نشریه فناوری اطلاعات و ارتباطات ایران
سال یازدهم شماره 39 (بهار و تابستان 1398)

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.

Market forecasting, like stock's market, with high volume of transactions have an effect on researchers and investors and get their attention. Important issue factors in any investment decision are risk and turnover. Understanding market momentum gives the ability to predict future movements. The ability to predict in a market economy, enables to achieve a higher turnover by reducing risk and avoiding financial losses. News plays an important role in the process of evaluating the current stock price. The development of data mining methods, computational intelligence and machine learning algorithms have led to new models of prediction. phpCrawler is a php base content crawler that use of DomCralwer and Guzzle packages for storing web data. With this tool, the news releases are stored and categorized from 17 News Agency.    Then, by using text mining and Support Vector Machine with different kernel, predict stock price direction. In this research use 948990, news has been stored from 17 news agencies. More than 300,000 news regarding political and economic categories were used and stock prices of chemicals between November 2017 till March 2018 (123 trading days) were studied. The results show that by using the linear kernel Support Vector Machine algorithm, the prediction accuracy of average price movement reached to 83%. Using nonlinear kernel Support Vector Machine with poly kernel increased two percent prediction accuracy to 85% on average and other kernel had poorer prediction.

One of the big challenges in software defined networks (SDN) is to find appropriate locations for controllers to shorten the latency between controllers and switches in wide area networks (WAN). In the literature, the majority of approaches are focused on the reduction of latency, but latency is only one of the factors of the overall cost between controllers and their associated switches. In this paper, we explore and investigate more possible factors of the cost, including the links utilization. In order to decrease the end-to-end cost, the concept of network partitioning is introduced and an Enhanced Clustering-based Network Partitioning Algorithm (ECNPA) is then proposed to partition the network. The proposed algorithm can guarantee that each partition is able to shorten the maximum end-to-end cost between controllers and their associated switches and improve routing by calculating bottleneck links. Extensive simulations are conducted under some real network topologies from the Internet Topology Zoo. The simulation results show that in the case of a busy network and the probability of congestion in it, the proposed algorithm has been able to well control congestion in the network by identifying the bottleneck links in each node's communication paths with other nodes. As a result, by taking into account the two factors of delay and the rate of busy links, the process of placement and distribution of controlers has been done with higher accuracy, and reducing the average of maximum end-to-end cost between controllers and their associated switches in Chinanet topology of China, Uunet topology of USA, DFN topology of Germany, and Rediris topology of Spain to 41.4694, 29.2853, 21.3805, and 46.4829 percent, respectively.

The present study aims Design and explanation Human Resources Knowledge Architecture Model Done In knowledge-based Organizations Using ISM Approach. This research is based on quantitative and qualitative cross-sectional research that is descriptive survey in terms of purpose, applicability and nature of nature. The statistical population of the present study consists of knowledge-based Organizations workers of Lorestan province which 30 of their experts have been selected based on the principle of theoretical adequacy and using a targeted sampling method. The information gathering tool in the qualitative research section is semi-structured interview and in the quantitative section the questionnaire is also used. In the qualitative section, the data and information obtained from the interview using Atlas.ti software and the coding method were analyzed and components and indicators of human resources knowledge architecture were identified. Also in the quantitative part of the research, the final model of research has been developed and presented using Matlab software and interpretive structural modeling technique. The results of the research include the indicators and components of human resources knowledge architecture and the presentation of the model of human resource knowledge architecture in the knowledge-based organizations of the students. Results in addition to the development of the human resources knowledge architecture model, That the identification of the main components of the architecture of human resources, Knowledge Management Infrastructure, Professional features, situational features and achievements of human resources knowledge architecture.

The aim of this study was to investigating the effect of using a variety of marketing strategies in social networks to build customers’ trust. The population of this study consisted of all Iranian users of social networking sites that affected by companies advertisement. Also the sample size by using snowball sampling is 446. The research method was descriptive survey research and data collection tool was questionnaire. To test Hypotheses the partial least squares (PLS) technique and SmartPLS 3 software has been used. The results showes that all four variables, transactional, relationship, database and knowledge-based marketing strategies in social networks have a significant impact to build customersʼ trust.

Due to the increasing popoularity of social media, Analyzing influence behavior of the users in these media is one of the main priorities of different businesses. Since, social media due to their social network structure and egalitarian nature has been known to mainly differ from other old fashioned online media, These changes need an individual measurement approach as a necessity for apt evaluation and later management. In this regard, we highlight social media mining as an important techniques associated with analyzing that influence and review these techniques by categorized them into three groups including: user-based tasks, relation-based tasks and content-based tasks. Based on the our literature review, A theoretical framework for analyzing influential behavior was proposed. Two main dimensions that are conisderd including: potential of the influence and the level of the influence. ranked of useres, number of actived users, quality and the Subjectiveness of content has been defined to measure each of the aforementioned dimensions. Therefore, different businesses can utilize these framework as a guidline to Analyze the Behavior of the Influentials and providing the best facilities to manage users in social media sites.