Dana Tyulemissova

Educational early-warning systems rank students for review when support capacity is limited. We examined how removing background information changes retrospective review-list performance in the Open University Learning Analytics Dataset (OULAD) and two separately analyzed UCI education datasets. Full-information models were compared with reduced-input models across several educational stages and four review capacities (5%, 10%, 20%, and 30%). The primary outcome was the difference in review-list precision, with paired bootstrap intervals adjusted across stages and capacities within each policy family. Sex/gender removal showed no statistically detectable difference after this adjustment; this does not establish equivalence. Removing socioeconomic/family information reduced whole-cohort predictive performance in OULAD and UCI 697 and reduced precision in selected larger OULAD lists and at UCI 697 enrollment. UCI 320 estimates were inconclusive. Some adjusted precision findings varied across calibration methods. Diagnostic models showed that excluded attributes remained partly predictable from retained records. These post-development analyses evaluate predictive performance, not causal effects on educator workload, interventions, or student outcomes. The findings support evaluating data minimization at the intended educational stage and review capacity, while separately assessing whole-cohort performance and list membership. Prospective evaluation with educators and students is needed before drawing conclusions about educational benefit.

In the context of the active and uncontrolled dissemination of information in the digital environment, especially on social media and news aggregators, the task of automatically identifying fake news has become especially relevant. The growing volume of user-generated content and the high speed of its distribution significantly complicate manual information verification, necessitating the use of machine learning methods. The aim of this study is to analyze, comparatively evaluate, and optimize baseline machine learning models for fake news detection, focusing on hyperparameter selection, computational efficiency, and interpretation of classification results. This study utilizes the ISOT Fake News Dataset, a text dataset containing Englishlanguage news items with binary labels labeled «true» and «false», cleaned and vectorized using the TF-IDF method. The study implemented and analyzed logistic regression, decision tree, random forest, and gradient boosting models. Classification quality was assessed using the following metrics: Accuracy, Precision, Recall, F1-score, as well as ROC analysis and the area under the curve (AUC). It was shown that the gradient boosting model provides the highest classification accuracy, while logistic regression demonstrates comparable quality with significantly lower computational costs and training time. Additionally, the study included data leakage monitoring and an analysis of the causes of inflated quality metrics, ensuring the correct interpretation of the results. The findings confirm that optimized classical machine learning models are capable of providing highquality fake news detection and can be considered a resource-efficient alternative to more complex neural network approaches in applied cybersecurity and information flow monitoring.

In the context of the rapid growth of security threats to mobile devices, information protection becomes a critically important task. The article is an overview of modern advanced technologies used for the development of secure mobile communication devices and crypto-smartphones. A bibliometric analysis was conducted using the Bibliometrix tool. The main principles for organizing secure communication have been classified. Innovative approaches are being considered to ensure data integrity and confidentiality. The latest achievements in the field of mobile communication security are also being analyzed. During the research, an informational and patent search for inventions in the field of mobile communication protection was conducted, and a comparative analysis of the advantages and limitations was carried out. The analysis conducted showed that some decisions create a false sense of security, as they propose the adoption of unconventional methods that do not align with well-established and widely accepted safety recommendations. The article describes the main crypto smartphones, each possessing unique characteristics and designed for specific user needs based on their functional capabilities. The provided examples of successful implementations and research demonstrate the significance of innovative solutions for enhancing the security of mobile communications and protecting personal data in the context of the modern digital world.