使用改进的SSA-CBAM-GNNs进行青铜葡萄酒容器分类的研究
Weifan Wang1, Siming Miao2, Yin Liao2
1School of Design, Jiangnan University, Wuxi, China.
PloS one
|March 21, 2024
概括
本研究介绍了一种先进的算法,用于使用Sparrow Search Algorithm (SSA),CBAM和图形神经网络 (GNNs) 来分类古代青铜饮用器. 新的SSA-CBAM-GNNs方法显著提高了识别文化特征的准确性,以便更好地进行历史分类.
科学领域:
- 考古测量考古测量方法
- 计算机科学 计算机科学
- 材料科学 材料科学 材料科学
背景情况:
- 对古代青铜饮用器具的准确分类对于理解历史王朝和文化背景至关重要.
- 传统的分类方法面临挑战,因为文化特征的复杂性和跨王朝的变化.
- 开发自动化和精确的识别技术对于考古研究至关重要.
研究的目的:
- 为青铜饮用器具提出一个先进的分类算法.
- 提高识别和分类文化特征的准确性和效率.
- 为了应对基于文物分析的王朝分类的挑战.
主要方法:
- 集成Sparrow搜索算法 (SSA) 进行网络优化和加速融合.
- 利用卷积块注意模块 (CBAM) 在图形神经网络 (GNN) 中优化特征提取权重.
- 开发一种新的SSA-CBAM-GNNs算法,将SSA,CBAM和GNN结合起来进行文物分类.
主要成果:
- SSA-CBAM-GNNs算法在准确识别和分类青铜饮用器具的文化特征方面表现出色.
- 通过各种性能指标验证的实验结果证实了算法的有效性.
- 对比实验表明,拟议的算法优于现有方法.
结论:
- 该研究成功开发了一种高效的识别和分类算法,用于青铜饮用器具.
- SSA-CBAM-GNNs算法有效提取和识别关键的文化特征,有助于精确的历史分类.
- 拟议的方法在考古学文物分析领域取得了重大进展.
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