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- W4366376218 abstract "Food recognition has captivated much significance for health-related appliances. The traditional schemes mainly concern on the classification of food depending on its names and disregards the basic ingredient compositions. However, owing to shortage of datasets with labels of ingredient, the issues on food ingredient recognition (FIR) is often unnoticed. This work develops a new FIR approach, where, textual features, SIFT and deep features are derived. Then, the DBN classifier is deployed for the recognition of food ingredients. To get a precise recognition, tuning the weights of DBN is done via Improved TDO (ITDO) model." @default.
- W4366376218 created "2023-04-21" @default.
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- W4366376218 date "2023-02-24" @default.
- W4366376218 modified "2023-10-17" @default.
- W4366376218 title "Fine Tuned DBN Model for Food Ingredient Recognition: Introduction to Self-improved Tasmanian Devil Optimization Algorithm" @default.
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- W4366376218 doi "https://doi.org/10.1109/icicacs57338.2023.10099841" @default.
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