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The fuzzy object oriented database model is often used to handle the existing imprecise and complicated objects for many real-world applications. The main focus of this paper is on fuzzy queries and tries to analyze a complicated and complex query to get more meaningful and closer responses. The method permits the user to provide the possibility of allocating the weight to various parts of the query, which makes it easier to follow better goals and return the target objects.

Online advertisement is a cheap and powerful tool which has targeted internet users. At the moment there is a multibillion dollar market for online advertising which is the main income of some popular websites. Some people use this cheap tool to achieve their personal goals i.e. they use incorrect advertisement techniques. For instance a user refers to Bing search engine and tries to search "Photoshop software download". The question is whether the returned results are related to the searched sentence or not. Unfortunately, there are some advertisement websites which use phony techniques (such as using fake key words) to attract users to their websites. Consequently, the user is not able to find his desired webpage and lose his time. In this paper Persian websites are investigated. A fuzzy system is proposed which performs identification and analysis of websites using two parameters; "url feature" and "number of important key words".

Abstract:In recent years, the increasing use of e-mails has led to the emergence and increase of problems caused by mass unwanted messages which are commonly known as spam. In this study, by using decision trees, support vector machine, Naïve Bayes theorem and voting algorithm, a new version for identifying and classifying spams is provided. In order to verify the proposed method, a set of a mails are chosen to get tested. First three algorithms try to detect spams, and then by using voting method, spams are identified. The advantage of this method is utilizing a combination of three algorithms at the same time: decision tree, support vector machine and Naïve Bayes method. During the evaluation of this method, a data set is analyzed by Weka software. Charts prepared in spam detection indicate improved accuracy compared to the previous methods.