We also combine 3D and 2D convolutions to generate feature maps, and the fully connected block is configured using the ADSSCA philosophy with dropout calculated as a function of model capacity. The dropout serves to randomly drop hidden computing units and their connections during training, thus ...
The results are compared with some similar state of the art models based on SCNN, including fuzzy neural network model (FNN), Convolution Neural Network (CNN), and LSTM neural network. The results show that the proposed model achieves a mean squared error (MSE) of 0.021 on the test dataset...