# On the Development of a Neural Network Architecture for Magnetometer-Based UXO Classification
## 5 Results
### Neural Network Design  
Initial results from [9] highlighted the advantages of using neural networks for UXO classification. However, when applied to the updated dataset--which includes the effects of remanent magnetization--the performance of the original network dropped significantly. This performance degradation is likely due to the increased complexity of the magnetic field distribution, which may exceed the capacity of simpler network architectures. To address these limitations, we transitioned from a basic MLP to a more expressive Convolutional Neural Network (CNN). Our proposed architecture is built upon modular building blocks, each comprising two convolutional layers, element-wise batch normalization, and residual skip-connections to facilitate effective gradient flow. The network was trained using the hyperparameters detailed in Table 3. The selection of these parameters was guided by empirical analysis. For instance, the choice of training duration and the implementation of an early stopping protocol were informed by the learning curve shown in Figure 3a. As the figure illustrates, the model's validation performance typically plateaus after approximately 100 epochs. This observation justified setting a fixed training length while using early stopping to prevent overfitting and improve efficiency. Similarly, the choice of a batch size of 512 is supported by the analysis in Figure 3b. While the performance was comparable for batch sizes of 256 and 512, the larger value was selected for its theoretical benefits. A larger batch size provides more stable estimates of the mean and standard deviation for the batch normalization layers, which is particularly important given the high variance observed across the input signals in our dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Accuracy** &
\begin{tabular}{c} **Balanced** \\ **Accuracy** \\ \end{tabular} & \(\mathbf{F1_{macro}}\) & **Parameters** \\ \hline \multirow{6}{*}{kNN} & 83.39 \(\pm\) 0.54 & 79.19 \(\pm\) 0.78 & 79.46 \(\pm\) 0.72 & \(k=3\) \\ \cline{2-5}  & **83.51 \(\pm\) 0.54** & **79.31 \(\pm\) 0.76** & **79.59 \(\pm\) 0.70** & \(k=5\) \\ \cline{2-5}  & 83.31 \(\pm\) 0.64 & 79.09 \(\pm\) 0.91 & 79.36 \(\pm\) 0.85 & \(k=7\) \\ \cline{2-5}  & 83.00 \(\pm\) 0.63 & 78.62 \(\pm\) 0.88 & 78.94 \(\pm\) 0.83 & \(k=9\) \\ \cline{2-5}  & 82.70 \(\pm\) 0.61 & 78.32 \(\pm\) 0.78 & 78.60 \(\pm\) 0.76 & \(k=13\) \\ \hline \multirow{6}{*}{RF} & 86.08 \(\pm\) 0.66 & 82.78 \(\pm\) 0.94 & 82.89 \(\pm\) 0.85 & \#_trees_ = 50 \\ \cline{2-5}  & 86.23 \(\pm\) 0.59 & 83.12 \(\pm\) 0.80 & 83.13 \(\pm\) 0.74 & \#_trees_ = 100 \\ \cline{2-5}  & **86.39 \(\pm\) 0.66** & **83.34 \(\pm\) 0.84** & **83.38 \(\pm\) 0.82** & \#_trees_ = **300** \\ \cline{2-5}  & 86.36 \(\pm\) 0.45 & 83.40 \(\pm\) 0.64 & 83.32 \(\pm\) 0.57 & \#_trees_ = 500 \\ \hline \multirow{6}{*}{MLP} & 83.75 \(\pm\) 0.85 & 78.84 \(\pm\) 1.67 & 79.54 \(\pm\) 1.23 & \(struct=100\) \\ \cline{2-5}  & 83.37 \(\pm\) 0.62 & 79.91 \(\pm\) 1.34 & 79.73 \(\pm\) 0.97 & \(struct=(10,10)\) \\ \cline{1-1} \cline{2-5}  & **85.26 \(\pm\) 0.28** & **80.94 \(\pm\) 0.89** & **81.55 \(\pm\) 0.48** & \(struct=(100,10)\)** \\ \cline{1-1} \cline{2-5}  & 84.34 \(\pm\) 0.65 & 79.63 \(\pm\) 0.54 & 80.34 \(\pm\) 0.67 & \(struct=(200)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the performance obtained from classical machine learning methods across different hyperparameter configurations. The best results for each model are marked in bold. Table 5 summarizes the effects of varying the width (number of filters per block) and depth (number of blocks) of the network on classification performance. The best-performing architecture consists of the following: an initial convolutional block with 32 filters; two building blocks with 128 and 512 filters, respectively; a fully connected layer with 100 neurons; and 4 neurons for processing meta-features. This model  
Figure 3: The influence of the network hyperparameters on prediction performance. (**a**) Learning curve. (**b**) Batch size.