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基于区块链的电子教育文档管理与基于角色的访问控制,使用机器学习模型

P Chinnasamy¹, B Subashini², Ramesh Kumar Ayyasamy³

¹Department of Computer Science and Engineering, School of Computing, Kalasalingam Academy of Research and Education, Srivilliputtur, India.

Scientific reports

|May 29, 2025

概括

本研究介绍了一种基于区块链的新型系统,使用模糊的前卷积时间神经网络 (FCTNN) 和remora群集优化 (SRSO) 来增强教育凭证管理和检测恶意用户. B-FCTNN_SRSO系统显著改善了数字教育中的安全和验证流程.

科学领域:

计算机科学计算机科学
信息安全信息安全
教育技术的教育技术

背景情况:

数字化转型需要安全的教育凭证管理.
传统方法面临着伪造,数据改和缓慢验证等挑战.
机器学习 (ML) 和区块链为教育数据安全提供先进的解决方案.

研究的目的:

提出一种新的电子教育文档管理系统.
通过使用先进的AI和区块链检测恶意用户来增强安全性.
提高凭证验证和访问控制的效率和可靠性.

主要方法:

基于区块链的模糊前卷积时神经网络 (B-FCTNN) 的开发.
集成模拟的remora群集优化 (SRSO) 用于访问控制.
在培训期间使用自然语言处理 (NLP) 进行文档文字重量索引.
在区块链上记录访问请求以进行身份验证的用户验证.

主要成果:

B-FCTNN_SRSO系统实现了98%的预测准确度,95%的平均精度 (MAP) 和97%的F1得分.
记录的延迟率为96%,证明了高效的性能.
区块链安全分析显示,97%的服务质量 (QoS),94%的精度和96%的吞吐量.

结论:

拟议的B-FCTNN_SRSO系统有效地管理电子教育文件,并检测恶意用户.
区块链和先进的ML技术的整合为教育凭证管理提供了强大的和安全的解决方案.
该系统展示了高性能指标,验证了其在现实应用中的有效性.

关键词:

区块链区块链区块链区块链电子教育文档管理电子教育文档管理雷莫拉群集优化雷莫拉群集优化基于角色的访问权限.时间神经网络的神经网络

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