New Machine Learning Research from K.N. Toosi University of Technology Outlined (Stacking Ensemble Technique Using Optimized Machine Learning Models with Boruta-XGBoost Feature Selection for Landslide Susceptibility Mapping: A Case of ...)

k.n的机器学习新研究Toosi技术大学概述(使用优化机器学习模型和Boruta-XGBoost特征选择的堆积集成技术用于滑坡易感性映射：一个案例.)

Robotics & Machine Learning Daily News2024，Issue(Dec.6) ：117-117.

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New Machine Learning Research from K.N. Toosi University of Technology Outlined (Stacking Ensemble Technique Using Optimized Machine Learning Models with Boruta-XGBoost Feature Selection for Landslide Susceptibility Mapping: A Case of ...)

k.n的机器学习新研究Toosi技术大学概述(使用优化机器学习模型和Boruta-XGBoost特征选择的堆积集成技术用于滑坡易感性映射：一个案例.)

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摘要

由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-调查人员讨论人工智能的新发现。根据新闻报道来自伊朗德黑兰的NewsR X新闻记者的研究表明，“山体滑坡造成重大的人力和财力损失。”l世界不同地区的损失。需要高精度滑坡敏感性图(LSM)以减少山体滑坡的不利影响。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Investigators discuss new findings in artificial intelligence. According to news reportingfrom Tehran, Iran, by NewsR x journalists, research stated, “Landslides cause significant human and financial losses in different regions of the world. A high-accuracy landslide susceptibi lity map (LSM) is requiredto reduce the adverse effects of landslides.”

Key words

K.N. Toosi University of Technology/Teh ran/Iran/Asia/Cyborgs/Emerging Technologies/Investment and Finance/Machine Learning

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出版年

2024

Robotics & Machine Learning Daily News

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