List of Articles خوشه¬بندی

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  1 - Application of clustering in AODV routing protocol for intercity networks on the highway scenario
  amin feyzi Vahid Sattari-Naeini majid mohammadi
  Intercarous networks are a subset of mobile networks in which vehicles are considered as network nodes. The main difference with case mobile networks is the rapid mobility of nodes, which causes rapid topology change in this network It becomes. Rapid changes in network More

  Intercarous networks are a subset of mobile networks in which vehicles are considered as network nodes. The main difference with case mobile networks is the rapid mobility of nodes, which causes rapid topology change in this network It becomes. Rapid changes in network topology are a major challenge for routing, for routing in these networks, routing protocols must be robust and reliable. One of the well-known routing protocols in intercity networks is the AODV routing protocol. The application of this routing protocol on intercity networks also has problems that increase the number of control messages in the network by increasing the scale of the network and the number of nodes. One way to reduce overhead in the AODV protocol is to cluster network nodes. In this paper, the modified K-Means algorithm is used to cluster the nodes and the particle swarm algorithm is used to select the cluster head. The results of the proposed method improve the normal routing load and increase the packet delivery rate compared to the AODV routing protocol. Manuscript profile
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  2 - Matched grouping of learners in e-learning environment using council clustering method
  malihe kamareiy gholamali montazer
  Despite the individual differences of learners such as their abilities, goals, knowledge, learning styles and backgrounds, most of the electronic learning systems has presented an equal learning content for all of the learners. This is happening while producing a specia More

  Despite the individual differences of learners such as their abilities, goals, knowledge, learning styles and backgrounds, most of the electronic learning systems has presented an equal learning content for all of the learners. This is happening while producing a specialized content for the individuals. Increasing appliances of artificial memory in teaching the adaptation learning systems will require recommended teaching methods which are appropriate to the learner’s individual differences. In order to grouping learners based on their learning styles in their own similar groups, we are presenting a new method in this text. This method is mainly about combining the result of clustering methods which is certainly reducing choosing an unreliable method. Meanwhile it is preventing method`s complication which is because of using simpler and more useful clustering algorithms that subsequently will cause a better result and it may happen due to the fact that different methods will overlap each other’s defections. In this article we are using Felder- Silverman learning style which consist of 5 dimensions: processing (active-reflective) , input (visual-verbal) , understanding (sequential-global) , perception (sensing-intuitive) and organization (inductive-deductive). Firstly, proper behavioral indicators to different learning style dimension of Silverman-Feedler will recognize and then based on these behaviors learners will be able to be groups by one of these 5 methods. In the case of evaluating the proposed method, utilizing the c++ programming electronic teaching period information is necessary. Learner members of experiment environment were 98 ones which were extracting the expressed indicators connected to their network behaviors in 4 dimensions of Perception , process , input and understanding of Felder- Silverman model. On the other hand students were asked to fill the questionnaire forms and their learning styles were calculated between 0-11 and then based on the behavioral information they were being grouped. We are using 5 clustering grouping methods : k-means , FCM , KNN , K-Medoids and SVM to produce ensemble clustering in generation step and co-occurrence samples or majority votes were used in Integration step. Evaluating the results will require the followings : Davies-bouldin index , Variance index , and gathering purity index. Due to the fact that the expressed methods are not able to indicate automatically the best cluster, clustering 3,4,5,6,7 clusters were using this method. And with calculating Davies-bouldin index the best cluster in each method were selected. In FCM each data were contributed to the cluster which has the most dependence to that . Numerical results of Davies-bouldin index have shown that ensemble clusters have the exact accumulation clusters among the others. Clustering variance in different size is indicating that ensemble clustering has the most accumulation and the least dispersion and also purity-gathering results has shown that proposed grouping method has the ability to gather learners with the similar style in each cluster and has a better efficiency compared to the others. So with this idea while maintaining simplicity, more accurate results based on the Davies-bouldin index , Variance index , and gathering purity index is obtained. Due to the importance of high accuracy and high speed and low computational complexity in the clustering methods, instead of a more complex approach, combining the weaker and easier clustering methods, better and more accurate results reached. Manuscript profile
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  3 - Routing improvement to control congestion in software defined networks by using distributed controllers
  saied bakhtiyari Ardeshir Azarnejad
  Software defined networks (SDNs) are flexible for use in determining network traffic routing because they separate data plane and control plane. One of the major challenges facing SDNs is choosing the right locations to place and distribute controllers; in such a way th More

  Software defined networks (SDNs) are flexible for use in determining network traffic routing because they separate data plane and control plane. One of the major challenges facing SDNs is choosing the right locations to place and distribute controllers; in such a way that the delay between controllers and switches in wide area networks can be reduced. In this regard, most of the proposed methods have focused on reducing latency. But latency is just one factor in network efficiency and overall cost reduction between controllers and related switches. This article examines more factors to reduce the cost between controllers and switches, such as communication link traffic. In this regard, a cluster-based algorithm is provided for network segmentation. Using this algorithm, it can be ensured that each part of the network can reduce the maximum cost (including delays and traffic on links) between the controller and its related switches. In this paper, using Topology Zoo, extensive simulations have been performed under real network topologies. The results of the simulations show that when the probability of congestion in the network increases, the proposed algorithm has been able to control the congestion in the network by identifying the bottleneck links in the communication paths of each node with other nodes. Therefore, considering the two criteria of delay and the degree of busyness of the links, the process of placing and distributing the controllers in the clustering operation has been done with higher accuracy. By doing so, the maximum end-to-end cost between each controller and its related switches, in the topologies Chinanet of China, Uunet of the United States, DFN of Germany, and Rediris of Spain, is decreased 41.2694%, 29.2853%, 21.3805% and 46.2829% respectively. Manuscript profile
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  4 - context-aware travel recommender system exploiting from Geo-tagged photos
  rezvan mohamadrezaei larki Reza Ravanmehr milad  amrolahi
  Recommender systems are the systems that help users find and select their target items. Most of the available events for recommender systems are focused on recommending the most relevant items to the users and do not include any context information such as time, locatio More

  Recommender systems are the systems that help users find and select their target items. Most of the available events for recommender systems are focused on recommending the most relevant items to the users and do not include any context information such as time, location . This paper is presented by the use of geographically tagged photo information which is highly accurate. The distinction point between this thesis and other similar articles is that this paper includes more context (weather conditions, users’ mental status, traffic level, etc.) than similar articles which include only time and location as context. This has brought the users close to each other in a cluster and has led to an increase in the accuracy. The proposed method merges the Colonial Competitive Algorithm and fuzzy clustering for a better and stronger processing against using merely the classic clustering and this has increased the accuracy of the recommendations. Flickr dataset is used to evaluate the presented method. Results of the evaluation indicate that the proposed method can provide location recommendations proportionate to the users’ preferences and their current visiting location. Manuscript profile