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基于模块化优化的LM和SCA优化的CNN的商业犯罪和法律保护风险评估
1HeBei Sport University, Shijiazhuang, Hebei, China.
PeerJ. Computer science
|December 11, 2023
概括
本研究引入了一个新的框架,使用模块化优化的卢瓦恩方法算法来检测商业犯罪,以及用于法律援助可行性的卷积神经网络模型. 这些模型在识别犯罪集团和分类法律指导方面达到90%以上的准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 犯罪学 犯罪学
背景情况:
- 物联网 (IoT) 在公共安全方面提出了数据挑战.
- 传统的深度学习模型与计算复杂性和检测犯罪网络的效率作斗争.
研究的目的:
- 开发一个最优化的框架,用于商业犯罪的检测.
- 通过法律文本分析,创建一个评估法律援助可行性的模型.
- 通过先进的数据分析技术,加强公共安全.
主要方法:
- 模块化优化的卢瓦恩方法 (LM) 算法用于检测商业违法行为.
- 卷积神经网络 (CNN) 模型与TFIDF和Word2vec用于特征转换.
- 对于超参数优化而使用的 sinus cosine 算法 (SCA).
主要成果:
- 商业犯罪识别模型在识别犯罪集团方面取得了超过90%的准确性.
- 基于CNN的模型证明了对相关法律指导的有效分类.
- 实验结果证实了该模型的特殊训练效率.
结论:
- 拟议的框架有效地识别商业网络中的犯罪集团.
- 这项研究为司法领域提供了宝贵的法律见解.
- 这种方法通过改善犯罪发现和法律援助评估来提高公共安全.
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