## 6 Results
### One-Dimensional CNN for EIT Data Analysis
The impact of input dimensionality reduction, where the number of BVs is decreased from 40 to 20, and the CNN model compression on the model's performance was assessed. Indeed, the evaluation considered four critical metrics: Model size, accuracy, computational complexity measured in FLOPs, and loss. The proposed approaches were examined in detail to determine their effectiveness and trade-offs, as illustrated in Figure 6. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Aspect** & **CNN Model 1** & **CNN Model 2** \\ \hline
**Input** & 40 BVs & 20 BVs \\ \hline
**First convolutional layer** & 8 filters & 16 filters \\ \hline
**Second convolutional layer** & 16 filters & 32 filters \\ \hline
**First fully-connected layer** & 64 neurons & 128 neurons \\ \hline
**Output layer** & 36 neurons & 36 neurons \\ \hline
**Dropout** &
\begin{tabular}{c} 0.5 dropout \\ (After the first fully connected layer) \\ \end{tabular} & - \\ \hline
**FLOPs** & 3.813M & 3.783M \\ \hline
**Mean accuracy** & \(94.72\%\pm 1.19\%\) & \(96.94\%\pm 1.19\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of CNN architectures for different EIT inputs. The results for model size show significant differences between the initial and compressed models. For the initial models, the initial model size for 40 BVs is 173.73 KB, which increases to 347.73 KB when the input is reduced to 20 BVs. This counter-intuitive increase suggests that reducing the input dimensionality forces the model to increase its internal capacity, such as its parameters or layers, to achieve a comparably high performance. This highlights that reducing input data alone does not inherently optimise resource usage, leaving room for further optimisation. However, the compressed models show a drastic reduction in size for both input configurations. For 40 BVs, the size decreases by approximately 91.75% to 14.35 KB, while for 20 BVs, it decreases by approximately 97.49% to 8.73 KB, resulting in the smallest overall size. This result emphasises the effectiveness of compression techniques in significantly minimising the model footprint, particularly for the 20-BV configuration. These results suggest that while input reduction alone can lead to resource inefficiencies, the combination of input reduction and compression creates a highly compact and efficient architecture, ideal for an efficient edge device implementation. Figure 6: Effects of input reduction from 40 to 20 BVs and CNN model compression on model size (a), Accuracy (b), FLOPS (c), Loss (d), and F1 Score (e). In terms of accuracy, the initial models have approximately comparable accuracy, 94.44% for the 40 BVs model and 95.83% for the 20 BVs model. While the overall model structure remains similar, the different hyperparameter settings, such as the number of channels and neurons, contribute to this difference. In our case, the reduced input dimensionality coupled with increased model complexity forces the network to focus on more important features, leading to improved performance. After compression, the accuracy also remains comparable with small variations. For 40 BVs, compression leads to an increase in accuracy to 97.22%, which indicates better generalisation due to reduced overfitting. The slight increase in accuracy for the compressed 40 BV model may indicate that it benefits from the structural sparsity introduced during compression. For 20 BVs, the accuracy drops slightly to 94.44%, which is still very competitive. These results confirm that compression effectively maintains performance, ensuring that significant reductions in model size and computational complexity do not compromise recognition capability, particularly for real-time applications. The computational complexity of the models, measured in FLOPs, demonstrates the significant efficiency gains achieved through compression. For the original models, the FLOPs are extremely high at 3.813M and 3.783M for the 40 BVs and 20 BVs CNN models, respectively, reflecting the significant computational requirements of the original architectures.