一个轻量级的深度证据融合框架,用于通过物联网对智能家电设备进行检测和分类
Nuha Alruwais1, Hadeel Alsolai2, Mohammed Maray3
1Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Saudi Arabia, P.O. Box 22459, Riyadh, 11495, Saudi Arabia.
本研究介绍了一种用于智能家居设备检测和分类 (LDEFF-SHADC) 的轻量级深度证据融合框架,以准确识别使用物联网 (IoT) 数据的设备. LDEFF-SHADC模型的准确度达到了98.90%,超过了现有的方法.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 物联网的物联网,就是物联网.
背景情况:
- 物联网 (IoT) 产生了大量数据,需要先进的设备管理和对象识别方法.
- 智能环境在流程控制,服务器保护和家庭安全方面面临着挑战.
- 深度学习 (DL) 为分析物联网传感器数据提供了强大的工具,以准确识别设备.
研究的目的:
- 利用物联网设备数据开发一个强大的框架来检测和分类智能家居设备.
- 介绍智能家居设备检测和分类 (LDEFF-SHADC) 轻量级深度证据融合框架模型.
- 在没有用户行为限制的情况下实现设备分类.
主要方法:
- 使用线性缩放规范化 (LSN) 来实现统一性和效率的数据规范化.
- 使用改进的蛇优化 (ISO) 进行特征选择,以减少维度.
- 一个混合分类器,集成关闭反复单位 (GRU) 和多头注意力 (MHA).
- 使用改进的子搜索优化算法 (ISSA) 优化GRU-MHA模型的参数.
主要成果:
- LDEFF-SHADC技术在检测和分类智能家居设备方面表现出卓越的性能.
- 在物联网设备识别数据集上实现了98.90%的高精度.
- 在设备识别任务中表现优于现有模型.
结论:
- LDEFF-SHADC框架为智能家电检测和分类提供了有效的解决方案.
- 整合DL技术,优化算法和特征选择可以提高识别准确性.
- 该模型提供了一种灵活而准确的方法来管理和理解连接设备的数据.
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