Fuzzy Logic-based Expert System Research Articles

Machining Data Selection by Using a Fuzzy Logic Model

The fuzzy model has been developed for machining parameter selection. The correlation between the hardness of the work-piece and cutting velocity is not exact, but a proper relationship can be established by using a developed model. The fuzzy model was applied to machining data for carbon steel (wrought) that are collected from the ASM handbook, and a proper relationship between handbook data and predicted data by the fuzzy application was obtained. The model was developed by considering input hardness and output speed. A linear fuzzy equation has been proposed to represent the machining data with good correlation. The aim of the present work is to develop a fuzzy logic model to enable the computerization process of machining data that is available in the machining data handbook. As a result, a fuzzy logic-based expert system for machining data selection can be created.

Diagnosis of heart diseases: A fuzzy-logic-based approach.

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.

A Systematic Study of the Literature on Career Guidance Expert Systems for Students: Implications for ODL

The continual evolution of employment opportunities in the present industrial era has raised the need for career-long expert advice. Similar to other fields, thankfully technology has come to our rescue in the area of career guidance also. This paper presents a systematic review of Expert Systems (ES) developed for career guidance, course selection and evaluation of students in the past ten years. The popular research databases Google Scholar and Science Direct were used for obtaining the relevant research papers through broad keywords. The keywords were refined to identify the articles related to rule-based, case-based and fuzzy logic-based ES used for career guidance. A total of twenty-five peer-reviewed relevant articles with full-text available online was selected for the final study. In order to avoid duplicity, technical reports and unreferenced literature were excluded. The review identifies the relatively high weight given by the researchers to rule-based systems owing to their simplicity and broad applicability. However, the relative merits and demerits of rule-based, case-based and fuzzy logic-based ES are highly dependent on the field of application. Nevertheless, ES find wide applications in the area of career guidance and have the potential to enhance the career guidance accessibility of the most remote students.

A fuzzy expert system for sustainable local development

ABSTRACT This research develops a fuzzy logic-based expert system that models the three main dimensions of sustainable development (SD) (social, economic and environmental) in relation to local areas. We applied this system to a lagging Southern Italian province that includes 97 municipalities with different levels of development. This approach allowed for the inclusion of experts’ opinions, which ensured a more comprehensive assessment of SD than methods based solely on quantitative indicators. The results denote gaps in the SD of the examined municipalities, highlighting differences in the social, economic and environmental dimensions that may inform the design of SD policies.

Design and development of fuzzy logic-based expert system for forward and reverse mappings in resin bonded sand systems

Design and development of fuzzy logic-based expert system for forward and reverse mappings in resin bonded sand systems

Clinical decision support system to predict chronic kidney disease: A fuzzy expert system approach

Background and objectivesDiagnosis and early intervention of chronic kidney disease are essential to prevent loss of kidney function and a large amount of financial resources. To this end, we developed a fuzzy logic-based expert system for diagnosis and prediction of chronic kidney disease and evaluate its robustness against noisy data. MethodsAt first, we identified the diagnostic parameters and risk factors through a literature review and a survey of 18 nephrologists. Depending on the features selected, a set of fuzzy rules for the prediction of chronic kidney disease was determined by reviewing the literature, guidelines and consulting with nephrologists. Fuzzy expert system was developed using MATLAB software and Mamdani Inference System. Finally, the fuzzy expert system was evaluated using data extracted from 216 randomly selected medical records of patients with and without chronic kidney disease. We added noisy data to our dataset and compare the performance of the system on original and noisy datasets. ResultsWe selected 16 parameters for the prediction of chronic kidney disease. The accuracy, sensitivity, and specificity of the final system were 92.13 %, 95.37 %, and 88.88 %, respectively. The area under the curve was 0.92 and the Kappa coefficient was 0.84, indicating a very high correlation between the system diagnosis and the final diagnosis recorded in the medical records. The performance of the system on noisy input variables indicated that in the worse scenario, the accuracy, sensitivity, and specificity of the system decreased only by 4.43 %, 7.48 %, and 5.41 %, respectively. ConclusionConsidering the desirable performance of the proposed expert system, the system can be useful in the prediction of chronic kidney disease.

A computer vision-based weed control system for low-land rice precision farming

<span>Agricultural sector is one of the economic pillars of developing nations, because it provides means of boosting gross domestic profit. However, weeds pose a threat to food crop by competing with it for nutrients and undermining the profit to be made from it. The treatment of these weeds is necessary, but at minimal impact on the actual food crop. Herbicide usage is one major means of weed control, owning to the expensive and labour-intensive nature of hand weeding. Recently, the need for site specific spraying has been on the rise because of health concerns which have been raised on the effect of herbicides on food crops and the effect on the environment. Most research on the field focuses on accurately identifying the weeds whilst neglecting the weed control. In this research, we apply fuzzy logic-based expert system to control how herbicide is sprayed on low-land rice in order to reduce excessive herbicide usage. The system supplies the control with weed density (Box size) and confidence level. The values of both are then passed to the fuzzy logic control for spray decision. The Sugeno as well as Mamdani models were tested using generated values for detected weed box size and confidence levels of the computer vision. The mean absolute error obtained was 0.9 for both, and 0.3 and 0.2 respectively, for the mean square error. The error shows how accurate the system can be and with low error value, it shows that the system implementation is capable of providing control for spraying of herbicides which in turn will yield more returns for low-land rice farmers.</span>

Investigation of thermal performance of SAC variables using fuzzy logic based expert system

The thermal performance of a solar air collector (SAC) is investigated experimentally under the different climatic conditions of north eastern India using fuzzy logic based expert system (FLES). The FLES based on subtractive clustering (SC) with the fuzzy logic method where here, SC is used for extraction of optimal fuzzy IF-THEN rules while a fuzzy logic is used for modeling of SAC variables. This work considered four input variables [like mass flow rate (m), collector tilt angles (θ), solar radiation (Q), temperature (T)] and the four output variables [i.e. efficiency (η), exergetic efficiency (ηII), temperature rise (∆T), and pressure drop (∆P)]. First, 272 trials of experimentation on SAC are performed by varying m from 0.0078 to 0.0118 kg/s and θ from 30 to 60°, whereas the variation of metrological data is obtained in different working days. Then modeling and parametric analysis is carried out for SAC. Experimental results reveal that the value of η increases with the increase in m, Q, T and θ up to 45°. The higher value of m results in a higher value of ∆P and that reduces the value of ηII. Also, FLES model provides comparable and acceptable values for SAC. At last, validation of the FLES model is done via published data to confirm the results.

An expert system-based modeling and optimization of corrugated plate solar air collector for North Eastern India

The present study introduces a fuzzy logic-based expert system (FLES) for evaluating the thermal performance of corrugated plate solar air collector (CPSAC) under different climatic conditions of North Eastern India. The FLES model consists of subtractive clustering with Takagi–Sugeno–Kang fuzzy logic (TSK-FL) model. Work considered mass flow rate (m) 0.0039–0.018 kg/s, collector tilt angles (θ) 30–60°, solar radiation (Q) 230–1086 W/m2, ambient temperature (T) 21–34 °C as input and efficiency (η), exergetic efficiency ( $$\eta_{II}$$ ), temperature rise (∆T), and pressure drop (∆P) as output parameters for the study. First, 270 trails of experimentation have been carried out on CPSAC by varying the input parameter to get a historical database. Second, modeling of the historical data and optimization of CPSAC parameters have been performed. Third, the parametric analysis is also performed to study the effect of parameters on the thermal performance of CPSAC. Parametric results reveal that η increases with m, while up to a certain value of θ, Q, T. At last, the effectiveness and accuracy of the model is judged via various validation tests with experimental data, published data, and artificially generated data. It is observed that FLES model predicts accuracy results with an accuracy of ≈ 97.5% and optimal conditions are at m = 0.00785 kg/s, θ = 45°, Q = 727 W/m2, and T = 29.6 °C, and the corresponding outputs are η = 35.9%, $$\eta_{II}$$ = 12.8%, ∆T = 34.7 °C and ∆P = 48.8 Pa.

A fuzzy logic-based expert system for substrate selection for soil construction in land reclamation

Abstract The mining industry can be one of the most impacting human activities. In the southern region of Santa Catarina (Brazil), open pit coal mining has left an extensive environmental impact. Since there was no topsoil in the abandoned open pit sites, it is necessary to provide a substrate for vegetation growth. However, the selection of the best substrate between multiple options is difficult. Thus, a fuzzy logic-based model is proposed. The proposed model was compared to reference models and to experts’ knowledge. Statistical analysis and validation were carried out with a correlation coefficient, a Kappa coefficient, along with the Accuracy, Precision, Sensibility Specificity, F-Score and Mathews correlation coefficients. The data set used to assess the proposed model presented a wide range of data, but for values such as aluminum saturation, higher values were common. The fuzzy logic-based expert system presented better results when assessing the behavior of the defuzzified output values with the crisp input values. The fuzzy model also followed the trend of the reference models (with R2 between 0.3639 and 0.5250). The comparison to the experts’ opinion demonstrated that agreement comes easily with extreme values (such as not suitable and suitable). However, using a Winner-Takes-All approach, the proposed fuzzy model had high scores for suitable soils for land reclamation’s soil construction. The proposed model can be used to define the best substrate for land reclamation. Some improvements, such as different parameters and increases in the number of interviews rounds, should be also tested.

Fuzzy Logic Based Expert System for Academic Staff Evaluation and Progress Monitoring

Academic teaching performance is recognized as a top instrument in reducing student dropout. This paper proposes a fuzzy logic based expert system which helps the teachers perform their own evaluation of academic teaching and research activities, in order to match them to requirements for each study cycle (introductory, specialization, master). The developed expert system also offers a way of monitoring the progress of teachers’ performances and to provide additional explanations about which aspects still need improvement. The system was tested using artificial data. The results show the most appropriate teacher for every study cycle.

Fuzzy Logic-Based Expert System for Prediction of Wear Rate in Selective Inhibition Sintered HDPE Parts

Selective inhibition sintering (SIS) is a novel additive manufacturing process to build parts with least human effort and cost. The SIS made high density polyethylene (HDPE) parts undergo wear, which is robustly influenced by the SIS process parameters like layer thickness, heat energy, heater feed rate and printer feed rate. Predicting the wear rate being a complex phenomenon, a fuzzy logic based expert system is proposed to evaluate the wear characteristics of SIS made HDPE specimens. Experiments are conducted using pin-on-disc wear testing apparatus to examine the wear rate. Comparative evaluation of experiments and fuzzy approach suggested that the obtained average error of wear rate using fuzzy system is concord with experimental results. Hence, the developed fuzzy rules can be effectively utilized to predict the wear rate of SIS polymer parts in automated manufacturing environments.

A brief history and technical review of the expert system research

The expert system is a computer system that emulates the decision-making ability of a human expert, which aims to solve complex problems by reasoning knowledge. It is an important branch of artificial intelligence. In this paper, firstly, we briefly introduce the development and basic structure of the expert system. Then, from the perspective of the enabling technology, we classify the current expert systems and elaborate four expert systems: The Rule-Based Expert System, the Framework-Based Expert System, the Fuzzy Logic-Based Expert System and the Expert System Based on Neural Network.

Adaptable Fuzzy Expert System for Ship Lock Control Support

A ship lock zone represents a specific area on waterway, and control of the ship lockage process requires a comprehensive approach. This research is a practical application of a Mamdani-type fuzzy inference system and particle swarm optimisation to control this process. It presents an optimisation process that adapts control logic to the desired criteria. The initially proposed Fuzzy Expert System (FES) was developed using suggestions from lockmasters (ship lock operators) with extensive experience. Further optimisation of the membership function parameters of the input variables was performed to achieve better results in the local distribution of ship arrivals. The presented fuzzy logic-based expert system was designed as part of a Programmable Logic Controller (PLC) and Supervisory Control And Data Acquisition (SCADA) system to support decision making and control. The developed fuzzy algorithm is a rare application of artificial intelligence in navigable canals and significantly improves performance of the ship lockage process. This adaptable FES is designed to be used as a support in decision-making processes or for the direct control of ship lock operations.

Fuzzy logic expert system for calculating the parameters of coupled numerical analysis of the fluid and thin-walled structures interaction

This paper deal with structural deformation of one rectangular, closed, thin walled, steel made, thermally loaded channel and influence of this deformation on the air flow through the channel, recognised as coupled fluid-structure interaction problem. The geometrically non-linear deformation of thermally loaded channel was solved using commercial finite-element analysis software ANSYS. The unsteady Navier-Stokes equations in their conservation form defining fluid flow are solved using commercial computer fluid dynamics software ANSYS CFX. A fuzzy logic-based expert system has been developed to predict some results of structural and fluid flow numerical simulation instead of directly evaluating it by a time-consuming software coupling of finite-element and computer fluid dynamics numerical analysis. Three input parameters, namely temperature of channel inner walls, difference in temperature of inner and outer surface of channel wall, and factor of mesh size, are varied while solving the fluid-structure interaction problem. A pressure drop downstream the flow was considered as a computer fluid dynamics output parameter of importance, while Von Misses maximum stresses were an finite-element analysis output parameter observed. The developed FLES can predict the results of finite-element and computer fluid dynamics analysis, a pressure drop and Von Misses maximum stresses respectively, within a reasonable accuracy limit and at lower computation cost compared to the finite-element, and CFD computer fluid dynamics packages combined into series of multi-physics analyses.

Fuzzy logic-driven expert system for the diagnosis of heart failure disease

The heart is a key organ of the human system which pumps and circulates blood throughout the body. Its failure to perform thesefunctions often leads to total breakdown of the entire body system and in most cases results in death. In several countries of theworld, significant numbers of heart failure patients are being reported dead due to inaccurate and untimely diagnosis. Recently,expert system approach, which is anchored on Information Technology, has emerged as a strong tool for solving this problem.This paper reports on the development of aWeb and Fuzzy Logic-based Expert System for the diagnosis of heart failure disease.The proposed system consists of a Knowledge Base (which is made up of a Database), a Fuzzy Logic component, a FuzzyInference Engine and a Decision Support Engine which comprises of cognitive and emotional filter as well as Tele-medicinefacilities. The system was implemented using Hypertext Preprocessor (PHP), JavaScript and Hypertext Mark-up Language(HTML) with My Structured Query Language (MySQL) as the Database Management System. Performance analysis based ondata on selected heart failure patients and survey of some experts on heart failure disease at the State Specialist Hospital, Akure,Nigeria shows satisfactory performances of the system.

Expert System in Detecting Coffee Plant Diseases

Coffee is an important commodity in the world economy. But unfortunately, productivity and quality of those commodities results are still quite low. This is caused by the disease in coffee plants. The research objective is to create an application that can help researchers or observers working in coffee plantation to diagnose diseases of coffee plants. The method used is fuzzy logic-based expert systems, and decision tree using a hierarchical classification. Knowledge about coffee, its symptoms, and its disease is extracted from human expert and then is converted into a decision tree. It will result on the fuzzy logic-based expert systems. From the experiments, accuracy calculation of the system is about 85%. Based on the accuracy, it can be concluded that this application can be a bit much to help researchers or observers of the coffee plants in diagnosing coffee plants diseases earlier. 

Shooting-inspired fuzzy logic expert system for ready-mixed concrete plants

With a goal of reducing the overall production process variability of Ready-mixed concrete RMC and decreasing production costs, a fuzzy logic-based expert system was developed. Inspired by shooting sports, the system guides the plant chief technician by recommending the actions to be taken to reduce variability. Such guidance is based on the evaluation of the compressive strength and coefficient of variation results from the samples molded by those responsible for testing concrete. The system was tested in a RMC plant with monthly production of ≈ 3300 m3. The results indicate that no major interventions are necessary for the investigated plant; however, training is likely recommended for some of the staff so as to attain a lower overall variability. These conclusions are in agreement with the technician's interpretation, proving that the system is satisfactory for application in the process. Additionally, a cost analysis was performed, and the results show that a potential reduction of 5.6 % in the monthly production cost can be reached. This represents an attractive reduction since the RMC profit margin is about 8.0 %.

Fuzzy logic-based expert system for prediction of depth of cut in abrasive water jet machining process

The development of an expert system for abrasive water jet machining (AWJM) process is considered in the present work. The expert system has been developed by using fuzzy logic (FL). It is to be noted that the performance of AWJM in terms of depth of cut depends on various process parameters, such as diameter of focusing nozzle, water pressure, abrasive mass flow rate and jet traverse speed. Three approaches have been developed to predict the depth of cut in AWJM using FL system. The first Approach deals with the construction of Mamdani-based fuzzy logic system. It is important to note that the performance of the FL depends on its knowledge base. In Approach 2, the data base and rule base of the FL-system are optimized, whereas in the third Approach, the total FL-system is evolved automatically. A binary-coded genetic algorithm has been used for the said purpose. The developed expert system eliminates the need of extensive experimental work, to select the most influential AWJM parameters on the depth of cut. The performances of the developed FL-systems have been tested to predict the depth of cut in AWJM process with the help of test cases. The prediction accuracy of the automatic FL-system (i.e. Approach 3) is found to be better than the other two approaches.

Anaesthesia monitoring using fuzzy logic

Humans have a limited ability to accurately and continuously analyse large amount of data. In recent times, there has been a rapid growth in patient monitoring and medical data analysis using smart monitoring systems. Fuzzy logic-based expert systems, which can mimic human thought processes in complex circumstances, have indicated potential to improve clinicians' performance and accurately execute repetitive tasks to which humans are ill-suited. The main goal of this study is to develop a clinically useful diagnostic alarm system based on fuzzy logic for detecting critical events during anaesthesia administration. The proposed diagnostic alarm system called fuzzy logic monitoring system (FLMS) is presented. New diagnostic rules and membership functions (MFs) are developed. In addition, fuzzy inference system (FIS), adaptive neuro fuzzy inference system (ANFIS), and clustering techniques are explored for developing the FLMS' diagnostic modules. The performance of FLMS which is based on fuzzy logic expert diagnostic systems is validated through a series of off-line tests. The training and testing data set are selected randomly from 30 sets of patients' data. The accuracy of diagnoses generated by the FLMS was validated by comparing the diagnostic information with the one provided by an anaesthetist for each patient. Kappa-analysis was used for measuring the level of agreement between the anaesthetist's and FLMS's diagnoses. When detecting hypovolaemia, a substantial level of agreement was observed between FLMS and the human expert (the anaesthetist) during surgical procedures. The diagnostic alarm system FLMS demonstrated that evidence-based expert diagnostic systems can diagnose hypovolaemia, with a substantial degree of accuracy, in anaesthetized patients and could be useful in delivering decision support to anaesthetists.