Жарияланымдар / Публикации
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- Некоторые метрики заблокированы вашиминастройками согласия
Тип записи:Публикация, Offensive Language Detection on Social Media using Machine Learning(Science and Information Organization, 2024-01-01); ; ; Elmira AbdrashovaThis research paper addresses the critical issue of cyberbullying detection within the realm of social networks, employing a comprehensive examination of various machine learning and deep learning techniques. The study investigates the performance of these methodologies through rigorous evaluation using standard metrics, including Accuracy, Precision, Recall, F-measure, and AUC-ROC. The findings highlight the notable efficacy of deep learning models, particularly the Bidirectional Long Short-Term Memory (BiLSTM) architecture, in consistently outperforming alternative methods across diverse classification tasks. Confusion matrices and graphical representations further elucidate model performance, emphasizing the BiLSTM-based model's remarkable capacity to discern and classify cyberbullying instances accurately. These results underscore the significance of advanced neural network structures in capturing the complexities of online hate speech and offensive content. This research contributes valuable insights toward fostering safer and more inclusive online communities by facilitating early identification and mitigation of cyberbullying. Future investigations may explore hybrid approaches, additional feature integration, or real-time detection systems to further refine and advance the state-of-the-art in addressing this critical societal concern.Цитирования (OpenAlex) 11Цитирования (OpenCitations) 7 - Некоторые метрики заблокированы вашиминастройками согласия
Тип записи:Публикация, A Multi-Modal Deep Learning Framework for Student Soft Skills Development in Adaptive Learning Environments(Science and Information Organization, 2026-01-01); ; Nuraim IbragimovaThe rapid evolution of artificial intelligence and adaptive educational technologies has created increasing demand for intelligent systems capable of automatically assessing and enhancing student soft skills within digital learning environments. This study proposes MST-SoftNet, a multimodal transformer-based deep learning framework designed for adaptive soft skill assessment and personalized educational recommendation. The proposed architecture integrates heterogeneous educational modalities, including textual interactions, speech signals, facial expressions, behavioral engagement patterns, performance indicators, and feedback information, within a unified hierarchical transformer fusion framework. Modality-specific encoders, cross-modal attention mechanisms, explainable attention visualization modules, and adaptive recommendation components were incorporated to improve both predictive performance and interpretability. Experimental evaluation was conducted using multiple baseline deep learning architectures, including CNN, LSTM, Transformer, and multimodal CNN-LSTM models. The proposed MST-SoftNet framework achieved superior performance across all evaluation metrics, attaining 93.67% accuracy, 92.11% F1-score, and 96.05% AUC, while simultaneously demonstrating reduced inference latency and improved computational efficiency. Attention visualization analysis further confirmed the capability of the framework to identify semantically meaningful multimodal behavioral patterns associated with communication, collaboration, leadership, creativity, emotional intelligence, and self-regulation competencies. Longitudinal adaptive learning experiments additionally demonstrated substantial improvement in student soft skill progression over time. The obtained results indicate that MST-SoftNet establishes a robust, interpretable, and scalable foundation for next-generation intelligent educational systems focused on personalized soft skill development and adaptive learning optimization. - Некоторые метрики заблокированы вашиминастройками согласия
Тип записи:Публикация, Gamifying Lower-Secondary School Computing: A Quasi-Experimental Study with Implications for Open and Distance e-Learning(Commonwealth of Learning, 2026-09-21); ;