Isotope Identification in Gamma ray Spectra using Machine Learning Techniques

Performing environmental radiation monitoring is very necessary in order to evaluate the effects of radiation on ecosystems and on the health of humans. When it comes to analyzing radiation spectra, traditional procedures are often labor-intensive and time-consuming. The purpose of this work is to investigate the use of machine learning (ML) methods to improve the analysis of radiation spectra from the environment. We want to achieve our goal of improving the accuracy and efficiency of radiation detection and categorization by using modern machine learning techniques such as neural networks, KNN. According to the findings of our research, machine learning approaches are capable of efficiently processing massive datasets, recognizing intricate patterns, and providing accurate predictions. The findings suggest that there is a substantial possibility of incorporating machine learning-based techniques into real-time environmental monitoring systems, which would result in an improvement in both public safety and environmental protection.