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KLCBL: An Improved Police Incident Classification Model

arXiv 2411.06749 ↗First posted 11 Nov 2024cs.AIcited by 0

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Abstract (arXiv)

Police incident data is crucial for public security intelligence, yet grassroots agencies struggle with efficient classification due to manual inefficiency and automated system limitations, especially in telecom and online fraud cases. This research proposes a multichannel neural network model, KLCBL, integrating Kolmogorov-Arnold Networks (KAN), a linguistically enhanced text preprocessing approach (LERT), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM) for police incident classification. Evaluated with real data, KLCBL achieved 91.9% accuracy, outperforming baseline models. The model addresses classification challenges, enhances police informatization, improves resource allocation, and offers broad applicability to other classification tasks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 33 references that are in the map. First posted on arXiv 11 Nov 2024.

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References · 16

  1. 2017Attention Is All You Needcited by 59,932
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  7. 2024KAN: Kolmogorov-Arnold Networkscited by 634
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  11. 2019Understanding LSTM -- a tutorial into Long Short-Term Memory Recurrent Neural Networkscited by 103
  12. 2019Multimodal Intelligence: Representation Learning, Information Fusion, and Applicationscited by 89
4 more
  1. 2020A Survey on Text Classification: From Shallow to Deep Learningcited by 80
  2. 2023The Efficiency Spectrum of Large Language Models: An Algorithmic Surveycited by 25
  3. 2017Medical Text Classification using Convolutional Neural Networkscited by 19
  4. 2022LERT: A Linguistically-motivated Pre-trained Language Modelcited by 10

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