## 4 Results and Discussions
### Lknn  
Using this method, four different network structures working with different training functions with one or two hidden layers were studied. Table 9 presents the main characteristics of the networks. Performance comparisons of the networks are also included in Table 10. According to the test results, the fourth experiment with two hidden layers and the _trainoss_ training function produced the best results. The results of this training are shown in Figure 11a. Figures 11b and 11c, respectively, illustrate network structure and representation. The results in the first five columns in Table 10 are taken from the neural network toolbox. Only the fifth column data are included here because the data of the first four columns are not displayed in the toolbox. Only visual representations are available in the MSE result graph. The data in the other column were calculated from the relevant formulas. In order to understand whether a material has dosimetric properties, dose-response experiments, varying heating rate experiments, and reusability and fading experiments should be performed against the exposed dose. The amount of research on how machine learning can be used in this regard continues to increase. [PERSON] et al. determined the TL signal intensity depending on the applied dose [32]. It is stated by the authors that the method, which was designed for specific zinc borates and allowed the application of standard learning algorithms to the neural network, encountered limitations that limited its applicability to different examples. [PERSON] et al. used artificial intelligence methods in fading time [11], heating rate, and reusability studies [33]. In addition, artificial intelligence methods were used by [PERSON] et al. on the duration or source of the exposure dose [34]; [PERSON] et al. used them on the estimation of the irradiation dose [35]; and [PERSON] et al. used them to determine kinetic properties such as activation energy and frequency factor. [PERSON] et al. [36] studied the effect of machine learning on TL features in rock salt samples. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **Train.** & **Valid.** & **Test.** & **All.** & **MSE** & **Train & **Test.** & **Train.** & **Test.** & **Train** & **Test** \\  & **R** & **R** & **R** & **R** & **R\({}^{2}\)** & **R\({}^{2}\)** & **R\({}^{2}\)** & **R\({}^{2}\)** & **R\({}^{2}\)** & **MAE** & **MAE** \\ \hline
1 & - & - & - & - & 7.96 \(\times\) 10\({}^{7}\) & 0.45 & 0.40 & 6575.9 & 3965.81 & 1.87 & 1.45 \\
2 & - & - & - & - & 3.67 \(\times\) 10\({}^{5}\) & 0.99 & 0.72 & 507.67 & 2389.23 & 0.25 & 0.39 \\
3 & - & - & - & - & 1.06 \(\times\) 10\({}^{6}\) & 0.99 & 0.98 & 762.05 & 476.37 & 0.35 & 0.19 \\
4 & - & - & - & - & 8.47 \(\times\) 10\({}^{5}\) & 0.99 & 0.98 & 706.91 & 463.16 & 0.31 & 0.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance comparisons of the LRNNs. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **Train.** & **Valid.** & **Test.** & **All.** & **MSE** & **Train** & **Test.** & **Train.** & **Test.** & **Train** & **Test** \\  & **R** & **R** & **R** & **R** & **R\({}^{2}\)** & **R\({}^{2}\)** & **R\({}^{2}\)** & **R\({}^{2}\)** & **R\({}^{2}\)** & **R\({}^{2}\)** & **R\({}^{2}\)** & **MAE** & **MAE** \\ \hline
1 & 0.9581 & 0.9591 & 0.9412 & 0.9561 & 1.37 \(\times\) 10\({}^{7}\) & 0.91 & 0.90 & 2554.6 & 1220.40 & 1.15 & 0.57 \\
2 & 0.9978 & 0.9968 & 0.9977 & 0.9976 & 8.37 \(\times\) 10\({}^{5}\) & 0.99 & 0.98 & 845.97 & 628.65 & 0.43 & 0.30 \\
3 & 0.9992 & 0.9990 & 0.9990 & 0.9991 & 2.45 \(\times\) 10\({}^{5}\) & 0.99 & 0.72 & 314.62 & 2227.87 & 0.21 & 0.54 \\
4 & 0.9992 & 0.9991 & 0.9984 & 0.9991 & 2.39 \(\times\) 10\({}^{5}\) & 0.99 & 0.82 & 366.35 & 1798.40 & 0.20 & 0.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance comparisons of the FFBNNs.