The selection of ERP Systems has been considered as one of the most influential and risky managerial decisions due to its heavy burden of purchase and implementation costs. The purpose of this paper is designing a Fuzzy Expert System for selecting the appropriate ERP System based on the internationally selected and validated criteria for minimizing the risks of inappropriate selection. If an option does not have the sufficient correspondence to the organizational criteria; the Fuzzy logic provides the opportunity to the other options to taken into account for not only cover the weaknesses of measurement, but also easily lower the opportunity of selecting the appropriate alternatives. This leads to a comprehensive Fuzzy evaluation of all of the alternatives. From another point of view, the Fuzzy Expert System rules evaluate the options according to the demands and requirements of organization and accordingly, the selection process will require no direct bi-comparison of options for the desired organizational status. For the sake of better validation and presentation, this paper has been conducted and provided through a case study of one of the Iranian industrial organizations.

The aim of this study is to construct appropriate portfolios by considering investor’s risk profile and his/her preferences in a flexible, applicable and realistic manner. For this purpose, a Fuzzy rule- based Expert System is developed to support investment managers in their middle term decision makings. The proposed Expert System is validated by using 106 stocks, traded in Tehran Stock Exchange between 1384 and 1389. Performance of the proposed Expert System is analyzed in terms of risk profile and investment length compared to market average. The results revealed that performance of the Expert System is better than the market average in most cases. Also, in parallel to our expectation, the proposed Expert System performance is better for risk-averse investors and in middle term.

Shewhart charts are the main tools for statistical process control. They are used for detecting assignable causes which affect quality of process output. From them, X and MR charts are two univariate control charts for monitoring mean and variation of measurable quality characteristics. The main drawbacks of these charts are: weakness of X chart against non-normal distribution of process data, autocorrelation of consecutive MR values, and inability of MR chart in detecting decrease in process variation, These drawbacks have declined their effectiveness. This paper presents a simple method for monitoring two parameters of normal distribution, based on Fuzzy logic, resolving above drawbacks, the proposed method can detect non-random patterns of the process data. Here a Fuzzy Expert System is developed to assess individual measurements, This results in considerable improvement in decreasing out-of-control average run length versus the traditional control charts, Moreover, performance of Fuzzy control chart for recognizing unnatural process behaviors has investigated using Mont-Carlo simulation and advantages of this novel method have been investigated.

Purpose Nowadays, due to the rapid development of the Internet and the rapid growth of web pages, many electronic websites are using product recommendation Systems to guide users to the products that they need. Such Systems usually provide a list of suggested items that the user may prefer. These Systems are provided as a support tool to help users obtain information that best meets their needs. These Systems can actually improve user decisions, resulting in increased sales and mutual customer satisfaction. The purpose of the paper is to improve user decisions in online shopping using Fuzzy Expert System. Methodology: The statistical population of this study consists of 30 Experts in the field of e-commerce who were selected by combining two methods of deliberate sampling and snowball sampling. To analyze the status of improvement of users' decisions, a Fuzzy Expert System was created using input variables business reputation status, environmental factors status in e-commerce, online store features; product specifications; user/customer characteristics. Findings: The final results showed that there is no significant difference between the results of the created Expert System and the mean of Expert opinions. Originality/Value: In this paper, a conceptual Model to improve user decisions in online shopping using a Fuzzy Expert System is designed.

Gestational diabetes mellitus (GDM) is a kind of diabetes that requires persistent medical care in patient self management education to prevent acute complications. One of the common and main problems in diagnosis of the diabetes is the weakness in its initial stages of the illness. This paper intends to propose an Expert System in order to diagnose the risk of GDM by using FIS model. The knowledge base was based on the opinion of Experts and also by using ANFIS model in order to extract rules that based on real data. The data were collected from the patient's file in a medical health care center that contains effective risk factors in giving rise to GDM. The effective factors consist of fast blood suger, weight and age in the initial months of pregnancy and the output is the risk of GDM in pregnant women . Based on the results , the level of minimum square error in the Fuzzy Expert System was calculated as 0.227. For diagnosis of illnesses such as diabetes, in lack of accesses to Experts ,one can rely on intelligent softwares such as Expert Systems, wich can assist the speedy and better diagnosis.

Asthma is a chronic lung disorder of which the number of sufferers estimated to be between 1.4-27.1% of the population in different areas of the world. Results of various studies show that asthma is usually under-diagnosed, especially in developing countries, because of limited access to medical specialist and laboratory data. The purpose of this paper is to design a Fuzzy rule-based Expert System to alleviate this hazard by diagnosing asthma at initial stages. A knowledge representation of this System is provided from a high level, based on patient perception, and organized into two different structures called Type A and Type B. Type A is composed of six modules, including symptoms, allergic rhinitis, genetic factors, symptom hyper-responsiveness, medical factors and environmental factors. Type B is composed of 8 modules including symptoms, allergic rhinitis, genetic factors, and response to short-term drug use, bronchodilator tests, challenge tests, PEF tests and exhaled nitric oxide. The final result of every System is de-fuzzifed in order to provide the assessment of the possibility of asthma for the patient. Verification and validations criteria are considered throughout a life-cycle; the System was developed by the participation of general physicians, experienced asthma physicians and asthmatic patients.

Introduction: Headache is one of the most common medical complaints. The treatment types of headaches require the diagnosis of these types. The aim of this study was to distinguish and diagnose common headaches by Fuzzy logic and Fuzzy System.Methods: A Fuzzy Expert System for the distinguish types of common headaches is presented, the Mamdani modeL was used in Fuzzy inference engine using Max-Min as OR-AND operators and Centroid method was used as defuzzification technique.Results: The Fuzzy System was evaluated using data obtained from 150 patients and showed 82% good agreement and high ability in terms of correct diagnosis. Accuracy, Precision, sensitivity, specificity of the System were 86%, 93%, 85%, 88% for migraine, 93%, 91%, 55%, 99% for tension, 97%, 86%, 66%, 99% for headaches resulting from infection and 95%, 85%, 88%, 97% for headaches resulting from increase of ICP, respectively. The Binomial and Chi-Square Test evaluated that between correct and incorrect diagnosis was meaningful difference and the proportion of correct diagnosis (82%) was more than the incorrect diagnosis (18%) (P<0.001).To measure agreement of System results with the physician diagnosis, Kappa statistics was employed and showed a high relation, 71%, 65%, 74% and 84% for migraine, tension, headaches resulting from infection and headaches resulting from increase of ICP, respectively.Conclusion: According to proximity of common headaches symptoms, and importance of early diagnosis of headache, and favorable results of the implementation and evaluation of the suggested Expert System, therefore this System can be very useful for diagnosis of common headaches with similar symptoms.

One of the most important stages in the urban transportation planning procedure is predicting the rate of trips generated by each trac zone. Currently, multiple linear regression models are frequently used as a prediction tool. This method predicts the number of trips produced from, or attracted to each trac zone according to the values of independent variables for that zone. One of the main limitations of this method is its huge dependency on the exact prediction of independent variables in future (horizon of the plan). The other limitation is its many assumptions, which raise challenging questions of its application. The current paper attempts to use Fuzzy logic and its capabilities to estimate the trip generation of urban zones. A Fuzzy Expert System is introduced, which is able to make suitable predictions using uncertain and inexact data. Results of the study on the data for Mashhad (Lon: 59.37 E, Lat: 36.19 N) show that this method can be a good competitor for multiple linear regression method, specially, when there is no exact data for independent variables.