• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2030-2052
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• 2023

Analysis of a bipartite (two-mode) network is a significant research area to understand the formation of social communities, economic systems, drug side effect topology, etc. in complex information systems. Most of the previous works talk about a projection-based model or latent feature model, which predicts the link based on singular similarity. The projection-based models suffer from the loss of structural information in the projected network and the latent feature is hardly present. This work proposes a novel method for link prediction in the bipartite network based on an ensemble of composite similarities, overcoming the issues of model-based and latent feature models. The proposed method analyzes the structure, neighborhood nodes as well as latent attributes between the nodes to predict the link in the network. To illustrate the proposed method, experiments are performed with five real-world data sets and compared with various state-of-art link prediction methods and it is inferred that this method outperforms with ~3% to ~9% higher using area under the precision-recall curve (AUC-PR) measure. This work holds great significance in the study of biological networks, e-commerce networks, complex web-based systems, networks of drug binding, enzyme protein, and other related networks in understanding the formation of such complex networks. Further, this study helps in link prediction and its usability for different purposes ranging from building intelligent systems to providing services in big data and web-based systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1571-1576
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• 2022

This study aims to analyze the interest of modern people in non-face-to-face medical counseling in the medical industrys. Big data was collected on two social platforms, 지식인, a platform that allows experts to receive medical counseling, and YouTube. In addition to the top five keywords of telephone counseling, "internal medicine", "general medicine", "department of neurology", "department of mental health", and "pediatrics", a data set was built from each platform with a total of eight search terms: "specialist", "medical counseling", and "health information". Afterwards, pre-processing processes such as morpheme classification, disease extraction, and normalization were performed based on the crawled data. Data was visualized with word clouds, broken line graphs, quarterly graphs, and bar graphs by disease frequency based on word frequency. An emotional classification model was constructed only for YouTube data, and the performance of GRU and BERT-based models was compared.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2020.11a
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• pp.161-162
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• 2020

Major advanced ports around the world are preparing for environmental regulations such as increased efficiency of ports and low emission of pollutants in ports by utilizing fourth industrial technologies and ICT technologies such as AI, big data, self-driving cars and connected cars. It is also investing in developing fully unmanned terminals to solve the problem of workforce reduction caused by avoidance of 3D industries. However, the introduction of advanced technology is being delayed in domestic ports, which has led to a drop in port efficiency. In addition, port safety accidents have also occurred frequently, seriously affecting port marketing. Thus, the characteristics and types of each container terminal in Korea were analyzed and the factors for introducing autonomous cooperative driving were classified into five section factors and 15 division factors. Hierarchically classified factors will be surveyed on workers working in shipping lines, port construction, container terminals and related ministries.

• Smart Structures and Systems
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• v.31 no.3
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• pp.229-245
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• 2023

The absence of excitation measurements may pose a big challenge in the application of structural damage identification owing to the fact that substantial effort is needed to reconstruct or identify unknown input force. To address this issue, in this paper, an iterative strategy, a synergy of Tikhonov regularization method for force identification and modified Jaya algorithm (M-Jaya) for stiffness parameter identification, is developed for damage identification with partial output-only responses. On the one hand, the probabilistic clustering learning technique and nonlinear updating equation are introduced to improve the performance of standard Jaya algorithm. On the other hand, to deal with the difficulty of selection the appropriate regularization parameters in traditional Tikhonov regularization, an improved L-curve method based on B-spline interpolation function is presented. The applicability and effectiveness of the iterative strategy for simultaneous identification of structural damages and unknown input excitation is validated by numerical simulation on a 21-bar truss structure subjected to ambient excitation under noise free and contaminated measurements cases, as well as a series of experimental tests on a five-floor steel frame structure excited by sinusoidal force. The results from these numerical and experimental studies demonstrate that the proposed identification strategy can accurately and effectively identify damage locations and extents without the requirement of force measurements. The proposed M-Jaya algorithm provides more satisfactory performance than genetic algorithm, Gaussian bare-bones artificial bee colony and Jaya algorithm.

• The Research Journal of the Costume Culture
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• v.31 no.1
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• pp.34-52
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• 2023

In this study, text mining was conducted on the product names of skirts, pants, shirts/blouses, and dresses to analyze the characteristics of keywords appearing in online shopping product names. As a result of frequency analysis, the number of keywords that appeared 0.5% or more for each item was around 30, and the number of keywords that appeared 0.1% or more was around 150. The cumulative distribution rate of 150 terms was around 80%. Accordingly, information on 150 key terms was analyzed, from which item, clothing composition, and material information were the found to be the most important types of information (ranking in the top five of all items). In addition, fit and style information for skirts and pants and length information for skirts and dresses were also considered important information. Keywords representing clothing composition information were: banding, high waist, and split for skirts and pants; and V-neck, tie, long sleeves, and puff for shirts/blouses and dresses. It was possible to identify the current design characteristics preferred by consumers from this information. However, there were also problems with terminology that hindered the connection between sellers and consumers. The most common problems were the use of various terms with the same meaning and irregular use of Korean and English terms. However, as a result of using co-appearance frequency analysis, it can be interpreted that there is little intention for product exposure, so it is recommended to avoid it.

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.305-318
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• 2022

We focused on the care effectiveness of the interactive AI robots. developed an AI toy robot called 'Agaya' to contribute to personalization with more human-centered care. First, by applying P-TTS technology, you can maximize intimacy by autonomously selecting the voice of the person you want to hear. Second, it is possible to heal in your own way with good memory storage and bring back memory function. Third, by having five senses of the role of eyes, nose, mouth, ears, and hands, seeking better personalised services. Fourth, it attempted to develop technologies such as warm temperature maintenance, aroma, sterilization and fine dust removal, convenient charging method. These skills will expand the effective use of interactive robots by elderly people and contribute to building a positive image of the elderly who can plan the remaining old age productively and independently

• Human Ecology Research
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• v.61 no.4
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• pp.687-707
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• 2023

This study was conducted to obtain useful information on the development of the future second-hand fashion market by obtaining information on the current situation through unstructured text data distributed as news articles related to 'purchase of second-hand clothing' and 'purchase of second-hand luxury goods'. Text-based unstructured data was collected on a daily basis from Naver news from January 1st to December 31st, 2022, using 'purchase of second-hand clothing' and 'purchase of second-hand luxury goods' as collection keywords. This was analyzed using text mining, and the results are as follows. First, looking at the frequency, the collection data related to the purchase of second-hand luxury goods almost quadrupled compared to the data related to the purchase of second-hand clothing, indicating that the purchase of second-hand luxury goods is receiving more social attention. Second, there were common words between the data obtained by the two collection keywords, but they had different words. Regarding second-hand clothing, words related to donations, sharing, and compensation sales were mainly mentioned, indicating that the purchase of second-hand clothing tends to be recognized as an eco-friendly transaction. In second-hand luxury goods, resale and genuine controversy related to the transaction of second-hand luxury goods, second-hand trading platforms, and luxury brands were frequently mentioned. Third, as a result of clustering, data related to the purchase of second-hand clothing were divided into five groups, and data related to the purchase of second-hand luxury goods were divided into six groups.

• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.5
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• pp.779-803
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• 2023

The growth of digital news media and the stock price index has resulted in economic fluctuations in the fashion industry. This study examines the impact of fashion industry news and macroeconomic changes on the Textile & Wearing Apparel KOSPI over the past five years. An auto-regressive integrated moving average exogenous time series model was conducted using the fashion industry stock market index, the news topic index, and macro-economic indicators. The results indicated the topics of "Cosmetic business expansion" and "Digital innovation" impacted the Textile & Wearing Apparel KOSPI after one week, and the topics of "Pop-up store," "Entry into the Chinese fashion market," and "Fashion week and trade show" affected it after two weeks. Moreover, the topics of "Cosmetic business expansion" and "Entry into the Chinese fashion market" were statistically significant in the macroeconomic environment. Regarding the effect relation of Textile & Wearing Apparel KOSPI, "Cosmetic business expansion," "Entry into the Chinese fashion market," and consumer price fluctuation showed negative effects, while the private consumption change rate, producer price fluctuation, and unemployment change rate had positive effects. This study analyzes the impact of media framing on fashion industry business cycles and provides practical insights into managing stock market risk for fashion companies.

• Nutrition Research and Practice
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• v.18 no.2
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• pp.269-281
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• 2024

BACKGROUND/OBJECTIVES: The objective of this study was to compare dietary habits and mental health among middle school students in urban and rural areas based on bedtime, and to provide evidence supporting appropriate bedtime for Korean middle school students in relation to their healthy dietary habits and mental well-being. SUBJECTS/METHODS: The study population consisted of 25,681 second-year middle school students who participated in the Korea Youth Risk Behavior Survey in 2020-2022. Participants were asked about their bedtime and wake-up time during the past 7 days and were classified into five categories. The study compared the general characteristics, academic factors, dietary habits, and mental health of urban and rural students based on their bedtime. RESULTS: Bedtime was found to be later in the following order: urban female students, rural female students, urban male students, and rural male students. As bedtime got later, the rates of smoking and alcohol consumption increased. Students who went to bed before 11 p.m. had lower academic performance, while rural male students who went to bed after 2 a.m. had lower academic performance. Later bedtime was associated with increased smartphone usage, skipping breakfast, consuming fast food, and drinking carbonated beverages. Later bedtime was also associated with higher perceived stress levels, particularly among students who went to bed after 2 a.m., higher rates of suicidal ideation, experiencing sadness and despair, as well as the prevalence of clinically significant anxiety disorders. CONCLUSION: These results suggest that middle school students who go to bed too late have higher rates of smoking and alcohol drinking, as well as unhealthy eating habits, stress, suicidal ideation, sadness, and anxiety. Therefore, it is necessary to provide educational and social institutional support to promote adequate sleep for the health of adolescents.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.161-175
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• 2024

In this paper, we examined the causes of privacy concern and related factors in personalized recommendation service of OTT. On the basis of the 'Big Five Personality model,' we established factors such as agreeableness, neuroticism, conscientiousness, extraversion, and openness to experience. Additionally, we established factors such as accuracy, diversity, and novelty of OTT recommendation's services, and perceived transparency. we analyzed the relationship between privacy concern, service benefit, and intention to give personal information. Finally, we analyzed the mediating effect of service benefits on the relationship between privacy concern and intention to give personal information. The results of this study showed that (1) neuroticism, extraversion and openness to experience had the significant effects on privacy concerns, (2) perceived transparency had the significant effects on privacy concern, 3) privacy concern and service benefit had the significant effect on intention to give personal information, and (4) as a result of multi-group analysis towards low and high groups to verify the moderating effect by service benefits, a significant difference was observed between privacy concern and intention to give personal information. The findings of the study are expected to help the OTT firms' understanding towards users' privacy protection behaviors.