To explore the effects of data balancing, we maintained each dataset in its original imbalanced state and also created balanced versions by randomly undersampling the majority class to match the size of the minority class. This balancing process was exclusively applied to the training sets, while the validation sets preserved their original, imbalanced distributions. The specifics of the imbalanced and balanced dataset sizes, along with the active/inactive ratios, are comprehensively detailed in Table .