相关实验视频

IBKA-MSM:基于改进的群体智能优化算法,循环验证的语义对齐和信心意识融合的新型多式假新闻检测模型

Guangyu Mu¹, Jiaxiu Dai¹, Chengguo Li²

¹School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, China.

Biomimetics (Basel, Switzerland)

|November 26, 2025

概括

由于语义上的不一致性,检测多式联络虚假新闻具有挑战性. 我们提出的IBKA-MSM框架使用群集智能和深度学习来提高虚假新闻检测的准确性和语义一致性.

科学领域:

人工智能的人工智能
计算机科学计算机科学
数据科学数据科学数据科学

背景情况:

由于各种模式和跨语义相关性,社交媒体错误信息带来了复杂的挑战.
准确检测假新闻受到语义不一致和不均的模式依赖的阻碍.

研究的目的:

提出一个新的多式联络语义表示框架,IBKA-MSM,用于增强假新闻检测.
解决在语义不一致和不均的模式依赖条件下检测错误信息的关键挑战.

主要方法:

开发了IBKA-MSM框架,将群集智能优化与深度神经建模相结合.
采用了改进的黑翼风算法 (IBKA) 进行特征选择,具有自适应步骤大小控制和增强的内存机制.
引入了用于语义对齐的模式生成循环验证 (MGLV) 和用于自适应多式融合的模式结合交互的语义信心矩阵 (SCM-MCI).

主要成果:

IBKA-MSM实现了95.80%的高精度,超过了现有的主流混合型号.
F1得分显著改善:比粒子群优化 (PSO) 大约2.8%,比基本的黑翼算法 (BKA) 大约1.6%.

结论:

IBKA-MSM框架在维护虚假新闻检测的多式模式语义一致性方面表现出强大的稳定性和强大的能力.
拟议的方法有效地解决了多式联运虚假信息的复杂性,在检测准确性和可靠性方面取得了重大进展.

关键词:

改进了黑翼风算法信任意识的融合意识的融合多式联络假新闻检测多式联络假新闻检测语义对齐有意义对齐群众情报是一个群众情报.

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