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Center for Open Access in Science (COAS) OPEN JOURNAL FOR INFORMATION TECHNOLOGY (OJIT) ISSN (Online) 2620-0627 * ojit@centerprode.com |
Time-Series Prediction of Gamma-Ray Counts Using XGB Algorithm Vincent Mutuku * mulwamutukuu@gmail.com * ORCID: 0000-0002-2469-6482 Joshua Mwema * joshuamke@gmail.com * ORCID: 0000-0002-9002-6794 Mutwiri Joseph * mutwirij3@gmail.com Open Journal for Information Technology, 2022, 5(1), 33-40 * https://doi.org/10.32591/coas.ojit.0501.03033m LICENCE: Creative Commons Attribution 4.0 International License. ARTICLE (Full Text - PDF) |
ABSTRACT: KEY WORDS: radioactivity, extreme gradient boost, regression, Gamma-rays, photo-peaks, NaITi. CORRESPONDING AUTHOR: |
REFERENCES: Akkurt, I., Gunoglu, K., & Arda, S. (2014). Detection efficiency of NaI(Tl) Detector in 511–1332 keV energy range,” Science and Technology of Nuclear Installations, vol. 2014, 2014. Althnian, A., AlSaeed, D., Al-Baity, H., Samha, A., Dris, A., Alzakari, N., Abou, A., Elwafa, & Kurdi, H. (2021). Impact of Dataset Size on Classification Performance: An empirical evaluation in the medical domain. Appl. Sci.,11, p. 796. Asaduzzaman, K., Mannan, F., Khandaker, M., Farook, M., Elkezza, A., & Amin, Y. (2015). Assessment of natural radioactivity levels and potential radiological risks of common building materials used in Bangladeshi dwellings. PLOS ONE,10. Aslam, M., Gul, R., Ara, T., & Hussain, M. (2012). Assessment of radiological hazards of naturally occurring radioactive materials in cement industry. Radiation Protection Dosimetry, 3(151), 483-488. Connell, C., & Pike, G. (2005). Encyclopedia of Analytical Science (Second Edition). Ebbing, D., & Wentworth (1995). In Experiments in Introductory Chemistry, p. 221. Hossain, I., Sharip, N., & Viswanathan, S. (2012). Efficiency and resolution of HPGe and NaI(Tl) detectors using gamma-ray spectroscopy. Scientific Research and Essays, 7(1), 86-89. Keeble, J., & Rios, A. (2020). Machine learning the deuteron. Physics Letters B. Klaus, G., & John, C. (1995). Neural networks that learn to predict probabilities: Global models of nuclear stability and decay. Neural Networks, 8(2), 291-311. James, P., & Christine, C. (2015). Compton scattering of Cs-137 Gamma rays. Department of Physics and Astronomy, The University of Tennessee, 401 Nielsen Physics Building, Knoxville. Niu, M., Liang, Z., Sun, H., Long, W., & Niu, F. (2019). Predictions of nuclear β-decay half-lives with machine learning and their impact on r-process nucleosynthesis, Physics Review C. NRCC (1999). Natural radioactivity and radiation. National Academies Press (US), Washington (DC). S., P., Freitas, A., & John, C. (2019). Experiments in machine learning of alpha-decay half-lives. Nuclear Theory. Saxena, G., Sharma, K., & Prafulla, S. (2021). Modified empirical formulas and machine learning for α-decay systematics. Journal of Physics G: Nuclear and Particle Physics, 48(5). Sharma, N., Singh, J., Esakki, C. A., & Tripath, R. (2016). A study of the natural radioactivity and radon exhalation rate in some cements used in India and its radiological significance. Journal of Radiation Research and Applied Sciences, 9(1), 47-56. Viruthagiri, G., Rajamannan, B., & S. J. K. (2013). Radioactivity and associated radiation hazards in ceramic raw materials and end products. Radiation Protection Dosimetry, 3(157), 383-391.
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