首页|Recent Research from School of Resources & Safety Engineering High light Findings in Machine Learning (Comparative Analysis of Machine Learning Alg orithms for Identifying Cobalt Contamination In Soil Using Spectroscopy)
Recent Research from School of Resources & Safety Engineering High light Findings in Machine Learning (Comparative Analysis of Machine Learning Alg orithms for Identifying Cobalt Contamination In Soil Using Spectroscopy)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily NewsInvestigators publish new report on Machine Learn ing. According to news reporting from Changsha, People's Republic of China, by N ewsRx journalists, research stated, "Cobalt (Co) has been recognized as one of t he most hazardous elements by the United Nations Environmental Program; however, it has received limited attention in previous studies of identifying heavy meta l contamination and has been limited to small, site-scale datasets and few machi ne learning algorithms. To fill this research gap, eight machine learning algori thms were combined with visible and near-infrared reflectance spectroscopy in th is study to develop a large-scale model for classifying Co content in soil." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), National Natural Science Foundation of China (NSFC), Natural Science Foundation of Hunan Province, Young Elite Scientists Sponsorship Progra m by CAST, Unveiling and Commanding Project from Fankou Lead-Zinc Mine, High-Per formance Computing Center of Central South University.
ChangshaPeople's Republic of ChinaAs iaAlgorithmsCobaltCyborgsEmerging TechnologiesHeavy MetalsMachine Le arningTransition ElementsSchool of Resources & Safety Engineer ing
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