# A Bagged Ensemble Convolutional Neural Networks Approach to Recognize Insurance Claim Frauds
## 3 Materials and Methods
### Deep Convolutional Neural Network
#### 3.1.4 Minimized 1D CNN  
After the analysis of pre-trained models, a minimized, yet efficient sequential network is proposed as a productive method of extracting time series data' deep features. The main idea of this model is to obtain better results with a CNN architecture having minimal complexity. The proposed network only has 19 layers in total, including one output layer, two max pooling, dropout, and fully connected layers, four convolutional, batch normalization, and ReLU layers, and four of each. This model has fewer layers then AlexNet model yet performs better than all three pre-trained CNN models. The proposed 1D CNN model's parameters, inputs, and outputs are all included in Table 1 thorough overview.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**\#** & **Layer** & **Parameters** & **Input** & **Output** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{Convolution1D\_1} & Channels = 32 & \multirow{3}{*}{64 \(\times\) 1 \(\times\) 32 \(\times\) 480} & \multirow{3}{*}{64 \(\times\) 32 \(\times\) 32 \(\times\) 480} \\  & & Kernels = 14 & & \\  & & Pooling = 0.32 & & \\ \hline
2 & Batch\_Normalization\_1 & Channels = 32 & 64 \(\times\) 32 \(\times\) 32 \(\times\) 480 & 64 \(\times\) 64 \(\times\) 32 \(\times\) 480 \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{Convolution1D\_2} & Channels = 64 & \multirow{3}{*}{64 \(\times\) 64 \(\times\) 32 \(\times\) 480} & \multirow{3}{*}{64 \(\times\) 64 \(\times\) 1 \(\times\) 480} \\  & & Kernels = (28,1) & & \\  & & Pooling = 0.0 & & \\ \hline
4 & Batch\_Normalization\_2 & Channels = 64 & 64 \(\times\) 64 \(\times\) 1 \(\times\) 480 & 64 \(\times\) 64 \(\times\) 1 \(\times\) 480 \\ \hline
5 & ReLU\_1 & - & 64 \(\times\) 64 \(\times\) 1 \(\times\) 480 & 64 \(\times\) 64 \(\times\) 1 \(\times\) 480 \\ \hline
6 & Max-Pooling\_1 & [1,2] & 64 \(\times\) 64 \(\times\) 1 \(\times\) 480 & 64 \(\times\) 64 \(\times\) 1 \(\times\) 228 \\ \hline
7 & Dropout & 0.5 & 64 \(\times\) 64 \(\times\) 1 \(\times\) 228 & 32 \(\times\) 32 \(\times\) 1 \(\times\) 114 \\ \hline
8 & Convolution1D\_3 & Channels = 16 & \multirow{3}{*}{32 \(\times\) 32 \(\times\) 1 \(\times\) 114} & \multirow{3}{*}{64 \(\times\) 16 \(\times\) 32 \(\times\) 114} \\  & & Kernels = 7 & \multirow{3}{*}{64 \(\times\) 32 \(\times\) 1 \(\times\) 114} & \multirow{3}{*}{64 \(\times\) 32 \(\times\) 1 \(\times\) 114} \\  & & Pooling = 0.64 & & \\ \hline
9 & Batch\_Normalization\_3 & Channels = 32 & 64 \(\times\) 16 \(\times\) 32 \(\times\) 114 & 64 \(\times\) 32 \(\times\) 32 \(\times\) 114 \\ \hline
10 & Convolution1D\_4 & Channels = 64 & \multirow{3}{*}{64 \(\times\) 32 \(\times\) 32 \(\times\) 114} & \multirow{3}{*}{64 \(\times\) 32 \(\times\) 1 \(\times\) 114} \\  & & Kernels = (28,1) & & \\  & & Pooling = 0.0 & & \\ \hline
11 & Batch\_Normalization\_4 & Channels = 32 & 64 \(\times\) 32 \(\times\) 1 \(\times\) 114 & 64 \(\times\) 32 \(\times\) 1 \(\times\) 114 \\ \hline
12 & ReLU\_2 & - & 64 \(\times\) 32 \(\times\) 1 \(\times\) 114 & 64 \(\times\) 32 \(\times\) 1 \(\times\) 114 \\ \hline
13 & Max-Pooling\_2 & [1,1] & 64 \(\times\) 32 \(\times\) 1 \(\times\) 114 & 64 \(\times\) 32 \(\times\) 1 \(\times\) 57 \\ \hline
14 & Dropout & 0.5 & 64 \(\times\) 32 \(\times\) 1 \(\times\) 57 & 32 \(\times\) 16 \(\times\) 1 \(\times\) 27 \\ \hline
15 & Fully\_Connected\_1 & Input = 32 & \multirow{3}{*}{32 \(\times\) 16 \(\times\) 1 \(\times\) 27} & 32 \(\times\) 16 \(\times\) 32 \\  & & Output = 32 & & \\ \hline
16 & ReLU\_3 & - & 32 \(\times\) 16 \(\times\) 32 & 32 \(\times\) 16 \(\times\) 32 \\ \hline
17 & Fully\_Connected\_2 & Input = 16 & \multirow{3}{*}{32 \(\times\) 16 \(\times\) 32} & 32 \(\times\) 16 \(\times\) 16 \\  & & Output = 16 & & \\ \hline
18 & ReLU\_4 & - & 32 \(\times\) 16 \(\times\) 32 & 32 \(\times\) 16 \(\times\) 32 \\ \hline
19 & Label Output & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed information of proposed 1D CNN model.