• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.33-56
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• 2017

As Internet usage is becoming more common worldwide and smartphone become necessity in daily life, technologies and applications related to mobile Internet are developing rapidly. The results of the Internet usage patterns of consumers around the world imply that there are many potential new business opportunities for mobile Internet technologies and applications. The location-based service (LBS) is a service based on the location information of the mobile device. LBS has recently gotten much attention among many mobile applications and various LBSs are rapidly developing in numerous categories. However, even with the development of LBS related technologies and services, there is still a lack of empirical research on the intention to use LBS. The application of previous researches is limited because they focused on the effect of one particular factor and had not shown the direct relationship on the intention to use LBS. Therefore, this study presents a research model of factors that affect the intention to use and actual use of LBS whose market is expected to grow rapidly, and tested it by conducting a questionnaire survey of 330 users. The results of data analysis showed that service customization, service quality, and personal innovativeness have a positive effect on the intention to use LBS and the intention to use LBS has a positive effect on the actual use of LBS. These results implies that LBS providers can enhance the user's intention to use LBS by offering service customization through the provision of various LBSs based on users' needs, improving information service qualities such as accuracy, timeliness, sensitivity, and reliability, and encouraging personal innovativeness. However, privacy concerns in the context of LBS are not significantly affected by service customization and personal innovativeness and privacy concerns do not significantly affect the intention to use LBS. In fact, the information related to users' location collected by LBS is less sensitive when compared with the information that is used to perform financial transactions. Therefore, such outcomes on privacy concern are revealed. In addition, the advantages of using LBS are more important than the sensitivity of privacy protection to the users who use LBS than to the users who use information systems such as electronic commerce that involves financial transactions. Therefore, LBS are recommended to be treated differently from other information systems. This study is significant in the theoretical point of contribution that it proposed factors affecting the intention to use LBS in a multi-faceted perspective, proved the proposed research model empirically, brought new insights on LBS, and broadens understanding of the intention to use and actual use of LBS. Also, the empirical results of the customization of LBS affecting the user's intention to use the LBS suggest that the provision of customized LBS services based on the usage data analysis through utilizing technologies such as artificial intelligence can enhance the user's intention to use. In a practical point of view, the results of this study are expected to help LBS providers to develop a competitive strategy for responding to LBS users effectively and lead to the LBS market grows. We expect that there will be differences in using LBSs depending on some factors such as types of LBS, whether it is free of charge or not, privacy policies related to LBS, the levels of reliability related application and technology, the frequency of use, etc. Therefore, if we can make comparative studies with those factors, it will contribute to the development of the research areas of LBS. We hope this study can inspire many researchers and initiate many great researches in LBS fields.

• Journal of The Korean Association For Science Education
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• v.44 no.1
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• pp.57-75
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• 2024

This study aimed to investigate the current state and educational needs of risk education related to science and technology as perceived by secondary science teachers. A survey was conducted with a total of 366 secondary science teachers. The results are as follows. First, There were more teachers who had not provided education on risks arising from science and technology in terms of risk perception, risk assessment, and risk management than those who had not. Global warming was the most common risk taught by teachers, followed by earthquakes, artificial intelligence, and traffic accidents. Second, teachers recognized that they lacked understanding that the achievement standards of the 2022 revised science curriculum include risks that may occur due to science and technology, but they thought they were prepared to teach. Third, teachers recognized that their understanding of risk perception was higher than that of risk management and risk assessment. Fourth, the experience of teachers in training on risk was very limited, with fewer having training in risk assessment and risk management compared to risk perception. The most common training experienced was in laboratory safety. Fifth, teachers recognized that their capabilities for the 10 goals of risk education were not high. Middle school teachers or teachers majoring in integrated science education evaluated their capabilities relatively highly. Sixth, many teachers thought it was important to address risks in school science education. They prioritized 'information use', 'decision-making skills', and 'influence of mass media', in that order, for importance and called for urgent education in 'action skills', 'information use', and 'influence of risk perception'. Seventh, as a result of deriving the priorities of education needs for each of the 10 goals of risk education, 'action skills', 'influence of risk perception', and 'evaluate risk assessment' were ranked 1st, 2nd, and 3rd, respectively.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.237-246
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• 2020

this paper, a data modeling method based on decision-making conditions is proposed for making combat and battlefield management systems to be intelligent, which are also a decision-making support system. A picture of a robot seeing and perceiving like humans and arriving a point it wanted can be understood and be felt in body. However, we can't find an example of implementing a decision-making which is the most important element in human cognitive action. Although the agent arrives at a designated office instead of human, it doesn't support a decision of whether raising the market price is appropriate or doing a counter-attack is smart. After we reviewed a current situation and problem in control & command of military, in order to collect a big data for making a machine staff's advice to be possible, we propose a data modeling prototype based on decision-making conditions as a method to change a current control & command system. In addition, a decision-making tree method is applied as an example of the decision making that the reformed control & command system equipped with the proposed data modeling will do. This paper can contribute in giving us an insight of how a future AI decision-making staff approaches to us.

• Journal of Service Research and Studies
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• v.13 no.4
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• pp.191-205
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• 2023

Artificial Intelligence (AI), especially in the domain of text-generative services, has witnessed a significant surge, with forecasts indicating the AI-as-a-Service (AIaaS) market reaching a valuation of $55.0 Billion by 2028. This research set out to explore the quality dimensions characterizing synthetic text media software, with a focus on four key players in the industry: ChatGPT, Writesonic, Jasper, and Anyword. Drawing from a comprehensive dataset of over 4,000 reviews sourced from a software evaluation platform, the study employed the Latent Dirichlet Allocation (LDA) topic modeling technique using the Gensim library. This process resulted the data into 11 distinct topics. Subsequent analysis involved comparing these topics against established AI service quality dimensions, specifically AICSQ and AISAQUAL. Notably, the reviews predominantly emphasized dimensions like availability and efficiency, while others, such as anthropomorphism, which have been underscored in prior literature, were absent. This observation is attributed to the inherent nature of the reviews of AI services examined, which lean more towards semantic understanding rather than direct user interaction. The study acknowledges inherent limitations, mainly potential biases stemming from the singular review source and the specific nature of the reviewer demographic. Possible future research includes gauging the real-world implications of these quality dimensions on user satisfaction and to discuss deeper into how individual dimensions might impact overall ratings.

• Smart Media Journal
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• v.13 no.4
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• pp.114-123
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• 2024

This paper proposes AR stone tower content, an experiential content based on wearable augmented reality (AR). Although wearable augmented reality is gaining attention, the acceptance of the technology is still focused on specialized applications such as industrial sites. On the other hand, the proposed AR stone tower content is based on the material of 'stone tower' so that general users can relate to it and easily participate in it, and it is organized to utilize space in a moving environment and find and stack stones based on natural hand gestures. The proposed AR stone tower content was implemented in the HoloLens 2 environment and evaluated by general users through a pilot exhibition in a small art museum. The evaluation results showed that the overall satisfaction with the content averaged 3.85, and the content appropriateness for the stone tower material was very high at 4.15. In particular, users were highly satisfied with content comprehension and sound, but somewhat less satisfied with object recognition, body adaptation, and object control. The above user evaluations confirm the resonance and positive response to the material, but also highlight the difficulties of the average user in experiencing and interacting with the wearable AR environment.

• Journal of The Korean Association For Science Education
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• v.38 no.2
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• pp.259-271
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• 2018

This study examined the effect of the SSI argumentation program based on social and emotional learning(SEL). The program consisted of 3 stages: (1) express their own feelings about SSI, identify the issues of SSI, and define a goal; (2) think of many possible solutions and envision results through argumentation; (3) select the best solution and make a decision based on warrants, data, and rebuttals. In each stage, the social-emotional strategies of self-awareness, self-management, social-awareness, relationship-management, and responsible decision making were used. Seventeen preservice biology teachers participated in this study during one semester dealing with four socioscientific issues. The results indicated that the preservice teachers, as time went on, became accustomed to expressing identifiable rebuttals, dispute talk, and asking questions. At the first SSI argumentation, argumentation mainly consisted of cumulative talk with no rebuttals, representing level 2 argumentation. Level 3 argumentation represented rebuttals that were implicit and weak, with cumulative talk. In level 2 and 3 argumentation, the preservice teachers represented understanding of others and compassion for self and others. Level 4 argumentation had rebuttals that were explicit, asking critical questions of the opposite sides. In addition, level 5 argumentation represented more than two controversial points with several instances of dispute talk. In levels 4 and 5, the preservice teachers became actively engaged in communication, inquiry self with others, managing vulnerability and negotiation.

• Journal of the Korean Society of Radiology
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• v.15 no.5
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• pp.667-673
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• 2021

As we enter the era of the 4th industrial revolution, it is judged that the scope of work of radiologists will be further expanded according to the innovation and advancement of radiation medical technology development. In this study, the current status of medical equipment and radiology technicians was identified, and basic data were provided for the plan for nurturing talents in the field of radiation medical technology in the era of the 4th industrial revolution, as well as career and employment counseling. Data from the second quarter of 2020 and the second quarter of 2021 were analyzed using health and medical big data. As a result of comparing the status of medical equipment by type in 2021 compared to 2020, C-Arm X-ray examination equipment increased by 5.83% to 6,638 units, followed by MRI examination equipment 1,811 units 5.29%, and angiography equipment 725 units 5.22% , general X-ray examination equipment 21,557 units increased 3.99%, CT examination equipment 2,136 units 3.03%, and breast examination equipment 3,425 units increased 3.00%. As a result of a comparison of the total number of radiologists in 2021 compared to 2020, the number was 29,038, an increase of 2.73%. As a result of comparing the status of radiographers by region, the increase was highest in the Gyeonggi region with 5.96%, followed by the Gangwon region with a 5.66% increase and the Chungnam region with a 3.81% increase. In a situation where the number of medical equipment and radiologist manpower is increasing, universities are developing specialized knowledge and practical competency through subject development related to the understanding and utilization of customized artificial intelligence and big data that can be applied in the medical radiation technology field in the era of the 4th industrial revolution. It is necessary to nurture qualified radiographers, and at the level of the association, it is thought that active policies are needed to create new jobs and improve employment.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Journal of the Korean Society of Food Science and Nutrition
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• v.33 no.1
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• pp.59-65
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• 2004

Hot water-extracts prepared from Cordyceps militaris of silkworm pupa (CMP) or Cordyceps militaris of silkworm larva (CML) were tested for tumor growth inhibitory and immunomodulatory activities in ICR mice bearing sarcoma-180 cells solid tumor, and compared with those of the known compound, cordycepin, found in Cordyceps militaris. Mice were subcutaneously injected with sarcoma-180 cells, and i.p. injected with either saline (Control), 50 mg/kg or 100 mg/kg of CMP (CMP50 or CMP100, respectively), or CML (CML50 or CML100, respectively), or 1 or 2 mg/kg of cordycepin (C1 or C2, respectively) for 10 days. Mice injected with CMP50 or CMP100 showed a 47.3% or 57.6% inhibition in the solid tumor growth (P<0.05), while those injected with CML50 or CML100 exhibited a 35.5% or 37.1% reduction (p<0.05) in solid tumor size compared to the value for control mice treated with saline. Animals injected with corcycepin showed a 26∼30% inhibition in the solid tumor growth (P<0.05). Mice bearing solid tumor and injected with CMP or CML showed a significantly increased thymus weight (38∼44% increase), lymphocyte percentages of CD4+ T-cell, CD8+ T-cell, and NK-cell (63∼110% increase) in the spleen, and interleukin-2 excretion (33∼51% increase) by the isolated splenocytes compared to those in control mice (p<0.05). These results indicate that the anti-tumor activity of hot water extracts of Cordyceps militaris, raised on both silkworm pupa and silkworm larva, appears to be associated with their immunomodulatory activity, and these activities found in Cordyceps militaris are superior to those for the single compound, cordycepin.