The science parks have important role in development of technology and are able to make economic growth of the countries. The purpose of this article is the presentation of a fuzzy expert system to evaluate the science and technology parks. One of the problems for evaluating Science and Technology parks is to have the high number of criteria and science parks and presenting a system which is able to compare this high number of science parks, with many criteria, is one of the findings of this paper. At the end, we have described a numerical example. This article is an useful information resource for managers of Science and Technology parks and interested parties to invest and to recognize the science parks better.

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Abstract-Credit risk assessment is one of the crucial issues which financial institutions particularly banks are faced and determining the effective variables is one of the critical parts in this type of studies. The purpose of this research is to present a hybrid method for evaluating credit risk of bank customers. Here, after extracting significant financial ratios from balance sheet, Kolmogorove-Smirnov test has been used to specify kind of financial ratios distribution. Then, T test has been run to select meaningful variables and DEMATEL method to determine effective ones. Finally, a Fuzzy Expert system has been developed to assess credit risk according to specified effective financial ratios as the system inputs. The presented steps have been studied in an Iranian Bank as empirical study.

Abstract-Credit assessment is a very typical classification problem in Data Mining. A type of classification technique that has attracted an increasing number of attempts in recent years is finding classification rules based on association rule mining techniques. This paper aims to contribute to this kind of research by classifying the bank's customers via association rules with the use of the APRIORI algorithm and CRISP-DM methodology and considering the Experts' opinions to filter the obtained rules and define the Membership functions for the considered criteria, finally a Fuzzy Expert System is designed based on the selected rules from association rules to specify the Credit Degree of banks' customers. The presented steps have been studied in an Iranian Bank as empirical study.