The SVM models underperform in comparison to the neural networks due to their formulation, whereby the C and parameters allow the misclassification of some faults on the wrong side of the hyperplanes. Therefore, adding a small amount of D 2 to the SVM training set may have no effect on its performance, since it is fundamentally designed in such a way to allow some outliers to be misclassified. Hence, if data from another MEA is significantly different and causes overlap, the SVM model will treat them as outliers and the hyperplanes will not be updated. In contrast, the Adam optimiser used in the neural networks will attempt to minimise loss and update their fitting parameters regardless of overlap or noise, enabling them to be influenced by a wider range of data and enhancing their generalisation potential. This is directly reflected in their improved accuracies over the SVMs when a small quantity of new data is used for training.