## 4 Experimental Validation
Finally, the SIDTLD-AIC model has recognized class 20 samples with \(accu_{y}\), \(prec_{n}\), \(reca_{l}\), \(F_{score}\), and \(G_{mean}\) of 99.21%, 88.57%, 96.88%, 92.54%, and 98.10%, correspondingly. \begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{5}{c}{**Training Phase (70\%)**} \\ \hline
**Class Labels** & **Accuracy** & **Precision** & **Recall** & **F-Score** & **Geometric Mean** \\ \hline
0 & 99.73 & 98.48 & 95.59 & 97.01 & 97.73 \\ \hline
1 & 99.39 & 90.41 & 97.06 & 93.62 & 98.27 \\ \hline
2 & 99.52 & 96.88 & 92.54 & 94.66 & 96.13 \\ \hline
3 & 99.66 & 98.53 & 94.37 & 96.40 & 97.11 \\ \hline
4 & 99.86 & 100.00 \begin{table}
\begin{tabular}{c c c c c c c} \hline \multicolumn{5}{c}{**Testing (30\%)**} \\ \hline
**Class Labels** & **Accuracy** & **Precision** & **Recall** & **F-Score** & **Geometric Mean** \\ \hline
0 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
1 & 99.68 & 100.00 & 93.75 & 96.77 & 96.82 \\ \hline
2 & 99.52 & 91.67 & 100.00 & 95.65 & 99.75 \\ \hline
3 & 99.52 & 96.43 & 93.10 & 94.74 & 96.41 \\ \hline
4 & 99.68 & 100.00 & 93.55 & 96.67 & 96.72 \\ \hline
5 & 99.37 & 92.86 & 92.86 & 92.86 & 96.20 \\ \hline
6 & 99.84 & 96.30 & 100.00 & 98.11 & 99.92 \\ \hline
7 & 99.37 & 96.30 & 89.66 & 92.86 & 94.61 \\ \hline \end{tabular}
\end{table} 
Table 2: Result analysis of SIDTLD-AIC technique with various measures on 30% of UCM datasets as testing datasets. Figure 5: Confusion matrix of SIDTLD-AIC technique on 30% of UCM datasets as testing datasets. The training accuracy (TA) and validation accuracy (VA) attained by the SIDTLD-AIC model on UCM dataset is demonstrated in Figure 6. The experimental outcome implied that the SIDTLD-AIC model has gained maximum values of TA and VA. In specific, the VA seemed higher than TA. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Testing (30\%)**} \\ \hline
**Class Labels** & **Accuracy** & **Precision** & **Recall** & **F-Score** & **Geometric Mean** \\ \hline
8 & 99.52 & 95.65 & 91.67 & 93.62 & 95.66 \\ \hline
9 & 99.21 & 86.11 & 100.00 & 92.54 & 99.58 \\ \hline
10 & 99.68 & 96.67 & 96.67 & 96.67 & 98.24 \\ \hline
11 & 99.84 & 100.00 & 97.06 & 98.51 & 98.52 \\ \hline
12 & 99.05 & 85.19 & 92.00 & 88.46 & 95.60 \\ \hline
13 & 99.21 & 93.10 & 90.00 & 91.53 & 94.71 \\ \hline
14 & 99.21 & 96.77 & 88.24 & 92.31 & 93.85 \\ \hline
15 & 99.37 & 91.43 & 96.97 & 94.12 & 98.23 \\ \hline
16 & 99.52 & 100.00 & 90.00 & 94.74 & 94.87 \\ \hline
17 & 99.84 & 96.88 & 100.00 & 98.41 & 99.92 \\ \hline
18 & 99.68 & 100.00 & 92.86 & 96.30 & 96.36 \\ \hline
19 & 99.21 & 89.66 & 92.86 & 91.23 & 96.12 \\ \hline
20 & 99.21 & 88.57 & 96.88 & 92.54 & 98.10 \\ \hline
**Average** & **99.50** & **94.93** & **94.67** & **94.70** & **97.15** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 6: TA and VA analysis of SIDTLD-AIC technique on UCM dataset. The training loss (TL) and validation loss (VL) achieved by the SIDTLD-AIC model on UCM dataset are established in Figure 7. The experimental outcome inferred that the SIDTLD-AIC model has been able least values of TL and VL. In specific, the VL seemed that lower than TL. A brief precision-recall examination of the SIDTLD-AIC method on UCM dataset is portrayed in Figure 8. By observing the figure, it can be noticed that the SIDTLD-AIC method has been able maximal precision-recall performance under all classes. Figure 8: Precision-recall curve analysis of SIDTLD-AIC technique on UCM dataset. Figure 7: TL and VL analysis of SIDTLD-AIC technique on UCM dataset. A detailed ROC investigation of the SIDTLD-AIC approach to UCM dataset is represented in Figure 9. The results indicated that the SIDTLD-AIC model has exhibited its ability in categorizing different classes on the UCM dataset. Figure 10 depicts the average image classification results of the SIDTLD-AIC model on 70% of UCM datasets as training datasets and 30% of UCM datasets as testing datasets. The figure shows that the SIDTLD-AIC model has resulted in better classification results under both aspects.