基于联合算法的学院信息构建的评估方案设计.
Caiyou Shen1, Yingjuan Shi1, Jing Fang2
1Institute of Cyberspace Security, Jinhua Advanced Research Institute, Jinhua, Zhejiang, China.
PeerJ. Computer science
|June 22, 2023
概括
本研究介绍了一种新的大学信息构建 (UIC) 评估方法,使用分析层次过程 (AHP) 和基于粒子群优化的反向传播神经网络 (PSO-BPNN). 开发的模型准确地评估了UIC的有效性,帮助大学管理和决策.
科学领域:
- 教育技术的教育技术
- 计算机科学 计算机科学
- 信息管理 信息管理
背景情况:
- 评估信息化建设 (IC) 的影响对于大学管理和决策至关重要.
- 评估教育信息化 (EI) 的现有方法面临的挑战是等级评估的模糊性.
- 大学信息建设 (UIC) 需要强大的评估框架来指导发展.
研究的目的:
- 为大学信息建设 (UIC) 开发一种先进的评估方法.
- 为了解决评估信息化建设 (IC) 的有效性固有的模糊性.
- 为大学管理和战略决策提供数据驱动的方法.
主要方法:
- 为UIC建立了一个数据驱动的评估指数系统,包含16个二级和4个一级指标 (基础设施,资源管理,信息管理,保护措施).
- 应用了分析层次过程 (AHP) 来确定IC模型中的一级指标的权重.
- 利用基于粒子优化的反向传播神经网络 (PSO-BPNN) 算法,优化惯性重量和学习因子,以适应和分析UIC级别.
主要成果:
- 拟议的PSO-BPNN模型显示,与传统方法相比,培训效果优越.
- 评估方法准确地反映了大学信息建设 (UIC) 的有效性.
- 综合的AHP和PSO-BPNN方法有效地减轻了IC等级评估中的模糊性.
结论:
- 开发的AHP-PSO-BPNN模型为评估UIC提供了一个精确可靠的方法.
- 这种方法提高了评估信息化建设 (IC) 和其对教育信息化 (EI) 的影响的准确性.
- 这些发现支持改善大学管理和在高等教育技术发展方面做出明智决策.
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