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Updated: Sep 13, 2025

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Design and Analysis for Fall Detection System Simplification
Published on: April 6, 2020
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一个新的DLDRM:基于深度学习的洪水灾害风险管理框架,采用多式联网社交媒体数据
S Sheeba Rachel1, S Srinivasan2
1Department of Information Technology, Sri Sai Ram Engineering College, Chennai, India.
概括
本研究引入了一种新的多式联网深度学习方法,用于使用来自社交媒体的文本和图像数据进行灾害信息分类. DLDRM模型显著提高了灾害风险管理的准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 灾害管理 灾害管理
背景情况:
- 社交媒体对于传播灾难信息至关重要.
- 现有的研究经常使用单模数据 (文本或图像) 来进行灾害分类.
- 在有效整合多式联网社交媒体数据以应对灾害方面存在差距.
研究的目的:
- 开发一种多式联网深度学习方法,整合来自灾难相关社交媒体帖子的文本和视觉数据.
- 引入基于DL的新型灾害风险管理 (DLDRM) 结构,用于分类多式联运灾害数据.
- 评估DLDRM的性能与既有多式联运模式相比.
主要方法:
- 通过结合灾难期间推特帖子中的文本和图像数据,开发了一种多式联网深度学习方法.
- 基于DL的新型灾害风险管理 (DLDRM) 结构被提议用于多式联运灾害数据分类.
- 使用基准数据集,DLDRM与VGG 16,VGG 19,ResNet 50,DenseNet 121和RegNet Y320进行了比较.
主要成果:
- 拟议的DLDRM模型实现了高性能指标:99%的准确性,92.5%的精度,84.08%的回忆和98.5%的F1分数.
- 与现有的最先进的融合技术相比,DLDRM在基准多式联运灾害数据集上表现出优越的性能.
- 该模型有效地强调文本和图像推特的相关方面,以改进分类.
结论:
- 开发的多式联络深度学习技术在灾害信息分类方面取得了重大进展.
- 通过利用集成的社交媒体数据,DLDRM为灾害风险管理提供了有效的框架.
- 这种方法超越了当前的方法,突出了多式联运分析在灾难应对中的潜力.
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