这项研究为刑侦领域提供了AI驱动的精准识别方案，做犯罪数据分析或公共安全研究的团队值得关注，能显著降低误判率。
该研究提出使用深度确定性策略梯度（DDPG）深度学习算法来改进犯罪调查中的嫌疑人识别。传统方法依赖有限数据分析，易产生误报和漏报。DDPG模型通过训练犯罪现场材料、证人陈述和嫌疑人档案等复杂数据集，最大化识别罪犯的可能性，同时减少噪声和无关数据的影响。实验结果显示，该方法在识别罪犯时准确率高达95%，优于现有多种方法。
Identifying Culprits Through Deep Deterministic Policy Gradient Deep Learning Investigation
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.