## 4 Experimental Results
### Result and Discussion
#### 4.4.1 SVMR Method  
The support vector machine regression method was tested with various learning parameters. An epsilon is the loss function for SVMR. The model was tested with various values, which range from [0.11, 0.12...0.16]. The result illustrated in Figure 3. shows that 0.12 is the optimal value for predicting cell voltage by using SVMR (Table 2). The experiment was stopped at 0.16 (Table 3). as there is an increase in error value. In addition to this test, the experiment was conducted with various kernel functions to identify the optimal kernel function (RBF, poly, linear, sigmoid) for the data. The kernel functions help to solve the complexity of the data [38]. It is necessary to select optimized function for data. It is observed that the kernel function RBF gives better result as listed in Table 4 when compared to other functions. Figures 4-6 illustrate the comparison of various kernel functions with humidity levels of 10%, 50%, and 100% (Table 5). The training of the SVM regression model turns out to be the most expensive, as it necessitates the cross-validation tweaking  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Kernel Function** & \multicolumn{5}{c}{**Rbf**} \\ \hline
**RMSE** & 0.0033 & 0.0026 & 0.0029 & 0.0035 & 0.0043 & 0.0054 \\ \hline \(R^{2}\) & 0.9574 & 0.9603 & 0.956 & 0.9469 & 0.9344 & 0.9184 \\ \hline
**Kernel Function** & \multicolumn{5}{c}{**poly**} \\ \hline
**RMSE** & 0.0123 & 0.0128 & 0.0131 & 0.0136 & 0.0146 & 0.0158 \\ \hline \(R^{2}\) & 0.8325 & 0.8252 & 0.8208 & 0.8139 & 0.80007 & 0.784 \\ \hline
**Kernel Function** & \multicolumn{5}{c}{**Linear**} \\ \hline
**RMSE** & 0.0043 & 0.0044 & 0.0059 & 0.0085 & 0.0111 & 0.0134 \\ \hline
**\(R^{2}\)** & 0.9405 & 0.9391 & 0.9191 & 0.8839 & 0.0848 & 0.8176 \\ \hline
**Kernel Function** & \multicolumn{5}{c}{**sigmoid**} \\ \hline
**RMSE** & 0.0057 & 0.0062 & 0.0072 & 0.0084 & 0.0103 & 0.0115 \\ \hline
**\(R^{2}\)** & 0.9139 & 0.9066 & 0.8908 & 0.8734 & 0.8449 & 0.8261 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Kernel function comparison (10% humidity).  
Figure 3: Learning parameter epsilon.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**epsilon** & 0.11 & 0.12 & 0.13 & 0.14 & 0.15 & 0.16 \\ \hline
**Kernel Function** & \multicolumn{5}{c}{**rbf**} \\ \hline
**RMSE** & 0.0011 & 0.0015 & 0.002 & 0.0027 & 0.003 & 0.0036 \\ \hline
**\(R^{2}\)** & 0.97 & 0.9606 & 0.9478 & 0.9307 & 0.9199 & 0.9079 \\ \hline
**Kernel Function** & \multicolumn{5}{c}{**poly**} \\ \hline
**RMSE** & 0.0049 & 0.0055 & 0.0062 & 0.0071 & 0.0068 & 0.0074 \\ \hline
**\(R^{2}\)** & 0.8745 & 0.8596 & 0.8404 & 0.8184 & 0.825 & 0.8088 \\ \hline
**Kernel Function** & \multicolumn{5}{c}{**Linear**} \\ \hline
**RMSE** & 0.0025 & 0.0036 & 0.0036 & 0.0037 & 0.0039 & 0.0039 \\ \hline
**\(R^{2}\)** & 0.934 & 0.9068 & 0.9068 & 0.904 & 0.8991 & 0.8991 \\ \hline
**Kernel Function** & \multicolumn{5}{c}{**sigmoid**} \\ \hline
**RMSE** & 0.0049 & 0.0048 & 0.0048 & 0.006 & 0.0073 & 0.0079 \\ \hline
**\(R^{2}\)** & 0.8742 & 0.875 & 0.8758 & 0.8459 & 0.8119 & 0.7983 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Kernel function comparison (50% humidity).  
Figure 4: Kernel function comparison for 10% humidity.  
Figure 5: Kernel function comparison for 50% humidity.  
Figure 6: Kernel function comparison for 100% humidity.  
Figure 7: SVMR model with various humidity levels.