摘要
一位新闻记者兼机器人与机器学习每日新闻的新闻编辑-一项关于人工智能的新研究现在是可行的。根据土木工程系B y NewsRx编辑的新闻报道，研究表明："这项研究计划使用12种机器学习(ML)算法预测和分析司机伤害严重程度(DIS)。"新闻编辑们从土木工程部的研究中引用了一句话：“本研究使用了2011年至2020年印度两个城市（Itanagar和Imphal）发生的单车和双车事故的警方报告。预测Itanagar DIS的最佳模型是梯度Boosting Tree(GBT)。事故原因变量对DIS的影响最大。”在Imphal的案例中，列车比率0.70、0.80和0.90的所有K倍交叉验证都是GBT、额外树木和随机森林模型。'事故原因'和'车辆类型'对DIS的影响最大。"
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – A new study on artificial intelligence is now ava ilable. According to news reporting out of the Department of Civil Engineering b y NewsRx editors, research stated, “This study planned to predict and analyze th e driver injury severity (DIS) using twelve machine learning (ML) algorithms.” The news editors obtained a quote from the research from Department of Civil Eng ineering: “Police reports of single and two-vehicle accidents that occurred duri ng 2011-2020 in the two cities of India (Itanagar and Imphal) were used in this study. The best-performing model to predict the DIS for Itanagar was Gradient Bo osting Trees (GBT). ‘Causes of Accident’ variable had shown maximum impact on th e DIS. In the case of Imphal, it was the GBT, Extra Trees, and Random Forest mod els across all k-fold cross-validation for train ratios 0.70, 0.80, and 0.90, re spectively. ‘Causes of Accident’, and ‘Vehicle Type’ had shown maximum impact on the DIS.”
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NSTL