Journal of Information Technology Management
Volume:7 Issue: 2, 2015

Withdrew is one of the challenges facing higher education. This paper considers the approach to admissions fees paid students as a business and the withdrawal of the student as customer churn. It aims to investigate the attrition of predictions for interventionist policies deterrent. This study is a descriptive and applied research and uses quantitative and qualitative data. The data are derived from the system of university of Tehran education, including 21,420 fees paid students entering in the years 2009-14. It uses data mining technology Crisp. The goal is to analyze the behavior of students to identify students at risk of attrition and withdrawal may be predictive modeling. After data analysis, predictive modeling, probability table attrition and withdrawal model were presented. Results show that the first and second semester (especially the freshmen and the age range 31-24), MSc for withdrawal and nightly for withdrawal are risky.

Insurance industry experts believe that fraud is a destructive disaster in the insurance industry. Over the years, many methods have been used in the literature for fraud detection, one of which is expert systems. Fraud detection expert systems based on the knowledge of experts in the field of insurance identify fraud. Judgment of experts is mostly based on evidence, documents, qualitative information which is often presented in verbal words to describe the fraudulent behavior. In the presented model, 61 qualitative and quantitative criteria related to the detection of fraud in car insurance were identified. Then, these criteria were prioritized according to expert opinion and 17 criteria with the highest priority classified into eight factors were selected. In the suggested system fuzzy inference was performed using Mamdani algorithm. Finally, the designed system was implemented to an Iranian private insurance company and the validity of the system assessed by a questionnaire and came up to 69.45%. The obtained results indicate that the proposed model is able to detect the fraud quite significantly.

Apriori algorithm is the most popular algorithm in association rules mining. One of the problems the Apriori algorithm is that the user must specify a minimum support threshold. Consider that a user wants to implement the Apriori algorithm on a database with millions of transactions; Users will not have the necessary knowledge about all the transactions in the database and therefore cannot determine an appropriate threshold. The aim of this paper is improved the Apriori algorithm to automatically determine the minimum support. To achieve this, we will try to use fuzzy logic before of using the Apriori algorithm on data contained in the database, put the data in different clusters and try the offer to user the most appropriate threshold automatically. We hope this will be any rule that may be of interest not lost, because of inappropriate threshold specified by the user and also not extracted any rule useless.

The banks as the most important symbol of monetary market in any country are not exception. As the optimum function of the banks have important role in economic development of the country, creating the ground for qualitative and quantitative promotion of the banks performance in healthy competition can play important role in achieving the goals. One of the methods helping the bank’s branches to identify the competitive position and performance quality is evaluation of their performance from various aspects and their ranking. The aim of present study is to design a decision support system based on Promethee II method as a complete and comprehensive method and by automatic ranking, despite considering the qualitative and quantitative indices, it is done in by low time and costs with high precision. Thus, it is possible to analyze the sensitivities to be sure of the initial selections and changing the indices and values dependent upon the environmental changes are provided for branches evaluators. The system output is the rank associated to each branch based on Promethee II method.

Recent studies have indicated that the rate of successful Enterprise Resource Planning (ERP) implementation projects is reduced and risk of implementation is increased to the same extent. Accordingly, this study provides a model in support of ERP implementation using system dynamics approach is by using behavioral simulation in the Vensim software environment. This study is an applied research and due to the nature, this is a descriptive and analytical research. For the purpose of research, proposed simulation model is used in paksan organization in order to present policies for the improvement in the organization's ERP system implementation project by testing different strategies in simulation environment. Since in this research the improvement of ERP system implementation project has been considered in terms of time and cost, policies have been proposed to reduce these two factors that their results were this: increasing the number of experienced users, eliminating customization and increasing the amount of training cause improvement in project perfpemance.

Today, Information Technology Outsourcing (ITO) has developed and caused organizations to become more flexible and dynamic than they were before. The ever-increasing development of ICT is one of the main reasons for the promotion of outsourcing. That is due to the fact that many organizations are not able to adapt their hardware and software in accordance with the fast-paced technology development. Cloud Computing is considered as one of newest paradigms in ITO. Due to its flexible nature, this paradigm has been able to protect organizations against extreme changes of IT during recent years. Furthermore, by better understanding their needs and prioritizing them, organizations can experience a more successful outsourcing in the context of Cloud Computing. SMI can be a considerable help in identifying organization’s needs to use Cloud Computing. This article suggests that organizations use Fuzzy DEMATEL Technique to prioritize their needs. In this research which is conducted as a case study, all the universities in Semnan Province are examined. The results show that the most important criteria for outsourcing in the context of Cloud Computing are Compliance, Operability and Contracting Experience respectively. Moreover, the model has identified Security Management, Ownership and Contracting Experience as the most effective criteria and Learnability, Maintainability and Recoverability as the most affected one.

Rapid growth of networks and social networks results in more access to people’s opinion. These opinions contain useful information. By analyzing these opinions, people’s preferences and their positive and negative opinions about different subjects can be identified.. Opinion mining is the process of analyzing people’s emotions, feelings and opinions to identify their preferences. In this article, a method for opinion mining in Persian language is introduced that is a combination of SVM and lexicon as a set of features. The lexicon is created by using SentiWordNet. To assess the algorithm, data of hotel domain is collected. Four cases were defined and among those cases, the case in which frequency of word multiplies with its orientation got the best result. The proposed method performs better compared to other methods in Persian opinion mining.

Understanding and changing the energy consumption behavior requires extensive knowledge about the motives of behavior. In this research, Rough Set Theory is used to investigate the energy consumption behavior of employees in organizations. So, thirteen condition attributes and a decision attribute have been selected and the decision system has been created. Condition attributes include demographic, values, attitudes and organizational characteristics of employees and decision attribute relates to energy consumption behavior. 482 employees have been selected randomly from 37 office buildings of ministry of Petroleum and rough modeling has been performed for them. By combining different methods of discretizing, reduction algorithms and rule generating, nine models have been made using ROSETTA software. The results show that four of the 13 condition attributes, involving “organizational citizenship”, “satisfaction”, “attitude toward behavior” and “lighting control” have been selected as the main features of the system. After cross validation of the various models, the model of manually discretizing using genetic algorithms and ORR approach to extract reducts has been had the most accuracy and selected as the most reliable model.

Recommender systems provide personalised recommendations to users, helping them find their ideal items and they also play a key role in encouraging users to make their purchases through websites thus leading to the success of online stores. The collaborative filtering method is one of the most successful techniques utilized in these systems which facilitate the provision of recommendations close to that of the customer's taste and need; however the proliferation of both customers and products on offer, the technique faces some issues such as "cold start" and scalability. As such in this paper a new method has been introduced in which user-based collaborative filtering is used at a base method along with a weighted clustering of users based upon demographics in order to improve the results obtained from the system. The implementation of the results of the algorithms demonstrate that the presented approach has a lower RMSE, which means that the system offers improved performance and accuracy and that the resulting recommendations are closer to the taste and preferences of the users.

Developing of mobile technologies in the field of health, create a new branch of e-Health called mobile-health. The goal of this study, is evaluation of communications infrastructure for mobile-health. For this purpose, fuzzy analytic hierarchy process is used for modeling and ranking the criterions and applications. Three main criteria in mobile communication network namely, data transmission, application services, and the accessing to the network were chosen. Then, sub-criteria of technological infrastructure were classified according to three main criteria using the Delphi and fuzzy Delphi techniques and thirteen applications for mobile-health was added to it as alternatives. The results show that the accessing to the mobile network has the highest weight (0.5656) and priority. The second criterion is data transmission (0.4184) and finally application services in mobile communication networks (0.0159) are the last factor that it has a large gap from other two factors. According to the obtained results, community mobilization, electronic health records of patient and patient monitoring have the highest weight 0.0981, 0.0878 and 0.0876, respectively, and these are the top priorities of mobile-health applications.

Regarding strategic importance of KM and its significant effect on sustaining competitive advantage, achieving to KM objectives are difficult in practice and has faced with challenges which one of the major challenges could be reluctance of users in KMS acceptance and use. In this paper in order to exploitation of comprehensive model, effective factors of knowledge initiatives are extracted via mixed method in which systematic literature review along with open codding of grounded theory are applied to enrich extracted factors and identifying native factors. Finally, in order to validating developed model, number of KM experts and specialists are asked to assess the factors and collected data were analyzed with structural equation modelling (SEM) The findings of the study can be used as a tool for evaluating KM initiative and identifying weaknesses and strengths of KM project in user’s point of view. Findings also propose some suggestions to assist KMS developers for developing systems which has more participation of users.

The purpose of this study is to consider and modeling E-Commerce variables based on FAHP, FTOPSIS and Possibility Theory to create a new approach in decision-making techniques. Thus, modeling Market Orientations and Innovation Orientation indicators as the E-Satisfaction factors and Fulfillment & Reliability, Responsiveness and Security indicators as the E-Trust factors is done for selected banks. Data collected from customers and experts via distributing questionnaires and the data were analyzed by FAHP, FTOPSIS and Possibility Theory techniques. The results show that E-Satisfaction, Innovation Orientation and Security are the important indicators in E-Loyalty and Mellat Bank takes the highest rank in covering these indicators.