首页|Studies from University of Tennessee at Knoxville Add New Findings in the Area o f Machine Learning (Predicting Silicate Glass Geochemistry Using Raman Spectrosc opy and Supervised Machine Learning: Partial Least Square Applications To Amorph ous …)
Studies from University of Tennessee at Knoxville Add New Findings in the Area o f Machine Learning (Predicting Silicate Glass Geochemistry Using Raman Spectrosc opy and Supervised Machine Learning: Partial Least Square Applications To Amorph ous …)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators publish new report on Ma chine Learning. According to news reportingoriginating from Knoxville, Tennesse e, by NewsRx correspondents, research stated, “Here, Raman spectroscopyis used to develop a univariate partial least squares (PLS) calibration capable of quant ifyinggeochemistry in synthetic and natural silicate glass samples. The calibra tion yields eight oxide-specificmodels that allow predictions of silicon dioxid e (SiO2), sodium oxide (Na2O), potassium oxide (K2O),calcium oxide (CaO), titan ium dioxide (TiO2), aluminum oxide (Al2O3), ferrous oxide (FeOT), andmagnesium oxide (MgO) (wt%) in glasses spanning a wide range of compositions, while also providingcorrelation-coefficient matrices that highlight the import ance of specific Raman channels in the regressionof a particular oxide.”
KnoxvilleTennesseeUnited StatesNor th and Central AmericaChemistryCyborgsEmerging TechnologiesGeochemistryMachine LearningMineralsSilicatesSilicic AcidUniversity of Tennessee at Knoxville
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