## 4 Results and Performance Discussion  
### Performance Analysis  
Figure 5 compares the F1 scores of the two feature vectors when they are used as inputs to the three ML classifiers (SVM, RF, and k-NN) of the validation set. The validation has been repeated five times to extract statistical parameters. In all cases, the proposed vector (V1) outperforms the combined approach (V2), achieving higher F1 scores with lower variance. Hence, it can be concluded that the larger number of pixel intensity features in the case of V2 (256) masks the unique features (16) that are used by V1, substantially reducing the performance of the ML classifiers. No significant difference can be observed between different ML algorithms. Note that a comparison between the training and validation sets indicates that the data has not been over-fitted. Table 3 summarizes the two vectors' performance using the other output metrics: accuracy, recall, and precision. As can be seen, V1 performs better across all categories. We note that V1 as a feature vector and RF as a classifier is the best combination for performance evaluation, achieving 99.6% accuracy. Table 4 compares the F1 scores obtained in this study and scores reported in the literature for thorough analysis. The obtained F1 scores of V1 are higher than the scores reported for the isolated in-depth training and transfer learning approaches [8; 14]. They are also higher than those obtained by the Kaze/VGG feature vector combined with an SVM classifier and spectral clustering algorithm [14; 21]. It is noticeable that our scores are similar to the best-reported scores, despite the relatively small dataset (2185 images) and without data augmentation. Moreover, the proposed method requires less computational time in terms of feature extraction and training time because the feature vector's size is curta  
Figure 5: Statistical boxplot of the F1 score for the three ML classifiers. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Feature Vectors** & \multicolumn{3}{c}{**Statistical Parameters (V1)**} & \multicolumn{3}{c}{**Pixel Intensity Histogram + Statistical Parameters (V2)**} \\ \hline
**ML Classifiers** & **SVM** & **RF** & **k-NN** & **SVM** & **RF** & **k-NN** \\ \hline F1 score (\%) & 94.3 & 98.3 & 97.1 & 80.9 & 83.9 & 82.1 \\ Accuracy (\%) & 96.7 & 99.2 & 98.4 & 76.6 & 83.1 & 83.9 \\ Recall (\%) & 93.8 & 97.9 & 96.3 & 79.9 & 81.9 & 80.6 \\ Precision (\%) & 92.8 & 99.2 & 97.9 & 82.9 & 86.2 & 85.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overall results of implemented feature vectors and the different ML classifiers. Table 4 also summarizes the reported accuracies. This study's obtained accuracy is similar to the highest reported accuracy that uses a two-image region/area detection algorithm for classification [22]. Despite the high overall precision, the two-region algorithm for EL cell images achieved a low F1 score (5.1%) and recall (27.4%) values, probably due to an unbalanced dataset. The output metric results can be improved by calculating the geometric mean for unbalanced class sizes. Moreover, this study's obtained accuracy is higher than recorded in Reference [15], which compares the supervised classifiers (SVM and RF) with CNN using the stochastic gradient descent method. They achieved the overall best accuracy, 98.77%, with the least computation time of (85.52 s) using the SVM classifier compared to 98.13% accuracy with (2250 s) of computation time using the CNN method. Moreover, Reference [19] recorded 98% accuracy computing Haralicks features as a feature vector for detecting different failures using an SVM classifier, as mentioned in Table 4.  
The weighted accuracy of detecting each fault class is presented in Figure 6, evaluating the performance for all the three individual classes independent of the number of observations considering a balanced dataset. Each class's accuracy is calculated to ensure that each label is correctly predicted and that no specific class dominates the overall accuracy.