Journal of Information Technology Management
Volume:9 Issue: 1, 2017

In order to gain more illicit profit, some traders in the stock market try to make a targeted impact on prices by placing fake orders and false advertising. Due to the high customer population, it is not possible to discover these frauds using traditional methods. The present study seeks to provide a system for preventing the frauds in future market-trading coins based on Bayesian classifier model for Iran Mercantile Exchange. The proposed model has polynomial time complexity and high accuracy because of considering important dependencies among different features of data. The primary labeling of data has been done by Kmeans clustering. The test of model shows 94.55 percent similarity between model's output and labeled data. Using this system can helps to identify the fraudulent from non-fraudulent traders.

Nowadays, considering the focus of international community on the field of IT services as well as the move of large organizations toward profitability by providing appropriate services to the needs of organization and business markets, it is necessary to draw more consideration to the related projects than ever. In this regard, identification and selection of IT service's indicators and evaluation of these services, and projects, are effective methods to increase profitability and effectiveness of IT in organizations. Therefore, in this study, first the effective criteria in selecting IT projects and services identified, and then a decision support system designed based on fuzzy analytical hierarchy process. And also, by applying the proposed system, the analysis was conducted to determine the sensitivity of various criteria against changes and the effects of each on the final ranking results. Finally, comparing the results of the study with others, the advantages of proposed decision support system have been described.

Systems related to knowledge management can improve quality and efficiency of knowledge used for decision making process. Approximately 80 percent of corporate information are in textual data formats. That is why text mining is useful and important in service chain knowledge management. For example, one of the most important applications of text mining is in managing on-line source of digital documents and the analysis of internal documents. This research is based on text-based documents and textual information and interviews processed by Grounded theory. In this research clustering techniques were applied at first step. In the second step, Apriori association rules techniques for discovering and extracting the most useful association rules were applied. In other words, integration of datamining techniques was emphasized to improve the accuracy and precision of classification. Using decision tree technique for classification may result in reducing classification precision. But, the proposed method showed a significant improvement in classification precision.

These days, Emergence of e-commerce web sites is one of the important consequences of the Internet in modern times, but products data is growing exponentially. In such environment, customers face a problem in finding optimized information among huge data bases about the items or desired products. In order to assist buyers, large e-commerce companies are planning to introduce their own recommender systems to help their customers in making a better choice among the items. Due to high percentage error , a basic method to build such systems is not usually being applied. In this essay, two methods have been suggested in order to improve recommendations in recommender systems. Collaborative filtering method is one of the most successful methods used in the system, but using this method that it has common problem the increasing number of users and products, therefore system do not inability to request the requirement of cold start and data sparsity. Two methods have been suggested in order to improve recommendations in recommender systems. To resolve this problem, a new method has been introduced in which by integrating rating time by Pearson also combining semantic technology with social networks offers a solution to reduce issues such as "cold start" and generally "data sparsity" in recommender systems. The result of simulating showed that the proposed approach provided better performance and more accurate predictions in addition of more consistent with user preferences.

The design of e-government websites with improper service quality is one of the common problems in the contemporary era. Users’ perceptions of e-service quality are affected by their perceived service content and delivery quality. In this study, based on the model of e-government service quality, first we identify the factors of service content and delivery related to Value Added Tax (VAT) system in Iran. Then, we develop a conceptual model that depicts the influence of these factors on service quality and users’ satisfaction. Data gathering is performed through electronic questionnaire and the case study of taxpayers using VAT services. The results show that transactional performance and accessibility respectively, have the greatest impact on quality of service content and delivery. In addition, the users’ positive perceptions of service content and delivery quality influence their positive perceptions of overall service quality. This study offers the design of a user-centric governmental website with effective service quality to improve users’ satisfaction and also to encourage the adoption and continuance use of these services.

Nowadays portions of the national income in developed countries are due to income of the electronic businesses and electronic commerce. Realizing these businesses and their strategical governance priorities regarding the development of those business with most profitability are among the most important necessities in this field. Accordingly, indicating the status quo of electronic business in our country can be of great value in having an effective policy-making. In this research we aim to compare the status of Iranian electronic commerce with the most successful ones in the globe. To do so, first we have considered the top hundred website according to Alexa ranking, nationally and globally, and we have extracted their main business models as well. Afterward, the frequency of these business models in addition to the average ranking of their websites have been extracted and compared accordingly. Results of this study show that those electronic business models which are highly valued globally are less attractive for Iran’s electronic commerce.

In recent years, rough set theory has been considered as a strong solution to solve artificial intelligence problem such as data mining. But, the classic rough set theory is not effective in the case of attribute reduction in incomplete information systems. Since there are null values for some of attributes in a data set, an incomplete information system is created. In this paper, a novel method proposed to solve attribute reduction in incomplete information system based on rough set theory by combining and modifying imperialist competitive algorithm with fuzzy logic. Utilizing the fuzzy logic to control the parameters of the algorithm was useful and generated better solutions compared to its classic draft. In this research, no changes imposed on incomplete data, and it was just considered as a complete systems. The fuzzy imperialist competitive algorithm acted intelligently to reduce the number of attribute in incomplete information system, providing appropriate results that is worthy of attention.

Due to the absence of a comprehensive framework for IT governance, the main objective of the study is to identify all components of IT governance and present them in the form of a comprehensive IT governance framework .The localization of provided framework for the automotive industry is the secondary objective of the study. In this regard, the research questions are: what is the comprehensive framework of IT governance? What are the components and dimensions of a comprehensive framework of IT governance? What is the localized comprehensive framework of IT governance for the Iranian automotive industry? All researches on IT governance were investigated using meta-synthesis qualitative method and were limited to 96 selected articles by performing a meta-synthesis process. Then, the five categories, 19 concepts and 79 codes of IT governance were identified through detailed study of these articles. Then, a comprehensive framework of IT governance was presented. For localizing, a questionnaire designed based on the identified IT governance components, and distributed among the automotive industry experts. Statistical hypothesis testing of collected data led to the rejection of cross/functional job rotation component in the automotive industry. To demonstrate the applicability of the framework, the IT governance status of ATLAS holding company was evaluated based on the comprehensive framework that localized for automotive industry.