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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
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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.
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DeepGuard:使用Golden Jackal优化与深度学习模型进行实时威胁识别.

Fatma S Alrayes1, Hamed Alqahtani2, Wahida Mansouri3,4

  • 1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.

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本研究介绍了DeepGuard模型,用于使用深度学习和金子优化在监控视频中实时检测暴力. 该模型在识别威胁方面实现了高精度,提高了公共安全.

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深度学习是一种深度学习.这就是DeepGuard的DeepGuard.金子优化优化 黄金子优化视频监控系统 视频监控系统暴力的承认和承认

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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
  • 安全系统安全系统

背景情况:

  • 在监控中承认暴力对于公共安全和关键基础设施安全至关重要.
  • 深度学习 (DL) 为分析视频录像中复杂的视觉模式提供了先进的功能.
  • 传统的安全措施可以通过智能,实时威胁检测系统来增强.

研究的目的:

  • 开发和评估DeepGuard模型,用于监控视频中实时识别暴力和非暴力事件.
  • 利用深度学习和优化技术来提高威胁检测准确度.
  • 加强公共空间和关键基础设施的积极安全措施.

主要方法:

  • 利用改进的ShuffleNetv2模型从监控图像中提取内在特征.
  • 应用黄金子优化 (GJO) 为ShuffleNetv2模型的最佳超参数调整.
  • 使用长期短期记忆神经网络 (LSTM-NN) 进行最终的暴力检测分类.

主要成果:

  • 在暴力检测任务中,DeepGuard模型表现出最佳的性能.
  • 在基准数据集上达到99.00%和98.63%的高准确率.
  • 在识别威胁的不同性能指标中,其表现优于其他现有技术.

结论:

  • DeepGuard模型有效地使用DL和GJO的组合识别监控视频中的暴力.
  • 拟议的系统提供了一种复杂而聪明的方法来增强传统的安全措施.
  • 实时威胁识别能力有助于积极保护公共安全和关键基础设施.