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Item Unknown Road traffic sign recognition using computer vision(Faculty of Engineering and Natural Science, 2024) Alsiyeu U.Road traffic accidents are a major public problem in Kazakhstan, with driver inattention and ignorance of traffic signs among the leading causes. Current driver assistance systems integrated in map apps may be inaccurate and irrelevant, especially in rural areas and on highways. The solution proposed by the research includes a computer vision algorithm for accurate and robust detection and recognition of road traffic signs in real time which will be integrated into a mobile application with a notification system. The algorithm will use deep learning neural networks to detect and recognize traffic signs in real time. The algorithm will be trained on a dataset, which will be collected manually and augmented using machine learning techniques. The proposed system has the potential to improve road safety in Kazakhstan by helping drivers to be more aware of traffic signs and to reduce driver inattention.Item Open Access Detecting social conflicts in kindergartens using deep learning and computer vision(SDU University, 2025) Kengesbay D.Early conflict detection in kindergartens plays a significant role in ensuring a harmonious learning atmosphere and in promoting the social growth of young children. While most previous works have only addressed conflict detection through adults, in this paper, we specifically address conflict detection in kindergartens using deep learning, utilizing both spatial and temporal information to improve performance. The application of deep learning and computer vision in automatically detecting and analyzing early conflicts among young children is discussed in this paper. Using video footage, we leverage state-of-theart RNNs and 3D CNNs for high-accuracy detection of conflict instances. Crucial visual cues—facial expressions, gestures, poses, vocal tone, and movement—are examined for the extraction of tension or aggression signs. The model is evaluated on real kindergarten video data, with promising conflict detection and classification results. The findings indicate the potential of AI-supported tools in assisting teachers in class management, child behavior monitoring, early intervention mechanisms, and the fostering of a good social environment