分析
核辐射
计算机科学
数据分析
过程(计算)
核武器
数据科学
计算机安全
工程类
数据挖掘
核工程
核物理学
物理
操作系统
作者
Miltiadis Alamaniotis,Alexander Heifetz
出处
期刊:Learning and analytics in intelligent systems
日期:2021-08-06
卷期号:: 97-115
被引量:4
标识
DOI:10.1007/978-3-030-76794-5_6
摘要
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.
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