Summary of Klcbl: An Improved Police Incident Classification Model, by Liu Zhuoxian et al.
KLCBL: An Improved Police Incident Classification Model
by Liu Zhuoxian, Shi Tuo, Hu Xiaofeng
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes a multichannel neural network model, KLCBL, for police incident classification. The model integrates Kolmogorov-Arnold Networks (KAN), linguistically enhanced text preprocessing approach (LERT), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM) to improve accuracy and efficiency. Compared to baseline models, KLCBL achieved 91.9% accuracy on real data, addressing classification challenges and enhancing police informatization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for police to quickly and accurately classify incidents using artificial intelligence. The model is made up of different parts that work together to understand and analyze large amounts of information. It’s more accurate than other methods and can help the police make better decisions about how to use their resources. This could be useful in many areas, not just policing. |
Keywords
» Artificial intelligence » Classification » Cnn » Neural network
