Xin-Yu Xiao
ABSTRACT
The quality control and classification of wine during the winemaking process are of great importance. Therefore, wineries must obtain information related to wine quality during red wine fermentation and aging through a fast, simple, accurate, and economical approach. In this research paper, we focus on the quality of red wine and have taken various measures to evaluate our proposed framework, such as accuracy and sensitivity. The introduced LGBM model significantly improves prediction accuracy and compares the performance of the proposed framework with existing literature. The results show that our framework achieves an accuracy of 81.5%, surpassing previous works. This will aid wine manufacturers in controlling quality before producing wine.
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Index Terms
- Research on red wine quality prediction model based on Deep Learning architecture.
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