碎形和混乱地图增强的灰狼优化,用于在深度卷积神经网络中强大的火灾检测
Yassine Bouteraa1,2, Mohammad Khishe3,4,5
1Department of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, 11942, Al-Kharj, Saudi Arabia. yassine.bouteraa@isbs.usf.tn.
Scientific reports
|April 3, 2025
概括
这项研究通过使用灰狼优化和碎形混乱地图来增强深层卷积神经网络架构. 这种新的方法实现了87.37%的准确性,在9个数据集上表现优于其他23个分类器.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 深度卷积神经网络 (CNN) 对于图像分类至关重要.
- 美国有线电视新闻网 (CNN) 的架构自我设计带来了重大挑战.
- 优化CNN的探索和利用是提高性能的关键.
研究的目的:
- 引入一种新的方法来增强CNN架构的自我设计.
- 提高CNN的勘探和开发能力.
- 为了提高深度学习模型的分类准确性.
主要方法:
- 利用灰狼优化器 (GWO) 进行增强搜索.
- 实施一个多尺度的碎形混乱地图搜索方案.
- 集成GWO和混乱地图用于CNN架构优化.
主要成果:
- 在95个试验中实现了87.37%的分类准确性.
- 在九个基准数据集上表现优于23个最先进的分类器.
- 在CNN架构增强中表现出卓越的性能.
结论:
- 提出的生物灵感和混乱/碎形方法显著提升了CNN架构.
- 这种方法为未来的深度学习研究提供了一个有希望的方向.
- 神经架构的有效优化导致了更好的分类任务.
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