首页|University of Jaffna Researchers Have Provided New Study Findings on Machine Lea rning (Prediction of moisture content of cementstabilized earth blocks using so il characteristics, cement content, and ultrasonic pulse velocity)
University of Jaffna Researchers Have Provided New Study Findings on Machine Lea rning (Prediction of moisture content of cementstabilized earth blocks using so il characteristics, cement content, and ultrasonic pulse velocity)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ar tificial intelligence. According to news reporting originating from the Universi ty of Jaffna by NewsRx correspondents, research stated, "This article investigat es the importance of moisture content in cement-stabilized earth blocks (CSEBs) and explores methods for their prediction using machine learning." The news journalists obtained a quote from the research from University of Jaffn a: "A key aspect of the research is the development of accurate moisture content prediction models. The study compares the performance of various machine learni ng models, and XGBoost emerges as the most promising model, demonstrating superi or accuracy in predicting moisture content based on factors like soil properties,cement content, and ultrasonic pulse velocity (UPV). The study employs SHAP (S Hapley Additive exPlanations) to understand how these features influence the mod el's predictions. UPV is the most significant factor affecting predicted moistur e content, followed by cement content and soil properties like uniformity coeffi cient. Also, the study explores the possibility of using a reduced set of featur es for moisture content prediction."
University of JaffnaCyborgsEmerging TechnologiesMachine Learning
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