利用转移学习与深度学习进行犯罪预测
Umair Muneer Butt1,2, Sukumar Letchmunan1, Fadratul Hafinaz Hassan1
1School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia.
PloS one
|April 17, 2024
概括
本研究介绍了一种转移学习方法,使用双向长期短期记忆 (BiLSTM) 网络进行准确的犯罪预测. 这种方法有效地将犯罪模式转移到社区之间,提高执法效率.
科学领域:
- 计算机犯罪学 计算机犯罪学
- 人工智能在公共安全中的作用
- 深度学习用于预测分析.
背景情况:
- 犯罪预测对公共安全至关重要,深度学习显得有前途.
- 有限的犯罪数据和资源阻碍了高级深度学习模型的培训.
- 现有的统计和深度学习方法需要大量数据来有效预测犯罪.
研究的目的:
- 通过采用转移学习模式来解决犯罪预测中的数据短缺问题.
- 微调和评估各种犯罪预测的统计和深度学习模型.
- 提出并验证基于BiLSTM的新型转移学习架构,以提高犯罪预测的准确性和效率.
主要方法:
- 微调统计方法:简单的移动平均线 (SMA),加权移动平均线 (WMA),指数移动平均线 (EMA).
- 深度学习模型的微调:长期短期记忆 (LSTM),双向长期短期记忆 (BiLSTM) 和卷积神经网络和长期短期记忆 (CNN-LSTM).
- 基于BiLSTM的转移学习架构的开发和评估,用于在不同社区中转移犯罪预测知识.
主要成果:
- 使用BiLSTM提出的转移学习方法与其他方法相比,表现优越.
- 在不同数据集 (芝加哥,纽约,拉合尔) 中预测每周和每月的犯罪趋势时,取得了显著的低误差值.
- 证明了执行时间的缩短,这表明实际执法应用的计算效率.
结论:
- 转移学习,特别是BiLSTM,有效地克服了犯罪预测中的数据限制.
- 拟议的模型提高了犯罪预测的准确性和效率.
- 这种方法为执法机构提供了一种有价值的工具,以改善犯罪控制和预防战略.
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