[1]HU Kang,HE Siyu,ZUO Min,et al.Classification model for judging illegal and irregular behavior for cosmetics based on CNN-BLSTM[J].CAAI Transactions on Intelligent Systems,2021,16(6):1151-1157.[doi:10.11992/tis.202104001]
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Classification model for judging illegal and irregular behavior for cosmetics based on CNN-BLSTM

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 6 Page number: 1151-1157 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2021-11-05
Title:
Classification model for judging illegal and irregular behavior for cosmetics based on CNN-BLSTM
Author(s):
HU Kang1;  HE Siyu2;  ZUO Min2;  GE Wei2
1. Information Center, China National Institute for Food and Drug Control, Beijing 102629, China;
2. National Engineering Laboratory for Agri-product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China
Keywords:
cosmetics; double-dimensional model; natural language processing; location awareness; attention mechanism; CNN; BLSTM
CLC:
TP391
DOI:
10.11992/tis.202104001
Abstract:
Aiming at the difficulties in the automatic recognition and classification of illegal and irregular behaviors of cosmetic supervision departments in sampling inspection, an automatic semantic classification model is established to assist relevant departments in building an intelligent management system so as to realize scientific decision-making and effective supervision with data. In this study, a Chinese word vector and character vector are used as two-way model input. The convolutional neural network (CNN) model is used to train the character vector, and the bidirectional long short-term memory (BLSTM) network model is used to train the word vector. Then, the positional attention mechanism is introduced to BLSTM to construct a character–word double-dimensional classification model for illegal and irregular behaviors in cosmetic sampling inspection based on CNN-BLSTM. The results of the comparative experiments on the sampling inspection dataset of hair dye cosmetics show that the accuracy of the CNN-BLSTM model is significantly higher than that of several commonly used deep neural network models, which verifies this model’s rationality and effectiveness.
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