基于混合预训练模型的特征提取,用于加强联合学习环境中的室内场景分类
Monica Dutta1, Deepali Gupta2, Vikas Khullar2
1Department of Computer Engineering & Applications, Institute of Engineering & Technology, GLA University, Mathura, India.
Scientific reports
|August 21, 2025
概括
这项研究介绍了室内场景分类的新型MultiData模型,将深度学习与联合学习相结合,以提高准确性和数据隐私. 该模型实现了近乎完美的分类,在智能环境中表现优于现有方法.
科学领域:
- 计算机视觉
- 人工智能
背景情况:
- 室内场景分类对于智能应用至关重要,但受到复杂的环境因素的挑战.
- 像SVM和KNN这样的传统方法在各种室内环境中提供有限的性能.
- 深度学习 (DL) 模型,特别是CNN,具有高级特征提取,以提高准确性.
研究的目的:
- 提出和实施一个新的多数据模型来加强室内场景的分类.
- 将DL与线性差异分析 (LDA) 和联合学习 (FL) 整合起来,以获得更高的性能和数据隐私.
- 评估MultiData模型与已有的DL架构和基于FL的培训的有效性.
主要方法:
- 开发了新的多数据模型,结合了DL,LDA和FL.
- 对MultiData与VGG16,VGG19和ResNet152进行比较分析.
- 在四个客户端实现联合学习,包括IID和非IID数据集.
主要成果:
- 与其他模型相比,MultiData实现了近乎完美的准确性 (99.99%) 和最小的验证损失 (0%).
- 联合培训证明了模型的稳定性,培训准确率为100%,验证准确率超过95%.
- 该模型在IID和非IID数据场景中被证明是有效的.
结论:
- 多数据模型在保护隐私的室内场景分类方面取得了重大进展.
- 这项研究支持智能,可持续环境和基于物联网的自动化.
- 这些发现适用于医疗保健,智能基础设施和监测部门,符合可持续发展目标9,11和12.
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