摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器学习的研究结果在一份新的报告中讨论。根据NewsRx记者从印度西孟加拉邦发回的新闻报道，Research称：“本研究通过实验室规模模型的实验试验，评估了阶梯式曝气系统的最佳曝气效率，旨在确定阶梯式曝气系统的几何特性和水流特性，随后将收集的数据用于评价四种先进机器学习算法的有效性。”即K近邻(KNN)、梯度提升回归器(GBR)、决策树回归器(DTR)、随机森林回归器(RFR)，预测了20℃(E20)时梯级曝气系统的曝气效率。这项研究的财政支持来自印度贾达夫普尔大学水资源工程学院。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Machine Learning are discussed in a new report. According to news reporting originating from West Bengal, India, by NewsRx correspondents, research stated, "The study assesses t he optimal aeration efficiency of a stepwise cascade aeration system through exp erimental trials in a lab scale model setup, aimed at determining the geometric and flow characteristics of the cascade system. Subsequently, the collected data sets are employed to evaluate the efficacy of four advanced machine learning alg orithms, namely K-nearest neighbour (KNN), gradient boosting regressor (GBR), de cision tree regressor (DTR), and random forest regressor (RFR), in predicting th e aeration efficiency at 20 degrees C (E20) of the cascade aeration system." Financial support for this research came from School of Water Resources Engineer ing of Jadavpur University in India.
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