Purpose: The purpose of the present research was to determine the effect of social-emotional skills training on motivational beliefs and SELF-REGULATED LEARNING strategies in students with specific LEARNING disorder. Method: This study is a quasi-experimental with pre-test/post-test and a control group. The sample of consisted of 26 elementary school students with specific LEARNING disorder in Rasht city in the school year 2016-2017 that were selected by available sampling method and assigned to two experimental and control groups. To collect the data, Motivational Beliefs and SELF-REGULATED LEARNING Strategies Questionnaire were used before and after training. The 12 sessions of social-emotional skills training were implemented for experimental group. Results: The results of multivariate analysis of covariance showed that this training were significantly improve motivational beliefs and SELF-REGULATED LEARNING strategies in students with specific LEARNING disorder (P<0. 001). Conclusion: According to the findings of the research, social-emotional skills training as a technique can be effective in improving metacognitive beliefs and motivations of these children.

To investigate the predictive power of language LEARNING strategy types on meta-cognitive and motivational SELF-REGULATED LEARNING components (task value, control of LEARNING beliefs, and test anxiety), 149 male and female BA level students, majoring in English translation and English language teaching were selected to participate in this study. To collect data, a Michigan Test of English Language Proficiency (MTELP) was given to homogenize the students. In addition, the participants were also asked to answer the Strategy Inventory for Language LEARNING (SILL) and Motivated Strategies for LEARNING Questionnaire (MSLQ). Four separate stepwise multiple regression procedures were used to analyze the obtained data. The results showed that cognitive, meta-cognitive and compensation strategies had predictive power for meta-cognitive SELF-REGULATED LEARNING. Moreover, meta-cognitive, memory, and affective strategies were predictors of task value. The findings also showed significant relationships between meta-cognitive strategies and control of LEARNING beliefs. In addition, the only negative predictor of test anxiety were communication strategies.

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.