首页|Wuhan University of Science and Technology Reports Findings inMachine Learning (Phase Stability of CH4 and CO2 Hydrates underConfinement Predicted by Machine Learning)
Wuhan University of Science and Technology Reports Findings inMachine Learning (Phase Stability of CH4 and CO2 Hydrates underConfinement Predicted by Machine Learning)
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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 Hubei, Peopl e’s Republic of China, by NewsRx correspondents, research stated,“Understanding the phase stability of gas hydrates under confinement is fundamental to the geo logicalstability evolutions of gas hydrate systems on Earth. Herein, the phase stability of CH and CO hydratesunder confinement is predicted by machine learni ng.”Our news editors obtained a quote from the research from the Wuhan University of Science andTechnology, “Three machine learning models, including support vecto r machine, random forest, andgradient boosting decision tree, are constructed t o predict the phase stability of CH and CO hydratesunder confinement. Our machi ne learning results show that the prediction accuracy of the support vectormach ine model is highest, yet the prediction accuracy of the random forest model is lowest among thosemachine learning models in determining the phase stability of confined gas hydrates. Based on theirperformance in predicting the phase stabi lity of confined gas hydrates, the support vector machine modelwith a training set fraction of 0.7 is finally chosen to deal with the unknown phase stability o f confinedgas hydrates. Importantly, the average accuracy of the support vector machine model can reach more than90% in predicting the unknown p hase stability of both CH and CO hydrates.”
HubeiPeople’s Republic of ChinaAsiaCyborgsEmergingTechnologiesMachine LearningSupport Vector MachinesVect or Machines
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