A Hybrid GCN and LSTM Structure Based on Attention Mechanism for Crime Prediction

Hu, Jia

doi:10.17762/converter.132

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…(3) Ensemble Model Ensemble models have been developed in the literature to predict the number of crimes and the level of crime risk, such the hybrid LSTM and GCN model, ST-GCN [59]. This framework consists of three modules: the spatio-temporal feature extraction, temporal feature extraction, and attention mechanism modules.…”

Section: ) Neural Networkmentioning

confidence: 99%

A Systematic Review of Multi-Scale Spatio-Temporal Crime Prediction Methods

Ding

2023

IJGI

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Crime is always one of the most important social problems, and it poses a great threat to public security and people. Accurate crime prediction can help the government, police, and citizens to carry out effective crime prevention measures. In this paper, the research on crime prediction is systematically reviewed from a variety of temporal and spatial perspectives. We describe the current state of crime prediction research from four perspectives (prediction content, crime types, methods, and evaluation) and focus on the prediction methods. According to various temporal and spatial scales, temporal crime prediction is divided into short-term prediction, medium-term prediction, and long-term prediction, and spatial crime prediction is divided into micro-, meso-, and macro-level prediction. Spatio-temporal crime prediction classification can be a permutation of temporal and spatial crime prediction classifications. A variety of crime prediction methods and evaluation metrics are also summarized, and different prediction methods and models are compared and evaluated. After sorting out the literature, it was found that there are still many limitations in the current research: (i) data sparsity is difficult to deal with effectively; (ii) the practicality, interpretability, and transparency of predictive models are insufficient; (iii) the evaluation system is relatively simple; and (iv) the research on decision-making application is lacking. In this regard, the following suggestions are proposed to solve the above problems: (i) the use of transformer learning technology to deal with sparse data; (ii) the introduction of model interpretation methods, such as Shapley additive explanations (SHAPs), to improve the interpretability of the models; (iii) the establishment of a set of standard evaluation systems for crime prediction at different scales to standardize data use and evaluation metrics; and (iv) the integration of reinforcement learning to achieve more accurate prediction while promoting the transformation of the application results.

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Section: ) Neural Networkmentioning

confidence: 99%

A Systematic Review of Multi-Scale Spatio-Temporal Crime Prediction Methods

Ding

2023

IJGI

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Exploration of Deep-Learning-Based Approaches for False Fact Identification in Social Judicial Systems

Zou,

Chen,

Cai

et al. 2024

Electronics

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With the many applications of artificial intelligence (AI) in social judicial systems, false fact identification becomes a challenging issue when the system is expected to be more autonomous and intelligent in assisting a judicial review. In particular, private lending disputes often involve false facts that are intentionally concealed and manipulated due to unique and dynamic relationships and their nonconfrontational nature in the judicial system. In this article, we investigate deep learning techniques to identify false facts in loan cases for the purpose of reducing the judicial workload. Specifically, we adapt deep-learning-based natural language processing techniques to a dataset over 100 real-world judicial rules spanning four courts of different levels in China. The BERT (bidirectional encoder representations from transformers)-based classifier and T5 text generation models were trained to classify false litigation claims semantically. The experimental results demonstrate that T5 has a robust learning capability with a small number of legal text samples, outperforms BERT in identifying falsified facts, and provides explainable decisions to judges. This research shows that deep-learning-based false fact identification approaches provide promising solutions for addressing concealed information and manipulation in private lending lawsuits. This highlights the feasibility of deep learning to strengthen fact-finding and reduce labor costs in the judicial field.

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A Hybrid GCN and LSTM Structure Based on Attention Mechanism for Crime Prediction

Cited by 2 publications

References 19 publications

A Systematic Review of Multi-Scale Spatio-Temporal Crime Prediction Methods

A Systematic Review of Multi-Scale Spatio-Temporal Crime Prediction Methods

Exploration of Deep-Learning-Based Approaches for False Fact Identification in Social Judicial Systems

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