Fuzzy Rule-based Expert System Research Articles

Contamination depth prediction in sandy soils using fuzzy rule-based expert system

AbstractAs a result of rainfall in large quantities, the leachate generated under the municipal solid waste (MSW) is increased, which leaks to the groundwater aquifers and pollutes it. Accurate evaluation of leachate leaks levels has long been regarded as a problem in Iraq due to a lack of reliable data and costly measuring costs. This work proposes a novel fuzzy expert system to predict the pollution status of the underground water in sandy soils. The expert system consists of two subsystems; fuzzy logic system and crisp logic system. The expert system is trained using a data set developed by finite element analysis of sandy soil subjected to contamination materials.

A fuzzy set theory-based fast fault diagnosis approach for rotators of induction motors.

Induction motors have been widely used in industry, agriculture, transportation, national defense engineering, etc. Defects of the motors will not only cause the abnormal operation of production equipment but also cause the motor to run in a state of low energy efficiency before evolving into a fault shutdown. The former may lead to the suspension of the production process, while the latter may lead to additional energy loss. This paper studies a fuzzy rule-based expert system for this purpose and focuses on the analysis of many knowledge representation methods and reasoning techniques. The rotator fault of induction motors is analyzed and diagnosed by using this knowledge, and the diagnosis result is displayed. The simulation model can effectively simulate the broken rotator fault by changing the resistance value of the equivalent rotor winding. And the influence of the broken rotor bar fault on the motors is described, which provides a basis for the fault characteristics analysis. The simulation results show that the proposed method can realize fast fault diagnosis for rotators of induction motors.

A Fuzzy Rule-Based Expert System to Determine Propofol Drug Dosage in Anesthesia

Introduction: Inadequate anesthetic, including under or over dosage, may lead to intraoperative awareness or prolonged recovery. Fuzzy expert systems can assist anesthesiologist to manage drug dosage in a right manner. Designing a fuzzy rule-based expert system to determine the Propofol anesthetic drug dosage was the main objective of this study.Material and Methods: This is a retrospective study. Fuzzy IF-THEN rules were defined based on evidences and experts’ linguistic rules for Propofol dose determination. Fuzzy toolbox in MATLAB software was used to design the system. Validation of system conducted with calculation of mean absolute error (MAE) and root mean squared error (RMSE). Also, difference mean between actual and predicted doses was tested with paired t-test in SPSS V.26 software. Data from 50 ENT (ears, nose, and throat) surgeries were used to validate the fuzzy system.Results: MAE for induction and maintenance doses was 0.128 and 1.95 respectively. RMSE for induction and maintenance doses was 0.228 and 3.383 respectively. Based on paired t-test result, there was no significant correlation between actual and predicted values (P>0.05).Conclusion: Obtained value from test and validation of system demonstrated a high performance and satisfying accuracy of the system. Therefore, this expert system can be used as a decision support system to determine initial dosage of anesthetic drugs. It can also be used for anesthesia students to learn drug administration.

A Novel Fuzzy Logic-Based Medical Expert System for Diagnosis of Chronic Kidney Disease

Chronic kidney disease is a life-threatening complication. Primary diagnosis and active control avoid its progression. To increase the life span of a patient, it is necessary to detect such diseases in early stages. In this research paper, design and development of a fuzzy expert system (FES) to identify the current stage of chronic kidney disease is proposed. The proposed fuzzy rule-based expert system is developed with the help of clinical practice guidelines, database, and the knowledge of a team of specialists. It makes use of input variables like nephron functionality, blood sugar, diastolic blood pressure, systolic blood pressure, age, body mass index (BMI), and smoke. The normality tests are applied on different input parameters. The input variables, i.e., nephron functionality, blood sugar, and BMI have more impact on the chronic kidney disease as shown by the response of surface analysis. The output of the system shows the current stage of patient’s kidney disease. Totally 80 tests were performed on the FES developed in this research work, and the generated output was compared with expected output. It is observed that this system succeeds in 93.75% of the tests. This system supports the doctors in assessment of chronic kidney disease among patients. The detection of chronic kidney disease is a serious clinical problem that comprises imprecision, and the use of fuzzy inference system is suggested to overcome this issue. The proposed FES is implemented in the MATLAB.

A Fuzzy Rule Based Expert System for Early Diagnosis of Osgood Schlatter Disease of Knee Joint

The proposed research work is for the early diagnosis of the inflammatory disease named Osgood-Schlatter disease of the knee joint. As the system deals with fuzzy values, a MATLAB (R2013a) fuzzy logic controller is used for the implementation. The knowledge engineering phase is done with the help of an orthopedic expert. Four symptoms are used for diagnosing the severity of disease. Also, this diagnosis provides the treatment for the respective level of disease. Data collection is completed by the survey method and various defuzzification methods are used to check the accuracy. The proposed system was tested on 25 patients.

A FUZZY DECISION SUPPORT SYSTEM FOR SUSTAINABLE CONSTRUCTION PROJECT SELECTION: AN INTEGRATED FPP-FIS MODEL

Sustainability has become a key concern for project selection in construction industries. Determining the best sustainable project based on various sustainability attributes is a very complicated decision. Accordingly, developing a suitable decision support framework can be very helpful for decision makers to attain planned business goals and complete projects at the right time with good quality. This research develops a decision support model which helps managers to understand the concept of sustainability in construction project selection and choose the best project using a new integrated Multi-Criteria Decision Making (MCDM) approach under uncertainty by integrating Fuzzy Preference Programming (FPP) as a modification of Fuzzy Analytical Hierarchy Process (FAHP), with Fuzzy Inference System (FIS) as a fuzzy rulebased expert system. In the first phase of the research, fifteen sustainability attributes were selected. In the second phase, the final weight of each attribute was computed by using FPP. In the last phase, the most appropriate project was selected by running the weighted FIS. The results showed that Project 3 (P3) is the best project. Finally, two different evaluative tests were also applied to verify the validity and robustness of the developed model.

Enhanced humanoid assisted human interaction model based on linear structural modeling for knowledge representation

Certain rules and regulations are adapted to the linguistic production model to support the decision of human computer interaction using linguistic protection rule method. This paper proposes linear linguistic advanced structural modeling (LLASM) approach to develop weighted linguistic reasoning (WLR) algorithm for the purpose of reasoning and knowledge representation. In this article human computer interaction faces optimal flow and job Characteristics experience using individual computers in the workplace which helps to overcome the issues using LLASM in a variety of organizations with Robotic assistance for verbal and non-verbal sequence using humanoid robot. This model introduces global weight and local knowledge fuzzy rules to determine the optimal flow and job characterization problem faced in linguistic reasoning method. Here, The Weighted linguistic reasoning algorithm allows fuzzy rule-based expert systems with the help of the LLASM method to execute the intelligent and flexible knowledge reasoning model with robotic assistance. Finally, case studies are presented to show the effectiveness of the re-scheduling process which benefits the proposed method using the Linguistic reasoning model.

A FUZZY RULE-BASED EXPERT SYSTEM FOR ASTHMA SEVERITY IDENTIFICATION IN EMERGENCY DEPARTMENT

The emergency department (ED) of a hospital is an important unit that deals with time-sensitive and life-threatening medical cases. Rapid treatment and accuracy in diagnosis are considered the main characteristics of excellent operational processes in ED. However, in reality, long waiting time and uncertainty in the diagnosis has affected the quality of ED services. Nonetheless, these problems can be improved by utilising computing technologies that assist medical professionals to make fast and accurate decisions. This paper investigates the issues of under-treatment and uncertainty condition of acute asthma cases in ED. A novel approach, known as the fuzzy logic principle is employed to determine the severity of acute asthma. The fuzzy set theory, known as Fuzzy Rule-based Expert System for Asthma Severity (FRESAS) determination is embedded into the expert system (ES) to assess the severity of asthma among patients in ED. The proposed fuzzy methodology effectively manages the fuzziness of the patient’s information data, and determines the subjective judgment of medical practitioners’ level on eight criteria assessed in severity determination. Knowledge acquisition and representation, fuzzification, fuzzy inference engine, and defuzzification are the processes tested by the FRESAS development that incorporates expert advice. The system evaluation is performed by using datasets that were extracted from the ED clerking notes from one of the hospitals in Northern Peninsular Malaysia. System evaluation demonstrates that the proposed system performs efficiently in determining the severity of acute asthma. Furthermore, the proposed system offers opportunities for further research on other types of diseases in ED, and improves other hybridisation approaches.

Diagnosis of hypothyroidism using a fuzzy rule-based expert system

BackgroundEarly and accurate diagnosis of many diseases is critical to their treatment. Today, the classification models based on fuzzy intelligent systems help the uncertainty conditions in medicine, as well as the classification of diseases. The main goal of this study is to diagnose the most common thyroid disorders, hypothyroidism, using a fuzzy rule-based expert system. MethodsIn this study, the data from patients who referred to Imam Khomeini Clinic and Shahid Beheshti Hospital in Hamadan west of Iran were collected. The data contain 305 subjects in three classes which are normal, subclinical hypothyroidism and hypothyroidism. Collected variables include demographic, symptoms as well as laboratory tests. In order to the diagnosis of Thyroid disorders, a fuzzy rule-based classifier was designed. Predictive performance of this model was compared with a multinomial logistic regression model in terms of the accuracy and the area under a ROC curve (AUC) as well as sensitivity and specificity. ResultsThe results showed that designed fuzzy rule-based system works well in thyroid disorder prediction with about 97% accuracy. Also, the fuzzy classifier has a better performance than the logistic regression model, especially for the subclinical hypothyroidism class. ConclusionsFuzzy rule-based classifier by utilizing overlapping sets has improved the efficiency of classification and decision-making systems. Also, by providing the possibility of using linguistic variables in the decision-making process, easier interpretation, can be used by doctors who are not familiar with modeling concepts.

A Fuzzy Rule based Expert System for T2DM Diagnosis

Expert system is an intelligent system to captures the knowledge of a human expert in a specific area. It is capable to make decisions and dealing with ambiguous data. It is used to take an expert view in the absence of a human expertise. Moreover, it is not possible that everyone is expert in every field; to overcome this kind of situation, the expert system is called to handle the complex cases [7]. Due to non availability of the doctor, sometimes patient's life is in risk and lead to death due to not diagnose the disease properly as there are several diseases whose symptoms are quite similar in initial stage. Hence, the objective of this paper is to design the expert system for diagnosing the diabetes to go for early treatment.

FESAEI: a fuzzy rule-based expert system for the assessment of environmental impacts : A fuzzy logic approach to impact assessment.

Currently, the method mostly used by practitioners of environmental impact assessment (EIA) is the "crisp numbers" method. Nevertheless, this arithmetic method is far away of giving correct values due to its rigidity and the lack of consideration of important aspects as the imprecision and incompleteness of data and the uncertainty that usually pervade our knowledge of environment. A more flexible model that considers uncertainty of knowledge and imprecision of data is necessary. Among the different approaches for the assessment of environmental impacts, the fuzzy logic-based one takes account of the aspects said before; this was our primal assumption. On this paper, we explain the structure and performance of the fuzzy rule-based inference model we built, how it works, and what can be obtained when used to assess environmental impacts. Our fuzzy expert system for the assessment of environmental impacts (FESAEI) is built as the combination of five subsystems, using a total of 120 fuzzy rules, and being the output and input for the next subsystem. We assessed the parameters of rarity, robustness, quality, recoverability, intrinsic value, extension, intensity, persistence, impact_character, cumulativeness, transmissivity, and impact prevalue in four subsystems. The fifth subsystem gives the definitive impact value corresponding to the impact type of "compatible," "moderate," "severe," and "critical." The model is verified and statistically validated. Weighted Cohen's kappa shows an almost perfect concordance among experts and FESAEI's evaluations.

Type-2 Fuzzy Rule-Based Expert System for Diagnosis of Spinal Cord Disorders

The majority of people have experienced pain in their low back or neck in their lives. In this paper a type-2 fuzzy rule based expert system is presented for diagnosing the spinal cord disorders. The interval type-2 fuzzy logic system permits us to handle the high uncertainty of diagnosing the type of disorder and its severity. The spinal cord disorders are studied in five categories using historical data and clinical symptoms of the patients. The main novelty of this paper lies in presentation of the interval type-2 fuzzy hybrid rule-based system, which is a combination of the forward and backward chaining approaches in its inference engine and avoids unnecessary medical questions. Using of parametric operations for fuzzy calculations increases the robustness of the system and the compatibility of the diagnosis with a wide range of physicians’ diagnosis. The outputs of the system are comprised of type of disorder, location and severity as well as the necessity of taking a M.R. Image. A comparison of the performance of the developed system with the expert shows an acceptable accuracy of the system in diagnosing the disorders and determining the necessity of the M.R. Image.

Fuzzy rule-based Fine–Kinney risk assessment approach for rail transportation systems

ABSTRACTRail transportation is one of the most crucial public transportation types for big and crowded cities. In rail transportation systems, stakeholders face serious issues involved in workshops, stations, lines and their environments, and general office buildings. In order to reach an increased awareness and better occupational health and safety (OHS) management, a new risk assessment approach is proposed in this study. This approach includes a combination of Fine–Kinney method and a fuzzy rule-based expert system. It captures nonlinear causal relationships between Fine–Kinney parameters. Since there is a high level of vagueness involved in the OHS risk assessment data, the rule-based expert system is developed for probability (P), exposure (E), and consequence (C) for evaluating risk score. A case study is carried out in a rail transportation system in Istanbul/Turkey, and a comparison with the classical Fine–Kinney method is discussed. Results of the case study reveal risk clusters and corresponding control measures that should be taken into consideration. The study methodologically contributes to risk assessment in the knowledge, while case study in a real rail transportation system offers an insight into public transport industry in safety improvement.

Design a Fuzzy Rule-based Expert System to Aid Earlier Diagnosis of Gastric Cancer.

Introduction:Screening and health check-up programs are most important sanitary priorities, that should be undertaken to control dangerous diseases such as gastric cancer that affected by different factors. More than 50% of gastric cancer diagnoses are made during the advanced stage. Currently, there is no systematic approach for early diagnosis of gastric cancer.Objective:to develop a fuzzy expert system that can identify gastric cancer risk levels in individuals.Methods:This system was implemented in MATLAB software, Mamdani inference technique applied to simulate reasoning of experts in the field, a total of 67 fuzzy rules extracted as a rule-base based on medical expert’s opinion.Results:50 case scenarios were used to evaluate the system, the information of case reports is given to the system to find risk level of each case report then obtained results were compared with expert’s diagnosis. Results revealed that sensitivity was 92.1% and the specificity was 83.1%.Conclusions:The results show that is possible to develop a system that can identify High risk individuals for gastric cancer. The system can lead to earlier diagnosis, this may facilitate early treatment and reduce gastric cancer mortality rate.

A fuzzy rule-based expert system for diagnosing cystic fibrosis.

BackgroundFinding a valid diagnosis is mostly a prolonged process. Current advances in the sector of artificial intelligence have led to the appearance of expert systems that enrich the experiences and capabilities of doctors for making decisions for their patients.ObjectiveThe objective of this research was developing a fuzzy expert system for diagnosing Cystic Fibrosis (CF).MethodsDefining the risk factors and then, designing the fuzzy expert system for diagnosis of CF were carried out in this cross-sectional study. To evaluate the performance of the proposed system, a dataset that corresponded to 70 patients with respiratory disease who were serially admitted to the CF Clinic in the Pediatric Respiratory Diseases Center, Masih Daneshvari Hospital in Tehran, Iran during August 2016 to January 2017 was considered. Whole procedures of system construction were implemented in a MATLAB environment.ResultsResults showed that the suggested system can be used as a strong diagnostic tool with 93.02% precision, 89.29% specificity, 95.24% sensitivity and 92.86% accuracy for diagnosing CF. There was also a good relationship between the user and the system through the appealing user interface.ConclusionThe system is equipped with information, knowledge, and expertise from certified specialists; hence, as a training tool it can be useful for new physicians. It is worth mentioning that the accomplishment of this project depends on advocacy of decision making in CF diagnosis. Nevertheless, it is expected that the system will reduce the number of false positives and false negatives in unusual cases.

Optimization Model of Fuzzy Rule Based Expert System Using Max-Min Composition and Schema Mapping Translation

Abstract— Fuzzy Decision Making involves a process of selecting one or more alternatives or solutions from a finite set of alternatives which suits a set of constraints. In the rule-based expert system, the terms following in the decision making is using knowledge based and the IF Statements of the rule are called the premises, while the THEN part of the rule is called conclusion. Membership function and knowledge based determines the performance of fuzzy rule based expert system. Membership function determines the performance of fuzzy logic as it relates to represent fuzzy set in a computer. Knowledge Based in the other side relates to capturing human cognitive and judgemental processes, such as thinking and reasoning. In this paper, we have proposed a method by using Max-Min Composition combined with Genetic Algorithm for determining membership function of Fuzzy Logic and Schema Mapping Translation for the rules assignment.Keywords— Fuzzy Decision Making, Rule-Based Expert System, Membership Function, Knowledge Based, Max-Min Composition, Schema Mapping Translation

Fuzzy Rule based Expert System for Evaluating Defaulter Risk in Banking Sector

Background/Objectives: Banking sector faced many problems like credit assessment, credit worthiness, and credit risk etc. Many techniques are used to solve these problems in artificial intelligence. But these problems are not fully resolved in previous years. Methods/Statistical Analysis: One of the most important problems is credit risk as customer defaulter risk. Recent studies have not discussed about defaulter or non-defaulter customer. This research work has discussed about customer’s defaulter risk as well as credit risk. Findings: A fuzzy expert system has been designed which can categorized customer as defaulter and non- defaulter. The defaulter risk is calculated by considering factors as CS (CIBIL Score), LVR (Loan to Value Ratio), AAL (Already Availed Loan), IRF (Income Ratio Factor). Data of customers are collected from Indian Overseas Bank (IOB) branches. Different defuzzification methods are used to verify the result of customers. It also verified by the expert in banking domain. Further it is also tested on the data of other branches of banks. Application/Improvements: The system can be helpful for the bank employees in decision making process. In addition to this the system can be used for training new employee for loan approval tasks.

Fuzzy Rule-Based Expert System for Determining Trustworthiness of Cloud Service Providers

Cloud Computing has a great potential to transform the way we use technology. People have started using cloud services but they still have a lot of apprehensions resulting from the lack of trust on Cloud Service Provider (CSP) and the associated security risks. In this paper, we have proposed a fuzzy rule-based expert system that evaluates trustworthiness of a CSP from the viewpoint of consumers (called Direct-Trust), their Public-Review, and the Cloud Auditor (called Auditor-Trust). A survey has been conducted to rate the five CSPs on the basis of their Reliability, Performance, Security, and Usability. The data collected from the survey serve as the dataset to calculate the Direct-Trust and Public-Review. Auditor-Trust has been calculated using the data generated by Nasuni, a cloud storage company which evaluates CSPs on the basis of their Performance, Availability, Scalability, and Accuracy. Final Trustworthiness is then calculated by combining Direct-Trust, Public-Review, and Auditor-Trust. All the simulations have been done using Fuzzy Logic Toolbox of MATLAB. The results generated rank the CSPs according to their Final Trustworthiness. A scale of 1–5 has been used in all the calculations for ratings.

Development of Sustainable Manufacturing Performance Evaluation Expert System for Small and Medium Enterprises

This study proposes a fuzzy rule based expert system for sustainable manufacturing performance assessment in small and medium enterprises (SMEs). The initial set of measures and metrics have been identified from the literature based on the characteristics of SMEs. Sixteen metrics were identified and categorized under four economic, five environmental and three social measures. Considering the involvement of human reasoning in the decision making process of manufacturing SMEs, it is proposed to gather the inputs in terms of linguistic variables. The fuzzy rule-based expert system is proposed to elicit the performances of all the aspects and overall sustainability of the organization based on triple bottom-line framework.

A cloud-based fuzzy expert system for the risk assessment of chronic kidney diseases

This paper aims to construct a cloud-based fuzzy expert system for the risk assessment of chronic kidney disease (CKD). The neural networks are firstly modelled for the detection of early chronic kidney disease while fuzzy rule-based expert system is employed for risk assessment. Firstly, three different neural network models are developed and experimented followed by the effectiveness measurement of three models based on the criteria of accuracy, sensitivity, and specificity among them. Secondly, the fuzzy rule-based expert system is developed and then is deployed to the cloud platform for conducting the self-risk assessment by the public users. In the ultimate goal, we anticipate that the expert system may also support CKD physicians an alternative to more accurately detect and assess the chronic kidney disease in its early stage of a patient. An online usefulness survey was also conducted. The result shows that there are 88.4% of all users responding to 'Helpful' based on our five-scale metrics. It was concluded that the system is helpful for a user to conduct a self-assessment on risk of kidney health status.