 regions of influence, proportional to the relevant 61 sites. According to the geographical location of the catchments in the region, it could be concluded that the geographical proximity doesn't necessarily involve homogeneity. The obtained results indicated the efficient potential of PSO-FES in the delineation of the homogenous catchments in the study area. [ABSTRACT FROM AUTHOR]