用深度强化学习提高犯罪嫌疑人识别准确率
Identifying Culprits Through Deep Deterministic Policy Gradient Deep Learning Investigation
- 基于DDPG算法构建智能识别模型
- 在复杂数据中实现95%的识别准确率
- 适合司法调查与智能刑侦场景
在人工智能与先进科技应用背景下，犯罪侦查中的嫌疑人识别面临重大挑战。传统方法依赖有限的数据分析，难以在复杂数据中高效定位罪犯并减少误判。本文提出一种基于深度确定性策略梯度（Deep Deterministic Policy Gradient, DDPG）的深度学习新方法。模型利用犯罪现场资料、目击者证词和嫌疑人画像等多源数据进行训练，通过特征优化提升识别概率，同时抑制噪声和无关信息干扰。实验表明，该方法在识别准确率上达到95%，显著优于多种现有技术，展现出在真实案件分析中的高有效性。
原文摘要 · Abstract (English)
In the world of AI and advanced technologies investigation aspects identification of a crime or criminal plays a major problem. In this research we focus on a Conventional ways of implicating criminal investigations usually rely on limited data analysis. Finding an optimal and efficient method that will effectively identify criminals from complex datasets and minimise false positives and false negatives is the considered as a challenge. The main novelty approach of this work is based on the deep learning algorithm Deep Deterministic Policy Gradient (DDPG) is presented in this paper. We train the DDPG model with a dataset of crime scene material, witness statements and suspect profiles. The algorithm uses features to maximise the likelihood of identifying the offender while minimising the noise impact and irrelevant data. We show the efficacy of the proposed method, where DDPG identified criminals with an amazing accuracy of 95% than other several existing methods.
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