## 4 Conclusions  
In this research, the TL properties of the plagioclase mineral and the prediction of TL glow curves at different ionizing radiation doses with an ANN were studied, and the following results were obtained:  
Figure 10: TL glow curves obtained from experimental, LM, and BR optimizers for (**a**) 0.5 Gy, (**b**) 4 Gy, (**c**) 250 Gy, and (**d**) 450 Gy radiation doses. * When the TL response of the mineral to ionizing radiation was evaluated, it was revealed that it showed a linear dose response in the dose range of 0.3 to 550 Gy. * The TL glow curve of the mineral consists of five different TL glow peaks--in other words, TL glow peaks corresponding to five different electron traps--in accordance with general order kinetics. * These glow peaks forming the TL glow curve have activation energies (trap depth) of 0.842, 0.848, 0.852, 0.852, 0.857, and 0.890 eV corresponding to temperatures of 83, 112, 163, 216, and 277 \({}^{\circ}\)C, respectively. * Among the optimization algorithms tested, it can be said that the BR algorithm is the most suitable optimization algorithm that can be used in the prediction of TL glow curves obtained at different radiation doses of plagioclase mineral. * Although the LM algorithm is advantageous in terms of fast convergence and high precision on small datasets, its tendency toward overfitting requires additional refinement techniques. * Although SCG is computationally efficient, it requires longer training times and larger training sets to achieve optimum accuracy. * Unlike LM and SCG, BR emerged as the most appropriate choice in this study by establishing a good balance between bias and variance and was able to produce reliable results in wide dose ranges thanks to its high generalization performance. Although BR emerged as the most appropriate algorithm approach in this study, future studies can use approaches such as hybrid and deep learning algorithms that combine the strengths of these three algorithms to further improve model performance on different datasets. The ANN can be trained on experimental radiation curve data to predict unknown doses, correct for fading effects, or reconstruct irradiation timing, as demonstrated in recent studies [12; 15] using both simulated and real-world datasets. Furthermore, the ANN architectures such as feed forward or shallow networks can accurately predict fade time and irradiation dose simultaneously, outperforming conventional deconvolution techniques in terms of robustness and computational time. Furthermore, in mineral characterization studies where TL signals are affected by complex defect distributions or anisotropic crystal structures (as seen in plagioclase feldspars), the flexibility of ANNs allows them to learn from data patterns without assuming specific physical models. This becomes particularly valuable when investigating variables such as trap distributions, compositional variations, or irradiation environments. Finally, as the volume of luminescence data increases, integrating machine learning (ML) frameworks, including convolutional networks or ensemble learning, with ANN-based regression models enables a method for real-time, automated dose assessment or classification tasks. Therefore, the synergy between luminescence analysis and ANN methodologies offers significant potential for researchers to advance both basic and applied research in the fields of dosimetry, geochronology, and nuclear physics. Conceptualization, M.Y. ; methodology, M.Y. and E.U. ; software, M.Y. and E.U. ; validation, M.Y. and E.U. ; formal analysis, M.Y. ; investigation, M.Y. and E.U. ; resources, M.Y. ; data curation, M.Y. and E.U. ; writing--original draft preparation, M.Y. ; writing--review and editing, M.Y. and E.U. ; visualization, M.Y. and E.U. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Data Availability Statement: The original contributions presented in this study are included in the article.