However, the instability and fluctuations during training are typical indicators of underfitting, where the network struggles to correctly predict the unseen data as the parameters have not been fully optimised . Nevertheless, the DNN was able to converge and performed well on the test data, achieving a diagnostic accuracy of 99.17%, only misclassifying one of the 120 test voltage responses as revealed in Figure , where the rows of the confusion matrix indicate the actual SoH while the columns indicate the predictions. Due to the stochastic nature of the Adam optimiser, random initialisation of the network parameters, and random train-test partition, the diagnosis results may vary slightly between executions .