Automatic identification of cyberbullying is a problem that is gaining traction, especially in the Machine Learning areas. Not only is it complicated, but it has also become a pressing necessity, considering how social media has become an integral part of adolescents’ lives and how serious the impacts of cyberbullying and online harassment can be, particularly among teenagers. This paper contains a systematic literature review of modern strategies, machine learning methods, and technical means for detecting cyberbullying and the aggressive command of an individual in the information space of the Internet. We undertake an in-depth review of 13 papers from four scientific databases. The article provides an overview of scientific literature to analyze the problem of cyberbullying detection from the point of view of machine learning and natural language processing. In this review, we consider a cyberbullying detection framework on social media platforms, which includes data collection, data processing, feature selection, feature extraction, and the application of machine learning to classify whether texts contain cyberbullying or not. This article seeks to guide future research on this topic toward a more consistent perspective with the phenomenon’s description and depiction, allowing future solutions to be more practical and effective.

This study investigates the efficacy of AI-enabled digital stethoscopes in enhancing physical performance, increasing student engagement and motivation, and improving psychological well-being among physical culture students. The experimental design involved two groups of 40 students each: the experimental group used AI-enabled stethoscopes for real-time cardiovascular monitoring, while the control group relied on traditional heart rate monitoring methods. The results indicated significant improvements in physical performance, engagement, and psychological well-being for the experimental group. Real-time monitoring facilitated personalized training adjustments, optimizing training loads and preventing overexertion, leading to superior performance outcomes. Additionally, the use of innovative monitoring tools significantly increased student motivation and engagement in physical culture classes, as reflected in higher attendance rates and more enthusiastic participation. Psychological assessments revealed that continuous health monitoring reduced anxiety levels and enhanced overall mental well-being, providing students with a sense of security and proactive health management. These findings underscore the transformative potential of integrating advanced monitoring technologies into physical education and rehabilitation programs, offering precise, real-time data that supports individualized and responsive interventions. The study concludes with a call for further research to explore the long-term impacts and broader applications of AI-enabled health monitoring tools in diverse educational and clinical settings, aiming to maximize their benefits and improve overall student and patient outcomes. Keywords: Sports therapy, Physical culture education, Health monitoring technology, Personalized training, Student engagement, Real-time cardiovascular monitoring, AI-enabled stethoscope.

This research paper explores the implementation and efficacy of real-time electrocardiogram (ECG) monitoring systems for athletes, emphasizing their potential to significantly enhance safety and performance in sports settings. By utilizing advanced ECG technology, the study investigates how continuous, real-time heart rate and rhythm monitoring can aid in the immediate detection of cardiovascular anomalies during high-intensity activities. The research methodology incorporates the deployment of portable ECG devices in a controlled experimental setup, analyzing data from athletes during training sessions and competitive events. Results from the study highlight the system's ability to provide swift and accurate cardiac assessments, thereby enabling timely medical interventions. Moreover, the paper discusses the technical challenges associated with real-time ECG monitoring, such as signal interference and data accuracy, and addresses privacy and ethical considerations concerning the continuous collection of health data. The discussion extends to the implications of integrating such technology within sports medicine, suggesting that while the systems offer substantial benefits in monitoring and preventing cardiac issues, they also necessitate rigorous standards for data security and ethical oversight. The conclusion advocates for a balanced approach to the adoption of these technologies, proposing future research directions that focus on enhancing system reliability and integrating artificial intelligence to predict potential health risks proactively. This study contributes to the ongoing discourse in sports health technology by providing a comprehensive analysis of real-time ECG monitoring as a transformative tool for athlete healthcare management.

This research introduces a novel convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) hybrid network for the automatic classification of heart arrhythmias using 12-lead electrocardiograms (ECGs). By merging the spatial feature extraction capabilities of CNNs with the temporal precision of BiLSTM networks, our approach sets a new standard in cardiac diagnostics. The proposed model was tested against the comprehensive CPSC2018 dataset, demonstrating superior performance with an accuracy of 90.67%, precision of 93.27%, recall of 96.35%, and an F-score of 94.78%, surpassing existing state-of-the-art methods. These results underscore the effectiveness of integrating spatial and temporal data analysis, offering a robust and reliable tool for medical practitioners. This study represents a significant advancement in automated ECG analysis, paving the way for improved diagnosis and treatment of heart diseases, and contributing to enhanced patient outcomes in cardiac care.

This study presents a new intelligent system based on deep learning methods for the automatic detection of hate speech in the Kazakh language. Particular attention is paid to the specific nature of Kazakh as a resource-poor language, where the limited linguistic data poses significant challenges in building reliable models. A multilingual data corpus covering a wide range of speech contexts was generated and preprocessed using various online sources-social media, forums, and news portals. To improve efficiency and generalization performance, a hybrid architecture was proposed, including convolutional neural networks (CNNs), bidirectional long short-term memories (BiLSTMs), and Transformer attention mechanisms. An evaluation using precision, recall, F1-criterion, and accuracy metrics demonstrated the superiority of the proposed model over traditional machine learning algorithms. The results of the study make a significant contribution to the development of automatic content moderation systems and promote the creation of a safer, inclusive, and linguistically sensitive digital space for Kazakh-speaking users.

Correct segmentation of brain tumors using multimodal magnetic resonance imaging (MRI) is critical for quantitative tumor evaluation, therapy planning and disease monitoring. However, the variability of tumor size, shape, location and appearance among MRI modalities poses a challenge for automatic segmentation. Heterogeneous structure of brain tumors and uneven boundaries of different subregions of a tumor also often impede the effectiveness of traditional segmentation methods. Hence, there is a need for strong approaches that can model local spatial features and long-range contextual dependencies. In this paper, we propose a deformable attention-guided Transformer architecture for multimodal brain tumor segmentation. The framework uses complementary information from T1, T1c, T2 and FLAIR MRI modalities and fuses multi-scale feature extraction, adaptive query refinement, deformable attention and skip connections to improve heterogeneous tumor region representation. We performed experiments on the dataset from the brain tumor image segmentation benchmark (BRATS) multimodal. The model was assessed in terms of Dice coefficient, Precision, Recall and F1-score on Whole Tumor (WT), Tumor Core (TC) and Enhancing Tumor (ET) regions. The suggested model attained Dice scores of 95.8%, 90.7%, and 86.1% for WT, TC and ET, respectively, while the average Dice score was 90.9%. Ablation analysis further highlighted the contribution of deformable attention, query refinement, skip connections and multimodal feature integration to the segmentation performance. The model also showed a good trade-off between segmentation accuracy and computational efficiency, which suggests the possible use of this approach in automated MRI processing workflows. The experimental results illustrate the potential of the proposed framework for accurate and robust multimodal MRI based brain tumor segmentation.