Prediction and Visual Analysis of Food Safety Risk Based on TabNet-GRA

ABSTRACT: Food safety risk prediction is crucial for timely hazard detection and effective control. This study proposes a novel risk prediction method for food safety called TabNet-GRA, which combines a specialized deep learning architecture for tabular data (TabNet) with a grey relational analysis (GRA) to predict food safety risk. Initially, this study employed a GRA to derive comprehensive risk values from fused detection data. Subsequently, a food safety risk prediction model was constructed based on TabNet, and training was performed using the detection data as inputs and the comprehensive risk values calculated via the GRA as the expected outputs. Comparative experiments with six typical models demonstrated the superior fitting ability of the TabNet-based prediction model. Moreover, a food saf

SUBMITTER: Chen Y 

PROVIDER: S-EPMC10453234 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature