OTERO, ANDRE G.L.POTIENS JUNIOR, ADEMAR J.CALZETA, EDUARDO P.MARUMO, JULIO T.2020-01-062020-01-06OTERO, ANDRE G.L.; POTIENS JUNIOR, ADEMAR J.; CALZETA, EDUARDO P.; MARUMO, JULIO T. Comparing deep learning architectures on gamma-spectroscopy analysis for nuclear waste characterization. In: INTERNATIONAL NUCLEAR ATLANTIC CONFERENCE, October 21-25, 2019, Santos, SP. <b>Proceedings...</b> Rio de Janeiro: Associação Brasileira de Energia Nuclear, 2019. p. 1278-1283. Disponível em: http://repositorio.ipen.br/handle/123456789/30561.http://repositorio.ipen.br/handle/123456789/30561Neural networks, particularly deep neural networks, are used nowadays with great success in several tasks, such as image classification, image segmentation, translation, text to speech, speech to text, achieving super-human performance. In this study, we explore the capabilities of deep learning on a new field: gamma-spectroscopy analysis, comparing the classification performance of different deep neural networks architectures. We choose VGG-16, VGG-19, Xception, ResNet, InceptionV3 and MobileNet architectures which are available through the Keras Deep Learning framework to identify several different radionuclides (Am-241, Ba- 133, Cd-109, Co-60, Cs-137, Eu-152, Mn-54, Na-24, and Pb-210). Using an HPGe detector to acquire several gamma spectra, from different sealed sources to created a dataset that was used for the training and validation of the neural networks comparison. This study demonstrates the strengths and weakness of applying deep learning on gamma-spectroscopy analysis for nuclear waste characterization.1278-1283openAccessartificial intelligencecomputer architecturegamma spectroscopyhigh-purity ge detectorsneural networksradioactive waste managementradioactive wastesradioisotopessealed sourcesTexto completo de evento0000-0003-3010-96910000-0002-4098-0272https://orcid.org/0000-0003-3010-9691