The schematic diagram of the three 3D CNN architectures, i.e., VGG-13 (a), ResNet-50 (b), and DenseNet-169 (c). VGG-13 includes several convolution and pooling layers as the building block of the architecture. The ResNet-50 has two types of residual blocks building the entire network...
Figure 3. Model architecture proposed in this study: (a) CNN-1, (b) CNN-2, and (c) CNN-3. In the first model, the number of filters employed in the convolution operation was gradually raised from 8 to 32, generating a small number of feature maps through a small operation at firs...
The architecture is characterized by using a new activating function, the Rectified Linear Unit (ReLU), to add non-linearity, solve the gradient evanescent problem, and accelerate network training. CNN consists of eight layers in total: the first five layers are of convolution, some of which ...
Figure 1 provides the graphical diagram of a single GRU unit within the proposed architecture, processing the parameter vector values according to the system of equations: 𝑧𝑡𝑟𝑡ℎ𝑡=𝜎𝑔(𝑊𝑧𝑥𝑡+𝑈𝑧ℎ𝑡−1+𝑏𝑧)=𝜎𝑔(𝑊𝑟𝑥𝑡+𝑈𝑟ℎ𝑡−...