• Journal of Food Hygiene and Safety
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• v.35 no.1
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• pp.45-50
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• 2020

In this study, seafood products (n=26) sold on sushi buffet restaurants in the city of Wonju were monitored by analyzing sequences of DNA barcode markers (cytochrome c oxidase subunit I and 16S ribosomal RNA genes). NCBI BLAST database was screened with the barcode sequences analyzed as a query for species identification. The BLAST search revealed that fifteen samples (58%) analyzed were consistent with their labeling information; however, the ingredients used in seven samples (27%) were not compliant with their label information. In the case of these mislabeled products, ingredients for sutchi catfish sushi and cherry bass sashimi were identified as Pangasianodon hypophthalmus and Lampris guttatus, respectively. For Japanese flying-fish roe sushi and Pacific herring roe sushi, roe of Mallotus villosus was used as an ingredient. Amphioctopus fangsiao and A. membranaceus were used in octopus sushi and soybean-marinated squid products, respectively. This monitoring result can contribute to the protection of consumer rights and the reduction of fraudulent practices in the food industry.

• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1306-1325
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• 2019

As the world is moving towards digitization, data is generated from various sources at a faster rate. It is getting humungous and is termed as big data. The financial sector is one domain which needs to leverage the big data being generated to identify financial risks, fraudulent activities, and so on. The design of predictive models for such financial big data is imperative for maintaining the health of the country's economics. Financial data has many features such as transaction history, repayment data, purchase data, investment data, and so on. The main problem in predictive algorithm is finding the right subset of representative features from which the predictive model can be constructed for a particular task. This paper proposes a correlation-based method using submodular optimization for selecting the optimum number of features and thereby, reducing the dimensions of the data for faster and better prediction. The important proposition is that the optimal feature subset should contain features having high correlation with the class label, but should not correlate with each other in the subset. Experiments are conducted to understand the effect of the various subsets on different classification algorithms for loan data. The IBM Bluemix BigData platform is used for experimentation along with the Spark notebook. The results indicate that the proposed approach achieves considerable accuracy with optimal subsets in significantly less execution time. The algorithm is also compared with the existing feature selection and extraction algorithms.