使用特征组合和BiGRU与自我注意力机制的恐怖组织预测
Mohammed Abdalsalam1,2,3, Chunlin Li1, Abdelghani Dahou4
1School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, Hubei, China.
PeerJ. Computer science
|September 24, 2024
概括
这项研究引入了一个新的AI框架,BiGRU-SA,用于识别恐怖组织. 该模型在对袭击负责的群体的分类方面取得了高准确性,增强了国家安全努力.
科学领域:
- 计算社会科学 计算社会科学
- 人工智能的人工智能
- 国家安全国家安全.
背景情况:
- 恐怖主义和极端主义对全球稳定和安全构成重大威胁.
- 识别恐怖组织对于有效的反恐战略至关重要.
- 人工智能,机器学习 (ML),深度学习 (DL) 和自然语言处理 (NLP) 的进步为安全分析提供了新的工具.
研究的目的:
- 开发和评估人工智能框架,用于分类和预测恐怖组织.
- 为了提高国家和地区安全,利用历史恐怖主义数据.
- 提高涉及恐怖活动的实体识别的准确性和稳定性.
主要方法:
- 一个新的框架,双向循环单位和自我注意力 (BiGRU-SA) 被开发出来.
- 从DistilBERT的文本特征和相关特征与全球恐怖主义数据库 (GTD) 数据进行了整合.
- 为了解决数据不平衡,使用了SMOTE-T (Synthetic Minority Over-sampling Technique with Tomek links) 的合成少数人过量采样技术.
主要成果:
- BiGRU-SA框架在对36个恐怖组织进行分类时取得了98.68%的准确性.
- 记录了高性能指标:96.06%的精度,96.83%的灵敏度,99.50%的特异性,97.50%的MCC.
- 拟议的模型表现优于其他十种比较模型,包括传统的ML和DL算法.
结论:
- BiGRU-SA框架在分类和预测恐怖组织方面表现出卓越的有效性和准确性.
- 这种人工智能驱动的方法显著提高了国家安全和反恐工作的能力.
- 该研究强调了先进的人工智能技术在分析使用GTD等大型数据集的复杂安全挑战方面的潜力.
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