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相关概念视频

Stratified Sampling Method01:16

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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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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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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Updated: May 7, 2025

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基于混合采样的预测分析技术,用于管理智能城市的不平衡数据.

Ayushi Chahal1, Preeti Gulia1, Nasib Singh Gill1

  • 1Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, Haryana, India.

Heliyon
|January 6, 2025
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概括

本研究引入了一种新的预测分析方法,用于处理智能城市物联网系统中的不平衡数据. 该技术通过在机器学习预测之前平衡数据来改善决策.

关键词:
在 ENN ENN ENN 里面.物联网 (IoT) 的物联网 (IoT) 的物联网.机器学习 机器学习在PCA中,PCA是PCA.预测分析是一种预测分析.在SMOTE中使用.传感器 传感器 传感器

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科学领域:

  • 智慧城市技术 智慧城市技术
  • 数据科学数据科学数据科学
  • 人工智能的人工智能

背景情况:

  • 智能城市依赖人工智能 (AI) 进行自主决策,需要从物理世界获得大量数据.
  • 物联网 (IoT) 传感器设备收集环境数据用于预测分析,但这些数据往往不平衡.
  • 不平衡的数据,如果未经预处理,可能会导致人工智能驱动的决策存在重大错误.

研究的目的:

  • 提出一种新的预测分析技术,用于管理智能城市物联网环境中的不平衡数据.
  • 开发一个强大的管道,整合数据预处理和机器学习,以准确预测.
  • 通过解决数据不平衡的挑战,提高智能城市的决策能力.

主要方法:

  • 设计了一个管道,包括主要组件分析 (PCA),混合采样方法 (SMOTE+ENN) 和机器学习 (ML) 预测模型.
  • 使用SMOTE+ENN将不平衡的数据集转化为平衡状态.
  • 使用ML算法对处理的数据集进行聚类和预测,使用大型智能城市物联网数据集 (405,184条记录).

主要成果:

  • 拟议的技术准确地预测了物联网设备附近的人类存在.
  • 评估指标包括准确性,精度,回忆,F1得分和曲线下面积 (AUC) 显示出强的表现.
  • 对于集群0,准确度为0.79,精度为1.0,召回为0.79,F1得分为0.87,AUC为0.88. 对于集群1,准确度为0.86,精度为0.99,回忆为0.86,F1得分为0.92,AUC为0.92.

结论:

  • 开发的技术有效地管理不平衡的数据用于智能城市的预测分析.
  • 拟议的方法在各种现实应用中有望改善决策.
  • PCA,SMOTE+ENN和ML的集成为智能城市物联网数据挑战提供了强大的解决方案.