The training of the model was configured to accommodate the memory constraints of the available hardware, setting the batch size at 40. Optimization was performed using the Adam optimizer , with an initial learning rate of 1×10 -5 . This learning rate was scheduled to decay by a factor of 0.2 starting from the second epoch to facilitate finer adjustments in the later stages of