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适应性深度聚类集成DINOv2嵌入,图表注意力和生物灵感优化

Mai Abdrabo1, Hossam Refaat2, Mohammed Abdallah2

  • 1Information Systems Department, Faculty of Computers and Informatics, Suez Canal University, Ismailia, 41522, Egypt. mai_abdrabo86@yahoo.com.

Scientific reports
|December 30, 2025
PubMed
概括

本研究介绍了一种新的自适应深度集群框架,集成DINOv2,图形注意网络 (GAT) 和蝙蝠算法优化. 该方法通过动态改进语义嵌入和结构信息以提高性能来增强无监督图像集群.

关键词:
蝙蝠的算法就是蝙蝠的算法.综合评价指数是指综合的评价指数.这就是DINOv2的意义.图表注意力网络 图表注意力网络没有监督的集群.

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