Design Of Fuzzy Expert System Research Articles

Fuzzy expert system design for detecting stunting

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.

A novel fuzzy expert system design to assist with peptic ulcer disease diagnosis

Peptic ulcer disease causes abdominal discomfort and pain. Helicobacter pylori bacteria, infection and long-term use of anti-inflammatory drugs are the most common causes of peptic ulcers. Untreate...

KONUT YAPILARININ MALİYET TAHMİNİNDE KULLANILAN YÖNTEMLERİN PERFORMANS ANALİZİ

Maliyet tahmini, yatırım kararlarını ve projenin kapsamını etkilemesi açısından işin başında yapılacak fizibilite çalışmalarının belki de en önemli kısmını oluşturmaktadır. Literatürde yapı maliyetlerinin çeşitli tahmin yöntemleri ile belirlenmesi kapsamında yapılmış çalışmalar bulunsa da, bu yöntemlerin geniş çaplı bir performans analizinin yapılmasına yönelik bir çalışmaya rastlanılmamıştır. Bu çalışma kapsamında; konut yapılarının maliyet tahmininde kullanılan gelişmiş tahmin yöntemlerinden altı farklı regresyon modeli, dört farklı makine öğrenmesi modeli ve Bulanık Uzman Sistem Tasarımı olmak üzere toplam on bir adet tahmin modeli oluşturulmuştur. Oluşturulan modeller üç farklı performans kriteri (Ortalama Hata Karesi, Ortalama Mutlak Yüzde Hatası ve Determinasyon Katsayısı) ile analiz edilerek en iyi tahmini yapan yaklaşım araştırılmıştır.

Design of fuzzy expert system for predicting of surface roughness in high-pressure jet assisted turning using bioinspired algorithms

AbstractThe surface roughness of the machined parts is one of the most important factors that have considerable influence on the quality and functional properties of products. The objective of this study is development of a surface roughness prediction model for machining Inconel 718 in high-pressure jet assisted turning using the fuzzy expert system, where the fuzzy system is optimized using two bioinspired algorithms: genetic algorithm and particle swarm optimization. The effect of various influential machining parameters, such as diameter of the nozzle, pressure of the jet, cutting speed, feed rate, and distance between the impact point of the jet and cutting edge were taken into consideration in this study. The predicted surface roughness values obtained from developed fuzzy expert systems were compared with the experimental data, and the results indicate that proposed systems can be effectively used to estimate the surface roughness in high-pressure jet assisted turning.

Subtractive Clustering Fuzzy Expert System for Engineering Applications

Abstract Performance of a fuzzy expert system is related with how good the membership functions are normalized, tuned for a problem statement and correlation of antecedents and consequents. This paper helps in tuning and designing the membership functions that are best suited for the problem statement by integrating subtractive clustering method for fuzzy expert system design. Subtractive clustering algorithm is used to generate the tuned membership functions automatically in accordance to the domain knowledge. The proposed integrated design of clustering based fuzzy expert system acts in improving the accuracy and leads to a precised decision making environment. A practical example for ageing assessment of transformer insulation oil has been included to illustrate the method much effectively; the discussed example has been validated practically in the laboratory and is found that the proposed method is efficient in decision making.

A fuzzy expert system design for forecasting return quantity in reverse logistics network

Purpose – The purpose of this study is to develop a fuzzy expert system to design robust forecast of return quantity in order to handle uncertainties from the return process in reverse logistic network. Design/methodology/approach – The most important factors which have impact on return of products are defined. Then the factors which have collinearity with others are eliminated by using dimension redundancy analysis. By training data of selected factors with fuzzy expert system, the return amounts of alternative cities are forecasted. Findings – The performance metrics of the proposed model are found as satisfactory. That means the result of this study indicates that fuzzy expert systems can be used as a supportive tool for forecasting return quantity of alternative areas. Research limitations/implications – In the future, the proposed model can be used for forecasting other uncertain parameters such as return quality and return time. Other fuzzy systems such as type-2 fuzzy sets can be used, or other exp...

Design of Fuzzy Expert System for Selection of Candidates using the Theory of Multiple Intelligence

The paper presents design of fuzzy expert system in order to identify performance of a candidate for recruitment. In order to achieve this task, the paper has integrated two application areas: education and human resources management for recruiting efficient candidates. Every organization requires candidates who acquire excellent skills in different areas. At the same time; it is also necessary that candidates are to be classified, analyzed, evaluated and then recruited according to their skills. The main contribution of education is to extend problem solving skills in individuals. Problem solving abilities depends on several qualities like problem solving capabilities, decision making skills, intelligence, as well as positive attitude. Among all of them intelligence is one of the most crucial factor required for professional success. It is obvious that intelligence is genetically achieved by human beings as well as it can be enhanced with the help of education and technology. According to the Theory of Multiple Intelligence, human intelligence is not limited to one or two directions but there are several other equally important and valuable aspects of intelligences. In order to classify intelligence of candidates using The Theory of Multiple Intelligence, fuzzy expert system has been designed. Fuzzy Logic is utilized for representing knowledge and human reasoning in such a way that it is amenable to be processed with knowledge engineering and expert systems. The main objective of this paper is to predict the level of capabilities to work with real life problems using fuzzy expert system. Keywords: Expert Systems (ES), Fuzzy Logic (FL), Fuzzy Expert Systems, Information and Communication Technology (ICT), Theory of Multiple Intelligence (MI)

Design of fuzzy expert system for diagnosis of cardiac diseases

Background: The logical thinking of medical practitioners play significant role in decision making about diagnosis. It exhibits variation in decisions because of their approaches to deal with uncertainties and vagueness in the knowledge and information. Fuzzy logic has proved to be the remarkable tool for building intelligent decision making systems for approximate reasoning that can appropriately handle both the uncertainty and imprecision. Aims & Objective: To develop a generic fuzzy expert system framework that can be used to design specific fuzzy expert systems for particular medical domain. Material and Methods: The generic fuzzy expert system has been designed for diagnosis of cardiac diseases. The interface between visual basic and MatLab is powerful feature of the system that offers user friendly graphical user interface. Results: Need to arrive at the most accurate medical diagnosis in a timely manner is the main outcome that may reduce the further complications. A generic fuzzy expert system for the diagnosis of various heart diseases yields better result than the classic designed systems, because this system simulates the manner of an expert in true sense. Conclusion: The particular focus is on diagnosis of heart disease by employing the fuzzy logic in expert systems. The system has been designed and tested successfully. Exhaustive rule base specifically formed for almost all heart diseases ensures the accuracy to arrive at certain decision.

Design of fuzzy expert system for microarray data classification using a novel Genetic Swarm Algorithm

Knowledge gained through classification of microarray gene expression data is increasingly important as they are useful for phenotype classification of diseases. Different from black box methods, fuzzy expert system can produce interpretable classifier with knowledge expressed in terms of if-then rules and membership function. This paper proposes a novel Genetic Swarm Algorithm (GSA) for obtaining near optimal rule set and membership function tuning. Advanced and problem specific genetic operators are proposed to improve the convergence of GSA and classification accuracy. The performance of the proposed approach is evaluated using six gene expression data sets. From the simulation study it is found that the proposed approach generated a compact fuzzy system with high classification accuracy for all the data sets when compared with other approaches.

Fuzzy expert system design for operating room air-condition control systems

In this study, a controlled fuzzy expert system (FES) was designed to provide the conditions necessary for operating rooms. For this purpose, existing operating rooms have been studied to see if there are more useful, reliable and comfortable ones. How an operating room can be controlled with FES and its advantages and disadvantages have also been researched. For a theoretically visible FES to show system’s advantage a prototype operating room was built and a suitable configuration was designed. In this system, heat, humidity, oxygen and particles were used as input parameters, and a fresh air entrance and fan circulation were chosen as output parameters. With the help of an expert, appropriate linguistic expressions and the membership function of these expressions were defined. The sensors were classified and sensor information was transferred to computer by means of an interface designed. In order to transfer the data to the system simultaneously, an interface was written in C#. Whether it provides the most suitable control for the system prototype was determined by simulating the operation with varying numbers of patients and operation personnel. In these trials, input, output and other necessary parameters were collected in the computer.In the study, we obtained excellent results in prototype operating room control with FES. The analyses of the results carried out indicated that the controls performed with FES provide more economical, comfortable, reliable and consistent controls and that they are feasible in a real operating room.

A fuzzy expert system design for analysis of body sounds and design of an unique electronic stethoscope (development of HILSA kit)

In this paper we have developed a fuzzy expert system (FES) for different sounds produced by different organs in the human body. We have also constructed a unique electronic stethoscope. The human body sounds produced by different organs like heart, lungs and intestine were analyzed. The doctor provided the data and relation between variables chosen for each organ sound. Using this information a rule base for fuzzy expert system was built. Such FES helps the medical doctor in arriving at appropriate decision in different difficult clinical situations. The examination of body sounds was done using conventional stethoscope (CS) and electronic stethoscope (ES), which was uniquely designed for this study. We have found that unique stethoscope developed by us is far superior to conventional stethoscope by its overall performance.