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相关概念视频

Levels of Use of a GIS01:29

Levels of Use of a GIS

52
Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
52
Manipulation and Analysis01:21

Manipulation and Analysis

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GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
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相关实验视频

Updated: Jul 6, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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一个基于车辆网络的智能交通系统,用于智能城市,使用机器学习算法.

J Prakash1, L Murali2, N Manikandan3

  • 1Computer Science and Engineering, Hindusthan College of Engineering and Technology, Coimbatore, Tamil Nadu, India.

Scientific reports
|January 3, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了使用机器学习预测智能城市交通拥堵的智能交通系统. 具有特征选择的基于树的模型显著提高了车辆互联网网络的准确性.

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 运输工程 运输工程

背景情况:

  • 交通拥堵是一个主要的城市挑战,特别是在基础设施和连接有限的地区.
  • 现有的交通监控解决方案通常依赖于广泛的物理基础设施和可靠的互联网,这在发展中国家是不可行的.
  • 互联网流量分析为城市移动等现实问题提供了潜在的解决方案.

研究的目的:

  • 通过使用车辆互联网 (IOV) 提出一个智能交通系统,用于预测智能城市的交通拥堵.
  • 评估各种机器学习模型的有效性,特别是基于树的算法,用于IOV流量分析.
  • 确定功能选择是否提高了这些机器学习模型在预测交通拥堵方面的性能.

主要方法:

  • 利用集体学习与基于树的机器学习策略:决策树,随机森林,额外的树和XGBoost.
  • 实施特征选择 (FS) 技术,以确定交通预测的关键特征.
  • 采用堆叠方法,平均特征选择,以提高检测准确度并最大限度地降低计算成本.

主要成果:

  • 基于树的机器学习方法与特征选择相结合,在基于IOV的车辆网络流量预测方面表现出卓越的性能.
  • 拟议的系统实现了高检测准确度,最小的计算开销.
  • 堆叠方法实现了最高的准确率99.05%,超过KNN (96.6%) 和SVM (98.01%).

结论:

  • 合体学习和特征选择在为智能城市开发智能交通系统方面是有效的.
  • 拟议的系统为IOV网络中交通拥堵预测提供了一个实际的解决方案,即使基础设施有限.
  • 机器学习,特别是基于树的方法,为智能运输解决方案提供了强大的框架.