## 4 Results and Discussions
### Gender-Wise Analysis
Our proposed model can be embedded with a hardware system to be deployed in a real-time classroom environment to detect the engagement and non-engagement state of the students. We compared various TL algorithms and models, including VGG16, AlexNet, InceptionV3, GoogleNet, Xception, MobileNet, SqueezeNet, and the proposed model. The classification results of the pretrained DL models for batch size 16 was calculated and presented in Table 10. The efficacy of the developed VGG16 extended with fine-tuned model achieved reasonable accuracy. However, group size had an influence on the number of parameters and computational time. However, the classification results for other pre-trained TL algorithms remain unchanged. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Independent Variable** & **Dependent Variable** & **Const.** & **Coefficient** & **Std. Err** & **Z Value** & **Prob** & **Test** \\ \hline \multirow{3}{*}{Session} & \multirow{2}{*}{engaged} & 3.568845 & \(-0.0985885\) & 0.0417993 & \(-2.36\) & 0.018 & PR \\  & & 3.568845 & \(-0.0985885\) & 0.0520437 & \(-1.89\) & 0.058 & NBR \\ \hline \hline \end{tabular}
\end{table} 
Table 10: All males (1) vs. all Females (0) with non-engagement. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Epochs** & **ACC** \\ \hline VGG16 & 40 & 79 \\ AlexNet & 40 & 81.3 \\ InceptionV3 & 40 & 82.7 \\ GoogleNet & 40 & 83.5 \\ Xception & 40 & 82.4 \\ MobileNet & 40 & 84.3 \\ SqueezeNet & 40 & 87.6 \\ Proposed VGG-16 & 40 & 90.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: All males (1) vs. All females (0) with engagement. In addition, Table 11 and Figure 10 demonstrate that the extended model of VGG16 is outperformed compared to other TL models in detecting multiple objects from live video camera scenes. Here are a few reasons why VGG16 may be outperformed by these architectures for object detection in videos:  
1. Spatial information vs. temporal information: VGG16 focuses on capturing spatial information within individual frames, but it does not explicitly model temporal dependencies between frames. In contrast, architectures such as InceptionV3 and GoogLeNet incorporate components such as temporal convolutional layers or recurrent neural networks (RNNs) that can capture temporal information and dependencies in video sequences. This can be beneficial for object detection in videos, where the motion and temporal context of objects play an important role. 2. Computational efficiency: VGG16 has a relatively high number of parameters and computational complexity due to its deeper architecture, which can make it computationally expensive for real-time object detection in videos. InceptionV3 and GoogLeNet, on the other hand, have been designed with computational efficiency in mind. They utilize techniques like 1 \(\times\) 1 convolutions and factorized convolutions, which reduce the number of parameters and computational cost while maintaining or even improving performance. This efficiency is particularly advantageous for video processing tasks that require real-time or near-real-time performance. 3. Architectural innovations: InceptionV3 and GoogLeNet incorporate architectural innovations that aim to address specific challenges in object detection, such as the problem of vanishing/exploding gradients or the efficient use of network capacity. These innovations, such as the use of inception modules, auxiliary classifiers, and reduction layers, can enhance the model's ability to detect objects accurately in videos.