In this section, the impact of the training dataset size on the performance of the ANN was explored. A numerical experiment was conducted using a smaller subset of 13 datasets from the original 61 discussed in Section 3.1. This subset spanned the entire range of 𝐷, with the only difference being an increase in the intervals between each dataset. The criteria for dataset selection are detailed in Table . The same optimization operations described in Sections 3.2, 3.4, and 3.6 were also performed using an ANN trained on a smaller dataset. For more detailed information on the results, please refer to Appendix D. A general comparison of the performance between the pre-trained ANN using both small and large datasets on different optimization operations can be found in Table . From an accuracy perspective, it is evident that an ANN trained on a larger dataset typically surpass those trained with smaller ones. Nevertheless, the training cost, i.e., the time required to train the ANN model, must be considered a crucial factor.