## 8 Qualitative and Quantitative Evaluation
### Quantitative Evaluation  
To evaluate our Hybrid 3D Convolutional-Transformer model to recognize the SMMs of the autistic children and compare them with the previous related works, we use the following well-known performance/evaluation metrics:  
* **Accuracy rate:** It is the correct classification of classified videos into normal behavior or abnormal behavior. It allows for the calculation of the overall classification performance.
* **Precision:** Also called Confidence, it denotes the proportion of Predicted Positive cases that are correctly Real Positives.
* **Recall:** It is referred to as the true positive rate or sensitivity. It is defined as the ratio of the total number of correctly classified positive/abnormal behavior instances divided by the total number of positive/abnormal behavior instances.
* **F1-score:** It is the harmonic mean of precision and recall.  
Table 5 provides a complete summary of the performance metrics over five folds of a validation process, including validation accuracy, precision, recall, and F1-score. Fold 1 has a validation accuracy of 0.910, with a precision of 0.91, a recall of 0.90, and an F1-score of 0.904. Fold 2 yields somewhat higher results, with a validation accuracy of 0.920, a precision of 0.92, a recall of 0.902, and an F1-score of 0.912. Fold 3 has a validation accuracy of 0.915, a precision of 0.915, a recall of 0.901, and an F1-score of 0.908. Fold 4 demonstrates a further improvement, with a validation accuracy of 0.930, a precision of 0.93, a recall of 0.902, and an F1 score of 0.916. Finally, Fold 5 achieves a validation accuracy of 0.925, with a precision of 0.935, a recall of 0.903, and an F1-score of 0.918. Averaging the findings over all folds, the **mean validation accuracy** is **0.920**, **Precision** is **0.922**, Recall is **0.9016**, and **F1-score** is **0.9116**. This report indicates consistent and robust performance throughout all validation folds.  
To expand the performance of our proposed model, we perform existing ablation experiences. Table 6 The table shows the findings of the ablation investigation, which emphasize the effect of various components and procedures on the performance of the proposed hybrid 3D-ResNet and Swin-3D Transformer models. The entire model, which includes all components and approaches, has the maximum performance, with a validation accuracy of 92.0%, precision of 0.922, recall of 0.902, and F1-score of 0.9116. When the Swin-3D Transformer is removed, the accuracy decreases to 87.5%, and removing the 3D-ResNet backbone reduces it even more to 85.0%. The absence of data augmentation causes a considerable fall in accuracy, decreasing to 83.0%, underscoring its necessity in dealing with the dataset's restricted size.  
Similarly, eliminating cross-validation results in the lowest accuracy of 81.5%, emphasizing its importance in avoiding overfitting and enhancing generalization. Overall,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fold** & **Val\_Accuracy** & **Precision** & **Recall** & **F-score** \\ \hline Fold 1 & 0.910 & 0.91 & 0.9 & 0.904 \\ Fold 2 & 0.920 & 0.92 & 0.902 & 0.912 \\ Fold 3 & 0.915 & 0.915 & 0.901 & 0.908 \\ Fold 4 & 0.930 & 0.93 & 0.902 & 0.916 \\ Fold 5 & 0.925 & 0.935 & 0.903 & 0.918 \\ The mean of all Folds & 0.92 & 0.922 & 0.9016 & 0.9116 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Quantitative evaluation results.  
the findings show how each component and approach contributes to the model's ideal performance.