首页|University of Tennessee Reports Findings in Machine Learning (Comparison of Mach ine Learning Approaches for Prediction of the Equivalent Alkane Carbon Number fo r Microemulsions Based on Molecular Properties)
University of Tennessee Reports Findings in Machine Learning (Comparison of Mach ine Learning Approaches for Prediction of the Equivalent Alkane Carbon Number fo r Microemulsions Based on Molecular Properties)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - New research on Machine Learning is th e subject of a report. According to newsreporting originating from Tennessee, U nited States, by NewsRx correspondents, research stated, “Thechemical propertie s of oils are vital in the design of microemulsion systems. The hydrophilic-lipo philicdifference equation used to predict microemulsions’ phase behavior expres ses the oils’ physiochemicalproperties as the equivalent alkane carbon number ( EACN).”
TennesseeUnited StatesNorth and Cent ral AmericaCyborgsEmerging TechnologiesMachine Learning
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