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• W4205621505 abstract "The increasing concerns over the use of nuclear materials for malevolent purposes (i.e., terrorist attacks) have fueled the interest in developing technologies that can detect hidden nuclear material before its use. The process of detecting and identifying nuclear materials for non-reported purposes is under the umbrella of nuclear security. Among several areas that contribute to the security and safeguards of nuclear materials, radiation data analytics has recently been marked as an area of high potential. Therefore, there is an increasing trend in applying machine learning for developing data analysis methods applied to radiation signals aiming at identifying patterns of interest associated with nuclear materials. This chapter aspires in providing a comprehensive survey and discussion of machine learning and data analytics methods pertained to nuclear security. The chapter will also discuss further trends and how data analytics can further enhance nuclear security by effectively analyzing radiation data." @default.
• W4205621505 created "2022-01-26" @default.
• W4205621505 creator A5038301956 @default.
• W4205621505 creator A5074118906 @default.
• W4205621505 date "2021-08-06" @default.
• W4205621505 modified "2023-09-24" @default.
• W4205621505 title "Survey of Machine Learning Approaches in Radiation Data Analytics Pertained to Nuclear Security" @default.
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• W4205621505 doi "https://doi.org/10.1007/978-3-030-76794-5_6" @default.
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