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BAOS-CNN: 一个新的深度神经进化算法,用于多种海草检测.

Md Kislu Noman1, Syed Mohammed Shamsul Islam1, Seyed Mohammad Jafar Jalali1

  • 1School of Science, Edith Cowan University, Perth, Australia.

PloS one
|June 25, 2024
PubMed
概括

本研究介绍了一种新的深度神经进化 (DNE) 模型,使用增强原子轨道搜索 (BAOS) 算法来自动化卷积神经网络 (CNN) 设计用于海草图像识别. BAOS-CNN模型在绘制海草物种的地图上取得了卓越的准确性.

科学领域:

  • * * 海洋生物学 海洋生物学
  • * 计算机科学 计算机科学
  • * 人工智能 * 人工智能

背景情况:

  • * 深度学习,特别是卷积神经网络 (CNN),显示出海草图像识别的前景.
  • *手动的架构工程和CNN的超参数调节是资源密集的.
  • * 自动化CNN设计对于推进海洋生态监测至关重要.

研究的目的:

  • * 提出一个深度神经进化 (DNE) 模型,用于自动化CNN架构工程和超参数调整.
  • * 引入一种新的元启发算法,即增强原子轨道搜索 (BAOS),用于优化CNN.
  • * 评估拟议的BAOS-CNN模型在海草图像识别中的性能.

主要方法:

  • * 增强原子轨道搜索 (BAOS) 算法的开发,这是一种增强原子轨道搜索 (AOS) 的增强,包含了莱维飞行.
  • *使用BAOS算法实现一个深度神经进化 (DNE) 模型 (BAOS-CNN).
  • *对BAOS-CNN模型在多种海草数据集上的训练和评估,并与其他六种优化算法进行比较.

主要成果:

  • *BAOS-CNN模型在多种海草数据集上的七个基于进化的CNN模型中获得了最高的整体准确性 (97.48%).
  • * 在"深海草"数据集上获得了最先进的整体准确性 (92.30%用于四个类别,93.5%用于五个类别).

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  • * 拟议的方法有效地自动化了CNN设计,用于复杂的生态图像分析.

    • 结论:

    • * 拟议的BAOS-CNN模型为CNN架构工程和超参数调整提供了一种有效的自动化方法.
    • * 这种方法显著提高了海草图像识别和绘制的准确性.
    • *开发的算法有可能通过先进的AI应用来彻底改变海洋生态研究.