Prediction Model of Suspect Number Based on Deep Learning

Abstract

With the development of public security informatization, crime prediction has become an important tool for public security organs to carry out accurate attacks and effective governance. In this paper, we propose an algorithm to predict the number of suspects through the feature modeling of historical data. We use Deep Neural Networks (DNN) and machine learning algorithms to extract features of different dimensions of case data. We also use Convolutional Neural Networks (CNN) to extract the text features of case description. These two types of features are combined and fed into fully connected layer and softmax layer. Compared with the DNN model which only uses numeric data, the DNN-CNN model combined with text data has improved the precision rate by 20%. The addition of text data significantly improves the precision and recall rate of prediction. To the best of our knowledge, it is the first time to combine numerical and textual data of case information in crime prediction.

This work is supported by National Key R&D Program Project (Grant No. 2018YFC0809802), the Fundamental Research Funds for the Central Universities (Grant No. 2018JKF609), Specialized Research Fund of Higher Education of China (Grant No. 2019ssky012).