## 4 Experimental Results
### Quantitative Evaluation  
The proposed IDOA-DBN model's effectiveness was validated utilizing dissimilar feature optimization techniques, such as Grasshopper Optimization Algorithm (GOA), genetic algorithm, Particle Swarm Optimization (PSO) algorithm, DOA, and IDOA, by means of precision, accuracy, F-score, and recall. In this study, the performance analysis was conducted for different patch configurations, such as Full Edge Division (FED), Homogeneous Grid Division (HGD), Small Edge Division (SED), Half Edge Division (HED), and  
Figure 5: Confusion matrix.  
Two-Band Division (TBD), respectively. By viewing Table 2, the IDOA with DBN classifier obtained efficient performance in tool wear classification compared to other existing optimizers, such as GOA, genetic algorithm, PSO, and DOA.  
The IDOA with DBN classifier achieved an F-score of 95.30%, recall of 94.53%, precision of 96.70%, and accuracy of 98.83% for FED patch configuration. Regarding HGD patch configuration, the IDOA with DBN classifier attained an F-score of 86.20%, recall of 87.90%, precision of 88.88%, and accuracy of 90.87%. For the SED, HED, and TBD patch configurations, the proposed IDOA with DBN achieved 93.20%, 90.45%, and 92.03% F-scores; 92.03%, 93.20%, and 94.57% recall values; 94.50%, 94.56%, and 95.55% precision values; and 96.78%, 95.60%, and 94.56% classification accuracy.  
AlexNet and ResNet models are capable of preserving the useful information in the network without overtrain or loss the features, while some of the recent CNN architecture pooling layers tend to lose useful features by overtraining the features. Furthermore, AlexNet and ResNet are suitable to handle large-scale data analysis. Therefore, in this study, these two models were preferred for the comparative analysis. In Table 3, it can be seen that the performance evaluation was conducted using various classification techniques, such as AlexNet, Autoencoder, ResNet-14, ResNet-18, and DBN, on five different patch configurations, namely FED, HGD, SED, HED, and TBD, by means of F-score, recall, precision, and accuracy. By viewing Table 3, the combination of DBN classifier with IDOA obtained maximum performance in tool wear classification as compared to other classifiers, such as AlexNet, Autoencoder, ResNet-14, and ResNet-18. Compared to other classification techniques, the DBN has two major advantages: reductions in both the over-smoothing problem and the training data fragmentation problem.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Patch Configuration** & **Optimizer** & **F-Score (\%)** & **Recall (\%)** & **Precision (\%)** & **Accuracy (\%)** \\ \hline \multirow{5}{*}{FED} & GOA & 80.90 & 78.90 & 80.87 & 79.08 \\  & Genetic & 82.34 & 86.30 & 85.60 & 83.49 \\  & PSO & 87.90 & 88.56 & 88.70 & 88.60 \\  & DOA & 90.80 & 92.30 & 93.94 & 94.59 \\  & IDOA & 95.30 & 94.53 & 96.70 & 98.83 \\ \hline \multirow{5}{*}{HGD} & GOA & 75.39 & 67.69 & 70.70 & 80.88 \\  & Genetic & 77.78 & 72.30 & 74.50 & 81.20 \\  & PSO & 85.49 & 75.60 & 78.90 & 85.60 \\  & DOA & 86.06 & 83.20 & 83.50 & 88.88 \\  & IDOA & 86.20 & 87.90 & 88.88 & 90.87 \\ \hline \multirow{5}{*}{SED} & GOA & 77.50 & 70.90 & 68.90 & 87.30 \\  & Genetic & 75.40 & 78.78 & 80.82 & 83.40 \\  & PSO & 85.06 & 83.40 & 84.50 & 86.67 \\  & DOA & 89.80 & 90.76 & 89.88 & 91.20 \\  & IDOA & 93.20 & 92.03 & 94.50 & 96.78 \\ \hline \multirow{5}{*}{HED} & GOA & 78.49 & 76.40 & 80 & 84.50 \\  & Genetic & 77 & 78.90 & 83.42 & 85.55 \\ \cline{1-1}  & PSO & 87.74 & 88.36 & 82.20 & 89.28 \\ \cline{1-1}  & DOA & 89.76 & 87.30 & 89.87 & 92.20 \\ \cline{1-1}  & IDOA & 90.45 & 93.20 & 94.56 & 95.60 \\ \hline \multirow{5}{*}{TBD} & GOA & 77.65 & 78.43 & 70.60 & 78.09 \\ \cline{1-1}  & Genetic & 78.70 & 82.49 & 74.05 & 80.93 \\ \cline{1-1}  & PSO & 84.50 & 85.60 & 85.60 & 86.70 \\ \cline{1-1}  & DOA & 90.10 & 89.40 & 92.30 & 91.29 \\ \cline{1-1}  & IDOA & 92.03 & 94.57 & 95.55 & 94.56 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results of various feature optimization techniques with the IDOA.