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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]

The increasing pollution of river systems poses a significant challenge to water quality management and public health. This study was conducted to evaluate the water quality of the Gomati River in India, focusing on the impact of anthropogenic activities on pollution levels across different seasons. The primary objective was to develop a comprehensive assessment model using an effective weighted fuzzy soft expert system (WFSES) to derive a weighted water pollution score (WWP-score) for rating water pollution levels based on various water quality parameters. To achieve this, we employed a fuzzy soft set (FSS) methodology integrated with a weighted fuzzy soft set (WFSS) model to analyze water quality indices at six sampling stations along the Gomati River, the largest river in Tripura, India. Originating from the Raima and Sarma streams, the river flows westward through key regions, providing 249.39 million cubic meters of water annually, essential for drinking, agriculture, fishing, and transportation. The study utilized crisp data summaries of key water quality parameters, including pH, total dissolved solids, total suspended solids, electrical conductivity, biochemical oxygen demand, dissolved oxygen, total hardness, chloride, total alkalinity, and total coliform, across the pre-monsoon, monsoon, and post-monsoon seasons from May 2022 to April 2023, guided by the distribution of waste discharge points. Fuzzy membership functions were defined for these parameters, and the root mean square (RMSQR) operation was used to combine multiple FSSs. The results revealed that the WWP-scores, a measure of water quality, ranged from 0.744 to 0.872 across the sampling stations, with Srimantapur exhibiting the highest level of pollution and Udaipur showing comparatively better quality. These scores indicate that water quality at all sites falls into the poor category, with Srimantapur experiencing the most severe contamination due to high levels of coliform bacteria and organic pollutants. The analysis highlights the impact of domestic wastewater and agricultural runoff on the river's health. This research offers a novel application of FSS and WFSS theory in environmental management, providing a robust framework for evaluating and ranking water quality across multiple parameters. The findings are valuable to the scientific community as they offer detailed insights into the pollution dynamics of the Gomati River and propose a methodological approach for assessing water quality in other river systems facing similar challenges. The study's results underscore the need for targeted pollution control measures and continuous monitoring to safeguard water resources and public health. [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]

In this study, the attractiveness of Azerbaijan economy for foreign direct investment (FDI) is assessed using the fuzzy expert system. The overall attractiveness index of Azerbaijan economy in terms of FDI inflows is calculated based on GDP per capita, GDP, GDP growth, labor cost, labor productivity, political stability, and economic freedom in a type I Mamdani inference system is 19.4 % (19.4\100). For the sake of simplicity of the model, factors such as natural resources, geographical location, membership in international organizations, and other factors influencing FDI inflows have not been considered. [ABSTRACT FROM AUTHOR]

Degumming and bleaching are critical steps in the palm oil refining process, as they are the precursors to the qualities of refined, bleached, and deodorized palm oil. In practice, plant operators often face oil rejections in these processes and solve the problem by trial and error. Hence, a fuzzy expert system is developed to troubleshoot the degumming and bleaching process, for identifying failures and suggesting actions. However, developing the knowledge base and inference engine in the fuzzy expert system for troubleshooting the degumming and bleaching process is challenging because the data in the actual palm oil refining process are poorly documented and must be obtained from various sources, including field observation, document analysis, and interviews, and need to be analyzed using thematic analysis. The results from the thematic analysis were represented as input and output variables of the fuzzy expert system. The developed fuzzy expert system is tested and validated against different data sets and industrial data to identify faults and suggest necessary actions. To evaluate the robustness of the troubleshooting system, the membership functions of the fuzzy expert system are adjusted based on the distributed control system (DCS). The results show that the troubleshooting system can effectively diagnose potential faults and provide necessary actions and can serve as a useful guidance for failures in the degumming and bleaching process. [ABSTRACT FROM AUTHOR]

Failure Mode and Effect Analysis (FMEA) is an essential risk analysis tool that is widely applicable in various industrial sectors. This structured technique allows us to identify and assign priority levels to potential failures that violate the reliability of a system or process. Failure evaluation occurs in a decision-making environment with uncertainty. This study proposes a probabilistic fuzzy system that integrates linguistic and stochastic uncertainty based on a Mamdani-type model to strengthen the FMEA technique. The system is based on analyzing the frequency of failures and obtaining the parameters to determine the probability of occurrence through the Poisson distribution. In addition, the severity and detection criteria were evaluated by the experts and modeled using the Binomial distribution. The evaluation result is a discrete value analogous to the process of obtaining the success or failure of the expert generating the evaluation of 10 Bernoulli experiments. Three fuzzy inference expert systems were developed to combine multiple experts' opinions and reduce linguistic subjectivity. The case study was implemented in the knitting area of a textile company in the south of Guanajuato to validate the proposed approach. The potential failure of the knitting machinery, which compromises the top tension subsystem's performance and the product's quality, was analyzed. The proposed system, which is based on a robust mathematical model, allows for reliable fault evaluation with a simple scale. The classification performed by the system and the one performed by the experts has similar behavior. The results show that the proposed approach supports decision-making by prioritizing failure modes. [ABSTRACT FROM AUTHOR]

• A fusion-driven Bayesian network fault diagnosis method is proposed. • Broad learning system-based benchmark model is developed to learn prior structure. • Fuzzy logic systems-based knowledge evaluation method is developed for structuring optimization. • Fusion field observation information to improve the accuracy and reliability of fault diagnosis. • The method was experimented on a chiller. Timely diagnosis and maintenance of faults in chiller units is beneficial for reducing energy consumption in buildings. In order to improve the diagnostic accuracy and provide reasonable explanations for fault occurrences of the heating, ventilation, and air conditioning (HVAC) systems in buildings, a fusion-driven Bayesian network fault diagnosis method is proposed based on broad learning system and fuzzy expert knowledge. Firstly, from the data-driven perspective, an efficient broad learning system is developed as benchmarking model to build the prior Bayesian network structure. Secondly, from the knowledge-driven perspective, the fuzzy logic system is used to fuzz expert knowledge, which then used to optimize the Bayesian network. Finally, the information obtained by experts on-site is incorporated into the optimized Bayesian network as new evidence nodes to determine the Bayesian network. The parameters of the Bayesian network are learned through Noisy-MAX processing of the conditional probability table. The performance of the proposed method is evaluated based on the fault diagnosis of a chiller system. The results demonstrate that the Bayesian network established through this method combines the advantages of data-driven and knowledge-driven approaches. It not only performs well in terms of diagnostic accuracy, with accuracy rates above 98 % for six typical faults, but also provides effective explanations for the underlying mechanisms of the faults through causal relationship diagrams. [ABSTRACT FROM AUTHOR]

Manufacturing companies operate in a complex environment and need efficient manufacturing processes to remain competitive. Therefore, evaluation methods are essential for decision makers when selecting manufacturing innovation projects (MIPs). However, most approaches are not suitable for strategic use and do not consider all relevant evaluation dimensions. To address this issue, this work presents an approach to evaluate and select MIPs holistically, considering potential, effort, and risk. The approach enables the analysis of the strategic impact of an MIP using a fuzzy expert system, and further evaluates the implementation effort and risk using a combination of the Analytical Hierarchy Process and the Exponential Risk Priority Number. The approach was developed using the results of a systematic literature review and expert-based methods. Finally, the approach was validated in an industrial case study and enabled a transparent evaluation of the strategic potential, effort, and risk of two MIPs, leading to informed project selection.

Unpredictable natural and man-made disasters highlight importance of humanitarian supply chain (HSC) to serve people and affected areas. The main challenges of applying effective relief operations are creating "swift trust" and "coordination" between aid organizations. Implementation of Industry 4.0 facilitates coordination and swift trust within HSC performance. The study intends to assess the readiness status of swift trust and coordination between stakeholders as well as to recommend the most suitable Industry 4.0 tools for improving relief operations. Firstly, a comprehensive set of critical success factors for implementing Industry 4.0 tools are introduced. The factors are categorized into limited groups using Exploratory Factor Analysis. In the next step, hierarchy fuzzy expert system is designed for assessing the readiness status of swift trust and coordination as well as to suggest the most suitable Industry 4.0 tool for enhancing HSC performance within given case study. The framework was applied for three aid organizations to address the pandemic disease in Iran. The outcome denotes that the organization has the highest readiness in logistic and transparency while information quality received the lowest readiness value. In addition to that, the organization should invest on the development of Industry 4.0 enablers including "Internet of Things and Big Data Analytics". The study extends organizational information process theory within HSC for reaching competitive advantage by information processing. The study suggests theoretical and practical implications by introducing a comprehensive set of critical success factors for implementation of Industry 4.0 and providing practical advice for enhancing HSC performance. [ABSTRACT FROM AUTHOR]

Stunting is a chronic nutritional problem that occurs in toddler due to lack of nutritional intake which results in impaired growth toddler. Usually, toddler who experience stunting are characterized by not increasing weight over a long period of time. Application utilization health which makes it easier for users to access information, one of which can be used to identify toddler who are stunted by selecting symptoms. The symptoms experienced by toddlers go through a system known as the system expert. In this research an expert system will be developed that is capable of early detection developmental disorders in toddlers using the Mamdani fuzzy method. The results obtained from this research are an expert system design for early detection of stunting using the Mamdani fuzzy method. The Mamdani fuzzy method was implemented to group the criteria for toddlers who fall into the stunting category or not from the initial data which is still gray because they are still unsure whether to categorize the toddler as having stunting or not. The detection accuracy rate using the Mamdani fuzzy method is 80.87% compared to expert diagnosis. [ABSTRACT FROM AUTHOR]

In the article proposes a new method, based on a hybrid fuzzy expert system, for assessing the QoE of web services. It also shows how different QoS parameters affect QoE. To do this, a subjective test was conducted in a controlled environment with real users to correlate QoS parameters with a subjective QoE score. Based on the test results, affiliation functions and rules for the fuzzy system were obtained. The membership function is derived using a probabilistic approach, and the derivation rules are generated using fuzzy set theory. The evaluation of the results was carried out in a simulation environment using the Matlab software package. The results of the simulation show that the quality of the website is rated and has a high correlation with the subjective quality assessment received from the participants of the control test. [ABSTRACT FROM AUTHOR]

In this study, a Fuzzy input Expert System (FES) is developed to detect the patients' PSG results to linguistic statements (apnea-hypopnea). All of the randomly selected 1318 PSG data taken from 15 patients (12 males (80%) and three females (20%)) from St. Vincent University Hospital / University College Dublin Sleep Apnea Database were studied and applied to the FES model. It is understood from the literature that three signals (airflow, SpO2, and Rib movements) are the primary indicators of apnea and hypopnea. Thus, this study's three important parameters were chosen as input variables to classify the apnea-hypopnea in this study. The output variable DIS (disease) was defined as A (Apnea) and H (Hypopnea). A rule base (consisting of 75 rules) was created using membership functions in the light of AASM's 2012 scoring criteria and an expert's opinion. Since it is the most preferred method, this study uses the center-of-gravity/area (centroid) method for defuzzification. The limit values for each fuzzy expression were created. These parameters were symbolically classified. Membership functions and the degree of the membership function were defined. 231 apnea and 1029 hypopnea events have been successfully detected at 97.5% and 95.2%, respectively. A confusion matrix has been formed for calculating the performances of FES, and accuracy was found to be 97.5%. An interface program was developed using Matlab Graphical User Interface programming language, where some sample results were checked. Thus, results have been converted into understandable linguistic expressions. It can be said that the detection performance of the system developed is good by looking at the results of the correct detection. It is shown that detecting apnea and hypopnea using FES are reliable, consistent, and successful results and helps doctors make quick and reliable diagnoses without any risks. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVD) also known as heart disease are now the leading cause of death in the world. This paper presents research for the design and creation of a fuzzy logic-based expert system for the prognosis and diagnosis of heart disease that is precise, economical, and effective. This system entails a fuzzification module, knowledge base, inference engine, and defuzzification module where seven attributes such as chest pain type, HbA1c (Haemoglobin A1c), HDL (high-density lipoprotein), LDL (low-density lipoprotein), heart rate, age, and blood pressure are considered as input to the system. With the aid of the available literature and extensive consultation with medical experts in this field, an enriched knowledge database has been created with a sufficient number of IF-THEN rules for the diagnosis of heart disease. The inference engine then activates the appropriate IF-THEN rule from the knowledge base and determines the output value using the appropriate defuzzification technique after the fuzzification module fuzzifies each input depending on the appropriate membership function. Moreover, the fusion of web-based technology makes it suitable and cost-effective for the prognosis of heart disease for a patient and then he can take his decision for addressing the problem based on the status of his heart. On the other hand, it can also assist a medical practitioner to reach a more accurate conclusion regarding the treatment of heart disease for a patient. The Mamdani inference method has been used to evaluate the results. The system is tested with the Cleveland dataset and cross-checked with the in-field dataset. Compared with the other existing expert systems, the proposed method performs 98.08% accurately and can make accurate decisions for diagnosing heart diseases. [ABSTRACT FROM AUTHOR]

Building on the contingency theory, the paper aims to shed light on the contribution provided by technological innovation on sustainability reporting quality. A fuzzy expert system (FES) was developed to evaluate the cumulative effects related to the adoption of digital devices in sustainability reporting practices. The analysis underlined the enabling role covered by Sustainable Enterprise Resources Planning (S‐ERP) systems on sustainability reporting processes. In detail, we found that the disclosure of environmental information using technological platforms can lead analysts to a more accurate evaluation. [ABSTRACT FROM AUTHOR]

Currently, the percentage of traffic accidents has increased, and according to statistics, this percentage will continue to increase every year, so it is necessary to develop new technologies to prevent this kind of accidents. This paper presents a drowsiness detection system based on electroencephalogram (EEG) signals using a pair of channels (Fp1 and Fp2) applied to drivers before entering their vehicles. First, this model detects the relationship between the area under the curve (AUC) of alpha brain waves, an effective parameter for detecting drowsiness. Then, the extracted information is passed to a fuzzy expert system (FES) that classifies the subject's state as "alert" or "sleepy"; the criterion used was a threshold and training with subjective levels. The proposed system was compared with neural network models, such as support vector machine (SVM), K nearest neighbors (KNN), and random forest (RF). Measurements of one hundred and twenty minutes were performed on each of the ten drivers for two days to test the system. The tests confirm that this system is suitable for preventive measures and that the fuzzy system is superior to traditional neural network methods. [ABSTRACT FROM AUTHOR]

The importance of Big Data and the analysis of this data in recent years is indisputable, and this boom has spread to all areas of life, including professional sports and, within this, soccer. The significant amounts of money involved in this sport have led to the need for the top clubs to employ these techniques to gain a competitive advantage over their competitors. Despite this, there is very little information on how these tools are used or what parameters they consider. Similarly, there are a multitude of amateur analyses that offer very few conclusions. They simply focus on collecting and presenting the data in the form of a comparison without any analysis or pre-processing. This work describes the implementation of an expert system based on fuzzy logic used to evaluate the talent of a soccer player at all levels, his/her aptitude and attitude, to face his/her individual and collective professional development. For this purpose, the above aspects will be evaluated specifically in the different aspects of the game, which will allow us to evaluate the performance of a soccer team and thus determine the probability of victory, draw, and defeat in a confrontation. [ABSTRACT FROM AUTHOR]

Expert systems have been widely used in medicine to diagnose different diseases. However, these rule-based systems only explain why and how their outcomes are reached. The rules leading to those outcomes are also expressed in a machine language and confronted with the familiar problems of coverage and specificity. This fact prevents procuring expert systems with fully human-understandable explanations. Furthermore, early diagnosis involves a high degree of uncertainty and vagueness which constitutes another challenge to overcome in this study. This paper aims to design and develop a fuzzy explainable expert system for coronavirus disease-2019 (COVID-19) diagnosis that could be incorporated into medical robots. The proposed medical robotic application deduces the likelihood level of contracting COVID-19 from the entered symptoms, the personal information, and the patient's activities. The proposal integrates fuzzy logic to deal with uncertainty and vagueness in diagnosis. Besides, it adopts a hybrid explainable artificial intelligence (XAI) technique to provide different explanation forms. In particular, the textual explanations are generated as rules expressed in a natural language while avoiding coverage and specificity problems. Therefore, the proposal could help overwhelmed hospitals during the epidemic propagation and avoid contamination using a solution with a high level of explicability. [ABSTRACT FROM AUTHOR]

This article examines the impact of reference groups, perceived value, and institution image on student behavior intention at a private STIE institution in Medan. The study, which involved 400 students, found that students' behavioral intentions are influenced by their own values and the presence of a supportive group. The article emphasizes the significance of reference groups and perceived value in attracting prospective students to choose a university. It also highlights the role of image and reputation in student retention and loyalty. The research methodology used was a descriptive survey, and the results indicate that reference groups have a stronger influence on student behavior intention compared to perceived value. The article further explores the relationship between client regard and commitment in higher education institutions, emphasizing the importance of providing regard to clients for client satisfaction, loyalty, and institutional improvement. Overall, the article concludes that when reference groups, perceived value, and institution image align positively, it can attract students to choose the institution. [Extracted from the article]

This article focuses on the development of algorithms for a smart neurorehabilitation system, whose core is made up of artificial neural networks. The authors of the article have proposed a completely unique transfer of ACE-R results to the CHC model. This unique approach allows for the saturation of the CHC model domains according to modified ACE-R factor analysis. The outputs of the proposed algorithm thus enable the automatic creation of a personalized and optimized neurorehabilitation plan for individual patients to train their cognitive functions. A set of tasks in 6 levels of difficulty (level 1 to level 6) was designed for each of the nine CHC model domains. For each patient, the results of the ACE-R screening helped deter-mine the specific CHC domains to be rehabilitated, as well as the initial gaming level for rehabilitation in each domain. The proposed artificial neural network algorithm was adapted to real data from 703 patients. Experimental outputs were compared to the outputs of the initially designed fuzzy expert system, which was trained on the same real data, and all outputs from both systems were statistically evaluated against expert conclusions that were available. It is evident from the conducted experimental study that the smart neurorehabilitation system using artificial neural networks achieved significantly better results than the neurorehabilitation system whose core is a fuzzy expert system. Both algorithms are implemented into a comprehensive neurorehabilitation portal (Eddie), which was supported by a research project from the Technology Agency of the Czech Republic. [ABSTRACT FROM AUTHOR]

The widespread application of reinforced concrete structures in different environmental conditions has underscored the need for effective maintenance and repair strategies. These structures offer numerous advantages, but are not impervious to the deleterious effects of chemical deterioration. The outcomes of this research hold significant implications for the management system of reinforced concrete structures. This study proposes the utilization of a fuzzy expert system as a means of enhancing the diagnosis of chemical deterioration in reinforced concrete structures that is a valuable tool for engineers and decision-makers involved in the maintenance and repair of these structures. The fuzzy expert system serves as an intelligent tool that can incorporate various symptoms of deterioration and inspection data to improve the accuracy and reliability of the diagnostic process. By integrating these inputs, the system evaluates 21 different data points, each representing a specific aspect of deterioration, on a scale ranging from 0 to 100. This numerical representation allows for a quantification of the level of deterioration, with 0 denoting minimal deterioration and 100 indicating severe deterioration. The effectiveness of the fuzzy expert system lies in its ability to process the vast amount of data and apply fuzzy operations on 352 fuzzy rules. These rules define the relationships between the inspection data, the type of deterioration, and its extent. Through this computational process, the fuzzy expert system can provide valuable insights into 10 distinct types of chemical deterioration, facilitating a more precise and comprehensive diagnosis. The implementation of the fuzzy expert system has the potential to revolutionize the field of diagnosing chemical deterioration in reinforced concrete structures. By addressing the limitations of traditional methods, this advanced approach can significantly improve the clarity and accuracy of the diagnostic process. The ability to obtain more precise information regarding the type and extent of deterioration is vital for developing effective maintenance and repair strategies. Ultimately, the fuzzy expert system holds great promise in enhancing the overall durability and performance of reinforced concrete structures in various environments. [ABSTRACT FROM AUTHOR]

Purpose: This study presents an image processing algorithm capable of calculating selected flight parameters requested by flight control systems to guide aircraft along the horizontal projection of the landing trajectory. The parameters identified based on the basics of the image of the Calvert light system appearing in the on-board video system are used by flight control algorithms that imitate the pilot's schematics of control. Controls were generated using a fuzzy logic expert system. This study aims to analyse an alternative to classical solutions that can be applied to some specific cases. Design/methodology/approach: The paper uses theoretical discussions and breakdowns to create the basics for the development of structures for both image processing algorithms and control algorithms. An analytical discussion on the first stage was transformed into laboratory rig tests using a real autopilot unit. The results of this research were verified in a series of software-in-the-loop computer simulations. Findings: The image processing method extracts the most crucial parameters defining the relative position of the aircraft to the runway, as well as the control algorithm that uses it. Practical implications: In flight control systems that do not use any dedicated ground or satellite infrastructure to land the aircraft. Originality/value: This paper presents the original approach of the author to aircraft control in cases where visual signals are used to determine the flight trajectory of the aircraft. [ABSTRACT FROM AUTHOR]

Here, we develop a fuzzy controller using a new online self-adapting design. The objective of this work is to control a nonlinear process by using a one-dimensional input rule variable, instead of error and error variation. The initial limits of the fuzzy logic membership functions are mostly depend on experiments and previous knowledge of the dynamic process behaviors. Generally, the membership function parameters have a significant impact on control signal amplitude and, consequently on the convergence and stability of the controller-plant system. The proposed technique determines the limits of the antecedent membership functions online using the kth and k - 1th outputs of the controlled plant and reference model, respectively. Meanwhile, the limits of the consequent membership functions are calculated using error and error variation. This approach ensures: (i) that the input/output variables have the required fuzzy space, (ii) the controlled plant follows the desired reference model, and (iii) the control signal amplitude is within acceptable limits. Additionally, (iiii) it takes into account the dynamic variability of the process and the existence of an overshoot. The membership function parameters are updated continuously through a self-adapting procedure, ensuring improved control performance. Ultimately, the proposed approach is improved using two nonlinear systems. [ABSTRACT FROM AUTHOR]

The article deals with mathematical formalism of the process of combining several inputs into a single output in fuzzy inteligent systems, the process known as aggregation. We are interested in logic aggregation operators. Such aggregators are present in most decision problems and in fuzzy expert systems. Fuzzy inteligent systems are equipped with aggregation operators (aggregators) with which reasoning models adapt well to human reasoning. A brief overview of the field of fuzzy aggregators is given. Attention is devoted to so called graded logic aggregators.. The role of fuzzy agregators in modelling reasoning and the way they are chosen in modelling are pointed out. The conclusions are given and research in the field is pointed out. [ABSTRACT FROM AUTHOR]

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]

Using fuzzy sets for reasoning is what we mean when we talk about fuzzy logic. Contradictory natures are prevalent phenomena in the medical field. Guidelines are used by anesthesiologists when taking care of patients. After gauging the patient’s vitals, he may make adjustments to the flow of medication and fluids, or even the ventilator settings. Knowledge about the real world is typically sketchy, inaccurate, and inconsistent. Due to the inherent representation of subjective human conceptions used in much medical decision making, fuzzy logic seems well-suited for use in anaesthesia. We have developed a fuzzy expert system using fuzzy methodology for the aim of fluid management during general anaesthesia. The desired intravenous fluid rate (IFR) is the defuzzified value that is output by the fuzzy expert system. Fuzzy inputs serve as antecedent parts of rules for a fuzzy expert system, and some examples of such inputs are mean arterial pressure (HUO), hourly urine output (HUO), and central venous pressure (CVP). It would only cost a little sum to have a human operator sometimes enter MAP, HUO, and CVP values into a personal computer for this purpose. The study’s overarching goal was to devise a method for approximating IFR by making use of a linguistic description of MAP and HUO. Fuzzy sets, including decreasing, constant, and growing MAP and HUO rates of change, would assist to illustrate the trend in a patient’s fluid state. To regulate fluid levels more precisely would be possible. Expert guidance in addition to the calculated use of fuzzy methods are essential for achievement of the desired outcome. Patients must be in generally good health before this mode may be used on them, and they must be undergoing minimally invasive surgery. Moderate to severe blood loss after surgery need more complex modalities involving more factors. [ABSTRACT FROM AUTHOR]

The use of intelligent systems in clinical diagnostics has evolved, integrating statistical learning and knowledge-based representation models. Two recent works propose the identification of risk factors for the diagnosis of obstructive sleep apnea (OSA). The first uses statistical learning to identify indicators associated with different levels of the apnea-hypopnea index (AHI). The second paper combines statistical and symbolic inference approaches to obtain risk indicators (Statistical Risk and Symbolic Risk) for a given AHI level. Based on this, in this paper we propose a new intelligent system that considers different AHI levels and generates risk pairs for each level. A learning-based model generates Statistical Risks based on objective patient data, while a cascade of fuzzy expert systems determines a Symbolic Risk using symptom data from patient interviews. The aggregation of risk pairs at each level involves a fuzzy expert system with automatically generated fuzzy rules using the Wang-Mendel algorithm. This aggregation produces an Apnea Risk indicator for each AHI level, allowing discrimination between OSA and non-OSA cases, along with appropriate recommendations. This approach improves variability, usefulness, and interpretability, increasing the reliability of the system. Initial tests on data from 4400 patients yielded AUC values of 0.74–0.88, demonstrating the potential benefits of the proposed intelligent system architecture. [ABSTRACT FROM AUTHOR]

Medical data, such as electronic health records, are a repository for a patient's medical records for use in the diagnosis of different diseases. Using medical data for individual patient care raises a number of concerns, including trustworthiness in data management, privacy, and patient data security. The introduction of visual analytics, a computing system that integrates analytics approaches with interactive visualizations, can potentially deal with information overload concerns in medical data. The practice of assessing the trustworthiness of visual analytics tools or applications using factors that affect medical data analysis is known as trustworthiness evaluation for medical data. It has a variety of major issues, such as a lack of important evaluation of medical data, the need to process much of medical data for diagnosis, the need to make trustworthy relationships clear, and the expectation that it will be automated. Decision-making strategies have been utilized in this evaluation process to avoid these concerns and intelligently and automatically analyze the trustworthiness of the visual analytics tool. The literature study found no hybrid decision support system for visual analytics tool trustworthiness in medical data diagnosis. Thus, this research develops a hybrid decision support system to assess and improve the trustworthiness of medical data for visual analytics tools using fuzzy decision systems. This study examined the trustworthiness of decision systems using visual analytics tools for medical data for the diagnosis of diseases. The hybrid multi-criteria decision-making-based decision support model, based on the analytic hierarchy process and sorting preferences by similarity to ideal solutions in a fuzzy environment, was employed in this study. The results were compared to highly correlated accuracy tests. In conclusion, we highlight the benefits of our proposed study, which includes performing a comparison analysis on the recommended models and some existing models in order to demonstrate the applicability of an optimal decision in real-world environments. In addition, we present a graphical interpretation of the proposed endeavor in order to demonstrate the coherence and effectiveness of our methodology. This research will also help medical experts select, evaluate, and rank the best visual analytics tools for medical data. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea (OSA), characterized by recurrent episodes of partial or total obstruction of the upper airway during sleep, is currently one of the respiratory pathologies with the highest incidence worldwide. This situation has led to an increase in the demand for medical appointments and specific diagnostic studies, resulting in long waiting lists, with all the health consequences that this entails for the affected patients. In this context, this paper proposes the design and development of a novel intelligent decision support system applied to the diagnosis of OSA, aiming to identify patients suspected of suffering from the pathology. For this purpose, two sets of heterogeneous information are considered. The first one includes objective data related to the patient's health profile, with information usually available in electronic health records (anthropometric information, habits, diagnosed conditions and prescribed treatments). The second type includes subjective data related to the specific OSA symptomatology reported by the patient in a specific interview. For the processing of this information, a machine-learning classification algorithm and a set of fuzzy expert systems arranged in cascade are used, obtaining, as a result, two indicators related to the risk of suffering from the disease. Subsequently, by interpreting both risk indicators, it will be possible to determine the severity of the patients' condition and to generate alerts. For the initial tests, a software artifact was built using a dataset with 4400 patients from the Álvaro Cunqueiro Hospital (Vigo, Galicia, Spain). The preliminary results obtained are promising and demonstrate the potential usefulness of this type of tool in the diagnosis of OSA. [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]

In this paper, a troubleshooting technique is developed to provide a sequence of necessary actions during deodorizer unit failure in a palm oil refinery. This paper introduces a fuzzy expert system that incorporates the knowledge of experienced plant workers in dealing with the complex problems of the palm oil deodorizer. The presented methodology uses both qualitative and quantitative data. Qualitative data are collected from several interview sessions with the technical staff in a palm oil refinery. Quantitative data are gathered from various sources, such as control and monitoring systems. The qualitative data are analyzed to discover the possible pattern of faults in the deodorizer failure. This tool can diagnose possible faults and provide a sequence of necessary actions when deodorizer failure occurs. The obtained results show the developed deodorizer troubleshooting tool is useful to guide plant workers/operators in diagnosing the possible faults and abnormalities during the troubleshooting process, as well as to provide field training for inexperienced staff. [ABSTRACT FROM AUTHOR]

Breast cancer is possibly the deadliest illness in the world and the risks are gradually increasing. One out of eight women has the chance to be detected with breast cancer in their lifetime. The utmost cause for the higher fatality rates is the prolonged prognosis for the detection of breast cancer. The focus of this study is therefore to develop a better fuzzy expert system for the detection of breast cancer using decision tree analysis for deriving the rule base. For this classification problem, the input features of the dataset are converted into human‐understandable terms‐linguistic variables. The Mamdani Fuzzy Rule‐Based system is deployed as the main inference engine and the centroid method for the defuzzification process to convert the final fuzzy score into class labels‐ benign (not cancerous) or malignant (cancerous). A decision tree algorithm is applied the creating a novel set of 27 fuzzy rules which are fed into FRBS. The investigation is performed on the publicly available Wisconsin Breast Cancer Dataset. The accuracy obtained by the proposed system is about 97%, recall is 99.58% and precision is about 93%. The experiments on this dataset yield higher performance as compared to the state‐of‐the‐art dataset. [ABSTRACT FROM AUTHOR]

Successful healthcare companies and illness diagnostics require data visualization. Healthcare and medical data analysis are needed to use compound information. Professionals often gather, evaluate, and monitor medical data to gauge risk, performance capability, tiredness, and adaptation to a medical diagnosis. Medical diagnosis data come from EMRs, software systems, hospital administration systems, laboratories, IoT devices, and billing and coding software. Interactive diagnosis data visualization tools enable healthcare professionals to identify trends and interpret data analytics results. Selecting the most trustworthy interactive visualization tool or application is crucial for the reliability of medical diagnosis data. Thus, this study examined the trustworthiness of interactive visualization tools for healthcare data analytics and medical diagnosis. The present study uses a scientific approach for evaluating the trustworthiness of interactive visualization tools for healthcare and medical diagnosis data and provides a novel idea and path for future healthcare experts. Our goal in this research was to make an idealness assessment of the trustworthiness impact of interactive visualization models under fuzzy conditions by using a medical fuzzy expert system based on an analytical network process and technique for ordering preference by similarity to ideal solutions. To eliminate the ambiguities that arose due to the multiple opinions of these experts and to externalize and organize information about the selection context of the interactive visualization models, the study used the proposed hybrid decision model. According to the results achieved through trustworthiness assessments of different visualization tools, BoldBI was found to be the most prioritized and trustworthy visualization tool among other alternatives. The suggested study would aid healthcare and medical professionals in interactive data visualization in identifying, selecting, prioritizing, and evaluating useful and trustworthy visualization-related characteristics, thereby leading to more accurate medical diagnosis profiles. [ABSTRACT FROM AUTHOR]

Summary: Cell planning in conventional networks has gained more attention as it directly affects the network performance and deployment cost. Existing cell planning methodologies are framed either with identical base stations or constructing a network without any infrastructure. But heterogeneous networks (HetNets) allow the service provider to deploy small cells over the region to enhance the network performance and signal coverage probability. Thus, a small‐cell planning procedure is presented in this research work considering the low‐powered base station and deployment cost to enhance the energy efficiency of the HetNet. An adaptive fuzzy expert system is used for cell dimensioning, and a nature‐inspired ant colony optimization model is employed for automatic base station placement. Simulation analysis demonstrates that the proposed small‐cell planning procedure attains better energy efficiency and user satisfaction ratio compared to conventional planning strategies. In the two cases of simulation analysis, the proposed model attains an average of 85% user satisfaction ratio for case 1 and 87% for case 2, which is better than existing strategies like density‐based spatial clustering of applications with noise (DBSCAN), k‐means, and number‐based spatial clustering (NBSC) algorithms. [ABSTRACT FROM AUTHOR]

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]

It is desirable for the work done in any construction process to be both cost-effective and durable. A thorough consideration of the matter reveals that the optimization of real-world problems involves multiple objectives. Bituminous hot mixtures, which are widely used in motorway construction, consist of aggregate and bitumen. The ratio between the different types of aggregate and bitumen forms the input to the real-world problem defined in this article, and the results of a test of the obtained asphalt in three different fields form the output. Our aim is to optimize these three outputs simultaneously to obtain a solution space with the most appropriate inputs. To optimize this problem, a new multi-objective optimization approach is proposed and tested in various ways and is finally adapted to the hot mix asphalt problem. Since the mathematical model of the objective function for this problem is fairly difficult, a fuzzy logic expert system is developed to act as the objective function. We believe that our approach to solving complex problems such as these forms a significant contribution to the literature. [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]

The IT industry occupies a significant place in the economy of the world. Trends in its development directly affect changes in various sectors of the economy. This paper considers the dynamics of IT industry under conditions of increased entropy of the functioning environment caused by crises, military conflicts, non-standard events. The mathematical basis of the study is an apparatus of fuzzy logic. The proposed technique for analyzing and forecasting the state and dynamics of the IT sector makes it possible to take into account the influences of a set of factors of different nature and direction of action, which contributes to obtaining a reasonable assessment under conditions of inaccurate information. The software platform for implementing the methodology is the authentically designed fuzzy expert system (ES) FuzzyKIDE. The conceptual foundations of the platform structure have been defined. A modification of the fuzzy inference mechanism has been proposed, which, unlike existing samples, makes it possible to eliminate saving intermediate results and reduces the load on the database. The composition and structure of the knowledge base (KB) for ES have been proposed. Fuzzy rules are based on predetermined relationships between key factors of influence. The technology of ES operation is represented using simulated scenarios with obtaining predictive results. The analysis of the final data of expert consultations proves the ES operability, providing for the possibility of KB significant expansion in the process of industrial operation. The use of ES is aimed at forming a holistic view of possible directions of IT sector development. Maintaining the actual state of ES KB is a condition for early warning of the emergent negative/crisis phenomena. The FuzzyKIDE expert consulting system is proposed as a tool to support management decision-making based on the analysis and forecasting of the state and dynamics of IT sector under conditions of high uncertainty. [ABSTRACT FROM AUTHOR]

Fused deposition modeling (FDM) is a well-liked additive fabrication method used to manufacture prototypes and components in industries. The quality of the 3D printed component depends on the temperature profile between the layers of the printed components and the process parameters. The deviations in the quality of manufactured components can be established using tools of metrology, including Coordinate-Measuring Machine and Machine Vision. This research is to determine the effect of temperature on the aforementioned phenomenon by using collected data to build a predictive model. The leading factor effect intrigue is stressed for the correlative closeness coefficient (Cn*) and Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS). The most favorable combinations of the experiment were obtained from the response diagram at a layer thickness of 0.3 mm, print speed of 80 mm/sec, and infill percentage of 20%. It is noted that the parameters have a contribution of 55.60%, 33.16%, and 0.15%, respectively. The majority of agreeable combinations of the investigations were acquired from the main factor effect response diagram, a layer thickness of 0.3 mm, printing FDM speed of 80 mm/sec, and an infill percentage of material is 20% for maximizing the temperature gradient and minimizing shrinkage and warpage. A fuzzy logic expert system was used to predict the shrinkage allowances precisely with less than 5% error. [ABSTRACT FROM AUTHOR]

The objective of the current study is to develop a model employing a fuzzy logic expert system (FLES) for predicting the noise reduction coefficient (NRC) of a banana-glass fiber composite. Banana fiber has a strong ability to muffle sounds while having low tensile strength. Glass fiber, on the other hand, has a limited capacity for sound absorption despite having considerable tensile strength. A composite consisting of banana and glass fiber has been developed in order to increase noise reduction capability while keeping appropriate tensile strength. The sound adsorption test was done by passing sound through composites via an impedance tube. It was found that 25% banana and 75% glass hybrid composite having 5 mm thickness shows 375.96% more NRC than 100% glass fiber composite. Only a 25% increase of banana fiber content increased NRC incredibly. Different compositions of glass fiber, banana fiber, and thicknesses of the composites were taken as the input variables and the output, noise reduction coefficient. To better comprehend the relationship between these variables, a fuzzy logic-based model was created and the model's coefficient of determination (R2) was found to be 0.9884. The model accurately anticipated the noise reduction coefficient of the composites with varying constituent percentages and thicknesses. [ABSTRACT FROM AUTHOR]

Taguchi L9 orthogonal array is used to conduct the IRB1410 robot spray painting experiments to investigate the surface characteristics of Cold Rolled close Annealed (CRCA) steel workpiece. The main parameter of spray painting is considered as Distance (D), Pressure (P) and Speed (S) of the Robot. The nanopaint is prepared through the ultra-sonication process for robot spray painting applications. To optimise the robot process parameters and to achieve the accuracy level, the fuzzy expert system is integrated with Taguchi analysis. The developed truth valued fuzzy logic model is assessed the surface roughness by robot nanopaint with Carbon Nanotube (CNT). ANOVA is used to identify the noteworthy factors influencing surface roughness in robot painting process and validated using the empirical regression model. Based on the model, the influence of Robot painting process parameters on surface roughness is analysed by using the sensitivity analysis method. Further, the Neural Network model is used to predict the thickness variation of Nano painted workpiece and compared the results with experimental values. [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]

One of the main challenges in short-term electrical load forecasting is extraction of nonlinear relationships and complex dependencies among different time instances of the load time series. To deal with this difficulty, a hybrid forecasting method is proposed in this paper that uses the fuzzy expert systems and deep learning methods. In the first step, dependency of previous time instances to the next instance to be load forecasted is achieved through a fuzzy system with 125 rules. Then, the obtained weights are used beside the actual load values as the input of a long short-term memory network for load forecasting. The obtained results on two popular datasets show the superior performance of the proposed method in terms of various evaluation measures. [ABSTRACT FROM AUTHOR]

In the city of Ambato - Ecuador, the company "Liargy" is dedicated among its services for analyzing transformer dielectric oils to determine the insulating oil's to physical and chemical electrical properties to anticipate possible failures. Currently, the results of the analysis of dielectric oils are diagnosed with the use of classical logic, a formal system of reasoning and argumentation based on well-defined principles and rules based on the parameters of current regulations, which causes slowness in the diagnostic process. The objective of the research is to develop an experimental fuzzy expert system that, based on the results of the physicochemical, chromatographic, and dielectric analysis and compound contents of insulating mineral oils and the current regulations, allows the diagnosis the condition of the transformer based on the calculation of a health index. The research work uses theoretical and empirical methods, where the research design is non-experimental transversal descriptive with an inductive-deductive method. The development methodology adopted is Scrum combined with fuzzy logic and classical analysis operational techniques to develop a system to automate the diagnosis of the state of electrical transformers based on the calculation of the health index with six variables of the physicochemical properties of the insulating oil, through fuzzy inference analysis and expert criteria rules. [ABSTRACT FROM AUTHOR]

Fuzzy logic plays an essential role in human life. It has been used in various fields such as facial pattern recognition, air conditioners, washing machines, vacuum cleaners etc. The main objective of this paper is to reveal the development of fuzzy logic-based system to improve the quality and reliability of medical diagnosis. Also deals with monitoring and modeling of the visual Prolog Programming were used to create the proposed medical diagnostic system. This article examines the evolution of fuzzy logic's application in the domain of healthcare. [ABSTRACT FROM AUTHOR]