Time-series prediction of gamma-ray counts using XGB algorithm

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dc.contributor.author Mutuku, Vincent
dc.contributor.author Mwema, Joshua M.
dc.contributor.author Mutwiri, Joseph
dc.date.accessioned 2022-09-15T07:21:54Z
dc.date.available 2022-09-15T07:21:54Z
dc.date.issued 2022
dc.identifier.citation Open Journal for Information Technology, 5 (1), 33-40. en_US
dc.identifier.issn 2620-0627
dc.identifier.uri https://centerprode.com/ojit/ojit0501/coas.ojit.0501.03033m.pdf
dc.identifier.uri http://repository.seku.ac.ke/handle/123456789/6867
dc.description.abstract Radioactivity is spontaneous and thus not easy to predict when it will occur. The average number of decay events in a given interval can lead to accurate projection of the activity of a sample. The possibility of predicting the number of events that will occur in a given time using machine learning has been investigated. The prediction performance of the Extreme gradient boosted (XGB) regression algorithm was tested on gamma-ray counts for K-40, Pb-212 and Pb-214 photo peaks. The accuracy of the prediction over a six-minute duration was observed to improve at higher peak energies. The best performance was obtained at 1460keV photopeak energy of K-40 while the least is at 239keV peak energy of Pb-212. This could be attributed to higher number of data points at higher peak energies which are broad for NaITi detector hence the model had more features to learn from. High R-squared values in the order of 0.99 and 0.97 for K-40 and Pb-212 peaks respectively suggest model overfitting which is attributed to the small number of detector channels. Although radioactive events are spontaneous in nature and not easy to predict when they will occur, it has been established that the average number of counts during a given period of time can be modelled using the XGB algorithm. A similar study with a NaITi gamma detector of high channel numbers and modelling with other machine learning algorithms would be important to compare the findings of the current study. en_US
dc.language.iso en en_US
dc.publisher Center for Open Access in Science en_US
dc.subject radioactivity en_US
dc.subject extreme gradient boost en_US
dc.subject regression en_US
dc.subject Gamma-rays en_US
dc.subject photo-peaks en_US
dc.subject NaITi en_US
dc.title Time-series prediction of gamma-ray counts using XGB algorithm en_US
dc.type Article en_US

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