首页|Federal Office for Radiation Protection (BfS) Reports Findings in Machine Learni ng (Development of a High-Resolution Indoor Radon Map Using a New Machine Learni ng-Based Probabilistic Model and German Radon Survey Data)
Federal Office for Radiation Protection (BfS) Reports Findings in Machine Learni ng (Development of a High-Resolution Indoor Radon Map Using a New Machine Learni ng-Based Probabilistic Model and German Radon Survey Data)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating from Berlin, Germ any, by NewsRx correspondents, research stated, “Radon is a carcinogenic, radioa ctive gas that can accumulate indoors and is undetected by human senses. Therefo re, accurate knowledge of indoor radon concentration is crucial for assessing ra don-related health effects or identifying radon-prone areas.”
BerlinGermanyEuropeCyborgsEmergi ng TechnologiesMachine LearningRadioactive ElementsRadon
NETL
NSTL
