Synthetic oversampling for advanced radioactive threat detection

DOIResolve DOI: http://doi.org/10.1109/ICMLA.2015.58
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TypeArticle
Proceedings title2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)
Conference2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), 9-11 December 2015, Miami, FL, USA
ISBN978-1-5090-0287-0
Pages948953
SubjectRadioactive waste; Gamma-ray spectra; Synthetic oversampling; Autoencoders; Class imbalance
AbstractGamma-ray spectral classification requires the automatic identification of a large background class and a small minority class composed of instances that may pose a risk to humans and the environment. Accurate classification of such instances is required in a variety of domains, spanning event and port security to national monitoring for failures at industrial nuclear facilities. This work proposes a novel form of synthetic oversampling based on artificial neural network architecture and empirically demonstrates that it is superior to the state-of-the-art in synthetic oversampling on the target domain. In particular, we utilize gamma-ray spectral data collected for security purposes at the Vancouver 2010 winter Olympics and on a node of Health Canada's national monitoring networks.
Publication date
PublisherIEEE
LanguageEnglish
AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number23000396
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Record identifier603adb86-bdee-4a71-80ec-7b55f87df7f8
Record created2016-07-13
Record modified2016-07-13
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