AutoEncoder-Based Data Completion Model for Theft Cases of Items Inside Cars

Authors

  • Enqi Cao
  • Fanliang Bu
  • Zhuxuan Han

DOI:

https://doi.org/10.54097/3b7gwn94

Keywords:

AutoEncoder, Theft Cases, Data Completion

Abstract

Accurate and reliable prediction results depend on high-quality data, and missing data is one of the key factors affecting data quality. This paper proposes a data completion model for car interior theft cases based on an AutoEncoder, which adopts an AutoEncoder architecture combining Re-parameterized Convolutional Neural Network and Recurrent Neural Network. Experimental results show that RepConv-RNN-AE can effectively complete the missing values in car interior theft case data and demonstrates superiority in completing missing data in car interior theft cases.

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References

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Published

29-07-2024

Issue

Section

Articles

How to Cite

Cao, E., Bu, F., & Han, Z. (2024). AutoEncoder-Based Data Completion Model for Theft Cases of Items Inside Cars. Frontiers in Computing and Intelligent Systems, 9(1), 1-4. https://doi.org/10.54097/3b7gwn94