## 4 Conclusions  
In the present article, we introduce a comparative study of two ML algorithms in material thickness classification using five input features, including the extracted dielectric constants from the NIST iterative method in order to improve the prediction of material thickness. Based on the initial scattering parameters, the new five features in the frequency domain can be integrated into two ML algorithms to train the classification model for detecting the thicknesses of test specimens. Four SVM kernels and their optimal pair values were determined by using the grid search method to build up the SVM model. The eight-layered DNN model was established and compared to the SVM one using the same dataset. While the SVM algorithm leads to a higher performance by employing a small amount of data, the DNN algorithm achieves a better performance with a large quantity of data. We also compared the results between the proposed method and the conventional approach. The comparison results state that once the classification model is constructed, it can help to decrease the calculation time as well as enhance the accurate and stable estimations. The high accuracy rate of classification results can show that the selected features are slightly representative and it strongly confirms that the proposed method can be useful in predicting the unknown thickness of a material, which is possibly integrated into the electromagnetic NDT methods.  
In future work, the monostatic measurement setup and regression model will be included instead of the bistatic setup and classification for an estimate of the thickness of specimens. Furthermore, the copula theory techniques will be studied to capture the non-linear dependency between the aforementioned correlated input features and an input feature selection method, such as principle component analysis (PCA), will be adopted to optimize this classification problem.  
Experimental work: P.-T.H.; conceptualization: P.-T.H. and L.-P.H.; data processing and statistical analysis: P.-T.H.; writing--original draft preparation: P.-T.H.; review of the text: I-P.H.; writing--review and editing: I-P.H.; supervision: I-P.H.; project administration: I-P.H.; funding acquisition: I-P.H., All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Basic Science Research Program under Grant 2020R1I1A3057142, and in part by the Priority Research Centers Program through the National Research Foundation of Korea under Grant 2019R1A6A1A03032988.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Thickness** \\ **(mm)** \\ \end{tabular} } & \multicolumn{3}{c}{**Average Estimation Accuracy (\%)**} \\ \cline{2-4}  & **NIST Iterative** & **SVM** & **DNN** \\ \hline
7 & 98.5\% & 100\% & 100\% \\
7.5 & 98.5\% & 100\% & 100\% \\
8 & 98.2\% & 100\% & 100\% \\
8.5 & 96.7\% & 99.9\% & 100\% \\
9 & 97.2\% & 99.1\% & 99.5\% \\
9.5 & 96.5\% & 97.8\% & 100\% \\
10 & 96.2\% & 100\% & 100\% \\
10.5 & 97.2\% & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Thickness estimation accuracy among the NIST iterative method, the SVM model, and the DNN model.