摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑-每日新闻-调查人员发布了关于人工智能的新报告。根据NewsRx记者从马尼拉发回的新闻报道，研究表明，“地震事件仍然是一个巨大的威胁，在脆弱地区造成生命损失和广泛破坏。”这项研究的财政支持者包括科学和技术工程研究和发展部。我们的新闻记者从德拉萨大学的研究中获得了一句话：“土壤液化是一种复杂的现象，土壤颗粒失去限制，带来了很大的风险。现有的液化评估常规简化程序和一些当前的机器学习技术揭示了这些模拟和缺点。利用公开的液化案例历史数据库，摘要:利用基于粗糙集的机器学习工具,建立了一个基于规则的液化触发分类模型。通过一系列的步骤,选择了一组以if-then语句形式存在的32条规则作为最佳规则集,其中一些规则显示了预期的输出。有几个规则提出了触发液化的临界值。管理细粒土的规则出现了，并挑战了对土壤液化的一些共同理解。
Abstract
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 Manila, Phil ippines, by NewsRx correspondents, research stated, "Seismic events remain a sig nificant threat, causing loss of life and extensive damage in vulnerable regions ." Financial supporters for this research include Department of Science And Technol ogy Engineering Research And Development For Technology. Our news reporters obtained a quote from the research from De La Salle Universit y: "Soil liquefaction, a complex phenomenon where soil particles lose confinemen t, poses a substantial risk. The existing conventional simplified procedures, an d some current machine learning techniques, for liquefaction assessment reveal l imitations and disadvantages. Utilizing the publicly available liquefaction case history database, this study aimed to produce a rule-based liquefaction trigger ing classification model using rough set-based machine learning, which is an int erpretable machine learning tool. Following a series of procedures, a set of 32 rules in the form of IF-THEN statements were chosen as the best rule set. While some rules showed the expected outputs, there are several rules that presented a ttribute threshold values for triggering liquefaction. Rules that govern fine-gr ained soils emerged and challenged some of the common understandings of soil liq uefaction."
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