首页|Studies from Guangzhou University Update Current Data on Machine Learning (Machi ne Learning Approaches for Monitoring Environmental Metal Pollutants: Recent Adv ances In Source Apportionment, Detection, Quantification, and Risk Assessment)
Studies from Guangzhou University Update Current Data on Machine Learning (Machi ne Learning Approaches for Monitoring Environmental Metal Pollutants: Recent Adv ances In Source Apportionment, Detection, Quantification, and Risk Assessment)
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 – Investigators discuss new findings in Machine Learning. According to news reporting originating from Guangzhou, People ’s Republic of China, by NewsRx correspondents, research stated, “Metal pollutan ts pose significant and enduring threats to human health and the environment, ma inly due to their non-biodegradable nature. Traditional monitoring of these poll utants involves costly and time-consuming analytical methods.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), National Natural Science Foundation of Guangdong Province, A nalysis and Center of Guangzhou University.
GuangzhouPeople’s Republic of ChinaA siaCyborgsEmerging TechnologiesMachine LearningGuangzhou University
NETL
NSTL
