混合蝶优化和反向传播神经网络,用于增强智能城市数据分类
Nandhini Natarajan1, Manikandan Venugopal2
1Coimbatore Institute of Technology, Coimbatore, India. nandhininatarajan469@gmail.com.
概括
这项研究引入了智能城市数据的新型混合分类框架 (HBPNNBO),达到94.76%的准确性. 该系统提高了城市分析的数据安全性和效率.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 智能城市从交通和公用事业等城市系统中产生了庞大的,不平衡的数据集.
- 准确分类这些异质数据对于资源效率和公民福祉至关重要.
- 现有的方法难以应对智慧城市数据的规模和复杂性.
研究的目的:
- 开发一种新的混合分类框架 (HBPNNBO) 来进行增强的智能城市数据分析.
- 提高分类不平衡城市数据集的准确性和效率.
- 通过区块链和高级加密来确保数据完整性和隐私.
主要方法:
- 一个混合分类框架,将蝶优化算法 (BOA) 与反向传播神经网络 (BPNN) 集成在一起.
- 使用HADASYNBSID进行数据集平衡和混合群遗传算法 (HCSGA) 进行特征选择的数据预处理.
- 区块链技术与混合AES-CSO加密方法的整合,用于安全处理数据.
主要成果:
- 通过HBPNNBO模型,分类准确度提高了94.76%.
- 该框架显示的最小处理时间为23.62 ms.
- 在入侵和流量数据集上进行评估时,HBPNNBO的表现优于传统的分类器.
结论:
- HBPNNBO框架为智能城市数据分类提供了高度准确和高效的解决方案.
- 基于区块链的安全性确保城市分析中的端到端数据完整性和隐私.
- 拟议的模型适用于实时,安全的智能城市数据处理和分析.
相关概念视频
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