## 3 Performance Validation  
The proposed DLSODC-GWM technique is simulated using the Python 3.6.5 tool. The proposed model is tested using a PC MSI Z370-A Pro with an i5-8600k processor, GeForce GTX 1050 Ti 4 GB, 16 GB of RAM, a 250 GB SSD, and a 1 TB HDD. For experimental validation, a ten-fold cross-validation process is employed. The parameter setting is given as follows: batch size: 128, learning rate: 0.001, momentum: 0.2, and optimizer: AOA. The experimental result analysis of the DLSODC-GWM technique is examined using the benchmark garbage classification dataset from the Kaggle repository [21]. The dataset contains 393 images under the cardboard class, 491 images under the glass class, 400 images under the metal class, 584 images under the paper class, 472 images under the plastic class, and 127 images under the trash class. A few sample images are illustrated in Figure 2.  
Figure 2: Sample images: (**a**) cardboard, (**b**) glass, (**c**) metal, and (**d**) paper. The heat map analysis of the objects that exist in the dataset is shown in Figure 3. Figure 4 visualizes the sample object detection outcomes of the DLSODC-GWM technique on the test images applied. From the figure, it is obvious that the DLSODC-GWM technique has identified glass, metal, and trash objects with the maximum \(acc_{y}\) of 99%. The confusion matrix generated by the DLSODC-GWM technique on the classification of waste under 1000 epochs is in Figure 5. The figure reported that the DLSODC-GWM technique has identified 372 images into cardboard, 472 images into glass, 380 images into metal, 570 images into paper, 463 images into plastic, and 107 images into trash. Figure 3: Heat map analysis. (**a**) Cardboard, (**b**–**d**) glass. Figure 4: Visualization of sample object detection. (**a**,**b**) Glass, (**c**,**d**) metal, and (**e**,**f**) trash. The classifier results of the DLSODC-GWM technique on the classification of waste objects with 1000 epochs are given in Table 1 and Figure 6. The table values pointed out that the DLSODC-GWM technique has effectually recognized all the class labels. For instance, the DLSODC-GWM technique has categorized into images into cardboard with \(prec_{n}\), \(reca_{l}\), \(accu_{y}\), and \(F_{score}\) of 95.88%, 94.66%, 98.50%, and 95.26%, respectively. On the other hand, the DLSODC-GWM technique has recognized images into paper with \(prec_{n}\), \(reca_{l}\), \(accu_{y}\), and \(F_{score}\) of 96.77%, 97.60%, 98.66%, and 97.19%, respectively. \begin{table}
\begin{tabular}{l l l l l} \hline
**Epoch-1000** & & & & \\ \hline
**Methods** & **Precision** & **Recall** & **Accuracy** & **F-Score** \\ \hline Cardboard & 95.88 & 94.66 & 98.50 & 95.26 \\ \hline Glass & 95.93 & 96.13 & 98.42 & 96.03 \\ \hline Metal & 95.48 & 95.00 & 98.46 & 95.24 \\ \hline Paper & 96.77 & 97.60 & 98.66 & 97.19 \\ \hline Plastic & 95.86 & 98.09 & 98.82 & 96.96 \\ \hline Trash & 91.45 & 84.25 & 98.78 & 87.70 \\ \hline Average & 95.23 & 94.29 & 98.61 & 94.73 \\ \hline \end{tabular}
\end{table} 
Table 1: Result analysis of DLSODC-GWM technique with different methods under 1000 epochs. Figure 5: Confusion matrix of DLSODC-GWM technique under 1000 epochs. The accuracy outcome analysis of the DLSODC-GWM technique on the test data is portrayed in Figure 7. The results demonstrated that the DLSODC-GWM technique has accomplished improved validation accuracy compared to training accuracy. It is also observable that the accuracy values become saturated with the epoch count of 1000. Figure 6: Result analysis of DLSODC-GWM technique under 1000 epochs. Figure 7: Accuracy graph analysis of DLSODC-GWM technique under 1000 epochs. The loss outcome analysis of the DLSODC-GWM technique on the test data is depicted in Figure 8. The figure reveals that the DLSODC-GWM technique has denoted the reduced validation loss over the training loss. It is additionally noticed that the loss values become saturated with the epoch count of 1000.