Table 5.

Model (4, 5, 6), (7, 8, 9) architecture

	Model 4/5/6	Model 7/8/9
Layers	Parameters	Parameters
Layer 1—FullyConnected	Input layer	Input layer
Layer 2—GRU/LSTM/BILSTM	50	50
Layer 3—GRU/LSTM/BILSTM	20	20
Layer 4—FullyConnected	2	10
Layer 5—FullyConnected	–	5
Layer 6—FullyConnected	–	2

The number of layers and the number of neurons in each layer can vary. Moreover, the hyper-parameters can be tuned to improve the final performance. The number of trainable and non-trainable layers can vary, but transfer learning does not perform well if all layers are trainable and the performance is improved