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Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
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Updated: Sep 18, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

650

通过使用基于深度maxout网络的入侵检测系统以及海优化来改进智能城市安全.

Wahid Rajeh1, Majed Aborokbah1, Manimurugan S1

  • 1Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Saudi Arabia.

PeerJ. Computer science
|June 26, 2025
PubMed
概括

本研究介绍了智能城市公共交通的资源效率高的入侵检测系统 (IDS). 深度Maxout网络与Walrus优化 (DMN-WO) 模型在检测网络威胁方面实现了高精度.

关键词:
网络安全 网络安全在DMN DMN上使用.在IDS IDS中,您可以使用这就是为什么物联网物联网物联网.在RFE中,使用RFE是必要的.树派. 树派.智慧城市是智慧城市.智能运输是一种智能运输.

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Design and Analysis for Fall Detection System Simplification
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相关实验视频

Last Updated: Sep 18, 2025

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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Design and Analysis for Fall Detection System Simplification
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Design and Analysis for Fall Detection System Simplification

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科学领域:

  • 网络安全 网络安全
  • 物联网 (IoT) 的物联网 (IoT) 的物联网.
  • 智慧城市基础设施 智慧城市基础设施

背景情况:

  • 智能城市利用物联网进行城市优化,增加对安全公共交通的需求.
  • 在城市环境中确保互联的数字基础设施至关重要.

研究的目的:

  • 开发一个强大的入侵检测系统 (IDS) 用于智能城市的公共交通.
  • 解决物联网支持的城市交通系统的独特安全挑战.

主要方法:

  • 开发了一个IDS模型,将深度Maxout网络 (DMN) 与Walrus优化 (WO) 集成在一起.
  • DMN-WO模型具有maxout激活功能,用于在物联网流量中复杂的模式识别.
  • 该模型是为了资源效率而设计的,适合在Raspberry Pi等设备上实时部署.

主要成果:

  • 使用CIC-IDS-2018,CIC-IDS-2029数据集和实时数据进行了DMN-WO模型的训练和验证.
  • 实现了高性能指标:98.06%的准确性,98.50%的检测率,98.81%的精度,98.24%的特异性,98.57%的F1得分.
  • 在智慧城市的公共交通网络中实时检测威胁的有效性.

结论:

  • 该研究为智能城市公共交通提供了弹性网络安全解决方案.
  • DMN-WO模型在基于物联网的城市基础设施中推进了威胁检测和缓解.
  • 这项工作为未来在智能城市环境中的真实世界部署奠定了基础.