نشریه فناوری اطلاعات و ارتباطات ایران
سال سیزدهم شماره 47 (بهار و تابستان 1400)

Increasing rate of novice technology in global marketing arises some challenges in the economic enterprise planning. One of the appropriate approaches to resolve these challenges is using rough set theory along with decision making. In this paper, a decision support system with an algorithm based on rough set theory is provided. The proposed algorithm is implemented for a product line in one of the organizations under supervision of mining, industry and trade ministry. The variable effects on the enterpise aims are evaluated by analysing the strength and support criteria of rough sets. The rules are classeified as three different classes and 3 out of 12 have high reasonable averagewhie the last 3 have a relatively high violation probability. The other rules have heterogenious distribution and are not certain. The advantages of the proposed system are avoidance of enterprse capital wasting, prevention of errors due to data uncertainty, and high precision of decitions. The decision makers in the enterprise validated the increasment of simplicity and speeds of vital decision making by using the proposed system.

As the Internet of Things (IoT) expands and becomes more widespread, we will soon see the dependence of human life on its services. At that time, it would be difficult to imagine the survival without the IoT, and disruption of its services would cause great loss of life and property. Disruption of IoT services can occur for two reasons: network errors due to congestion, collision, interruption and noise, and disruption due to the malicious activities of infiltrator. Also, the destructive activities of infiltrators can lead to various cyber attacks and violation of the privacy of individuals. Therefore, before the interdependence between human life and IoT, it is necessary to consider measures to ensure the quality and security of service and privacy. In this study, a solution to reduce service delay (improve quality) and ensure security and privacy of things by relying on the computing power of nodes available in the Fog Layer has been proposed. The proposed solution simultaneously improves service quality and maintains security and privacy. Other features of presented algorithm in proposed solution of fairness between objects are in terms of the quality of service received and minimizing overhead processing and transfer of expired packages (packages that will certainly experience a consumedly threshold delay). Adherence to fairness ensures that the quality of service of any of the things does not be a subject of the reduction of the delay of the service of the entire network; These aforementioned objects may be subjects of critical applications, such as health.

Forecasting a 10-fold increase in global data till 2025 by IDC indicates that the data journey for organizations has just begun. Collecting, storing, analyzing and using this valuable gold contributes to innovation in companies and organizations and leads businesses to a competitive future. The purpose of this article is to examine the various contexts of value creation and big-data-driven business for startups that rely on data as their primary source of business. Therefore, in this regard, after examining the various dimensions of big data, a conceptual model of this field has been presented. This model helps companies to carry out their activities in this field with more awareness and focus. Also in the continuation of this article, the results of the survey of knowledge-based companies with data-driven businesses are presented. The results show that most of these companies are more focused on the two areas of data analysis and visualization. Also, the two areas of "Fintech" and "Media" have been more receptive to the technologies of these two areas. On the other hand, products related to "big data management and processing", which includes providing solutions and launching big-data processing and storage services, have the highest sales volume and a significant amount of activity of these companies on Content of news, social networks and information related to stock exchange transactions.

The growing use of social media and online communication to express opinions, exchange ideas, and also the expanding use of of this platforms by Persian users has increased Persian texts on the Web. This remarkable growth, along with abusive use of the writer's anonymity, reveals the need for the author's automatic identification system in this language. In this research, the purpose of the study is to investigate the factors affecting the identification of authors of Persian reviews produced by cell-phone buyers and also to evaluate supervised and unsupervised methods. The factors considered in this research include lexical, syntactic, semantic, structural, grammatical, text-specific, and specific to social networks. After extracting these features, selecting the best features is tested by four algorithms including feature correlation, gain ratio, OneR, and principal components analysis. In the following, K-means, EM and density-based clustering will be used for clustering and Bayesian network, random forest, and Bagging will be used for categorization. The evaluation of the above algorithms on Persian comments of Samsung phone buyers indicates that the best performance among the clustering algorithms is 59/16% obtained by the EM algorithm on top-15 features selected by OneR, while the random forest algorithm using top-90 features selected by gain ratio with 79/57% achieves the best performance among the classification algorithms. Also, the comparison of features showed that syntactic features had the most effect on the identification of the author of short texts, and then, lexical, text-specific, specific to social networks, structural, grammatical and semantic features, respectively.

In this paper, a new personalized system based on learners' motivation with utilization of game elements have been implemented for the e-learning environment. Learners' motivation is measured by AMS questionnaire and then by finding the player's type based on AMS results, appropriate game elements are selected for each learner and added to their webpage. To implement the designed system, the intelligent tutoring system has been designed with 117 participants in a math course. To measure the performance of the designed system, academic performance and the time spent on learning management system (LMS) in the control and test groups, before and after personalization have been compared. The results show that personalization based on individual motivation by utilization of gamification leads to a significant improvement in learners’ academic performance compared to pre-personalization and compared to the control group.

Due to the significant growth of textual information and data generated by humans on social networks, there is a need for systems that can automatically analyze the data and extract valuable information from them. One of the most important textual data is people's opinions about a particular topic that are expressed in the form of text. Text published by users on social networks can represent their personality. Although machine learning based methods can be considered as a good choice for analyzing these data, there is also a remarkable need for deep learning based methods to overcome the complexity and dispersion of content and syntax of textual data during the training process. In this regard, the purpose of this paper is to employ deep learning based methods for personality recognition. Accordingly, the convolutional neural network is combined with the Adaboost algorithm to consider the possibility of using the contribution of various filter lengths and gasp their potential in the final classification via combining various classifiers with respective filter sizes using AdaBoost. The proposed model was conducted on Essays and YouTube datasets. Based on the empirical results, the proposed model presented superior performance compared to other existing models on both datasets.

Emotional analysis is used as one of the main pillars in various fields such as financial management, marketing and economic changes forecasting in different countries. In order to build an emotion analyzer based on users' opinions on social media, after extracting important features between words by convolutional layers, we use LSTM layers to establish the relationship behind the sequence of words and extract the important features of the text. With discovery of new features extracted by LSTM, the ability of the proposed model to classify the stock values of companies increases. This article is based on the data of Nguyen et al. (2015) and uses only the emotional information of people in social networks to predict stocks. Given that we categorize each user's message into one of the emotional classes "Strong Buy", "Buy", "Hold", "Sell", "Strong Sell", this model can predict the stock value of the next day, whether it will be high or low. The proposed structure consisted of 21 layers of neural networks consisting of convolutional neural networks and long short-term memory network. These networks were implemented to predict the stock markets of 18 companies. Although some of the previously presented models have used for emotion analysis to predict the capital markets, the advanced hybrid methods have not been performed in deep networks with a good forecasting accuracy. The results were compared with 8 baseline methods and indicate that the performance of the proposed method is significantly better than other baselines. For daily forecasts of stocks changes, it resulted in 19.80% improvement in the prediction accuracy, compared with the deep CNN, and 24.50% and 23.94% improvement compared with the models developed by Nguyen et al. (2015) and Derakhshan et al. (2019), respectively.

The Iran’s national enterprise architecture framework (INEAF) has been adapted from the TOGAF framework and its architectural development method. In this framework, the use of agility paradigm is emphasized, but there is no basis for using agile methods and techniques. Based on the results obtained for the researcher, the studied sources did not indicate all the necessary solutions and features to develop an agile methodology based on the Iran’s national EA framework. According to Mr. Gill research, each modeling standard is different in scope and function, and since a modeling standard alone cannot support all the requirements of agile enterprise architecture, combining modeling standards is a suitable solution. In this paper, an agile enterprise architecture modeling methodology including ten solutions with a combination of six modeling standards ArchiMate, UML, BPMN, FAML, SoaML and BMM based on the Iran’s national EA framework is presented. The evaluation of the applicability of the proposed methodology is performed by the combined method (qualitative + quantitative). Qualitative evaluation is performed through a case study and quantitative evaluation is performed using multi-criteria decision making methods including ANP and DEMATEL. Data collection and information gathering and determining options and criteria, is performed through library studies and field methods, and using questionnaire, interview and observation tools. Based on the case study, combination of six standards by agile enterprise architecture modeling method based on the Iran’s national enterprise architecture framework is applicable. For quantitative evaluation of applicability in this paper, according to experts, four options have been proposed, which are based on the final weight: support by known tools, the ability to cover the artifacts of the Iran’s national enterprise architecture framework, efficiency or effectiveness, ease of learning or teachability.

For many years, fake news and messages have been spread in human societies, and today, with the spread of social networks among the people, the possibility of spreading false information has increased more than before. Therefore, detecting fake news and messages has become a prominent issue in the research community. It is also important to detect the users who generate this false information and publish it on the network. This paper detects users who publish incorrect information on the Twitter social network in Persian. In this regard, a system has been established based on combining context-user and context-network features with the help of a conditional generative adversarial network (CGAN) for balancing the data set. The system also detects users who publish fake news by modeling the twitter social network into a graph of user interactions and embedding a node to feature vector by Node2vec. Also, by conducting several tests, the proposed system has improved evaluation metrics up to 11%, 13%, 12%, and 12% in precision, recall, F-measure and accuracy respectively, compared to its competitors and has been able to create about 99% precision, in detecting users who publish fake news.

According to enormous growths in communication networks, multimedia data will play a significant role on the Internet of Things in the near future. High volume of multimedia data leads to challenges such as reducing network lifetime and congestion. In this paper, a new objective function for the RPL routing protocol is proposed which addresses the characteristics of multimedia data in the routing process. In the objective function, node’s remaining energy and the remaining buffer capacity of nodes measures are combined using a weighted pair. In order to evaluate this method, input data is generated based on a video trace. Packet delivery ratio, network lifetime, nodes availability over the lifetime of the network, node energy distribution, and end-to-end delay are used to evaluate the proposed method. The evaluation results show that the proposed method increases the package delivery ratio compared to the standard RPL. This method also improves the lifetime of the nodes by distributing energy between the nodes in comparison with standard RPL and extends the node's availability over the lifetime of the network. Finally, it reduces the network congestion which led to a lower end-to-end delay.

Social network analysis’ link prediction has a diverse range of applications in different areas of science. Bipartite networks are a kind of complex network, which can be used to describe various real-world phenomena. In this article, a link prediction method for bipartite network is presented. Uni-partite link prediction methods are not effective and efficient enough to be applied to bipartite networks. Thus, to solve this problem, distinct methods specifically designed for bipartite networks are required. The proposed method is neighbor based and consisted of measures of such. Classic uni-partite link prediction measures are redefined to be compatible with bipartite network. Subsequently, these modified measures are used as the basis of the presented method, which in addition to simplicity, has high performance rates and is superior to other neighbor-based methods by 15% in average.