Table 4.

Performance analysis of test data using proposed technique (Technique 4).

	Base Models	Meta-learner	Accuracy	Precision	Recall	Specificity	F1-score	Time (s)
Feature Extraction	LR	Random Forest	0.9844	0.98	0.98	0.97	0.98	0.09
with CNN	SVM	XGBoost	0.9989	0.99	1.00	1.00	0.99	0.05
& Classification	DT	AdaBoost	0.9959	0.99	0.98	0.99	0.99	1.07
with Stacking	KNN	GradBoost	0.9922	0.99	1.00	0.99	0.99	0.09
Ensemble Model	NB	CATBoost	0.9958	1.0	0.99	0.99	1.0	1.12