Validation of the classifications was performed on the dataset for the test. For this, statistical metrics were used to evaluate and test the performance of the adjusted CNN. The metrics are specified in Table 3, of which are calculated according to the results of the classifications. The Kappa index is a measure of agreement used in nominal scales that gives us an idea of how far the observations deviate from those expected, at random, thus indicating to us how legitimate the interpretations are. It measures the percentage of the **data** values on the main diagonal of the table and then adjusts these values for the amount of agreement that might be expected [50]. Accuracy is an index that reflects the rate at which individuals are correctly classified into the category containing their true score. Ranking accuracy is usually attributed to the appropriateness and validity of your decisions based on the obtained score. A large value for the index indicates a high hit rate of individuals in the correct categories, and a low value indicates a lower rate of correct classification of individuals [51]. Adjusted F-score AGF =
5 × PPV × TPR (4 × PPV)+ TPR × (1 + 0.5 2 ) × NPV × TNR (0.5 2 × NPV)+ TNR
Use all confusion matrix elements and provide more weights to samples that are correctly classified in the lowest class.