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It is demonstrated that integrating image augmentation and class weighting within the ResNet50-CBAM framework effectively mitigates class imbalance and improves the reliability of automated pneumonia classification.
It is demonstrated that semantic representations and controlled lexical variation can jointly enhance minority-class recognition in short informal Indonesian text and highlight the importance of aligning embedding strategies with sequence architectures.
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