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1. METHOD FOR DETERMINING EFFECTIVE TYPES OF NEURAL NETWORK MODELS FOR RECOGNITION OF CYBER ATTACKS BASED ON PROCEDURAL RULES
2. AI-driven framework for automated competency formalization: from professional standards to adaptive learning outcomes
3. Correction: AI-driven framework for automated competency formalization: from professional standards to adaptive learning outcomes (Frontiers in Computer Science, (2025), 7, (1710358), 10.3389/fcomp.2025.1710358)
4. Comparison of Recommendation Models Based on GraphSAGE, Heterogeneous Graph Transformer and Heterogeneous Graph Attention Network for Educational Recommendation in Heterogeneous Knowledge Graphs
5. Curriculum–Vacancy–Course Recommendation Model Based on Knowledge Graphs, Sentence Transformers, and Graph Neural Networks
6. Development of a Knowledge Graph-Based Model for Recommending MOOCs to Supplement University Educational Programs in Line With Employer Requirements
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