• Journal of Food Hygiene and Safety
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• v.22 no.4
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• pp.323-331
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• 2007

This study was designed to estimate self-life of Kimbab and sandwiches marketed in convenience store. While the 12 different type of Kimbab (n=6) and sandwiches (n=6) were kept at $10^{\circ}C$ for 72 hours, quality changes including volatile basic nitrogen, aerobic plate count, pathogens detection and sensorial property was monitored, and effective quality indicators were selected. Volatile basic nitrogen, indicator for protein deterioration was slightly increased during storage periods in all samples. E. coli, Staphylococcus aureus, Salmonella spp. and Vibrio parahaemolyticus were not detected from any of samples. Change of aerobic plate count of Kimbab and sandwiches were increased moderately but increased dramatically after 48 hours of storage. Overall acceptability were maintained over 5, purchasing power limit, for 40 hours in 4 general Kimbab, 48 hours in 2 samgak Kimbab and 42 hours in 2 sandwiches. Shelf-life of each item was calculated from regression equation between reference limit from effective quality indicators, aerobic plate count and sensory property, and storage period. Estimated shelf-lives of general Kimbab were $15{\sim}33$ hours, samgak Kimbab were 32 hours and sandwiches were $27{\sim}30$ hours at $10^{\circ}C$ refrigerated condition.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.19-25
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• 2020

In frequent pattern mining, the uncertainty of each item is accompanied by a loss of information. AAlso, in real environment, the importance of patterns changes with time, so fuzzy logic must be applied to meet these requirements and the dynamic characteristics of the importance of patterns should be considered. In this paper, we propose a fuzzy utility mining technique for extracting frequent web page sets from web log databases through fuzzy utility-based web page set mining. Here, the downward closure characteristic of the fuzzy set is applied to remove a large space by the minimum fuzzy utility threshold (MFUT)and the user-defined percentile(UDP). Extensive performance analyses show that our algorithm is very efficient and scalable for Fuzzy Utility Mining using dynamic weights.

• Preventive Nutrition and Food Science
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• v.17 no.4
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• pp.274-279
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• 2012

For the quantitative analysis of genetically modified (GM) maize in processed foods, primer sets and probes based on the 35S promoter (p35S), nopaline synthase terminator (tNOS), p35S-hsp70 intron, and zSSIIb gene encoding starch synthase II for intrinsic control were designed. Polymerase chain reaction (PCR) products (80~101 bp) were specifically amplified and the primer sets targeting the smaller regions (80 or 81 bp) were more sensitive than those targeting the larger regions (94 or 101 bp). Particularly, the primer set 35F1-R1 for p35S targeting 81 bp of sequence was even more sensitive than that targeting 101 bp of sequence by a 3-log scale. The target DNA fragments were also specifically amplified from all GM labeled food samples except for one item we tested when 35F1-R1 primer set was applied. A reference plasmid pGMmaize (3 kb) including the smaller PCR products for p35S, tNOS, p35S-hsp70 intron, and the zSSIIb gene was constructed for real-time PCR (RT-PCR). The linearity of standard curves was confirmed by using diluents ranging from $2{\times}10^1{\sim}10^5$ copies of pGMmaize and the $R^2$ values ranged from 0.999~1.000. In the RT-PCR, the detection limit using the novel primer/probe sets was 5 pg of genomic DNA from MON810 line indicating that the primer sets targeting the smaller regions (80 or 81 bp) could be used for highly sensitive detection of foreign DNA fragments from GM maize in processed foods.

• Industrial Engineering and Management Systems
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• v.14 no.4
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• pp.335-346
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• 2015

The electronic commerce site (EC site) has become an important marketing channel where consumers can purchase many kinds of products; their access logs, including purchase records and browsing histories, are saved in the EC sites' databases. These log data can be utilized for the purpose of web marketing. The customers who purchase many product items are good customers, whereas the other customers, who do not purchase many items, must not be good customers even if they browse many items. If the attributes of good customers and those of other customers are clarified, such information is valuable as input for making a new marketing strategy. Regarding the product items, the characteristics of good items that are bought by many users are valuable information. It is necessary to construct a method to efficiently analyze such characteristics. This paper proposes a new latent class model to analyze both purchasing and browsing histories to make latent item and user clusters. By applying the proposal, an example of data analysis on an EC site is demonstrated. Through the clusters obtained by the proposed latent class model and the classification rule by the decision tree model, new findings are extracted from the data of purchasing and browsing histories.

• The KIPS Transactions:PartD
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• v.16D no.3
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• pp.423-432
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• 2009

Advances in information and communication technologies are changing our society greatly. In knowledge-based society, information can be obtained easily via communication tools such as web and e-mail. However, obtaining right and up-to-date information is difficult in spite of overflowing information. The concept of adaptive web site has been initiated recently. The purpose of the site is to provide information only users want out of tons of data gathered. In this paper, an algorithm is developed for adaptive web site construction. The proposed algorithm is based on association rules that are major principle in adaptive web site construction. The algorithm is constructed by analysing log data in web server and extracting meaning documents through finding behavior patterns of users. The proposed algorithm has the following characteristics. First, it is superior to existing algorithms using association rules in time complexity. Its superiority is proved theoretically. Second, the proposed algorithm is effective in space complexity. This is due to that it does not need any intermediate products except a linked list that is essential for finding frequent item sets.

• KIISE Transactions on Computing Practices
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• v.21 no.11
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• pp.706-712
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• 2015

These days, much information is provided as a list of summaries through mobile services. In this regard, users consume information in which they are interested by observing the list and not by expressing their interest explicitly or implicitly through rating content or clicking links. Therefore, to appropriately model a user's interest, it is necessary to detect latent interest content. In this study, we propose a method for reasoning latent interest of a user by analyzing mobile content consumption logs of the user. Specifically, since erroneous reasoning will drastically degrade service quality, a unanimity ensemble method is adopted to maximize precision. In this method, an item is determined as the subject of latent interest only when multiple classifiers considering various aspects of the log unanimously agree. Accurate reasoning of latent interest will contribute to enhancing the quality of personalized services such as interest-based recommendation systems.

• Korean Journal of Biological Psychiatry
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• v.9 no.2
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• pp.140-151
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• 2002

Object and Method:Minor physical anomalies(MPAs) are frequently seen in patients with schizophrenia. MPAs are considered to arise from the anomalous development of ectoderm-originated tissues in the developing fetus. Since the central nervous system originates from ectoderm, MPAs can be regarded as externally observable and objective indicators of the aberrant development which might have taken place in the central nervous system. To investigate whether MPAs are more frequent in schizophrenic patients, the frequencies of MPAs were compared between schizophrenic patients and normal controls. Total 245 schizophrenic patients diagnosed with DSM-IV(male : 158, female : 87), and 418 normal control subjects(male : 216, female : 202) were included in this study. The MPAs were measured using the modified Waldrop scale with fifteen items in six bodily regions; head, eye, ear, mouth, hand, and foot. Result:The total scores of Waldrop scale were $4.40{\pm}1.93$($mean{\pm}standard$ deviation) in patients and $3.43{\pm}1.68$ in controls for females, and for males, $4.58{\pm}1.75$ in patients and $4.28{\pm}1.59$ in controls. For females, the excess of MPAs in schizophrenic patients was statistically significant(t-test : p<0.001). For males, schizophrenic patients also showed more MPAs than normal controls, but this tendency did not reach statistical significance (t-test : p=0.094). When the modified Waldrop total scores excluding head circumference were compared, the total scores in schizophrenic patients were significantly higher for both male and female subjects(t-test : male p<0.001, female p=0.001). The individual anomaly items included in Waldrop scale were also investigated. The items of epicanthus, hypertelorism, malformed ears, syndactylia were significantly more frequent in schizophrenic patients. In contrast, the items of adherent ear lobes, asymmetric ears, furrowed tongue, curved fifth finger, single palmar crease and big gap between toes did not show any differences in frequency between schizophrenic patients and normal controls. Since a lot of statistical analyses showed different results between male and female subjects, it seems to be necessary to consider gender as an important controlling variable for the analysis, however only the item of head circumference showed statistically significant gender-related difference according to log-linear analysis. Conclusion:With a relatively large sample size, the frequencies of MPAs enlisted in Waldrop scale were compared between schizophrenic patients and normal controls in this study. MPAs were more frequently seen in schizophrenic patients and, especially, several specific items in the Waldrop scale showed prominent excess in schizophrenic patients. Although definite conclusions cannot be drawn due to the inherent limitation of the study using Waldrop scale, these results seem to support the possibility that aberrant neurodevelopmental process might be involved in the pathogenesis of schizophrenia in some of the patients.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.1-15
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• 2021

If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.