Research on a hierarchical intervention algorithm for violent crime based on CLGA-Net.

To achieve scientific and intelligent hierarchical intervention of violent criminals, this paper proposes a deep feature fusion model called the Convolutional Long Short Term Gated Attention Network (CLGA-Net), for violent crime temperament classification. First, the CNN is reconstructed to improve local feature extraction. We combine eigenvectors by filtering word vectors with multiple kernel sizes and convoluting separately to obtain feature maps with different granularities. Second, a multi-model fusion algorithm with attention mechanism is proposed by reconstructing CNN. After concatenate layer, Bi-LSTM and Bi-GRU are combined with Multi-Head Attention, and parallelly combined LSTM and GRU with Self-Attention to capture various structural patterns effectively in text sequences. Then, global average pooling layer is used to solve the overfitting of the traditional fully connected layer. Finally, after the softmax classifier, the arithmetic average algorithm is used to obtain the final classification result. To verify the performance of the proposed model, CLGA-Net was compared with other baseline models. The results show that CLGA-Net achieves the optimal effect under all evaluation indices with an accuracy of 99.25%. The AUC values under the macro average and micro average are 99.03% and 99.81%, respectively. By analyzing crime facts, CLGA-Net shows good violent crime temperament classification ability. Regulatory departments can develop a personalized correctional education program according to the type of attribution obtained. Therefore, the CLGA-Net model proposed in this paper can provide an accurate and scalable computerized hierarchical intervention service for offenders. [ABSTRACT FROM AUTHOR]