加强自然灾害分析和废物分类:一种新的VGG-FL方法
S Soundararajan1, R Josphineleela2, Anil Kumar Bisht3
1Department of Computer Science and Engineering, Velammal Institute of Technology, Chennai, Tamil Nadu, India. soundar07@gmail.com.
Environmental monitoring and assessment
|June 21, 2024
概括
一个新的视觉几何组与联合学习 (VGG-FL) 模型准确地分析自然灾害,并对废物进行分类. 这种人工智能方法为环境保护和灾害管理效率提供了显著的改进.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 分析自然灾害和分类废物是关键的,但耗时的任务.
- 传统方法经常面临这些复杂分析的准确性和效率方面的挑战.
- 需要先进的计算方法来改善灾害评估和废物管理.
研究的目的:
- 引入和评估一种新的视觉几何组与联合学习 (VGG-FL) 方法.
- 提高自然灾害分析和废物分类的准确性和效率.
- 将VGG-FL模型的性能与现有方法进行比较.
主要方法:
- 使用视觉几何组 (VGG) 架构与联合学习 (FL) 结合.
- 采用黄金搜索优化 (GSO) 算法进行有效的功能选择.
- 在灾难图像和废物分类数据集上训练了VGG-FL模型.
主要成果:
- VGG-FL模型实现了高性能指标,包括98.52%的准确性.
- 证明了卓越的精度 (97.48%),回忆力 (97.83%),F1得分 (97.58%) 和特异性 (97.12%).
- 在自然灾害分析和废物分类任务上都超过了现有的方法.
结论:
- 在分析自然灾害和分类废物方面,VGG-FL方法非常有效.
- 这种人工智能驱动的方法在环境保护和灾害管理方面取得了重大进展.
- 该模型的高精度和效率突出显示了其在现实世界应用中的潜力.
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