## 4 Experimental Evaluation  
The intention detection results of the CL-DLBIDC model were tested using the SNIPS dataset [25]. In this work, we considered 3500 samples with 7 class labels, as depicted in Table 1. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Label** & **Class** & **No. of Samples** \\ \hline
1 & CreativeWork & 500 \\
2 & PlayMusic & 500 \\
3 & RateBook & 500 \\
4 & ScreeningEvent & 500 \\
5 & GetWeather & 500 \\
6 & AddToPlaylist & 500 \\
7 & BookRestaurant & 500 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset details. Figure 2: Steps involved in MFO. Figure 3 depicts the classification outcomes of the CL-DLBIDC model in terms of the confusion matrix under distinct sizes of training (TR) data and testing (TS) data. The CL-DLBIDC model classified all of the distinct class labels of the intents under all sizes of TR and TS data. Table 2 exhibits a brief set of data regarding the intent-detection performance of the CL-DLBIDC model on 80% of the TR data and 20% of the TS data. These results depict that the CL-DLBIDC model has attained enhanced values under all classes. For instance, with Class 1, the CL-DLBIDC method has granted \(accu_{j}\), \(reca_{l}\), \(specy_{j}\), \(F_{score}\), the Matthews Correlation Coefficient (MCC), and \(G_{measure}\) of 99.04%, 97.48%, 99.29%, 96.63%, 96.07%, and 96.63%, respectively. Table 3 displays a detailed set of data on the intent-detection performance of the CL-DLBIDC method on 70% of the TR data and 30% of the TS data. The experimental values indicate that the proposed model has shown enhanced performance on the classification of distinct classes on 70:30% of the TR/TS data. Figure 3: Confusion matrices of CL-DLBIDC: (**a**) 80% of TR data, (**b**) 20% of TS data, (**c**) 70% of TR data, and (**d**) 30% of TS data. The training accuracy (TRA) and validation accuracy (VLA) obtained by the CL-DLBIDC methodology on the test dataset are shown in Figure 4. The experimental outcome denotes that the CL-DLBIDC approach has attained maximal values of TRA and VLA. Seemingly, the VLA is greater than the TRA. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Labels** & **Accuracy** & **Recall** & **Specificity** & **F-Score** & **MCC** & **G-Measure** \\ \hline \multicolumn{6}{c}{**Training Set (80\%)**} \\ \hline
1 & 99.04 & 97.48 & 99.29 & 96.63 & 96.07 & 96.63 \\
2 & 99.14 & 94.78 & 99.87 & 96.95 & 96.48 & 96.97 \\
3 & 99.11 & 99.75 & 99.00 & 96.91 & 96.44 & 96.95 \\
4 & 99.39 & 96.95 & 99.79 & 97.82 & 97.47 & 97.82 \\
5 & 99.64 & 98.53 & 99.83 & 98.77 & 98.56 & 98.77 \\
6 & 99.14 & 96.77 & 99.54 & 97.01 & 96.51 & 97.01 \\
7 & 99.54 & 98.27 & 99.75 & 98.39 & 98.12 & 98.39 \\ \hline
**Average** & **99.29** & **97.50** & **99.58** & **97.50** & **97.09** & **97.51** \\ \hline \multicolumn{6}{c}{**Testing Set (20\%)**} \\ \hline
1 & 98.86 & 98.06 & 98.99 & 96.19 & 95.54 & 96.21 \\
2 & 99.71 & 100.00 & 99.67 & 98.99 & 98.83 & 98.99 \\
3 & 99.57 & 99.07 & 99.66 & 98.60 & 98.35 & 98.61 \\
4 & 99.86 & 99.07 & 100.00 & 99.53 & 99.4 The training loss (TRL) and validation loss (VLL) obtained by the CL-DLBIDC method on the test dataset are exhibited in Figure 5. The experimental outcome represented that the CL-DLBIDC algorithm has established minimal values of TRL and VLL. Particularly, the VLL is lesser than TRL. Figure 4: TRA and VLA analysis of the CL-DLBIDC approach. Figure 5: TRL and VLL analysis of the CL-DLBIDC approach. A clear precision-recall inspection of the CL-DLBIDC approach on the test dataset is portrayed in Figure 6. The figure shows that the CL-DLBIDC technique has resulted in enhanced values of precision-recall values under all classes. A brief ROC analysis of the CL-DLBIDC algorithm on the test dataset is presented in Figure 7. The results indicate that the CL-DLBIDC method has shown an ability to categorize distinct classes in the test dataset. Figure 6: Precision-recall analysis of the CL-DLBIDC approach. Figure 2:0 ROC analysis of the CL-DLBIDC approach.