## 8 Experimental Studies  
The computer used in the experimental studies has a 4 GB graphics card, i7 intel 5500U processor (Intel, Santa Clara, CA, USA), and 16 GB RAM, and the MATLAB 2021a program was installed on Windows 11 and used to code the suggested technique. Training the suggested Parallel Attention-Residual CNN (PARC) model was carried out for multi-class and binary classification at the first step of the suggested methodology. The epoch value was chosen as 100 to achieve maximum performance. This ensured sufficient training iterations. The mini-batch size was chosen as 32. This was the maximum allowed by the hardware for the PARC model used. The initial learning rate was chosen as 0.001. For smaller values, the training time increased and for larger values, the classification performance decreased. The SGDM technique was used as the optimization solver. The  
The accuracy scores according to feature selection cases and different classifiers are given in Table 1 for two classification problems. DT, SVM, KNN, NB, LD, Subspace KNN (SK), Subspace Discriminate (SD), and RUSBoosted Trees (RT) were classifier algorithms in the Classification Learner (CL) tool in the Matlab program. The default hyperparameters were selected in the CL tool for the classification process. This ablation study was performed to evaluate the feature selection operation's effectiveness and detect which classifier algorithm gave the best accuracy. As seen in Table 1, the SVM with the Gaussian kernel provided the best accuracy for two classification problems. In the case without feature selection, for binary and multi-class classification problems, the accuracy scores were 95.4% and 94.6%, respectively. In the case of the INCA feature selection, for binary and multi-class classification problems, the accuracy scores were 98.3% and 97.5%, respectively. Subspace Discriminant and RUSBoosted Trees provided the worst accuracies for two classification problems. In multi-class classification and binary classification without feature selection, the worst performance of the accuracy rate was 88.7% (Subspace Discriminant) and 85.6% (RUSBoosted Trees), respectively. In multi-class classification and binary classification with the INCA feature selection, the worst performances of the accuracy rate were 90.1% (Subspace Discriminant) and 88.5% (RUSBoosted Trees), respectively. Figure 12 shows the confusion matrices of the proposed approach for the binary and multi-class classification tasks. In Figure 12a, the classes named 1, 2, 3, 4, and 5 represent the pitted surface, crazing, scratches, patches, and multi-class defect classes, respectively. In Figure 12b, the classes named 1, 2, 3, 4, and 5 represent the defect and non-defect classes, respectively. \begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{2}{c}{**No Feature Selection**} & \multicolumn{2}{c}{**INCA Feature Selection**} \\ \cline{2-5}  & **Binary** & **Multi–Class** & **Binary** & **Multi–Class** \\ \hline DT & 90.2 & 88.7 & 91.3 & 90 \\ SVM & 95.4 & 94.6 & 98.3 & 97.5 \\ KNN & 92.5 & 93.1 & 95.1 & 94.9 \\ NB & 93.2 & 87.9 & 95.1 & 90 \\ LD & 92.8 & 93.5 & 94.1 & 95,2 \\ SK & 90.1 & 92.8 & 92.3 & 95.6 \\ SD & 88.7 & 93.6 & 90.1 & 95.4 \\ RT & 89.3 & 85.6 & 91.8 & 88.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy variations according to feature selection cases and different classifiers. Figure 11: Three–dimensional feature representation for no feature selection and INCA feature selection cases (multi–class classification). respectively. For the binary and multi-class classification problems, the accuracy scores were 98.3% and 97.5%, respectively. Performance metrics, including the sensitivity (SN), specificity (SP), precision (PR), and F-score, were computed by using true positive (TP), true negative (TN), false positive (FP), and false negative (FN) values.