Review on Comparison of automated isotope identification algorithms for NaI (Tl) spectrometer
There are different algorithms for isotope identification in Gamma ray spectra. Previous research applying machine learning algorithms to isotope identification is promising. The algorithm should be able to perform well on spectra contains a mixture of isotopes. Spectral features are difficult to an...
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| Veröffentlicht in: | NeuroQuantology 2022-01, Vol.20 (9), p.5135 |
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| creator | Paleti, Bharathi Sastry, G Hanumat |
| description | There are different algorithms for isotope identification in Gamma ray spectra. Previous research applying machine learning algorithms to isotope identification is promising. The algorithm should be able to perform well on spectra contains a mixture of isotopes. Spectral features are difficult to analyze in case of low resolution detectors. It becomes hard to identify when features overlap. In this work, attempt made in comparison of all existing algorithms, their performance for isotope identification and issues related to each and every algorithm |
| doi_str_mv | 10.14704/nq.2022.20.9.NQ44594 |
| format | Article |
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Previous research applying machine learning algorithms to isotope identification is promising. The algorithm should be able to perform well on spectra contains a mixture of isotopes. Spectral features are difficult to analyze in case of low resolution detectors. It becomes hard to identify when features overlap. 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| fulltext | fulltext |
| identifier | EISSN: 1303-5150 |
| ispartof | NeuroQuantology, 2022-01, Vol.20 (9), p.5135 |
| issn | 1303-5150 |
| language | eng |
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| source | EZB-FREE-00999 freely available EZB journals |
| subjects | Algorithms Automation Calibration Datasets Discriminant analysis Gamma ray spectra Gamma rays Identification Isotopes Libraries Machine learning Maximum entropy method Neural networks Neurons Radiation Sensors Spectrum analysis |
| title | Review on Comparison of automated isotope identification algorithms for NaI (Tl) spectrometer |
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