• Journal of the Korean Applied Science and Technology
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• v.37 no.3
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• pp.473-483
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• 2020

This paper proposes a tourism recommendation system in intelligent cloud environment using information of tourist behavior applied with perceived value. This proposed system applied tourist information and empirical analysis information that reflected the perceptual value of tourists in their behavior to the tourism recommendation system using wide and deep learning technology. This proposal system was applied to the tourism recommendation system by collecting and analyzing various tourist information that can be collected and analyzing the values that tourists were usually aware of and the intentions of people's behavior. It provides empirical information by analyzing and mapping the association of tourism information, perceived value and behavior to tourism platforms in various fields that have been used. In addition, the tourism recommendation system using wide and deep learning technology, which can achieve both memorization and generalization in one model by learning linear model components and neural only components together, and the method of pipeline operation was presented. As a result of applying wide and deep learning model, the recommendation system presented in this paper showed that the app subscription rate on the visiting page of the tourism-related app store increased by 3.9% compared to the control group, and the other 1% group applied a model using only the same variables and only the deep side of the neural network structure, resulting in a 1% increase in subscription rate compared to the model using only the deep side. In addition, by measuring the area (AUC) below the receiver operating characteristic curve for the dataset, offline AUC was also derived that the wide-and-deep learning model was somewhat higher, but more influential in online traffic.

• Journal of Food Hygiene and Safety
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• v.38 no.6
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• pp.457-463
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• 2023

To investigate residual pesticide levels in agricultural products contained in Meal-kits, 27 Meal-kit products were collected from marts, Meal-kit shops, and online stores in Incheon City, South Korea. Seventy-six vegetable and thirty-seven mushroom products were analyzed for residual levels of 339 pesticides. Residual pesticides were detected in 23 out of 76 vegetables and were not present in the 37 mushroom products. The residual pesticide detection rate was 20.4% (23/113 cases). The pesticides famoxadone 0.034 mg/kg (standard: 0.01 mg/kg or less, PLS) and fenpyroximate 0.302 mg/kg (standard: 0.01 mg/kg or less, PLS) exceeded their maximum residue levels (MRL). This survey revealed that various types of pesticides remain in agricultural products in Meal-kits. Due to the nature of Meal-kit products, there is no separate standard for residual pesticides in agricultural products. Therefore, continuous monitoring of residual pesticides is necessary.