درخت حوزه‌های تخصصی

Research shows that narcissistic traits such as self-conceit and entitlement are associated with delinquency and aggression in childhood and adolescence. Therefore, the purpose of the present study was to investigate the relationship between narcissism and tendency to abnormal behavior and assertiveness with the mediating role of mindfulness among active students. The present study was a cross-sectional study of correlation with structural equation modeling. The statistical population was all male students in the second year of high schools in Torbat Heydarieh city, Northeastern Iran, in the academic year 2022-2023. According to the Morgan table, the size of the statistical sample was estimated to be 200 active male students who were selected stratified random sampling method. The data collection tools included Baer et al.'s Five Facet Mindfulness Questionnaire (FFMQ) (2006), Narcissistic Personality Inventory (NPI-40) from Raskin and Terry (1988), Rostami and Rad's Abnormal Behavior Tendency Questionnaire (2012), and Kotler and Giura's Assertiveness Questionnaire. Data analysis was done using structural equations with PLS version 3 software. The findings showed that there was a positive and significant relationship between narcissism and tendency to abnormal behavior, and there was no significant relationship between assertiveness and tendency to abnormal behavior. The mediating role of mindfulness on the effect of assertiveness and the tendency to abnormal behavior was confirmed, but the mediating role of mindfulness on the effect of narcissism on the tendency to abnormal behavior was not confirmed. In order to reduce the tendency toward abnormal behavior in active students, it is recommended to strengthen assertiveness by mindfulness training, as well as designing educational and behavioral therapy programs to control narcissism.

Today, users generate various data increasingly using the Internet when choosing a product or service. This leads to the generation of data about the purchases and services of various consumers. In addition, consumers often leave feedback about the purchase. At the same time, consumers discuss their attitudes about goods and services on social networks, messengers, thematic sites, etc. This leads to the emergence of large volumes of data that contain useful information about various manufacturers of goods and services. Such information can be useful to both ordinary users and large companies. However, it is practically impossible to use this information due to the fact that it is located in different places, that is, it has a raw, unstructured character. At the same time, depending on the target group of users, not the entire data set is needed, but a specific target sample. To solve this problem, it is necessary to have a tool for structuring information arrays and their further analysis depending on the set goal. This can be done with the help of various frameworks that use methods of machine learning and work with data. This work is devoted to elucidating the problem of creating means for evaluating consumer preferences based on the analysis of large volumes of data for its further use by the target audience.  The goal of the development of big data analysis systems is obtaining new, previously unknown information. The methodology of application of algorithms of work with large data sets and methods of machine learning is used, namely the pandas library for operations on a data set and logistic regression for information classification As a result, a system was built that allows the analysis of lexical information, translate it into numerical format and create on this basis the necessary statistical samples. The originality of the work lies in the use of specialized libraries of data processing and machine learning to create data analysis systems. The practical value of the work lies in the possibility of creating data analysis systems built using specialized machine learning libraries.