首页|Studies from Missouri University of Science and Technology Have Provided New Inf ormation about Machine Learning (Enhancing Frp-concrete Interface Bearing Capaci ty Prediction With Explainable Machine Learning: a Feature Engineering Approach and …)
Studies from Missouri University of Science and Technology Have Provided New Inf ormation about Machine Learning (Enhancing Frp-concrete Interface Bearing Capaci ty Prediction With Explainable Machine Learning: a Feature Engineering Approach and …)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Researchers detail new data in Machine Learning. According to news reporting out ofRolla, Missouri, by NewsRx editors , research stated, “This study introduces a novel approach to predict theshear bearing capacity of FRP-concrete interfaces using explainable machine learning. Eight algorithms areemployed: three standalone models (Artificial Neural Networ k, Support Vector Regression, and DecisionTree) and five ensemble learning mode ls (Bagging, Random Forest, Adaptive Boosting, Gradient Boosting,and Extreme Gr adient Boosting).”
RollaMissouriUnited StatesNorth an d Central AmericaCyborgsEmerging TechnologiesEngineeringMachine LearningMissouri University of Science and Technology
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