We utilize a supervised learning approach in a multi-label classification scenario. The labels are represented as binary vectors, where a value of 1 indicates the presence of a corresponding label, and 0 indicates its absence. To ensure unbiased performance evaluation, we divided the datasets into 80% training and 20% validation sets. Since the datasets exhibit class imbalance, it is crucial to mitigate any performance bias resulting from the training split. Hence, we employ a K-Fold cross-validation protocol with 5 folds. For training the neural network, we utilize the Adam optimizer, which minimizes the binary cross-entropy loss while employing linear learning rate decay. The reported results are the average validation scores obtained across the 5 folds. We trained the models over 50 epochs, depending on the specific experiment. On average, the 5-fold cross-validation process for our split model takes approximately 1 hour on a system with a Nvidia GeForce 1080 Ti GPU and an Intel Xeon CPU.