## 5 Experimental Results and Discussion
### Results of State-of-the-Art AI Model Examination  
The outcomes of investigating the state-of-the-art AI models are presented in Table 8. The experimental results are organized based on the test accuracy, ranging from the largest to the smallest. Xception led to the highest test accuracy of 88.91%, with a memory size of 244.9 MB. Figure 4 provides a clear visualization of the trade-off between the classification accuracy and memory footprint in Table 8. It is evident that these AI models exhibit a broad spectrum of accuracy and memory size variations. In this figure, Xception, EfficientNet, MobileNet, and DenseNet121 demonstrate much higher accuracies. It is normal that certain AI models produce better outcomes despite having a smaller number of parameters. This phenomenon is attributed to the models' architecture, efficient design, and optimization techniques, which allow them to effectively capture and utilize information from the training data. As a result, these models achieve better performance with fewer parameters compared to their larger, less optimized counterparts (for example, AlexNet and VGG-16 in this study). As discussed in Section 4.2, since EfficientNetB0 could not be simplified further, Xception, MobileNet, and DenseNet121 were chosen as the foremost candidate models for the subsequent phase of model simplification. Figures 5 and 6 plot the experimental results of the loss and classification accuracy on both the training and validation sets for DenseNet121, MobileNet, EfficientNetB1, and Xception. The purpose of the validation set is to evaluate the performance of a trained AI model and assess its generalization ability. It is clear that the validation loss started with a relatively high value and gradually diminished. At 150 epochs, the validation loss stabilized, indicating that the AI model fits well without over-fitting. Moreover, the training accuracy and validation accuracy ultimately settled at approximately 0.9 and 0.85, respectively. Two primary factors contributed to the wild fluctuation in the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**State-of-the-Art AI Model** & **The Number of Parameters** & **Memory Size** & **Test Accuracy** \\ \hline Xception [22] & 20,877,872 & 244.9 MB & 88.91\% \\ \hline L-w Xception 1 [22] & 9,549,464 & 112.1 MB & 86.52\% \\ \hline EfficientNetB1 & 6,585,487 & 78.2 MB & 86.08\% \\ \hline DenseNet121 [23] & 7,045,704 & 83.9 MB & 85.07\% \\ \hline EfficientNetB0 [18] & 4,059,819 & 48.3 MB & 84.47\% \\ \hline EfficientNetB3 [19] & 10,795,831 & 127.5 MB & 84.19\% \\ \hline MobileNet [17] & 3,237,064 & 38.2 MB & 83.66\% \\ \hline Inception-V3 [21] & 21,819,176 & 256.8 MB & 82.90\% \\ \hline ResNet50 [15] & 23,604,104 & 277.1 MB & 79.51\% \\ \hline ShuffleNetV3 & 1,991,372 & 23.5 MB & 77.94\% \\ \hline ShuffleNet & 1,482,368 & 17.5 MB & 76.85\% \\ \hline ResNet34 [13] & 21,318,728 & 250.3 MB & 76.29\% \\ \hline ShuffleNetV2 [14] & 755,004 & 9.0 MB & 75.23\% \\ \hline DenseNet169 [23] & 12,656,200 & 149.8 MB & 75.11\% \\ \hline ResNet101 [16] & 42,674,568 & 501.0 MB & 72.90\% \\ \hline MobileNetV2 [18] & 2,268,232 & 27.1 MB & 71.90\% \\ \hline NasNetMobile [18] & 4,278,172 & 53.3 MB & 71.64\% \\ \hline LeNet-5 & 4,059,372 & 47.6 MB & 58.33\% \\ \hline AlexNet & 26,822,280 & 314.4 MB & 24.21\% \\ \hline VGG-16 & 98,641,736 & 1156.1 MB & 23.80\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The number of parameters, memory size, and test accuracy of state-of-the-art AI models employed in indoor small suction classification as reported in the literature. validation curves compared to the training curves. Firstly, the limited number of samples within the validation set played a significant role in the fluctuations observed in the validation curves. In this paper, considering that the validation dataset consisted of just 5760 samples across eight classification categories, the prevalence of the pronounced fluctuations in validation performance was not unexpected.