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

Enqi Cao; Fanliang Bu; Zhuxuan Han

doi:10.54097/3b7gwn94

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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