## 3 Performance Validation
The confusion matrix generated by the DLSODC-GWM approach to the classification of waste under 2000 epochs in Figure 9. The figure described that the DLSODC-GWM methodology has identified 372 images into cardboard, 467 images into glass, 373 images into metal, 565 images into paper, 456 images into plastic, and 114 images into trash. Figure 8: Loss graph analysis of DLSODC-GWM technique under 1000 epochs. Figure 9: Confusion matrix of DLSODC-GWM technique under 2000 epochs. The classifier outcomes of the DLSODC-GWM algorithm on the classification of waste objects with 2000 epochs are provided in Table 2 and Figure 10. The table values pointed out that the DLSODC-GWM system has effectually recognized all the class labels. For instance, the DLSODC-GWM method has categorized images into cardboard with \(prec_{n}\), \(rec_{l}\), \(acc_{y}\), and \(F_{score}\) of 95.38%, 94.66%, 98.42%, and 95.02%, correspondingly. In addition, the DLSODC-GWM approach has recognized images into paper with \(prec_{n}\), \(rec_{l}\), \(acc_{y}\), and \(F_{score}\) of 95.28%, 96.75%, 98.09%, and 96.01%, correspondingly. The accuracy outcome analysis of the DLSODC-GWM technique on the test data is illustrated in Figure 5:0 The outcomes exhibited that the DLSODC-GWM approach has accomplished improved validation accuracy compared to training accuracy. It can also be observed that the accuracy values become saturated with the epoch count of 1000. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Epoch-2000** & & & & \\ \hline
**Methods** & **Precision** & **Recall** & **Accuracy** & **F-Score** \\ \hline Cardboard & 95.38 & 94.66 & 98.42 & 95.02 \\ \hline Glass & 95.89 & 95.11 & 98.22 & 95.50 \\ \hline Metal & 96.13 & 93.25 & 98.30 & 94.67 \\ \hline Paper & 95.28 & 96.75 & 98.09 & 96.01 \\ \hline Plastic & 97.44 & 96.61 & 98.87 & 97.02 \\ \hline Trash & 80.85 & 89.76 & 98.38 & 85.07 \\ \hline Average & 93.50 & 94.36 & 98.38 & 93.88 \\ \hline \end{tabular}
\end{table} 
Table 2: Result analysis of DLSODC-GWM technique with different methods under 2000 epochs. Figure 10: Result analysis of DLSODC-GWM technique under 2000 epochs. The loss outcome analysis of the DLSODC-GWM technique on the test data is outperformed in Figure 12. The figure shows that the DLSODC-GWM technique has denoted the lower validation loss over the training loss. It is additionally observed that the loss values become saturated with the epoch count of 1000. Figure 11: Accuracy graph analysis of DLSODC-GWM technique under 1000 epochs. Figure 12: Loss graph analysis of DLSODC-GWM technique under 1000 epochs. Table 3 and Figure 13 demonstrate the comparative \(accu_{y}\) analysis of the DLSODC-GWM technique with recent methods. The results show that the AlexNet model has resulted in lower performance with \(accu_{y}\) of 52.50%. In line with this, the VGG16 model has obtained a greatly improved \(accu_{y}\) of 73.10%; whereas, the ResNet50 model has accomplished and even greater increased \(accu_{y}\) of 74.70%. Though the MLH-CNN technique has resulted in a near-optimal \(accu_{y}\) of 92.60%, the presented DLSODC-GWM technique has accomplished superior performance with \(accu_{y}\) of 98.61%. Table 4 and Figure 14 showcase the comparative \(prec_{n}\), \(reca_{l}\), and \(F_{score}\) analysis of the DLSODC-GWM method with recent algorithms [22]. The outcomes demonstrated that the AlexNet approach has resulted in lower performance with \(prec_{n}\), \(reca_{l}\), and \(F_{score}\) of 42%, 50%, and 44%, correspondingly. Followed by, the VGG16 system has achieved a greatly improved \(prec_{n}\), \(reca_{l}\), and \(F_{score}\) of 69%, 68%, and 68%, respectively; whereas, the ResNet50 model has accomplished even increased \(prec_{n}\), \(reca_{l}\), and \(F_{score}\) of 72%, 72%, and 72%, respectively. \begin{table}
\begin{tabular}{l c} \hline \hline
**Methods** & **Accuracy (\%)** \\ \hline DLSODC-GWM & 98.61 \\ \hline MLH-CNN & 92.60 \\ \hline AlexNet & 52.50 \\ \hline ResNet50 & 74.70 \\ \hline VGG16 & 73.10 \\ \hline \hline \end{tabular}
\end{table}