## 4 Experimental Results
### Ablation Analysis of the Log-DRSN  
Ablation analysis is used to determine which module is more important and effective for the performance of the proposed model. Log-DRSN has three basic modules. These are the residual shrinkage network (RSN) module, the attention (Attn.) module, and the soft thresholding (ST) module. In the proposed model, the RSN module consists of three blocks. For this reason, the units were gradually deactivated while examining the RSN module in \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Classical DRSN (\%)** & **Log-DRSN (\%)** \\ \hline CIFAR-10 & 68.30 & 72.41 \\ Mini-ImageNet & 80.67 & 86.21 \\ MNIST & 96.05 & 97.20 \\ Dataset\_Fabric & **96.86** & **99.17** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of classification accuracy between classical DRSN and Log-DRSN on multiple datasets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Accuracy** & **Precision** & **Recall** & **AUC** \\ \hline ResNet50 & 0.55 & 0.67 & 0.38 & 0.80 \\ MobileNet & 0.59 & 0.59 & 0.59 & 0.78 \\ InceptionV3 & 0.67 & 0.67 & 0.65 & 0.85 \\ VGG19 & 0.71 & 0.72 & 0.64 & 0.92 \\ VGG16 & 0.72 & 0.84 & 0.63 & 0.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Transfer learning comparisons on our dataset. ablation analysis. Although the contents of these units are similar, the number of filters is different, which changes the effectiveness of the units in the model. RSN unit-1, unit-2, and unit-3 filter numbers are 32, 64, and 128, respectively. The Attention module, which creates a feature map, increases the model's performance, and makes it more consistent. This module trains the model faster and more consistently. In addition, it accelerates model training and reduces the risk of overfitting. It contributes to obtaining better results as it ensures that the model is flexible and adaptive. The purpose of the soft thresholding module is to clean noisy data. It helps filter out unwanted noise, especially in data with a low signal-to-noise ratio. It contributes to increasing the model's performance and plays an important role in training noisy image data. The RSN module is essential in training deep networks. This module not only simplifies the training process, but it also reduces overfitting. The network's memory requirements are reduced when the Shrinkage module diminishes the size of the features that the network learns. Based on this data and Table 8, the outcomes that follow can be reached:  
* The attention module is the most essential component of the designed model because it extracts features. The factor that significantly impacted network performance was the removal of this module from the model. Similarly, it is the module that minimizes the model's computation time most dramatically. The computation time was 365.0 s, and the accuracy rate was 26.48%. The removal of the attention module led to a dramatic 72-73% drop in accuracy compared to the baseline (over 96% range), underscoring its critical role in feature representation. The significant reduction in computation time (approximately 48% less than the full model's \(\sim\)705 s) also indicates this module's computational cost, which is justified by its contribution to accuracy. * When the first unit of the RSN module was omitted from the model, the accuracy of the model decreased slightly and the computation time slightly shortened as well. The computation time was 626.0 s, and the accuracy rate was 99.03%. The impact of removing RSN unit-1 was minimal, with an accuracy drop of only \(\sim\)0.72%, suggesting that this unit contributes modestly to model robustness. The 11% decrease in computation time shows that this unit is somewhat costly computationally but not essential for peak accuracy. * Once the second unit of the RSN module was eliminated from the model, the model's accuracy rate declined more than RSN unit-1 and the calculation time was reduced even further. The calculation time was 605.0 s, and the accuracy rate was 98.29%.