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Moving to our performance results, these curves provide clear evidence of our model¡¯s learning efficiency. On the left, the Training and Validation Accuracy graph shows a rapid climb that stabilizes near 97%. Notice how the validation curve closely tracks the training curve. This is a critical indicator that our hybrid framework generalizes exceptionally well to unseen data and is not suffering from overfitting. On the right, the Loss Curves show a steady, synchronized decline. The smooth stabilization of both training and validation loss further validates our early stopping mechanism, proving that the model reaches its peak predictive power without unnecessary computational waste. Together, these results confirm that the combination of BiLSTM and hEVOSWO creates a highly stable and reliable detection system. |
IEEE/ICACT20260103 Slide.17
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