• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.95-106
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• 2021

The purpose of this study is to derive major topics in content R&D and provide directions for academic development by exploring research trends over the past 20 years using topic modeling targeting 9,858 papers published in the Journal of the Korean Contents Association. To secure the reliability and validity of the extracted topics, not only the quantitative evaluation technique but also the qualitative technique were applied step-by-step and repeated until a corpus of the level agreed upon by the researchers was generated, and detailed analysis procedures were presented accordingly. As a result of the analysis, 8 core topics were extracted. This shows that the Korean Contents Association is publishing convergence and complex research papers in various fields without limiting to a specific academic field. Also, before 2012, the proportion of topics in the field of engineering and technology appeared relatively high, while after 2012, the proportion of topics in the field of social sciences appeared relatively high. Specifically, the topic of 'social welfare' showed a fourfold increase in the second half compared to the first half. Through topic-specific trend analysis, we focused on the turning point in time at which the inflection point of the trend line appeared, explored the external variables that affected the research trend of the topic, and identified the relationship between the topic and the external variable. It is hoped that the results of this study can provide implications for active discussions in domestic content-related R&D and industrial fields.

• Information Systems Review
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• v.25 no.1
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• pp.75-91
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• 2023

Thanks to information technologies, sharing economy services offer a new way of consumption. Carsharing appeared as a novel type of service that transformed the conventional way of personal transportation, from owning a vehicle to using an on-demand service. Allowing users to use a vehicle without owning a car, carsharing provides various social benefits such as the reduction of resource allocation inefficiencies and the alleviation of transportation problems. To strengthen such positive aspects of carsharing service, it is essential to understand an individual's service usage pattern and reveal factors that affect users' reuse behavior. This study investigates the factors that have an influence on carsharing reuse of users applying RFMC (Recency, Frequency, Monetary, and Clumpiness) model, the popular model for understanding the reuse likelihood of customers. Using data from a leading carsharing service provider in South Korea, we empirically analyze the effect of RFMC on carsharing reuse behavior. The findings show that recency and monetary values are negatively related to reuse while frequency is positively related to carsharing service reuse. Moreover, the impact of recency and monetary value are more salient whereas the impact of frequency is smaller among users with higher clumpiness. Based on these findings, this study elaborates on theoretical and practical implications.

• Journal of Digital Convergence
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• v.18 no.11
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• pp.215-226
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• 2020

Chatbot is emerging as an important interface window for business. This change is due to the continued development of chatbot-related research from NLP to NLU and NLG. However, the reality is that the methodological study of drawing domain knowledge and developing it into a user-friendly interactive interface is weak in the process of developing chatbot. In this paper, in order to present the process criteria of chatbot development, we applied it to the actual project based on the methodology presented in the previous paper and improved the development methodology. In conclusion, the productivity of the test phase, which is the most important step, was improved by 33.3%, and the number of iterations was reduced to 37.5%. Based on these results, the "3 Phase and 17 Tasks Development Methodology" was presented, which is expected to dramatically improve the trial and error of the chatbot development.

• Journal of Digital Convergence
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• v.18 no.2
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• pp.163-174
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• 2020

The market for electric vehicles is growing due to the public's interest in the environment and the expansion of electric vehicle support projects in terms of government policy. This study surveyed 2,332 people in Jeju, one of the nation's representative areas of electric vehicles, and the higher the perceived value in terms of the total cost of automobile ownership for electric vehicles, the higher the intention to purchase electric vehicles. The higher the level of knowledge and attachment, the higher the intention to purchase electric vehicles. While many previous studies considered economic value mainly as price, the study was conducted to approach economic value in terms of total cost of ownership. Marketing practitioners also look for practical contributions in that they can propose price framing so that customers can judge the economic value of the electric vehicle as a strategic way to increase the intention to purchase the electric vehicle, rather than just the purchase price. can see. In addition, the same research should be conducted in various regions besides Jeju, so that the research results can be generalized.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.25-30
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• 2022

This study is a study on the filter bubble reinforcement phenomenon of SNS recommendation algorithm such as YouTube, which is a characteristic of the Russian-Ukraine war (2022), and the victory or defeat factors of the hybrid war. This war is identified as a hybrid war, and the use of New Media based on the SNS recommendation algorithm is emerging as a factor that determines the outcome of the war beyond political leverage. For this reason, the filter bubble phenomenon goes beyond the dictionary meaning of confirmation bias that limits information exposed to viewers. A YouTube video of Ukrainian President Zelensky encouraging protests in Kyiv garnered 7.02 million views, but Putin's speech only 800,000, which is a evidence that his speech was not exposed to the recommendation algorithm. The war of these SNS recommendation algorithms tends to develop into an algorithm war between the US (YouTube, Twitter, Facebook) and China (TikTok) big tech companies. Influenced by US companies, Ukraine is now able to receive international support, and in Russia, under the influence of Chinese companies, Putin's approval rating is over 80%, resulting in conflicting results. Since this algorithmic empowerment is based on the confirmation bias of public opinion by 'filter bubble', the justification that a new guideline setting for this distortion phenomenon should be presented shortly is drawing attention through this Russia-Ukraine war.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.7
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• pp.1140-1147
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• 2022

With the development of digital technology, the marine environment is expected to change rapidly. In the case of autonomous vessels, technology is being developed in many countries, and the international community has begun to discuss ways to operate it. Changes in ships cause changes in the marine traffic environment and urge changes to aids to navigation. This study aims to analyze the cyber security management awareness of VTS personnel to improve the cyber security system for aids to navigation. To this end, the current status of cyber security management was reviewed with a focus on VTS, and a survey was conducted on VTS personnel. The survey analysis used the IPA methodology, and as a result of the analysis, a clear difference was observed in the perception of cybersecurity between those with experience in security and those without experience. In addition, technical measures related to cyber-attack detection and blocking should be implemented with the highest priority. The results of this study can be used as basic data for improving the cyber security management system for aids to navigation.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.133-153
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• 2023

In this paper, we propose a novel approach to investigating brain-signal measurement technology using Electroencephalography (EEG). Traditionally, researchers have combined EEG signals with bio-signals (BSs) to enhance the classification performance of emotional states. Our objective was to explore the synergistic effects of coupling EEG and BSs, and determine whether the combination of EEG+BS improves the classification accuracy of emotional states compared to using EEG alone or combining EEG with pseudo-random signals (PS) generated arbitrarily by random generators. Employing four feature extraction methods, we examined four combinations: EEG alone, EG+BS, EEG+BS+PS, and EEG+PS, utilizing data from two widely-used open datasets. Emotional states (task versus rest states) were classified using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) classifiers. Our results revealed that when using the highest accuracy SVM-FFT, the average error rates of EEG+BS were 4.7% and 6.5% higher than those of EEG+PS and EEG alone, respectively. We also conducted a thorough analysis of EEG+BS by combining numerous PSs. The error rate of EEG+BS+PS displayed a V-shaped curve, initially decreasing due to the deep double descent phenomenon, followed by an increase attributed to the curse of dimensionality. Consequently, our findings suggest that the combination of EEG+BS may not always yield promising classification performance.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.525-530
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• 2023

Digital drawing subjects require the subdivision of evaluation elements and the graduality of evaluation according to the recent movement of the innovative curriculum. The purpose of this paper is to present the criteria for evaluating the drawing and to propose it as a rubric evaluation. In the text, criteria for beginner evaluation were technical skills such as the accuracy and consistency of the line, the ratio and balance of the picture, and the ability to effectively utilize various brushes and tools at the intermediate levels. In the advanced evaluation section, it is a part of a new perspective or originality centered on creativity and originality, and a unique perspective or interpretation of a given subject. In addition, as an understanding of design principles, the evaluation of completeness was derived focusing on the ability to actively utilize various functions of digital drawing software through design principles such as placement, color, and shape. The importance of introducing rubric evaluation is to allow instructors to make objective and consistent evaluations, and the key to research in rubric evaluation in these art subjects is to help learners clearly grasp their strengths and weaknesses, and learners can identify what needs to be improved and develop better drawing skills accordingly through feedback on each item.

• Advanced Industrial SCIence
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• v.3 no.2
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• pp.8-16
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• 2024

This paper shows the system of drug classification, the goal of this is to foretell the apt drug for the patients based on their demographic and physiological traits. The dataset consists of various attributes like Age, Sex, BP (Blood Pressure), Cholesterol Level, and Na_to_K (Sodium to Potassium ratio), with the objective to determine the kind of drug being given. The models used in this paper are K-Nearest Neighbors (KNN), Logistic Regression and Random Forest. Further to fine-tune hyper parameters using 5-fold cross-validation, GridSearchCV was used and each model was trained and tested on the dataset. To assess the performance of each model both with and without hyper parameter tuning evaluation metrics like accuracy, confusion matrices, and classification reports were used and the accuracy of the models without GridSearchCV was 0.7, 0.875, 0.975 and with GridSearchCV was 0.75, 1.0, 0.975. According to GridSearchCV Logistic Regression is the most suitable model for drug classification among the three-model used followed by the K-Nearest Neighbors. Also, Na_to_K is an essential feature in predicting the outcome.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.565-574
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• 2024

ChatGPT is causing significant reverberations across all sectors of our society, and this holds true for the field of education as well. However, scholarly and societal discussions regarding ChatGPT in academic settings have primarily focused on issues such as plagiarism, with relatively limited research on the positive effects of utilizing generative AI. Additionally, amidst the educational crisis of the post-COVID era, there is a growing recognition of the need to enhance academic engagement. In light of these concerns, we investigated how academic engagement varies based on students' levels of ChatGPT literacy and examined whether students' academic confidence and perceived academic competence serve as mediators between ChatGPT literacy and academic engagement. An analysis using SPSS was conducted on the data collected from 406 college students. The results showed that ChatGPT literacy had a positive effect on academic engagement, and academic confidence mediated the relationship between ChatGPT literacy and academic engagement. Also, when the mediating effect of perceived academic competence was significant only when it was serially mediated. Based on these findings, we discussed the theoretical contributions of identifying the theoretical mechanism between ChatGPT literacy and academic engagement. In addition, practical implications regarding the importance of ChatGPT literacy education were described.