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基于云平台和数据挖掘的智慧系统设计

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在教育领域,云平台的数据安全直接关系到学生和教师的隐私保护以及教学内容的安全存储.研究为使入侵杂草算法在云平台数据安全防护中得到应用,采用多标签核映射对数据进行降维和支持向量机进行分类.实验结果表明,挖掘误差约为 2%,节能量在 400 J~500 J之间;挖掘时间最长为 2.2 s,远快于其他方法的 7.8 s和 8.4 s.结果表明,研究提出的方法在处理云计算平台上的高级持续威胁数据时,展现出了明显的优势,在云平台数据保护中具有明显效果.
Intelligent system design based on cloud platform and data mining
In the field of education,particular attention is paid to data security issues in cloud platforms,as they are directly re-lated to the protection of the privacy of students and teachers and the secure storage of teaching content.In this paper,the multi-label kernel mapping data dimensionality reduction method and least squares multi-classification twin support vector machine model are ap-plied,and the invasive weed optimization algorithm is combined.The experimental results show that the mining error is about 2%,and the energy saving is between 400J-500J,which is higher than the other two methods.The maximum mining time was 2.2 seconds,much faster than the 7.8 seconds and 8.4 seconds of other methods.The results show that the proposed method has obvious advantages in dealing with advanced persistent threat data on cloud computing platforms.

cloud platformdata miningtwin support vector machineinvasive weed optimization algorithm

邵临光、郝宇刚、张飞

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陕西能源职业技术学院,陕西 咸阳 712000

云平台 数据挖掘 孪生支持向量机 入侵杂草优化算法

2024

自动化与仪器仪表
重庆工业自动化仪表研究所,重庆市自动化与仪器仪表学会

自动化与仪器仪表

CSTPCD
影响因子:0.327
ISSN:1001-9227
年,卷(期):2024.(11)