## 3 Result and Discussion
### Comparative Analysis of Model Performance
MobileNetV2-LSTM reaches about a 99.1% training accuracy with a high validation accuracy and some sharp increases between 93% and 94%, indicating better generalization due to the LSTM layers, as shown in previous papers [15; 17]. MobileNetV2-CfC-NCP, despite a lower initial baseline, achieves around a 99.2% training accuracy and a consistently high validation accuracy of 93%-94% with minimal fluctuations, demonstrating superior generalization and robustness. In Figure 10, the fluctuation observed in the validation accuracy can be primarily attributed to the VGG-16 and ResNet50 models. Both of these models are highly complex with a large number of parameters, which makes them prone to overfitting. This is evident in the sharp peaks and troughs in the validation performance of VGG-16 and ResNet50. Addressing this issue is one of the main reasons why MobileNetV2 was chosen as the baseline CNN model in this research. MobileNetV2 is a simpler and more efficient model, which inherently reduces the risk of overfitting. Consequently, it exhibits much less fluctuation in validation accuracy compared to VGG-16 and ResNet50, as shown in Figure 9. However, this simplicity also leads to a slightly lower accuracy overall. To further enhance the performance, MobileNetV2 was combined with Recurrent Neural Network (RNN) models such as the LSTM and CfC-NCP. These hybrid models leverage the strengths of both CNN and RNN architectures, capturing spatial features with the CNN and temporal dependencies with the RNN. This combination not only improves the training accuracy but also stabilizes and enhances the validation accuracy, as seen in the smoother curves and higher overall performance in the graph. In summary, the fluctuations in the original models are mitigated by the use of MobileNetV2 due to its simpler architecture, and the integration of RNN components compensates for any loss in accuracy, leading to a more robust and reliable performance in both training and validation phases. Table 3 present the performance metrics for the five models, evaluated using a test dataset that was not seen during training or validation. This evaluation helps to validate each model's performance on unseen data, highlighting their generalization power and potential value in real-life applications. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline VGG-16 & 94.41 & 94.62 & 94.19 & 94.37 \\ ResNet-50 & 94.66 & 94.94 & 94.41 & 94.61 \\ MobileNetV2 & 94.66 & 94.5 & 94.31 & 94.33 \\ MobileNetV2-LSTM & 96.38 & 96.39 & 96.20 & 96.27 \\ MobileNetV2-CfC-NCP & 97.15 & 97.16 & 97.09 & 97.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative performance metrics of the baseline and proposed models. Figure 10: Training and validation accuracy comparison between 5 models over 50 epochs. From the results, it is evident that the baseline models VGG-16, ResNet-50, and MobileNetV2 performed well but did not match the performance of the hybrid model. This confirms the benefit of combining the CNN and RNN in image recognition tasks. Furthermore, the proposed model, MobileNetV2-CfC-NCP, outperformed all other models across all metrics. It achieved the highest accuracy (97.15%), precision (97.16%), recall (97.09%), and F1-score (97.11%). This result shows that the hybrid models outperformed the traditional single CNN models in all performance metrics. The proposed MobileNetV2-CfC-NCP model achieved the highest performance, surpassing the LSTM-based hybrid model that was applied in previous studies [15; 17], demonstrating the superior effectiveness of liquid neural architectures over the LSTM in this context.