• Multi-label model: one hidden layer with 512 nodes and it has a dropout layer with dropoutrate 0.64 between each layer. It uses ReLU as its activation layer between layers, except for the final layer which uses a softmax function. The multi-label model was trained binary cross-entropy loss and Adam optimizer with a learning rate of 9.2*10 -4 . It was trained for 20 epochs with a batch size of 64.