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IEEE/ICACT20250142 Slide.11        [Big Slide]	Oral Presentation
	Figure presents the confusion matrix. The model appropriately found 119 spam messages and 882 valid (ham) messages in this matrix. Still, there were 26 false negatives—spam mails misclassified as legitimate—and 7 false positives—legitimate messages misclassified as spam. With appropriate performance for spam detection as well, the great number of accurate classifications—especially in the genuine class—indicate the model's robustness in spotting ham messages.

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