درخت حوزه‌های تخصصی

The main purpose of this research is to present a model of film marketing process in Iran. The present research is developmental-applied in terms of purpose and is a kind of exploratory mixed research in terms of nature. The statistical population in the quality department is specialists in the field of work and academia, marketing and film industry. The number of samples was obtained by the available method and the sample size was obtained by theoretical similarity. Specialists were identified by purposive sampling and snowball sampling, which according to the adequacy of the data, a total of 30 specialists were interviewed. The statistical population in the quantitative section is 294 producers, directors and media experts. In the qualitative part, the interview was used as a data collection tool and in the quantitative part, a 34-item questionnaire was used. The research method in the qualitative part was the qualitative content analysis method in the MAXQDA software and in the quantitative part the researcher used the SPSS software and the structural equation approach. Based on the results of the qualitative study, nine hypotheses were proposed. The hypotheses were tested by distributing a researcher-made questionnaire and extracting the data and then the confirmatory factor analysis test. The results of the confirmatory factor analysis test and the dimension measurement model of the variables showed that all variables are in a significant state. And in the section of structural equations, it was proved that all the relations designed in the qualitative section are approved and all the components of the model have significant relations with each other.

One of the most important risks that the banks and financial institutes face, is credit risk which is related to not-paid instalments or the instalments paid with delay by borrowers. Banks use credit scoring models In order to prevent this type of risk. The goal of this research is to investigate the joint effect of economic time cycles and the industry sector on credit scoring models we are seeking to answer the key question: “when should bank change their credit scoring models based on economic time cycles and for which industry sectors?”. The dataset of the research involves all companies that were applied for a loan in one of the Iranian major banks during the years 2008-2011. The companies have been divided into four industry sectors including “Industry and Mine”, “Oil and chemical”, “Service and Infrastructure” and finally “Agriculture”. Based on the sector of the industry and year, 54 explanatory variables, both financial and non-financial, 12 distinct industry sectors and time-specific data sets are built then classification methods were used to classify customers into two groups of defaults and non-defaults. Finally, we compared the results by Wilcoxon Test. The results show that the companies that are in the groups of Industry and Mine and Agriculture, need their own special credit scoring based on industry type model and time but two other groups don’t need of course in the studies dataset duration. Finally, the study concluded by introducing the credit scoring strategies for different four-cycle of economies

SCImago Journal Rank (SJR) is one indicator of a journal's reputation.  The value is calculated based on several published journals, such as scholarly journals' scientific impact, representing the number of quotes sent to a journal and the relevance or reputation of journals from which the quotations originate. A high SJR value means that the corresponding journal has a high reputation. This study aims to approach the SJR classification by implementing a machine learning approach. A simple yet powerful method Naïve Bayes Classifier (NBC), is selected. NBC utilizes probability calculations based on Bayes' theorem. However, NBC has an assumption that the attribute values do not depend on each other. This method is optimized using Particle Swarm Optimization (PSO) to overcome this weakness. This study used SJR data of the computer science domain from 2014 to 2017. Publication without Q rank is filtered for better performance. As a result, the accuracy of the proposed method is higher than the baseline. The use of PSO  significantly improves the NBC performance based on the performed T-test. The PSO-NBC selects four of eight features: H index, Cites/ Doc (2 Years), and Ref. / Doc. Overall results show that using PSO-NBC is closer to SJR rather than using mere NBC.