• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.13-21
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• 2021

This study aims to analyze tourist inflow trends and consumption patterns using a geographic information system and big data analysis system. Songdo Central Park and Chinatown were selected among the major tourist destinations in Incheon, and floating population analysis and card sales analysis were conducted for one month in June 2017. The number of tourists visiting Songdo Central Park from metropolitan cities across the country was highest in the order of Incheon Metropolitan City, Gyeonggi-do, and Seoul Metropolitan City, and the proportion of foreign tourists was the highest in China. The number of card consumption used by Chinatown tourists was 12.4% higher for men than for women, and the amount of card consumption was also higher for men by 18%. This study has implications for proposing a strategic plan for tourism policy by analyzing the inflow trend and consumption pattern of tourists and deriving major issues in the establishment of tourism policy. Based on this study, it is expected that it can be helpful in improving the construction of tourism infrastructure in the future.

• Smart Media Journal
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• v.11 no.4
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• pp.62-68
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• 2022

To prevent the spread of disease in aquaculture, it is a need for a system to predict fish diseases while monitoring the water quality environment and the status of growing fish in real time. The existing research in predicting fish disease were image processing techniques. Recently, there have been more studies on disease prediction methods through deep learning techniques. This paper introduces the research results on how to predict diseases of Paralichthys Olivaceus with deep learning technology in aquaculture. The method enhances the performance of disease detection rates by including data augmentation and pre-processing in camera images collected from aquaculture. In this method, it is expected that early detection of disease fish will prevent fishery disasters such as mass closure of fish in aquaculture and reduce the damage of the spread of diseases to local aquaculture to prevent the decline in sales.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• Korean Journal of Clinical Pharmacy
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• v.33 no.1
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• pp.43-50
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• 2023

Background and Objective: With the advancement of cancer treatments and increased life expectancy, managing breakthrough cancer pain (BTcP) is essential to improve the quality of life for cancer patients. This study aimed to compare the major rapid onset opioids in Korea based on their characteristics and costs to determine the best option for each patient. Methods: Based on sales information from IQVIA-MIDAS, sublingual fentanyl tablet (SLF), fentanyl buccal tablet (FBT), and oral transmucosal fentanyl citrate (OTFC) were selected as the top three drugs for the treatment of BTcP in Korea, considering them the most comparable drugs. The cost and cost-pain relief ratio of the drugs for short-term (1 month) and long-term (1 year) treatment were compared and the ease of administration based on various factors, including pharmacokinetics, onset of action, and administration procedures were evaluated. Results: SLF was evaluated as the best overall in terms of rapid onset of action, ease of administration, and drug cost and also had the highest market share. SLF had the lowest cost pain relief ratio for both the initial and supplemental treatment for the 1-month pain intensity difference 15 (PID15) ratio. However, for the 1-month PID30 ratio, SLF was not superior to OTFC or FBT. The longer the breakthrough cancer pain duration, the more cost-effective the other rapid onset opioids. Conclusion: The rapid onset opioids that fit the patient's breakthrough cancer pain pattern have the best cost-effectiveness.

• Korean Journal of Community Nutrition
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• v.29 no.4
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• pp.318-335
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• 2024

Objectives: Despite the growing market share of online grocery shopping, there is a need to understand customer perceived value due to the ongoing advancements in information technology. This study explores the connections between attributes, consequences, and values. Additionally, it conducts a cross-country comparison of consumers' online grocery shopping behaviors to gain a deeper understanding of consumer market segments and any potential variations among them. Methods: Data was collected through an online questionnaire survey conducted from May 1 to 15, 2024, targeting 400 consumers in Seoul, Korea, and Shanghai, China, who have experience with online grocery shopping. The survey utilized the Means-End Chain theory and association pattern technique hard laddering. Data collation and analysis were conducted using the IBM SPSS Statistics 28.0 program. The LadderUX software was employed to analyze the links between attributes, consequences, and values and create the consumer purchasing process's implication matrix and hierarchical value map (HVM). Results: The study identified key attributes that influence online grocery shopping decisions, including delivery service, price, freshness, and quality. Korean consumers demonstrated a higher sensitivity to price (19.0%) and delivery service (17.0%). In contrast, Chinese consumers prioritized delivery service (15.0%) and after-sales service (14.8%). Commonly cited consequences included time saving (12.6% for Koreans, 11.3% for Chinese), whereas prevalent values encompassed convenience (36.8% for Koreans, 19.6% for Chinese) and economic value (26.6% for Koreans, 14.7% for Chinese). The HVM underscored these insights, highlighting diverse consumer preferences and country-specific nuances. Conclusions: The findings highlight the current state of online food consumption and consumers' value systems, revealing variations among countries. These findings offer empirical insights that can be used to create customized global marketing strategies that resonate with various consumer preferences and market dynamics.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.23 no.2
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• pp.89-95
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• 2017

Although the global coffee market is growing every year and the demand for coffee wrapping paper is increasing accordingly, research on the effect of PE material and PP material on the coffee aroma used in the sealant layer, which will directly contact the product, is lacking. In this study, we studied the change of aroma patterns and flavor materials by adding coffee to PP and PE pouches. In addition, we observed changes in aroma patterns depending on the temperature and the presence of the deoxidizer. As a result, it was found that the PP type packaging material was slightly better than the PE type packaging material, but the performance was hardly changed by the material. Rather, the change in the aroma pattern due to temperature was dominant rather than the material. It is ideal that refrigerated distribution ($4^{\circ}C$) is the best storage temperature and sales are done within a short period of time. Among the indicators, pyridine was the most suitable material to study and there are many data about pyridine. Therefore, it is expected that the results can be derived by using pyridine.

• Journal of Fashion Business
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• v.3 no.4
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• pp.83-91
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• 1999

The purpose of this study is to provide some useful references to the educational field in terms of providing on analysis of job characteristics of fashion designers working in the women's wear industries. The data were collected from 102 fashion designers working in women's wear industries through the questionnaire and were analyzed by SPSS packages of frequencies and percentiles for comparative study, and the results are as follows: 1. The demographic characteristics of the fashion designers are; unmarried (80.4%), working less than 2 years (20.2%), completion of junior college(68.6%), majority ages between 20-24 yrs(43.1%). An average length of working in one company war less than 6 months. 2. The ratio computer usage of the design room was approx. 52.0% especially in the management of sales (52.9%) and the ratio in fashion design was approx. 17.6% in merchandising planning. 3. 76.4% of respondents was working 10 hours a day, and 50% of them was dissatisfied on the job caused by excessive working hour (31.4%) and job over load (35.3%). In the developing fashion design with the relation of actual job, insufficient knowledges of the concerned technical and production fields (68.6%) were indicated as the most difficult area. In addition, fashion magazines were considered as the most helpful resource(94.1%). 4. It was noted that the target age groups for the brand were clearly divided into two groups, notably the early and middle of twenties and the early and middle forties. Among the produced items, formal wears were accounted for 52.9%. 5. As far as the contents of job are concerned, the fashion designers are mostly engaged in purchasing textile, collecting informations of fashion, quality control, whereas their actual job is apparel design. 6. The training that the fashion designer received beside formal education includes attendance of private institutes(62.7%), OJT(7.8%), seminars(4.9%). Regarding formal education, the respond indicated that they had least opportunity to received computer training. 7. The necessary subjects in the schools for the fashion designers in relation to the current job were fashion information, merchandising planning, pattern making, cutting, fashion marketing, knowledges of clothing material in sequence. Subjects which are necessary for the further development include pattern making(21.6%), fashion marketing(14.7%), and designing with computer(7.8%).

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.129-141
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• 2019

In recent years, fast growing cities in Korea are showing signs of being vulnerable to more disasters as their population and facilities increase and intensify. In particular, fire is one of the most common disasters in Korea's cities, along with traffic accidents. Therefore, in this study, we analyze what type of factors affect the fire that threatens urban people. Fire data were acquired for 10 years, from 2007 to 2017, in Jinju, Korea. Spatial distribution pattern of fire occurrence in Jinju was assessed through the spatial autocorrelation analysis. First, spatial autocorrelation analysis was carried out to grasp the spatial distribution pattern of fire occurrence in Jinju city. In addition, correlation and multiple regression analysis were used to confirm spatial dependency and abnormality among factors. Based on this, OLS (Ordinary Least Square) regression analysis was performed using space weighting considering fire location and spatial location of each facility. As a result, First, LISA (Local Indicator of Spatial Association) analysis of the occurrence of fire in Jinju shows that the most central commercial area are fire department, industrial area, and residential area. Second, the OLS regression model was analyzed by applying spatial weighting, focusing on the most derived factors of multiple regression analysis, by integrating population and social variables and physical variables. As a result, the second kind of neighborhood living facility showed the highest correlation with the fire occurrence, followed by the following in the order of single house, sales facility, first type of neighborhood living facility, and number of households. The results of this study are expected to be useful for analyzing the fire occurrence factors of each facility in urban areas and establishing fire safety measures.

• Knowledge Management Research
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• v.23 no.2
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• pp.193-210
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• 2022

The rapid development of mobile technology and the improvement of network speed are providing convenience to various services, and mobile shopping malls are no exception. Although efforts are being made to promote sales by combining various technologies such as customized recommendations using big data and specialized personalization services based on artificial intelligence, most mobile shopping malls have the same detailed page information structure including detailed product information. In this context, in this study, it was determined that the content layout of the product detail page and the mobile product detail page layout tailored to the consumer's preference should be presented according to the consumer's preference. Based on Higgins' Regulatory Focus Theory, a study of consumer propensity revealed that the content layout arrangement on a product detail page, when presented in an F-shape, informs the consumer that it is organized. If presented in a Z-shape, vivid information was recognized, and it was examined whether the product attitude and purchase intention were affected. As a result, when the content layout composition was presented as a layout arrangement in the form of a sense of unity and organization, prevention-focused consumers were positively affected by product attitudes and purchase intentions, and promotion-oriented consumers felt freedom. When presented in an arrangement, it was confirmed that the product attitude and purchase intention were affected.