• ETRI Journal
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• v.46 no.3
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• pp.473-484
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• 2024

We introduce a machine learning-based web application to help travel agents plan a package tour schedule. K-nearest neighbor (KNN) classification predicts the optimal tourists' dwelling time based on a variety of information to automatically generate a convenient tour schedule. A database collected in collaboration with an established travel agency is fed into the KNN algorithm implemented in the Python language, and the predicted dwelling times are sent to the web application via a RESTful application programming interface provided by the Flask framework. The web application displays a page in which the agents can configure the initial data and predict the optimal dwelling time and automatically update the tour schedule. After conducting a performance evaluation by simulating a scenario on a computer running the Windows operating system, the average response time was 1.762 s, and the prediction consistency was 100% over 100 iterations.

• Journal of Preventive Medicine and Public Health
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• v.55 no.3
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• pp.280-288
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• 2022

Objectives: One of the most widely used pesticides today is chlorpyrifos (CPF). Cytochrome P450 (CYP)2B6, the most prominent catalyst in CPF bioactivation, is highly polymorphic. The objective of our study was to evaluate the role of CYP2B6^*6, which contains both 516G>T and 785A>G polymorphisms, in CPF toxicity, as represented by the concentration of 3,5,6-trichloro-2-pyridinol (TCPy), among vegetable farmers in Central Java, Indonesia, where CPF has been commonly used. Methods: A cross-sectional study was conducted among 132 vegetable farmers. Individual socio-demographic and occupational characteristics, as determinants of TCPy levels, were obtained using a structured interviewer-administered questionnaire and subsequently used to estimate the cumulative exposure level (CEL). TCPy levels were detected with liquid chromatography-mass spectrometry. CYP2B6^*6 gene polymorphisms were analyzed using a TaqMan^® SNP Genotyping Assay and Sanger sequencing. Linear regression analysis was performed to analyze the association between TCPy, as a biomarker of CPF exposure, and its determinants. Results: The prevalence of CYP2B6^*6 polymorphisms was 31% for ^*1/^*1, 51% for ^*1/^*6, and 18% for ^*6/^*6. TCPy concentrations were higher among participants with CYP2B6^*1/^*1 than among those with ^*1/^*6 or ^*6/^*6 genotypes. CYP2B6^*6 gene polymorphisms, smoking, CEL, body mass index, and spraying time were retained in the final linear regression model as determinants of TCPy. Conclusions: The results suggest that CYP2B6^*6 gene polymorphisms may play an important role in influencing susceptibility to CPF exposure. CYP2B6^*6 gene polymorphisms together with CEL, smoking habits, body mass index, and spraying time were the determinants of urinary TCPy concentrations, as a biomarker of CPF toxicity.