with the explosive growth in amount of information, it is highly required to utilize tools and methods in order to search, filter and manage resources. one of the major problems in text classification relates to the high dimensional feature spaces. therefore, the main goal of text classification is to reduce the dimensionality of features space. there are many feature selection methods. however...

Recommendation systems aim at directing users toward the resources that best meet their needs and interests. One of the challenging tasks in improving web recommendation algorithms is the simultaneous use of users’s activity log and hyperlink graph of the web site. In this paper, we propose a new recommendation algorithm based on semantic web and web usage data and hyperlink graph of a web site...

In this paper we address the major bottleneck of active automata learning, the typically huge number of required tests, by investigating the impact of using a distributed testing environment (a crowd of teachers) to execute test cases (membership queries) in parallel. This kind of parallelization of automata learning has the best potential when the time for test case execution is dominant, an a...

A weakly connected dominating set (WCDS) of graph is a subset of so that the vertex set of the given subset and all vertices with at least one endpoint in the subset induce a connected sub‐graph of . Finding the WCDS is a new promising approach for clustering the wireless networks. The minimum WCDS (MWCDS) problem is known to be NP‐hard, and several approximation algo...

Learning Automata (LA) are adaptive decision making devices suited for operation in unknown environments [12]. Originally they were developed in the area of mathematical psychology and used for modeling observed behavior. In its current form, LA are closely related to Reinforcement Learning (RL) approaches and most popular in the area of engineering. LA combine fast and accurate convergence wit...

Cellular learning automata is a combination of learning automata and cellular automata. This model is superior to cellular learning automata because of its ability to learn and also is superior to single learning automaton because it is a collection of learning automata which can interact together. In some applications such as image processing, a type of cellular learning automata in which the ...