## 5 Discussion  
The results of hyperparameter optimization reveal that some combinations of parameters have a greater impact on the model's performance, while others have a minor impact. We noticed that the number of layers and the breadth of the filter had a significant impact on the prediction performance. The results also showed that great performance may be achieved for all filter widths. Furthermore, using several layers produced somewhat better performance than using only one layer since it tolerates additional model complexity, but it similarly required a longer training time. The training time was affected by the number of layers and the breadth of the filter, but not by the classification performance. Accordingly, provided that the number of layers is rigid, a high filter width takes fewer periods to train than a lesser filter width, even though the two alternatives provide similar predictions. The same can be said for the number of filters used. The higher the filters used, the longer the training period becomes, with no discernible gain in prediction accuracy. The number of filters used has a significant impact on the training time (bottom). The number of trainable parameters is determined by the number of filters, their breadth, and the number of layers and stacks. As a result, by utilizing fewer filters for the equivalent fuse of filter width and layers, training time is lowered. Adding extra layers to the network enhances the depth and, as a result, its complexity. Training time is influenced by the number of layers but not by performance. Although both would deliver equal outcomes provided the number of layers was fixed, an extensive filter width would require fewer layers and hereafter fewer training times than a narrower filter width. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Model** & **Accuracy** \\ \hline [PERSON] et al. [52] & Custom network & 87.0\% \\ [PERSON] et al. [53] & Modified ResNet & 93.5\% \\ [PERSON] et al. [54] & CVC-Net & 91.6\% \\ [PERSON] et al. [55] & Transformer network & 90.86\% \\ [PERSON] et al. [56] & UULPN & 85.7\% \\ [PERSON] et al. [57] & Mixture density network & Only qualitative results reported \\ [PERSON] et al. [58] & Modified ResNet & 88.8\% \\ [PERSON] et al. [59] & MobileNetV2 backbone & 88.1\% \\
**This study** & **MLP + HPO** & **89.9\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison with previous studies on posture recognition using MPII dataset. An independent test set was used to choose the most efficient network model with the optimal mix of hyperparameter values. These results show that the suggested VGG16 and AlexNet transfer learning models can reliably classify human pose images. As a result, transfer learning models are an excellent choice for replacing time-consuming classical machine learning models. Figures 9-19 depict the model training outcomes, including training accuracies, validation accuracies, training loss, and validation loss numbers. The fundamental downside of transfer learning models is that they demand a lot of processing resources, such as GPUs and a lot of RAMs. Transfer learning classifiers create synthetic data during the training phase, which requires a large amount of storage space. Image augmentation was used in the data preparation portion of the investigation. Data augmentation was used as a regularizer to help manage data overfitting. By producing additional training data and exposing the model to diverse versions of data, image augmentation helps to reduce the likelihood of overfitting. The image augmentation utilized in this study helped the model to operate better and more precisely by improving the results. It also reduces operating expenses by adding transformations in the datasets, as evidenced by the fact that the model calculation execution time is shorter than when the model is without image augmentation. It also helps with data cleansing, which is necessary for good model accuracy.