IRANIAN COMMUNICATION AND INFORMATION TECHNOLOGY
Year:2021 | Volume:12 | Issue:45-46|Papers Count:12

The clothing industry has seen great growth in social media in recent years. Many factors affect the purchase of clothing from social networks, and one of these important and influential factors is organizational factors. Therefore, the purpose of this study is to investigate the organizational factors affecting the consumer in shopping through social networks. In this research, a combined method has been used. In the qualitative section, by reviewing the literature and interviewing vendors active in social networks, texts were prepared and coded. In the quantitative part, based on the initial model, a questionnaire was developed and distributed to 385 clothing buyers in networks. The structural equation method was used to analyze the data and the Sobel test was used to examine the mediating role. This study examines the independent and combined effect of organizational variables affecting people's trust in buying clothes through social networks. The results showed that information quality, transaction security, company reputation and company location have a significant effect on people's trust and consumer willingness to buy clothing through social networks.

In this study Data envelopment analysis is used for the assessment of enterprise resources planning systems of 18 manufacturing companies, to determine whether the defined goals for ERP systems have been able to affect the performance after the implementation of the system. Considering the identification of effective factors in implementing ERP systems and using the previous researches, the performance evaluation criteria of this system were identified. Then, using experts’ views the most important input indicators were ranked by fuzzy DEMATEL and output indicators by ANP. In this ranking, time spent on implementation, the implementation infrastructure, training and user support were identified as top input indicators, and three indicators of productivity increase, proper resource management and user satisfaction were selected as top output indicators. Using selected indicators, the performance of ERP systems of selected companies was evaluated. The results of this research will be useful in identifying the strengths and weaknesses of companies compared to top ones and making their ERP system even better.

In this paper, a downlink network with two users as transmitter and relay, respectively, and a central station as a receiver is considered. The aim is to determine the optimal coefficient of non-orthogonal signal symbols and the optimal power allocation in the source-relay in order to maximize the average total rate in a cooperative communication system using the non-orthogonal multiple access technique. To achieve these goals, the average total rate of the cooperative communication system with non-orthogonal multiple access with decode and forward relay in the independent Rayleigh channel was calculated. Then, in the first step, the optimization problem of the non-orthogonal symbols coefficient is mathematically expressed for each power allocation and a closed form solution is proposed. In the second step, the power allocation optimization for the source-relay was introduced and solved. Finally, the joint optimization problem of the non-orthogonal symbols coefficient and power allocation is investigated and an algorithm proposed for the joint optimization problem. The proposed algorithm shows that the joint optimization of the non-orthogonal symbols coefficient and power allocation achieve a higher average rate than the separate optimization of each of these parameters. Also, simulations and numerical results are presented to confirm the theoretical equation, where the simulations show the 3 dB gain for the optimized system using the proposed algorithm compared to the non-optimized system.

With the pervasiveness of social networks and the growing information shared on them, users of these networks are exposed to potential threats to data security and privacy. The privacy settings included in these networks do not give users complete control over the management and privatization of access to their shared information by other users. In this article, using the concept of social graph, a new model of user access control was proposed to the user, which allows the expression of privacy policies and more accurate and professional access control in terms of pattern and depth of relationships between users in social networks. In this article, by using the regular index method, indirect relationships among users are examined and analyzed, and more precise policies than previous models are presented. The evaluation of the results showed that for 10 neighbors for each user, the probability accumulation of a qualified path for the first three counter loops is 1, 10. 5 and 67. 3%, respectively, and finally for the fourth counter it reaches 100%. As the defined counting characteristic increases, the average execution time of the proposed algorithm and previously proposed algorithms increases. However, for the higher limits of the counting characteristic, the proposed algorithm performs better than the previous ones.

Recent expansions of web demands for more capable information retrieval systems that more accurately address the users' information needs. Weighting the words and terms in documents plays an important role in any information retrieval system. Various methods for weighting the words are proposed, however, it is not straightforward to assert which one is more effective than the others. In this paper, we have proposed a method that calculates the weights of the terms in documents and queries as interval numbers. The interval numbers are derived by aggregating the crisp weights that are calculated by exploiting the existing weighting methods. The proposed method, calculates an interval number as the overall relevancy of each document with the given query. We have discussed three approaches for ranking the interval relevancy numbers. In the experiments we have conducted on Cranfield and Medline datasets, we have studied the effects of weight normalization, use of variations of term and document frequency and have shown that appropriate selection of basic term weighting methods in conjunction with their aggregation into an interval number would considerably improve the information retrieval performance. Through appropriate selection of basic weighting methods we have reached the MAP of 0. 43323 and 0. 54580 on the datasets, respectively. Obtained results show that he proposed method, outperforms the use of any single basic weighting method and other existing complicated weighting methods.

In many real-world frameworks, dealing with huge domains of nodes and online streaming edges are unavoidable. Transportation systems, IP networks and developed social medias are quintessential examples of such scenarios. One of the most important open problems while dealing with massive graph streams are finding frequent sub-graph. There are some approaches such as count-min for storing the frequent nodes, however performing these methods will result in inaccurate modelling of structures based on the main graph. Having said that, gMatrix is one of the recently developed approaches which can fairly save the important properties of the main graph. In this approach, different hash functions are utilized to store the basis of streams in the main graph. As a result, having the reverse of the hash functions will be extremely useful in calculation of the frequent subgraph. Though gMatrix mainly suffer from two problems. First, they are not really accurate due to high compression rate of the main graph and second, the complexity of returning a query is high. In this thesis, we have presented a new approach based on gMatrix which can reduce the amount of memory usage as well as returning the queries in less amount of time. The main contribution of the introduced approach is to reduce the dependency among the hash functions. This will result in less conflicts while creating the gMatrix later. In this study we have used Cosine Similarity in order to estimate the amount of dependency and similarity among hash functions. Our experimental results prove the higher performance in terms of algorithm and time complexity.

Underwater acoustic sensor networks (UASNs) have gained growing importance due to their desirable features and wide spread practical applications in many communication fields. Due to the high cost of underwater sensor nodes as well as implementation complexity, increasing the lifetime of UASNs is an important issue. Although relay nodes have an important role in reducing the transmission distance and energy consumption. But the efficient RNP (Relay Node Placement) to avoid the critical sensor nodes' elimination is the main problem, i. e., to preserve the connected network. For this aim this paper presents an innovative solution called an Efficient Relay node Setting (ERS) algorithm, which involves formulating the Relay Node Placement (RNP) as a non-convex optimization problem. Actually, due to the Difference Convex (DC) constraints the proposed RNP problem is a non-convex problem and finding an optimal solution is complicated. However, a novel transformation can be applied to DC constraints which converts the problem into its convex programming equivalent. Application of the convex programming offers the advantage of readily computing a global optimal solution. Simulation results confirm the superiority of the proposed scheme over the competing RA method in terms of network lifetime and efficiency.

n today's world, one of the most important media and entertainment sectors is the gaming industry. The number of users of this type of games is constantly increasing and Businesses in this industry have grown dramatically. This has led to a dollars million turnover boost for developer companies of this kind of games. Therefore, the main aim of this study is to provide a model for measuring the quality of services on the cloud platform in the gaming industry. The present research is a developmental-applied objective and is a part of qualitative research. This research is also a survey research in terms of data gathering. In this study, 52 indicators were identified in 9 main dimensions after evaluating theoretical literature of the research and existing models and Delphi method for assessing the quality of the cloud computing services industry. The results of the research provide a comprehensive model for assessing the quality of cloud computing services industry services that can be used by industry and researchers.

We consider the following similarity problem concerning the Fré chet distance. A directed path π is given as input and a horizontal segment Q is defined at query time by the user. Our goal is to preprocess and save the directed path π into a data structure such that based on the information saved in the data structure, one sub-path of the directed path can be reported which Fré chet distance between the sub-path and the horizontal query segment Q is minimum between all possible sub-paths. To the best of our knowledge, no theoretical results have been reported for this problem. In this paper, the first heuristic algorithm is proposed. We only experimentally show the quality of the algorithm in several datasets due to no existing algorithm.

It is regarded as a crucial task in a software project to estimate the criteria, and effort estimation in the primary stages of software development is thus one of the most important challenges involved in management of software projects. Incorrect estimation can lead the project to failure. It is therefore a major task in efficient development of software projects to estimate software costs accurately. Therefore, two methods were presented in this research for effort estimation in software projects, where attempts were made to provide a way to increase accuracy through analysis of stimuli and application of metaheuristic algorithms in combination with neural networks. The first method examined the effect of the cuckoo search algorithm in optimization of the estimation coefficients in the COCOMO model, and the second method was presented as a combination of neural networks and the cuckoo search optimization algorithm to increase the accuracy of effort estimation in software development. The results obtained on two real-world datasets demonstrated the proper efficiency of the proposed methods as compared to that of similar methods.