AutoEncoder-Based Data Completion Model for Theft Cases of Items Inside Cars
DOI:
https://doi.org/10.54097/3b7gwn94Keywords:
AutoEncoder, Theft Cases, Data CompletionAbstract
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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