## 4 Results and Discussions
K-fold cross-approval is a technique that endeavors to expand the utilization of accessible information for training and afterward testing a model. It is especially valuable for surveying model execution, as it gives a scope of accuracy scores across (to some degree) different datasets. K-fold CV is a method where a given informational index is part of a K number of segments/folds, where each fold is eventually utilized as a testing set. Let us consider the situation of fivefold cross-validation (K = 5). This cycle is repeated until each of the five folds has been utilized as the testing set. The validation accuracy using fivefold validation means that it makes five folds of all the input data from which the randomized instances are selected in each fold. Afterwards, one fold is trained and tested on the four other folds, where results are saved as one-fold predictions. Similarly, two folds selected for training and testing are performed on three folds, and the results of the predictions are saved. At last, when all folds' training and testing is performed, the mean of all predicting results is taken and shown as fivefold prediction results. By doing these cross-fold validation methods, the biases in predicting and training decreases almost to zero. Therefore, the proposed study uses these five- and tenfold methods to make the proposed results more promising. In Table 6, we can see almost all methods perform more than 99% of the results, except for the KNN-coarse, KNN-medium, and subspace-discriminant methods. The other results of these methods in terms of sensitivity, specificity, precision, F1-\(score\), and the statistical method of evaluation (kappa-cohen) index are used. Generally, the validation accuracy is the ratio between the summation of true positives, true negatives with the summation of true positives, true negatives, false positives, and false negatives. This tells us how many true results for positive and negative classes are found. The evaluation measures operational calculations are shown in Equations (14)-(19) [70]. \[Accuracy=\frac{(TP+TN)}{(TP+FN)+(FP+TN)} \tag{14}\] \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Accuracy (\%)** & **Sensitivity (\%)** & **Specificity (\%)** & **F1-Score (\%)** & **Precision (\%)** & **Kappa (\%)** \\ \hline Bag-Ensemble & 99.33 & 99.33 & 99.95 & 99.33 & 99.34 & 96.64 \\ \hline KNN-coarse & 96.17 & 96.17 & 99.73 & 96.17 & 96.23 & 69.20 \\ \hline KNN-fine & 99.40 & 99.40 & 99.96 & 99.40 & 99.41 & 95.18 \\ \hline KNN-medium & 96.30 & 96.30 & 99.74 & 96.30 & 96.34 & 70.27 \\ \hline LP-Boost-Ensemble & 99.1 & 99.10 & 99.94 & 99.10 & 99.12 & 92.77 \\ \hline Subspace-discriminant & 93.40 & 93.40 & 99.53 & 93.44 & 93.55 & 46.96 \\ \hline Total-boost-Ensemble & 99.17 & 99.17 & 99.94 & 99.17 & 99.18 & 93.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification validation accuracy of fivefold validation using self-activated features. In the proposed study, there are 15 classes, so it is not about two classes. It considers the multi-class problem validation accuracy. \[Sensitivity=\ \frac{(TP)}{(TP+FN)} \tag{15}\]  
The other measure called recall or sensitivity is used to measure true positivity predictions among actual true positives. \[Specificity=\ \frac{(TN)}{(FP+TN)} \tag{16}\]  
The specificity measure is used to measure the true negatives among actual negatives. The precision is similar to sensitivity, which is used to measure over the summation of true negatives and false negatives; precision is used to measure the summation of true positives and false positives. However, the proposed studies used both to cross check the true positive predictions. \[Precision=\frac{(TP)}{(FP+TN)} \tag{17}\]  
The F1-_score_ is used to measure the ratio of the product of precision and recall with the summation of precision and recall. Sometimes, the true positivity and true negativity predictions may lead to incorrect perceptions due to data imbalancing.