• Journal of the Korean Society of Marine Environment & Safety
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• v.30 no.1
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• pp.82-88
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• 2024

As the modern maritime industry rapidly progresses through technological advancements, data processing technology is emphasized as a key driver of this development. Natural language processing is a technology that enables machines to understand and process human language. Through this methodology, we aim to develop a model that predicts the proportions of outcomes when entering new written judgments by analyzing the rulings of the Marine Safety Tribunal and learning the cause-providing ratios of previously adjudicated ship collisions. The model calculated the cause-providing ratios of the accident using the navigation applied at the time of the accident and the weight of key keywords that affect the cause-providing ratios. Through this, the accuracy of the developed model could be analyzed, the practical applicability of the model could be reviewed, and it could be used to prevent the recurrence of collisions and resolve disputes between parties involved in marine accidents.

• Fashion & Textile Research Journal
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• v.26 no.1
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• pp.65-77
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• 2024

This study aimed to propose effective ways to integrate CLO into educational settings by conducting a comparative analysis of pattern functions in YUKA and CLO, specifically focusing on skirt prototypes and variations. CLO, being a 3D virtual sample CAD tool, is mainly used in education to facilitate the creation of 3D virtual clothing. In order to explore the applicability of CLO's pattern functions in pattern education, CAD education experts were asked to produce two types of skirt prototypes and two skirt variations. Subsequently, in-depth interviews were conducted. In addition, the skirt pattern creation process was recorded on video and used for comparative analysis of YUKA and CLO pattern functions. The comparison revealed that CLO provides the pattern tools necessary for drafting skirt prototypes. The learning curve for acquiring the skills necessary for drafting and transforming skirt prototypes was found to be relatively shorter for CLO compared to YUKA. In addition, due to CLO's surface-based pattern drawing method, it is difficult to move or copy only specific parts of the outline, and there are some limitations in drawing right angle lines. In the pattern transformation process, CLO's preview function proved to be advantageous, and it was highly rated on user convenience due to the intuitive UI. Thus, CLO shows promise for pattern drafting education and is deemed to have high scalability as it is directly linked to 3D virtual clothing.

• The Journal of Korean Society for School & Community Health Education
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• v.25 no.1
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• pp.29-41
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• 2024

Objectives: This study used an efficient data mining algorithm to explore association rules between the lifestyle risk behaviors and multimorbidity (having more than one chronic disease) in Korean adults. Methods: We used data from the 8th Korean National Health and Nutrition Examination Survey(2019-2020) for 7,609 adults aged ≥19 years. This study was undertaken where 6 lifestyle risk behaviors and 11 morbidities were analyzed using R and Rstudio for the ARM. Results: Among 117 association rules, combinations of hypertension, dyslipidemia and diabetes, hypertension were important role in inadequate sleep, physical inactivity and inadequate weight. Conclusion: The findings of this study are significant because they demonstrate the importance of lifestyle risk factors and the role of multiple chronic diseases using big data analytics such as association rule mining. We recommend developing selective and focused health education programs, such as exercise programs to address physical inactivity, dietary interventions to address inadequate weight, and mental health education programs to address inadequate sleep.

• Information Systems Review
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• v.20 no.4
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• pp.25-41
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• 2018

Applying predictive analytics to enterprise processes is an effective way to reduce operation costs and enhance productivity. Accordingly, the ability to predict business processes and performance indicators are regarded as a core capability. Recently, several works have predicted processes using deep learning in the form of recurrent neural networks (RNN). In particular, the approach of predicting the next step of activity using static or dynamic RNN has excellent results. However, few studies have given attention to applying deep learning in the form of dynamic RNN to predictions of process performance indicators. To fill this knowledge gap, the study developed an approach to using process mining and dynamic RNN. By utilizing actual data from a large domestic company, it has applied the suggested approach in estimating timely stocking in purchasing process, which is an important indicator of the process. The analytic methods and results of this study were presented and some implications and limitations are also discussed.

• Advances in nano research
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• v.16 no.6
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• pp.623-638
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• 2024

This study investigates the thermal post-buckling behavior of concrete eccentric annular sector plates reinforced with graphene oxide powders (GOPs). Employing the minimum total potential energy principle, the plates' stability and response under thermal loads are analyzed. The Haber-Schaim foundation model is utilized to account for the support conditions, while the transform differential quadrature method (TDQM) is applied to solve the governing differential equations efficiently. The integration of GOPs significantly enhances the mechanical properties and stability of the plates, making them suitable for advanced engineering applications. Numerical results demonstrate the critical thermal loads and post-buckling paths, providing valuable insights into the design and optimization of such reinforced structures. This study presents a machine learning algorithm designed to predict complex engineering phenomena using datasets derived from presented mathematical modeling. By leveraging advanced data analytics and machine learning techniques, the algorithm effectively captures and learns intricate patterns from the mathematical models, providing accurate and efficient predictions. The methodology involves generating comprehensive datasets from mathematical simulations, which are then used to train the machine learning model. The trained model is capable of predicting various engineering outcomes, such as stress, strain, and thermal responses, with high precision. This approach significantly reduces the computational time and resources required for traditional simulations, enabling rapid and reliable analysis. This comprehensive approach offers a robust framework for predicting the thermal post-buckling behavior of reinforced concrete plates, contributing to the development of resilient and efficient structural components in civil engineering.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.39-70
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• 2017

Data-drive analytics techniques have been recently applied to public surveys. Instead of simply gathering survey results or expert opinions to research the preference for a recently launched product, enterprises need a way to collect and analyze various types of online data and then accurately figure out customer preferences. In the main concept of existing data-based survey methods, the sentiment lexicon for a particular domain is first constructed by domain experts who usually judge the positive, neutral, or negative meanings of the frequently used words from the collected text documents. In order to research the preference for a particular product, the existing approach collects (1) review posts, which are related to the product, from several product review web sites; (2) extracts sentences (or phrases) in the collection after the pre-processing step such as stemming and removal of stop words is performed; (3) classifies the polarity (either positive or negative sense) of each sentence (or phrase) based on the sentiment lexicon; and (4) estimates the positive and negative ratios of the product by dividing the total numbers of the positive and negative sentences (or phrases) by the total number of the sentences (or phrases) in the collection. Furthermore, the existing approach automatically finds important sentences (or phrases) including the positive and negative meaning to/against the product. As a motivated example, given a product like Sonata made by Hyundai Motors, customers often want to see the summary note including what positive points are in the 'car design' aspect as well as what negative points are in thesame aspect. They also want to gain more useful information regarding other aspects such as 'car quality', 'car performance', and 'car service.' Such an information will enable customers to make good choice when they attempt to purchase brand-new vehicles. In addition, automobile makers will be able to figure out the preference and positive/negative points for new models on market. In the near future, the weak points of the models will be improved by the sentiment analysis. For this, the existing approach computes the sentiment score of each sentence (or phrase) and then selects top-k sentences (or phrases) with the highest positive and negative scores. However, the existing approach has several shortcomings and is limited to apply to real applications. The main disadvantages of the existing approach is as follows: (1) The main aspects (e.g., car design, quality, performance, and service) to a product (e.g., Hyundai Sonata) are not considered. Through the sentiment analysis without considering aspects, as a result, the summary note including the positive and negative ratios of the product and top-k sentences (or phrases) with the highest sentiment scores in the entire corpus is just reported to customers and car makers. This approach is not enough and main aspects of the target product need to be considered in the sentiment analysis. (2) In general, since the same word has different meanings across different domains, the sentiment lexicon which is proper to each domain needs to be constructed. The efficient way to construct the sentiment lexicon per domain is required because the sentiment lexicon construction is labor intensive and time consuming. To address the above problems, in this article, we propose a novel product reputation mining algorithm that (1) extracts topics hidden in review documents written by customers; (2) mines main aspects based on the extracted topics; (3) measures the positive and negative ratios of the product using the aspects; and (4) presents the digest in which a few important sentences with the positive and negative meanings are listed in each aspect. Unlike the existing approach, using hidden topics makes experts construct the sentimental lexicon easily and quickly. Furthermore, reinforcing topic semantics, we can improve the accuracy of the product reputation mining algorithms more largely than that of the existing approach. In the experiments, we collected large review documents to the domestic vehicles such as K5, SM5, and Avante; measured the positive and negative ratios of the three cars; showed top-k positive and negative summaries per aspect; and conducted statistical analysis. Our experimental results clearly show the effectiveness of the proposed method, compared with the existing method.

• Journal of the Korean Society for information Management
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• v.38 no.3
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• pp.141-174
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• 2021

In this study, by examining the actual usage data of the university library website before and after COVID-19 outbreak, the usage behavior of users was analyzed, and the data before and after the virus outbreak was compared, so that university libraries can provide more efficient information services in a pandemic situation. We would like to suggest ways to improve it. In this study, the user traffic made on the website of University C was 'using Google Analytics', from January 2018 to December 2018 before the oneself of the COVID-19 virus and from January 2020 to 2020 after the outbreak of the virus. A comparative analysis was conducted until December. Web traffic variables were analyzed by classifying them into three characteristics: 'User information', 'Path', and 'Site behavior' based on metrics such as session, user, number of pageviews, number of pages per session time, and bounce rate. To summarize the study results, first, when compared with data from January 1 to January 20 before the oneself of COVID-19, users, new visitors, and sessions all increased compared to the previous year, and the number of sessions per user, number of pageviews, and number of pages per session, which showed an upward trend before the virus outbreak in 2020, increased significantly. Second, as social distancing was upgraded to the second stage, there was also a change in the use of university library websites. In 2020 and 2018, when the number os students was the lowest, the number of page views increased by 100,000 more in 2020 compared to 2018, and the number of pages per session also recorded10.46, which was about 2 more pages compared to 2018. The bounce rate also recorded 14.38 in 2018 and 2019, but decreased by 1 percentage point to 13.05 in 2020, which led to more active use of the website at a time when social distancing was raised.

• Journal of Service Research and Studies
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• v.13 no.2
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• pp.181-203
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• 2023

YouTube exhibits a hybrid personality, incorporating traits of both over-the-top (OTT) and personal broadcasting platforms. However, limited research has investigated these hybrid characteristics, particularly in the context of paid YouTube channel memberships. Therefore, building upon consumption value theory and prior literature, this study examines the influence of consumption value factors associated with paid YouTube channel memberships on usage satisfaction and continuance intention. Specifically, the study identifies four perceived consumption value factors (functional, social, emotional, and epistemic values) within the paid YouTube channel membership context and assesses their impact on usage satisfaction and continuance intention. Additionally, the study explores the moderating role of conditional value (the experience of watching live streams on paid YouTube channels) in these relationships. Data was collected via an online survey from Korean adults who subscribed to multiple paid YouTube channel memberships, resulting in 274 responses. The proposed hypotheses were tested using structural equation modeling (SEM). The SEM results indicate that all four consumption value factors significantly influence usage satisfaction, with usage satisfaction in turn positively affecting continuance intention. Furthermore, the study reveals that conditional value moderates the relationships between functional/emotional values and usage satisfaction, as well as between usage satisfaction and continuance intention. This study is the first to focus on YouTube channel paid memberships, which encompass characteristics from both OTT and personal broadcasting platforms. It is anticipated that this research will offer insights to personal broadcasters and stakeholders regarding the motivational factors that impact user satisfaction and encourage subscriptions to channel memberships.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.311-328
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• 2022

Because of the spread of smartphones due to the development of information and communication technology, online shopping mall services can be used on computers and mobile devices. As a result, the number of users using the online shopping mall service increases rapidly, and the types of products traded are also growing. Therefore, to maximize profits, companies need to provide information that may interest users. To this end, the recommendation system presents necessary information or products to the user based on the user's past behavioral data or behavioral purchase records. Representative overseas companies that currently provide recommendation services include Netflix, Amazon, and YouTube. These companies support users' purchase decisions by recommending products to users using ratings, purchase records, and clickstream data that users give to the items. In addition, users refer to the ratings left by other users about the product before buying a product. Most users tend to provide ratings only to products they are satisfied with, and the higher the rating, the higher the purchase intention. And recently, e-commerce sites have provided users with the ability to vote on whether product reviews are helpful. Through this, the user makes a purchase decision by referring to reviews and ratings of products judged to be beneficial. Therefore, in this study, the correlation between the product rating and the helpful information of the review is identified. The valuable data of the evaluation is reflected in the recommendation system to check the recommendation performance. In addition, we want to compare the results of skipping all the ratings in the traditional collaborative filtering technique with the recommended performance results that reflect only the 4 and 5 ratings. For this purpose, electronic product data collected from Amazon was used in this study, and the experimental results confirmed a correlation between ratings and review usefulness information. In addition, as a result of comparing the recommendation performance by reflecting all the ratings and only the 4 and 5 points in the recommendation system, the recommendation performance of remembering only the 4 and 5 points in the recommendation system was higher. In addition, as a result of reflecting review usefulness information in the recommendation system, it was confirmed that the more valuable the review, the higher the recommendation performance. Therefore, these experimental results are expected to improve the performance of personalized recommendation services in the future and provide implications for e-commerce sites.

• Journal of Digital Convergence
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• v.12 no.8
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• pp.49-60
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• 2014

The primary purpose of this paper is to find out what issues are important in the Social Security sector, and then, through AHP methodology, this study analyzes what kind of big data methodologies and projects can be implemented to solves these issues. To the aim, this paper first confirmed 8 big data projects from reviewing all issues in the Social Security sector such as administrative works and social policies. After the result of pairwise comparison, policy validity is most important factors rather then effectiveness and practicability. With regard to the priorities among sub-big data projects, the project about preventing improper recipients has come out the most important project in terms of validity, effectiveness and practicability. And the results showed that the project about outreaching and reducing a blind spot on the welfare sector is weighed as a significant project. The results of this paper, in particular 8 sub-big data projects, will be useful to anyone who is interested in using big data and its methodologies for the social welfare sector.