一个集成的深度学习和多层次框架,以了解恐怖组织的行为.
Dong Jiang1,2, Jiajie Wu1,2, Fangyu Ding1,2
1Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
Heliyon
|August 28, 2023
概括
预测恐怖袭击是由一个新的深度学习框架增强. 该模型整合了位置,社交网络和群体行为数据,以识别高风险区域并预测未来的威胁.
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
- 安全研究 安全研究
背景情况:
- 恐怖主义在21世纪对人类安全构成重大威胁.
- 恐怖主义的预测模型一直受到单一视角方法的限制.
- 了解恐怖组织的行为对于有效的反恐战略至关重要.
研究的目的:
- 开发一个集成的深度学习框架来分析恐怖组织的行为模式.
- 改善恐怖袭击目标和高风险地区的预测.
- 为特定的恐怖组织提供连续的与袭击有关的信息.
主要方法:
- 开发了一个集成的深度学习框架.
- 整合了过去攻击地点的背景背景.
- 利用社交网络分析和恐怖组织过去的行动.
- 与传统基准模型相比,比较框架性能.
主要成果:
- 拟议的框架在预测恐怖主义方面明显优于传统模型.
- 该模型在各种时空分辨率上证明了有效性.
- 成功地预测了活跃的恐怖组织的未来目标.
- 确定了高风险区域,并提供了连续攻击的洞察力.
结论:
- 综合深度学习方法与多尺度数据相结合,为恐怖主义提供了新的见解.
- 这一框架促进了对恐怖组织行为模式的理解.
- 这些发现对制定更有效的反恐政策和应对有组织暴力犯罪有影响.
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