Fig. 2
From: Deep learning optimization of teaching schedules in sports dance education
Overall proposed architecture. The figure illustrates the deep learning-powered scheduling framework, which integrates data collection, preprocessing, model training, conflict resolution, reinforcement learning adaptation, and final optimized scheduling. The proposed system ensures workload balancing, real-time schedule learning, and improved scheduling efficiency using AI-driven optimizations.
