درخت حوزه‌های تخصصی

Social media allows people interact to express their thoughts or feelings about different subjects. However, some of users may write offensive twits to other via social media which known as cyber bullying. Successful prevention depends on automatically detecting malicious messages. Automatic detection of bullying in the text of social media by analyzing the text "twits" via one of the machine learning algorithms. In this paper, we have reviewed algorithms for automatic cyberbullying detection in Arabic of machine learning, and after comparing the highest accuracy of these classifications we will propose the techniques Ridge Regression (RR) and Logistic Regression (LR), which achieved the highest accuracy between the various techniques applied in the automatic cyberbullying detection in English and between the techniques that was used in the sentiment analysis in Arabic text, The purpose of this work is applying these techniques for detecting cyberbullying in Arabic.

Nowadays, organizational intelligence is very important in acquiring necessary information for making suitable decisions and gaining competitive benefits in active environments for generative corporations. So, the present study aims to identify and prioritize organizational intelligent criteria in the production cooperatives of Iran. In methodology,this is an applied (developmental) study objectively and a mixed (quantitative-qualitative) survey regarding the nature of data. The present study is a Grounded theory its nature and methodology in the qualitative section, while it is a descriptive-exploratory study in the quantitative section. The study population consists of experts including the managers of the Ministry of Cooperatives, Labor, and Social Welfare, who were 12 respondents selected using the non-probability purposive and snowball sampling techniques. The dimensions of organizational intelligence, including intelligent enablers, organizational intelligent capabilities, intelligent development contexts, and intelligent governance with 18 criteria were identified using the grounded method. Finally, the cause and effect relationships between the dimensions and criteria were determined by the Analytical Network Process method based on the fuzzy DEMATEL. According to DEMATEL findings, the “intelligent governance” and “intelligent development contexts” dimensions are the cause, while the “intelligent enablers” and “organizational intelligent capabilities” constitute the effect. The “customer relationship management”, “organizational learning”, and “human intelligence” criteria have respectively the most weight and priority in the organizational intelligence of production cooperatives in Iran.

Stock price crash risk is a phenomenon in which stock prices are subject to severe negative and sudden adjustments. So far, different approaches have been proposed to model and predict  the  stock price crash risk, which in most cases have been the main emphasis on the factors affecting it, and often traditional methods have been used for prediction. On the other hand, using  Meta Heuristic Algorithms, has led to a lot of research in the field of finance and accounting. Accordingly, the purpose of this research is to model the Stock price crash risk of listed companies in Tehran Stock Exchange using firefly algorithm and compare the results with multivariate regression as a traditional method. Of the companies listed on the stock exchange, 101 companies have been selected as samples. Initially, 19 independent variables were introduced into the model as input property of the particle accumulation algorithm, which was considered as a feature selection method. Finally, in each of the different criteria for calculating the risk Stock price crash risk, some optimal variables were selected, then using firefly algorithm and multivariate regression, the stock price crash risk was  predicted  and results were compared. To quantify the Stock price crash risk, three criteria for negative skewness, high fluctuations and maximum sigma have been used. Two methods of MSE and MAE have been used to compare the methods. The results show that the ability of meta-meta-heuristic methods to predict the risk Stock price crash risk is not  generally higher than the traditional method of multivariate regression, And the research hypothesis was not approved.

One of the basic assumptions of management accounting illustrate that costschanges has a significance Relationship with increasing and decreasing in the levelof activity, recently after being raised of sticky costs issue by Anderson and hiscolleagues this assumption was discussed. It means Increases in costs by increasingthe more activity level of reduction in costs is exchange for the reduction in thelevel of activity. Anderson et al (2003) changed the expression of Cost behavior tosticky costs. The Subject profit forecast error is one of the issues that can affectinvestment decisions to hold or transfer of shares, because purchase of shares ofthe new company's stock is riskier process than other companies; Because of itslack of trading Background, historical information related to their is low. Accordingly,in prediction two fundamental goals follow: The first one obviously is theproper planning and second one is Familiarity and deployment of predictive techniquesusing predictive techniques for decision making and problem solving process.The aim of this study was to investigate the relationship between forecasterror of earnings and sticky cost in Tehran Stock Exchange. To achieve this aim, amain hypothesis and sub-hypothesis has been proposed, and to test hypothesesdata 108 firms listed companies in Tehran Stock Exchange using systematic samplingpurposefully selected, and data of the period between 2007 to 2013 was usedfor statistical analysis. Finally, the results indicate that There is an inverse relationshipbetween sticky costs and forecast error of earnings, and also betweensticky cost of goods sold and sticky costs of sales.