IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0295690.html
   My bibliography  Save this article

Research on bronze wine vessel classification using improved SSA-CBAM-GNNs

Author

Listed:
  • Weifan Wang
  • Siming Miao
  • Yin Liao

Abstract

This article proposes an advanced classification algorithm for bronze drinking utensils, taking into account the complexity of their cultural characteristics and the challenges of dynasty classification. The SSA-CBAM-GNNs algorithm integrates the Sparrow Search Algorithm (SSA), Spatial and Spectral Attention (CBAM) modules, and Graph Neural Networks (GNNs). The CBAM module is essential for optimizing feature extraction weights in graph neural networks, while SSA enhances the weighted network and expedites the convergence process. Experimental results, validated through various performance evaluation indicators, illustrate the outstanding performance of the improved SSA-CBAM-GNNs algorithm in accurately identifying and classifying cultural features of bronze drinking utensils. Comparative experiments confirm the algorithm’s superiority over other methods. Overall, this study proposes a highly efficient identification and classification algorithm, and its effectiveness and excellence in extracting and identifying cultural features of bronze drinking utensils are experimentally demonstrated.

Suggested Citation

  • Weifan Wang & Siming Miao & Yin Liao, 2024. "Research on bronze wine vessel classification using improved SSA-CBAM-GNNs," PLOS ONE, Public Library of Science, vol. 19(3), pages 1-15, March.
  • Handle: RePEc:plo:pone00:0295690
    DOI: 10.1371/journal.pone.0295690
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0295690
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0295690&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0295690?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Shuzhan Ye & Xiaoliang Xu & Yuxiang Wang & Tao Fu, 2023. "Efficient Complex Aggregate Queries with Accuracy Guarantee Based on Execution Cost Model over Knowledge Graphs," Mathematics, MDPI, vol. 11(18), pages 1-28, September.
    2. Jun Wu & Wenzhe Luo & Jiaru Chen & Rungtai Lin & Yanru Lyu, 2023. "Design Ritual into Modern Product: A Case Study of Chinese Bronze Ware," Sustainability, MDPI, vol. 15(17), pages 1-18, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      More about this item

      Statistics

      Access and download statistics

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0295690. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.