An Effective Deep Learning Approach for the Estimation of Proton Energy by Using Artificial Neural Network

The prediction of proton energy shows a key part in various scientific and technological studies including particle physics, medical imaging, and radiation therapy. In the last years the regression techniques study a lot trying to discover the difficult relationships among proton properties energy, needing to be studying of additional advanced techniques. This study proposed Artificial Neural Networks (ANN) as a tool for predicting types of proton energy (Max, Total and A vg). This paper proposed a comprehensive methodology for developing an ANN model for proton energy predection, covering data preparation, model architecture design, evaluation, and prediction. This proposed can ability to learn difficult models and non-linear relationships from large datasets, making them well-suited for this task. The ANN proposed achieved the highest R2 on the Total of proton energy predation of 0.96 % while the best mean_squared_error on 0.03668 for testing dataset. The results displayed the efficiency and accuracy of the proposed ANN model in predicted proton energy. The findings highlight the ability of ANNs as a tool for proton energy prediction.