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Arak, Iran

Islamic Azad University-Arak is a private university in Arak, Iran.The university was established in 1985 to further satisfy the growing need for an academic institution in the city of Arak. Today, the university operates 7 research centers and hosts 22,000 students.The university currently offers degrees in 6 colleges in 148 fields for both undergraduate and graduate students. It is the largest institute of higher education in the province in terms of student enrollment, eclipsing both University of Arak and Arak University of Medical science. Wikipedia.

Akbari Torkestani J.,Islamic Azad University of Arak
Journal of Network and Computer Applications

In realistic mobile ad-hoc network scenarios, the hosts usually travel to the pre-specified destinations, and often exhibit non-random motion behaviors. In such mobility patterns, the future motion behavior of the mobile is correlated with its past and current mobility characteristics. Therefore, the memoryless mobility models are not capable of realistically emulating such a mobility behavior. In this paper, an adaptive learning automata-based mobility prediction method is proposed in which the prediction is made based on the Gauss-Markov random process, and exploiting the correlation of the mobility parameters over time. In this prediction method, using a continuous-valued reinforcement scheme, the proposed algorithm learns how to predict the future mobility behaviors relying only on the mobility history. Therefore, it requires no a prior knowledge of the distribution parameters of the mobility characteristics. Furthermore, since in realistic mobile ad hoc networks the mobiles move with a wide variety of the mobility models, the proposed algorithm can be tuned for duplicating a wide spectrum of the mobility patterns with various randomness degrees. Since the proposed method predicts the basic mobility characteristics of the host (i.e., speed, direction and randomness degree), it can be also used to estimate the various ad-hoc network parameters like link availability time, path reliability, route duration and so on. In this paper, the convergence properties of the proposed algorithm are also studied and a strong convergence theorem is presented to show the convergence of the algorithm to the actual characteristics of the mobility model. The simulation results conform to the theoretically expected convergence results and show that the proposed algorithm precisely estimates the motion behaviors. © 2012 Elsevier Ltd. Source

Akbari Torkestani J.,Islamic Azad University of Arak
Journal of Network and Computer Applications

Centralized or hierarchical administration of the classical grid resource discovery approaches is unable to efficiently manage the highly dynamic large-scale grid environments. Peer-to-peer (P2P) overlay represents a dynamic, scalable, and decentralized prospect of the grids. Structured P2P methods do not fully support the multi-attribute range queries and unstructured P2P resource discovery methods suffer from the network-wide broadcast storm problem. In this paper, a decentralized learning automata-based resource discovery algorithm is proposed for large-scale P2P grids. The proposed method supports the multi-attribute range queries and forwards the resource queries through the shortest path ending at the grid peers more likely having the requested resource. Several simulation experiments are conducted to show the efficiency of the proposed algorithm. Numerical results reveal the superiority of the proposed model over the other methods in terms of the average hop count, average hit ratio, and control message overhead. © 2012 Elsevier Ltd. All rights reserved. Source

Asady B.,Islamic Azad University of Arak
Expert Systems with Applications

Recently, Wang, Liu, Fan, and Feng (in press) proposed an approach to overcome the limitations of the existing studies and simplify the computational procedures based on the LR deviation degree of fuzzy number. However, there were some problems with the ranking method. In this paper, we want to indicate these problems of Wang's method and then propose a revised method which can avoid these problems for ranking fuzzy numbers. Since the revised method is based on the Wang's method, it is easy to rank fuzzy numbers in a way similar to the original method. The method is illustrated by numerical examples and compared with other methods. © 2009 Elsevier Ltd. All rights reserved. Source

Akbari Torkestani J.,Islamic Azad University of Arak
Soft Computing

Given a graph G and a bound d ≥ 2, the bounded-diameter minimum spanning tree problem seeks a spanning tree on G of minimum weight subject to the constraint that its diameter does not exceed d. This problem is NP-hard; several heuristics have been proposed to find near-optimal solutions to it in reasonable times. A decentralized learning automata-based algorithm creates spanning trees that honor the diameter constraint. The algorithm rewards a tree if it has the smallest weight found so far and penalizes it otherwise. As the algorithm proceeds, the choice probability of the tree converges to one; and the algorithm halts when this probability exceeds a predefined value. Experiments confirm the superiority of the algorithm over other heuristics in terms of both speed and solution quality. © 2012 Springer-Verlag. Source

Akbari Torkestani J.,Islamic Azad University of Arak
Applied Intelligence

The recent years have witnessed the birth and explosive growth of the Web. The exponential growth of the Web has made it into a huge source of information wherein finding a document without an efficient search engine is unimaginable. Web crawling has become an important aspect of the Web search on which the performance of the search engines is strongly dependent. Focused Web crawlers try to focus the crawling process on the topic-relevant Web documents. Topic oriented crawlers are widely used in domain-specific Web search portals and personalized search tools. This paper designs a decentralized learning automata-based focused Web crawler. Taking advantage of learning automata, the proposed crawler learns the most relevant URLs and the promising paths leading to the target on-topic documents. It can effectively adapt its configuration to the Web dynamics. This crawler is expected to have a higher precision rate because of construction a small Web graph of only on-topic documents. Based on the Martingale theorem, the convergence of the proposed algorithm is proved. To show the performance of the proposed crawler, extensive simulation experiments are conducted. The obtained results show the superiority of the proposed crawler over several existing methods in terms of precision, recall, and running time. The t-test is used to verify the statistical significance of the precision results of the proposed crawler. © 2012 Springer Science+Business Media, LLC. Source

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