## 4 Results and Discussion  
### Optimization of Model Parameters  
Python 3.6 was used to build the model. The two models were trained and tested under different parameter values. The main parameter of CNN was the number of convolutional layers. The prediction effect of layer 1 to 4 of CNN was studied. The main parameter of SVR was the type of kernel function. The accuracy of 10-fold cross validation was used to compare CNN and SVR, and 95% confidence interval, average precision, average recall, and average F1 score were given. The results are shown in Table 4. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Parameters**} & **\begin{tabular}{c} **KCV** \\ **Accuracy** \\ \end{tabular} & \begin{tabular}{c} **95\% Confidence** \\ **Hence** \\ **Interval** \\ \end{tabular} & \begin{tabular}{c} **AVG** \\ **Precision** \\ \end{tabular} & \
\begin{tabular}{c} **AVG** \\ **Recall** \\ \end{tabular} &
\begin{tabular}{c} **AVG** \\ **F1 Score** \\ \end{tabular} \\ \hline \multirow{4}{*}{CNN} & One layer & 0.61 & (\(\pm\)0.25) & 0.65 & 0.66 & 0.67 \\  & Two layer & 0.81 & (\(\pm\)0.11) & 0.85 & 0.73 & 0.74 \\  & Three layer & 0.77 & (\(\pm\)0.18) & 0.79 & 0.78 & 0.78 \\  & Four layer & 0.75 & (\(\pm\)0.11) & 0.69 & 0.68 & 0.55 \\ \cline{2-5}  & “linear” & 0.49 & (\(\pm\)0.08) & 0.55 & 0.56 & 0.56 \\  & “poly” & 0.57 & (\(\pm\)0.22) & 0.60 & 0.50 & 0.56 \\  & “rbf” & 0.61 & (\(\pm\)0.18) & 0.55 & 0.60 & 0.59 \\  & “sigmoid” & 0.34 & (\(\pm\)0.21) & 0.32 & 0.35 & 0.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Preliminary evaluation of the CNN and SVR using 10-fold cross-validation. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Evaluation**} & **
\begin{tabular}{c} **Predict** \\ **\end{tabular} \\ \cline{2-3}  & **1** & **0** \\ \hline Real & 1 & TP & FN \\
0 & FP & TN \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Fuzzy rule tables based on fuzzy control. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluation** & **Macro-Average** & **Micro-Average** \\ **Criteria** & \\ \hline Precision & \(Macro\_P=\frac{1}{n}\sum_{i=1}^{n}P_{i}\) & \(Micro\_P=\frac{\sum_{i=1}^{n}TP_{i}}{\sum_{i=1}^{n}TP_{i}+TP_{i}}\) \\ Recall & \(Macro\_R=\frac{1}{n}\sum_{i=1}^{n}R_{i}\) & \(Micro\_R=\frac{1}{n}\sum_{i=1}^{n}TR_{i}+TR_{i}\) \\ F1 score & \(Macro\_F1=\frac{2\times Macro\_P\times Macro\_R}{Micro\_P+Macro\_R}\) & \(Micro\_F1=\frac{2\times Micro\_P\times Micro\_R}{Micro\_P+Micro\_R}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Macro average and micro-average formulas. The kCV accuracy in Table 3 represents the highest 10-fold cross-validation accuracy of SVR and CNN. The minimum cross-validation accuracy of CNN (0.61) was higher than the minimum cross-validation accuracy of SVR (0.34). Therefore, CNN was more suitable for classification and recognition of this data set than SVR. In addition, by adjusting the structure of the neural network model, the cross-validation accuracy can reach 80%. The CNN model obtained the best 95% confidence interval, average precision, average recall, and average F1 score. The convolution long- and short-memory neural network hybrid model (GA-CNN-LSTM) optimized by genetic algorithm is shown in Figure 5. The model structure was composed of convolutional neural network and long- and short-memory neural network. First, the convolution neural network extracted the spatial features of the data [13], and then, the long- and short-memory neural network extracted the temporal features. The whole model combined the advantages of the two neural networks, and during the training of the superparameters of the LSTM neural network, the weight coefficient and offset value of the network were updated and calculated by GA, replacing the traditional gradient descent method. Hence, the whole training and learning process were optimized. Among them, the convolutional neural network (as shown in Figure 4) is a one-dimensional two-layer convolutional neural network model.