## 4 Performance Validation  
The experimental result analysis of the QWSA-HDLAR model is tested using two datasets, namely the KTH dataset [25] and the UCF Sports dataset [26]. The first KTH dataset includes 600 samples with six class labels, as given in Table 1. Next, the UCF Sports dataset contains 1000 samples with ten class labels, as provided in Table 2. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **KTH Dataset** & \\ \hline
**Class No.** & **Description** & **No. of Samples** \\ \hline
1 & Boxing & 100 \\
2 & Handclapping & 100 \\
3 & Handwaving & 100 \\
4 & Jogging & 100 \\
5 & Running & 100 \\
6 & Walking & 100 \\ \hline \hline \end{tabular} Total  
\end{table} 
Table 1: Details of KTH dataset. Figure 1 demonstrates the confusion matrices produced by the QWSA-HDLAR model. With the entire dataset, the QWSA-HDLAR model recognized 99 samples under class 1, 97 samples under class 2, 95 samples under class 3, 99 samples under class 4, 98 samples under class 5, and 100 samples under class 6. Similarly, with 70% of the training (TR) dataset, the QWSA-HDLAR model identified 74 samples under class 1, 74 samples under class 2, 63 samples under class 3, 67 samples under class 4, 69 samples under class 5, and 63 samples under class 6. Table 3 illustrates the overall HAR outcomes of the QWSA-HDLAR model on the test KTH dataset. The experimental output demonstrated that the QWSA-HDLAR model has shown enhanced performance on all datasets. For instance, on the entire dataset, the QWSA-HDLAR model has obtained average \(accu_{y}\) of 99.33%, \(reca_{l}\) of 98%, \(spec_{y}\) of 99.60%, \(F_{score}\) of 97.99%, and an area under the receiver operating characteristic curve (AUROC) score of 98.80%. Eventually, with 70% of the TR dataset, the QWSA-HDLAR model attained average \(accu_{y}\) of 99.21%, \(reca_{l}\) of 97.63%, \(spec_{y}\) of 99.52%, \(F_{score}\) of 97.62%, and AUROC score of 98.58%. Meanwhile, on 30% of the TS dataset, the QWSA-HDLAR model reached average \(accu_{y}\) of 99.63%, \(reca_{l}\) of 98.80%, \(spec_{y}\) of 99.78%, \(F_{score}\) of 98.83%, and an AUROC score of 99.29% as illustrated in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class Labels** & **Accuracy** & **Recall** & **Specificity** & **F-Score** & **AUROC Score** \\ \hline \multicolumn{3}{c}{Entire Dataset} \\ \hline
1 & 99.67 & 99.00 & 99.80 & 99.00 & 99.40 \\
2 & 99.00 & 97.00 & 99.40 & 97.00 & 98.20 \\
3 & 98.83 & 95.00 & 99.60 & 96.45 & 97.30 \\
4 & 99.33 & 99.00 & 99.40 & 98.02 & 99.20 \\
5 & 99.33 & 98.00 &Figure 3: Confusion matrix of QWSA-HDLAR model on KTH dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class Labels** & **Accuracy** & **Recall** & **Specificity** & **F-Score** & **AUROC Score** \\ \hline \multicolumn{6}{c}{Entire Dataset} \\ \hline
1 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\
2 & 99.44 & 95.83 & 100.00 & 97.87 & 97.92 \\
3 & 99.44 & 96.97 & 100.00 & 98.46 & 98.48 \\
4 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\
5 & 98.89 & 100.00 & 98.68 & 96.67 & 99.34 \\
6 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline Average & 99.63 & 98.80 & 99.78 & 98.83 & 99.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._The training and validation accuracies depicted by the QWSA-HDLAR technique with distinct epochs on the KTH dataset are demonstrated in Figure 4. The results assured that the accuracies are found to be higher with increased epochs. Additionally, the training accuracy seems to be superior to testing accuracy. The training and validation losses gained by the QWSA-HDLAR method on the test KTH dataset are reported in Figure 5. The figure identified that the QWSA-HDLAR technique has resulted in lower training and validation loss values.