Fuzzy expert systems are intelligent systems which can be used to obtain better results in evaluating the performance of the banking system. The purpose of this study is to evaluate the performance of bank branches using fuzzy variables beside financial variables. In this study, firstly, the rules of the data were extracted by implementing data mining algorithms on the financial data of branches. In the next step, by obtained rules of financial data and along with fuzzy variables, a fuzzy expert system is designed in order to achieve a system that can comprehensively evaluate the bank branches performance. For designing the considered expert system, nine fuzzy variables such as branch location, customer loyalty, employee satisfaction, customer satisfaction, creativity and innovation, branch appearance, staff appearance, employee stability and also the output of financial rates have been used. Decision tree and C. 5 algorithms have been used in order to extract the rules in the branch data. MATLAB fuzzy inference system has been used to design the fuzzy expert system also. The results of the research illustrated the hidden knowledge of the branch data can be extracted via data mining and the performance of bank branches can be evaluated as a comprehensive information system by fuzzy expert systems.

Recently, in the field of modem criminology, several solutions have been proposed for expediting crime detection and lessening the rate of crime occurrence. Among these solutions those having scientific backgrounds have grabbed the criminologists' attentions. In this paper, burglary methods have been investigated using neural networks in order to detect a crime before it happens. In fact, the analysis of burglar behavior is the key to crime detection and attribution. In addition, the issue of anticipative detection has been discussed with a new meaning and its relationship with crime for casting has been explained. In the current paper, other academic techniques in subjects like: comparative criminology, group detection and link analysis in criminal networks have been fully discussed. Crime pattern recognition is a common need in all these academic solutions. The paper structure is a combination of two approaches: crime detection before its occurrence and crime for casting.

Today, considering technology development, increased use of Internet in businesses, and movement of business types from physical to virtual and internet, attacks and anomalies have also changed from physical to virtual. That is, instead of thieving a store or market, the individuals intrude the websites and virtual markets through cyberattacks and disrupt them. Detection of attacks and anomalies is one of the new challenges in promoting e-commerce technologies. Detecting anomalies of a network and the process of detecting destructive activities in e-commerce can be executed by analyzing the behavior of network traffic. Data mining systems/techniques are used extensively in intrusion detection systems (IDS) in order to detect anomalies. Reducing the size/dimensions of features plays an important role in intrusion detection since detecting anomalies, which are features of network traffic with high dimensions, is a time-consuming process. Choosing suitable and accurate features influences the speed of the proposed task/work analysis, resulting in an improved speed of detection. In this article, by using data mining algorithms such as J48 and PSO, we were able to significantly improve the accuracy of detecting anomalies and attacks.

Customer churn is one of the challenges of business management in today's complex competitive environment. For this purpose, the organization must have an efficient system to detect and analyze the factors influencing customer churn. To conduct this research, an attempt has been made to build a hybrid model based on data mining approaches from information related to 5830 customers of a chain store (demographic information and information based on customer purchase records) with 17 qualitative and quantitative characteristics. The features of higher importance were identified to build the model in the first stage using the logistic regression algorithm. In the second stage, the support vector machine algorithm, a critical supervised learning algorithm, was used to classify the customers and rank the essential features. Finally, the proposed model has been implemented as a case study in the chain store industry. The results indicate the optimal efficiency of the proposed analysis method. This research has been done to identify the influential factors in customer churn and focus on providing new solutions to reduce churn in the retail industry. Also, the results show that age, marital status, and average monthly income from the set of demographic features and how to get to know the store, the share of online shopping, and special sales from the set of features related to customer transaction records are among the most important factors affecting customer churn. In addition, practical suggestions have been presented that can be used for tactical and strategic planning of chain stores to attract and retain customers.

Background & Aim: Today, with the advent of technology, due to the growing data in the field of health care, it is difficult to manage and analyze this type of data known as the Big Data. This analysis has many capabilities to improve the quality of care, reduce errors and reduce costs in care services. Methods: This study is based on search of databases (PubMed, Google Scholar, Science Direct, and Scopus). This investigation has done with the websites and the specialized books with standard key words. After a careful study, 50 sources were in the final article. Results: Since the Big Data Analysis in the field of health has been growing and also considered in recent years, this survey identified the necessity of these analyses, the definition of the Big Data, the benefits, resources, architecture, applications, analysis, platforms, Examples and challenges in the field of health care. Conclusions: Familiarity with the big data concepts in the field of healthcare can help researchers in conducting applied research and thus improve the quality of health care services and reduce costs.

Background and Objective: Paying attention to the health of workers as a significant part of the population is important as they play an important role in the development of the society, which also has caught the attention of government officials and World Health Organization (WHO). Based on the rules and regulations of workers in different occupations, each year they must undergo certain medical tests and examinations to ensure they have sufficient health to perform their duties. This study aimed to predict the results of examinations, extraction of knowledge and identifying patterns and agents that affect workers' health. Materials and Methods: This was a descriptive-analytic study conducted in Tehran among 1267 employees of various occupations who participated in annual examinations of labor medicine in 2017 and 80 variables related to their health and occupational and family background were collected during the examinations. Due to the size and type of data, the C5. 0 decision tree method was used to perform data mining and discovery process. Results: Using the C5. 0 decision tree, a model with accuracy of 99. 05% was introduced. According to this model, variables with the greatest impact on the health of the employees were identified. Hearing status, especially hearing loss at frequencies of 6000 and 4000 Hz, had the greatest impact on the results of employee health examinations. Conclusion: According to the extracted patterns and identification of determinants that had the greatest impact on the result of medical examinations, it is possible to control the specified factors to improve the health of workers.

One of the challenges that banks are faced with is recognition and differentiation of customers and providing customized services to them. Recognizing valuable customers based on their field of business is one of the key objectives and competitive advantages of banks. To determine guild patterns of the valuable customers based on their transactions and value of each guild for the bank, the banking tools on which the customer’s transactions take place need to be surveyed. Using deeper insights into the value of each guild, banks can provide customized services to ensure satisfaction and loyalty of their customers. Study population was comprised of the holders of point of sale (POS) devices in different guilds and the transactions done through the devices in an 18-months period. Datamining methods were employed on the set of data and the results were analyzed. Data preparation and analysis were done though online analytical processing (OLAP) method and to find guild patterns of the bank customers, value of each customer was determined using recency, frequency, monetary (RFM) method and clustered based on K-means algorithm. Finally, specifications of customers in the most valuable cluster were analyzed based on their guilds and the rules were extracted from the model developed using C5 decision tree algorithm.

With the fast growth of e-commerce and the emerging new retail trend—online and offline integration—it is important to recognize the target market and satisfy customers with different needs by analyzing their online search behaviors. Accordingly, in this study, several internet companies in Iran were investigated. The companies were divided into 5 categories based on their product type: food, cosmetics and luxury goods, industrial goods and their accessories, sanitary goods, detergents, and clothing. Then the trading data of the companies in a certain period are analyzed. The data of this research includes customer transaction records from 2018 to 2019, after removing incomplete and missing data, this number has reached 349 records or the company. According to the inquiry from the Ministry of Mining Industry and Trade, there are 51,307 internet shopping and service sites and 36,200,000 internet buyers in the country.Clustering provides a good understanding of customer needs and helps identify potential customers. Dividing customers into sectors also increases the company's income. It is believed that retaining customers is more important than finding new customers. For example, companies can employ marketing strategies specific to a particular segment to retain customers. This study first performed RFM analysis on transaction data and then applied clustering using k-means. Then the results obtained from the methods were compared with each other.

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.