## 5 Conclusions  
A well-trained neural network model could be used as a predictive model for a particular application. Here, a study was made in order to predict the glow curves from the thermoluminescence dose-response of the aluminosilica sample with the use of machine learning algorithms, and we tried to predict the results of four different dosimeter experi  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Network** & **Train.** & **Test.** & **Train.** & **Test.** & **Train.** & **Test.** \\  & **Type** & **5\% Error** & **5\% Error** & **10\% Error** & **10\% Error** & **15\% Error** & **15\% Error** \\ \hline
1 & EBNN & 37.30\% & 32.70\% & 58.35\% & 62.18\% & 80.80\% & 87.95\% \\
2 & LRNN & 33.69\% & 30.46\% & 52.55\% & 54.92\% & 64.71\% & 68.63\% \\
3 & FFBNN & 32.68\% & 20.16\% & 51.80\% & 37.63\% & 64.67\% & 52.24\% \\
4 & GRNN & 97.97\% & 13.53\% & 97.97\% & 24.46\% & 97.97\% & 36.11\% \\
5 & ANFIS & 26.09\% & 27.95\% & 45.12\% & 49.64\% & 57.57\% & 64.24\% \\
6 & CFBNN & 61.51\% & 14.06\% & 73.15\% & 23.92\% & 79.96\% & 33.33\% \\
7 & RBNN & 96.64\% & 0 & 97.13\% & 0 & 97.36\% & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Success rates of networks within 5%, 10%, and 15%.  
ments with seven different artificial intelligence methods (ANFIS, GRNN, RBNN, CFBNN, EBNN, FFBNN, LRNN). The aim here is to make an estimation of the test results that is not measured and to inform the user about the possible results. The application temperature values T (\({}^{\circ}\)C) and Gy dose values were used as input variables, and we attempted to estimate TL signal intensity values. While the networks were being trained, 73.3% of the total data was reserved for training and 26.6% for testing, and during the training phase, test data were never introduced to the networks. The studies were carried out in the direction of 0 error goals and 5000 epochs. The current situation provides valuable information about the network structures that need to be studied for better results. As a result of the studies, the best network performance was obtained with the neural network, which has the Elman backpropagation network type. When comparing the performance comparison of the neural networks, how close they are to 1 for the results of R\({}^{2}\) and also to 0 for the results of MSE, RSE, and MAE is essential for the results. The performance rankings of the other networks are shown in Table 12. In this study, it was observed that artificial intelligence provides valuable results in an application that has very unpredictable results, such as determining the change of signal intensity according to dose. It is predicted that the popularity of ANN in examining the dosimetric properties of materials will continue to increase in the future.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Acknowledgments:** The author expresses gratitude for the financial backing provided through the Scientific Research Projects of Cukurova University FBA 2023 15883 project.  
**Conflicts of Interest:** The author declares no conflict of interest.