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.

Introduction: The recent emphasis on learning strategies and their effects on success has promoted researchers from different disciplines to carry out research in this regard. Methods: This paper reports the results of the study performed on 41 students, randomly selected out of 120 students, in an attempt to find out their learning style preferences. It has replicated the study presented by Davis et al. (1994) entitled helping Teachers and students Understand learning Style. To determine the students learning styles two inventories Barsch learning Inventory and Brain- dominance inventory, have been given to the students. The former is used to find out whther the students are left or right hemisphere dominant.Results: The learning inventory revealed that there are four types of students. But majority of them (43.2%0) were predominantly visual with equal with equal frequency in males and females: The scored achieved from the other inventory divide the learners into six groups, but majority oh them (40.5%) had a slight preference to the left. The "Whole brain" is the next dominat preference (21.6%) Regarding sex. Males and females showed equal tendency toward usind left to righit hemisphere except for "left dominant" in which males scored three times greater than females.Conclusion: Regarding students dominant hemisphere and VAT, one can suggest some implications for teachers to find ways to foster learning that would meet the Learners preferences.

The multiple criteria ABC inventory analysis method is a well-known inventory management method for inventory classification. In most ABC classification techniques, a completely compensatory approach is adopted to classification problems with criteria aggregation. Hence, attention should be paid to noncompensatory approaches with criteria aggregation. Our literature review revealed a lack of research on the non-compensatory approach to this problem. The ELECTRE TRI model is based on outranking relations and adopts a compensatory approach to calculations.However, this model has not been popular in determining preferences of decision makers (parameters) due to its complexity and costly nature. To solve the aforementioned problems, a method was proposed in this research which uses the particle swarm optimization algorithm and simultaneously learns all of the decision maker’s preferences from the data through an evolutionary process and uses it in inventory classification. Unlike the standard data mining models, which carry out nominal classifications, the proposed method offers ABC inventory classification. Results of testing the proposed method on inventory datasets revealed its potential to compete with other standard classification models.

Observing a model let learners to make a plan of action that can be used for learning motor skills. Moreover, self-talk is a conversation that performers use it either apparently or secretly in order to think about their performance and reinforce it. Therefore, the purpose of this study was to investigate the effect of observational learning, self-talk and combination of both on boy’ s performance. The statistical samples of the present study included 60 students (12. 25± 1. 24) with forth and fifth grade that were selected as available subjects. After volleyball AAHPERD pre-test, subjects divided into 6 groups: Instructional self-talk (STI), motivational self-talk (STM), observational learning (OB), combination of Instructional self-talk and learning observation (STI-OB), combination of motivational self-talk and observational learning (STM-OB) and control group (CO) and during one day, they were participated in specific intervention that planed for each group. On the first day of intervention, subjects participated in acquisition phase and acquisition test was taken, after 48 hours' retention and transfer test was Held. In each phase of study self-efficacy was examined. The results of two-way ANOVA revealed that there was a significant difference among all training groups with control group (p≤ 0. 05). These results support the use of cognitive factors, self-talk and observational learning and the combination of observational learning and Instructional self-talk as learning facilitator.

Distributed Denial of Service (DDoS) attacks are among the primary concerns in internet security today. Machine learning can be exploited to detect such attacks. In this paper, a multi-layer perceptron model is proposed and implemented using deep machine learning to distinguish between malicious and normal traffic based on their behavioral patterns. The proposed model is trained and tested using the CICDDoS2019 dataset. To remove irrelevant and redundant data from the dataset and increase learning accuracy, feature selection is used to select and extract the most effective features that allow us to detect these attacks. Moreover, we use the grid search algorithm to acquire optimum values of the model’s hyperparameters among the parameters’ space. In addition, the sensitivity of accuracy of the model to variations of an input parameter is analyzed. Finally, the effectiveness of the presented model is validated in comparison with some state-of-the-art works.