**10. Performance evolution**

We will utilize Classification Accuracy, Confusion Matrix, and ROC curve to assess the effectiveness of our systems.

Classification Precision It is a model classification metric in which the number of right predictions is compared to the total number of predictions produced by each model. Matrix of Confusion is one of the evaluation techniques that use the model's output and four categories: True positives are values that are supposed to be positive, whereas false positives are values that are expected to be positive but turned out negatively, making them fake. True negative values are those that are predicted to be negative and hence are true. False negative denotes Values that are predicted to be negative but are positive, hence they are false [13, 14].
