## 4 Proposed System
### Output Layer
#### 4.3.2 BenSignNet: The Proposed CNN Architecture  
Figure 7 shows the proposed BenSignNet model architecture comprises nine convolution layers with a specific filter size, eight ReLU activation layers, two max-pooling layers, a batch normalization layer, a global average pooling layer, and an output layer with a softmax activation function. The output layer is a 38 neuron softmax, which defines the 38 classes. The dropout layer is placed after the convolution layer to reduce the overfitting of the model. The max-pooling layer is placed after the drop out layer to down-sample the value of the convolution feature map. The ReLU activation layer incorporates the each convolution layers. The global average pooling layer is used to replace the fully connected layer.  
The input image 124 \(\times\) 124 \(\times\) 3 is filtered at the first convolutional layer with 96 kernels of size 3 \(\times\) 3 \(\times\) 3. After applying the ReLU activation and dropout layer, the output of the first convolutional layer is fed into the second convolutional layer as input. In second convolution layer filtered the input with the 96 kernels of size 3 \(\times\) 3 \(\times\) 96 and passed into the third convolution layer after applying the ReLU function. Third convolutional layers filtered the input with the 96 kernels of size 3 \(\times\) 3 \(\times\) 96 based on the 2 stride and fed the output at the max-pooling layer.  
The fourth convolution layer takes as input the pooled output layer and filters it with 192 kernels of size 3 \(\times\) 3 \(\times\) 96, which fed in the fifth convolution layer by filtering it with 192 filters fo size 3 \(\times\) 3 \(\times\) 192. After applying the relu activation on the fifth output layer, it fed into the sixth convolution layer, whose stride is two and filtered with 192 kernels of size 3 \(\times\) 3 \(\times\) 192.  
The seventh convolutional layer takes as input the pooled output of the six convolutional layers and filters the feature map with 192 kernels in size of 3 \(\times\) 3 \(\times\) 192. After  
Figure 7: Proposed BenSignNet Architecture.  
applying the ReLU on the output of seventh convolutional layer its fed into the eigth convolution layer, which is filtered with the 192 kernels of size \(1\times 1\times 192\). The output of the eighth convolutional layer is fed into the ninth convolutional layer after applying the ReLU activation and filtered the feature map with the 38 kernels in size of \(1\times 1\times 192\). The value of the stride is 2 for the third and sixth convolutional layers and 1 for the rest convolutional layer.  
The pool size is 2, and the stride is 2 for both max-pooling layers after the third and sixth convolutional layers.  
The dropouts and global average pooling technique are used to reduce the overfitting of the model. Improving the generalization property and solving the layer's overfitting issue are the main advantages of the global average pooling because there is no parameter to optimize in the global average pooling. In addition, global average polling is more robust to spatial translations of the input than the fully connected layer because which sums out the spatial information. Table 2 depicts the details specification of the BenSignNet Model.  
Finally, the model is compiled and optimized with the Adam optimizer using the categorical cross-entropy loss function. Our actual output is converted into the one-hot-encoded, making them directly fitted with the categorical cross-entropy loss function.