Prediction of Suspect Activity Trajectory in Food Safety Area Based on Multiple U-Model Algorithm

Abstract EN

Modelling and predicting the suspect activity trajectory are of great importance for preventing and fighting crime in the food safety area.

Combing artificial intelligence and the multiple U-model algorithm, this paper represents a novel approach to predict the suspect activity trajectory.

Based on social text data, emotional assessment is conducted using the LSTM network to detect food safety criminal suspects.

Activity trajectories of criminal suspects are clustered using the graphic clustering method based on the GPS data.

U-model with the sliding window algorithm is proposed to model activity trajectories.

Further, the multiple U-model strategy is proposed to predict the activity trajectory based on the accumulated model error of previous positions and multiple clustered trajectories.

The simulation study shows that the proposed scheme can detect food safety criminal suspects and predict their activity trajectories effectively.