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IEEE/ICACT20260314 Slide.09        [Big Slide]       [YouTube]	Oral Presentation
	All models are implemented using the PyTorch deep learning framework. We use the AdamW optimizer with cosine learning rate scheduling and early stopping based on validation accuracy. Gaussian noise injection is applied as data augmentation to improve robustness. Model performance is evaluated using accuracy, macro F1-score, inference time, and the number of trainable parameters.

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