• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.123-134
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• 2018

Recently, due to the rising incidence of disasters in the nation, there has been a growing interest in the relevance and role of science and technology in solving disaster and safety related issues. In addition, the necessities of securing the human rights of all citizens in disaster risk reduction, identifying fields of technology development for effective disaster response, and improving the efficiency of R&D investment for disaster and safety are becoming more important as the different types of disasters and stages of disaster and safety management process have been considered. In this study, we analyzed bipartite or two-mode networks constructed from an expert survey dataset of technology development for disaster and safety management. The results reveal that earthquake and fire are the two disasters affecting an individual and society at large and demonstrate that AI and big data analytics are effective supports in managing disaster and safety. We believe that such a network analytic approach can be used to explore some important implications exist for the national science and technology effort and successful disaster and safety management practices in Korea.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.99-105
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• 2023

In the 4th industrial revolution, the field of criminal justice is paying attention to Legaltech using artificial intelligence to provide efficient legal services. This paper attempted to create a crime prediction model that can apply Recurrent Neural Network(RNN) to increase the potential for using legal technology in the domestic criminal justice field. To this end, the crime process was divided into pre, during, and post stages based on the criminal facts described in the judgment, utilizing crime script analysis techniques. In addition, at each time point, the method and evidence of crime were classified into objects, actions, and environments based on the sentence composition elements and the 8 principles of investigation. The case summary analysis framework derived from this study can contribute to establishing situational crime prevention strategies because it is easy to identify typical patterns of specific crime methods. Furthermore, the results of this study can be used as a useful reference for research on generating crime situation prediction data based on RNN models in future follow-up studies.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.4
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• pp.44-57
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• 2019

Various studies have been conducted to solve traffic congestions in many metropolitan cities through accurate traffic flow prediction. Most studies are based on the assumption that past traffic patterns repeat in the future. Models based on such an assumption fall short in case irregular traffic patterns abruptly occur. Instead, the approaches such as predicting traffic pattern through big data analytics and artificial intelligence have emerged. Specifically, deep learning algorithms such as RNN have been prevalent for tackling the problems of predicting temporal traffic flow as a time series. However, these algorithms do not perform well in terms of long-term prediction. In this paper, we take into account various external factors that may affect the traffic flows. We model the correlation between the multi-dimensional context information with temporal traffic speed pattern using deep neural networks. Our model trained with the traffic data from TOPIS system by Seoul, Korea can predict traffic speed on a specific date with the accuracy reaching nearly 90%. We expect that the accuracy can be improved further by taking into account additional factors such as accidents and constructions for the prediction.

• 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 Science Education
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• v.45 no.2
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• pp.156-171
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• 2021

This study aims at investigating the characteristics of trends of future education over time though the literature review and examining the accuracy of the framework for forecasting future education proposed by the previous studies by comparing the outcomes between the literature review and media articles. Thus, this study collects the articles dealing with future education searched from the Web of Science and categorized them into four periods during the new millennium. The new articles from media were selected to find out the present of education so that we can figure out the appropriateness of the proposed framework to predict the future of education. Research findings reveal that gradual tendencies of topics could not be found except teacher education and they are diverse from characteristics of agents (students and teachers) to the curriculum and pedagogical strategies. On the other hand, the results of analysis on the media articles focuses more on the projects launched by the government and the immediate responses to the COVID-19, as well as educational technologies related to big data and artificial intelligence. It is surprising that only a few key words are occupied in the latest articles from the literature review and many of them have not been discussed before. This indicates that the predictive framework is not effective to establish the long-term plan for education due to the uncertainty of educational environment, and thus this study will give some implications for developing the model to forecast the future of education.

• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.247-259
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• 2021

This study aimed to look for educational ways to prepare for the future society for education and people of talent who will lead the post-COVID-19 era. To this end, the factors necessary for the type of future talent in the post-COVID-19 era were identified by analyzing Big data. Based on the deducted factors composing the type of talent in the post-COVID-19 era, policy direction according to the emergence of the post-COVID-19 era were deducted through the interviews with the group of experts and delphi survey, and on the basis of this, this study sought for"a plan for the educational change in line with cultivation of people of talent in the post-COVID-19 era. The results of this study are as follows. First, through the big data analytics and analysis of the interviews, convergence, ICT utilization ability, creativity, self-regulated competency and leadership were found to be the factors necessary for the type of talent in the post-COVID-19 era. Second, it considered the innovation of digital education system and the support for vulnerable classes as the issue for cultivation of people of talent in the post-COVID-19 era. Third, the most important policy with regard to the educational direction for cultivation of people of talent in the post-COVID-19 era was cultivation of convergence talents. Convergence is a very important variable in the post-COVID-19 era since it creates new values by connecting things that are separated from each other. Hopefully, this study will build a basis for competency development, education and training in preparation for the post-COVID-19 era.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.207-218
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• 2020

Recently, due to the increase of online reviews and the development of analysis technology, the interest and demand for online review analysis continues to increase. However, previous studies have not considered the emotions contained in each vocabulary may differ from one reviewer to another. Therefore, this study first classifies the customer group according to the customer's grade, and presents the result of analyzing the difference by performing review analysis for each customer group. We found that the price factor had a significant influence on the evaluation of products for customers with high ratings. On the contrary, in the case of low-grade customers, the degree of correspondence between the contents introduced in the mall and the actual product significantly influenced the evaluation of the product. We expect that the proposed methodology can be effectively used to establish differentiated marketing strategies by identifying factors that affect product evaluation by customer group.

• Management & Information Systems Review
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• v.36 no.1
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• pp.273-290
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• 2017

Internet of Things (IoT) is considered to be the next wave of Information Technology transformation after the Internet has changed the process of doing business. Since the domain of IoT ranging from the sensor technology to service to the users is wide, the structure of the research domain is not delineated clearly. To do that we suggest to use the Technology Stack Model proposed by Porter et al.(2014) to measure the maturity level of IoT in organizations. Based on the Stack Model, for the general understandings of IoT, we do keyword analyses on the academic papers whose major research issue is IoT. It is found that the current status of IoT application from the perspectives of cloud and big data analytics is not active, meaning that the real value of IoT has not been realized. We also examine the cases which deal with the part of cloud process which is crucial for value accrual. Based on these findings, we suggest the future direction of IoT research. We also propose that IT is to value chain what IoT is to the Stack Model to derive value in organizations.

• Journal of Contemporary Eastern Asia
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• v.13 no.1
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• pp.5-18
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• 2014

This exploratory study investigates the use of a computational knowledge engine (WolframAlpha) and social networking sites (Gmail, Yahoo and Facebook) by 200 students at De La Salle-College of Saint Benilde, their "friends" and their "friends of friends" during the 2009 through 2013 school years, and how this appears to have added value in knowledge generation. The primary aim is to identify what enhances productiveness in knowledge generation in Philippine Tertiary Education. The phenomenological approach is used, therefore there are no specific research questions or hypotheses proposed in this paper. Considering that knowledge generation is a complex phenomenon, a stochastic modelling approach is also used for the investigation that was developed specifically to study un-deterministic complex systems. A list of salient features for knowledge generation is presented as a result. In addition to these features, various problem types are identified from literature. These are then integrated to provide a proposed framework of inclusive (friendly) and innovative social networks, for knowledge generation in Philippine tertiary education. Such a framework is necessarily multidisciplinary and useful for problem-solving in a globalized and pluralist reality. The implementation of this framework is illustrated in the three parts of the study: Part 1: Online lessons, discussions, and examinations in General Psychology, Introduction to Sociology, and Life and Works of Jose Rizal, for the author's students in De La Salle-College of Saint Benilde; Part 2: Facebook Report analytics of students and teachers, their friends and their friends of friends via WolframAlpha; and Part 3: Social Network Analysis of the people and groups influencing the courses' scope-and-sequence in the new General Education Curriculum for Tertiary Schools and Institutions in the Philippines.

• The Journal of Society for e-Business Studies
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• v.25 no.2
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• pp.49-63
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• 2020

The accurate prediction of box office in the early stage is crucial for film industry to make better managerial decision. With aims to improve the prediction performance, the purpose of this paper is to evaluate the use of machine learning methods. We tested both classification and regression based methods including k-NN, SVM and Random Forest. We first evaluate input variables, which show that reputation-related information generated during the first two-week period after release is significant. Prediction test results show that regression based methods provides lower prediction error, and Random Forest particularly outperforms other machine learning methods. Regression based method has better prediction power when films have small box office earnings. On the other hand, classification based method works better for predicting large box office earnings.