• Information Systems Review
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• v.25 no.3
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• pp.59-81
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• 2023

Elderly suicide problem has become worse in South Korea. With a rapid aging of the population, the trend of suicide among the elderly is expected to accelerate, preventing elderly suicide has been considered an important societal problem. Thus, we aim to investigate various factors that explain suicidal ideation and to develop a predictive model for suicidal ideation in the context of elderly people in South Korea. To this end, this study contributes to addressing the elderly suicide problem. By using seven-year panel data from the Korea Welfare Panel Survey, we extract various potential causal factors for elderly suicidal ideation based on interpersonal theory of suicide and social disorganization theory. Then a panel logit model was employed to assess the impacts of potential factors on suicidal ideation and deep learning and machine learning algorithms were used to develop a predictive model for suicidal ideation of elderly people. The results of our study provide practical implications for preventing elderly suicide by identifying causal factors of suicidal ideation and a high suicidal risk group of the elderly. This study sheds light on synergy of mixed methodology and provides various academic implications.

• Journal of Korean Society of Archives and Records Management
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• v.19 no.4
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• pp.63-86
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• 2019

UNESCO clearly has a good user interface compared to other sites. However, it does not have a structure in which user-centric knowledge curating is employed by users. As such, the knowledge structure should be expressed differently in advance for users to enjoy such benefits. At present, almost all current information systems are lacking with semantic and contextual information. Moreover, these systems are deemed insufficient of interlinking various kinds of thoughts in our minds. Thus, it is necessary to model in advance what users are likely to think and provide an interface that they can easily utilize based on that modeling. Furthermore, there is a need for a new structural theory based on semantic technology that can make that possible. Therefore, in this proposal, theoretical and practical insights were presented for user interface implementation to which relational facet navigation based on the structural theory is applied. Moreover, this proposal intends to suggest a "thinking expansion platform" that allows users' ideation of different concepts, including those unfamiliar to them.

• Journal of Convergence for Information Technology
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• v.9 no.4
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• pp.1-7
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• 2019

The medical environment, combined with IT technology, is changing the paradigm for medical services from treatment to prevention. In particular, as ICT convergence digital healthcare technology is applied to hospital medical systems, infrastructure technologies such as big data, Internet of Things, and artificial intelligence are being used in conjunction with the cloud. In particular, as medical services are used with IT devices, the quality of medical services is increasingly improving to make them easier for users to access. Medical institutions seeking to incorporate IoT services into cloud health care environment services are trying to reduce hospital operating costs and improve service quality, but have not yet been fully supported. In this paper, a patient information collection model from hospital IoT system, which has established a cloud environment, is proposed. The proposed model prevents third parties from illegally eavesdropping and interfering with patients' biometric information through IoT devices attached to the patient's body at hospitals in cloud environments that have established hospital IoT systems. The proposed model allows clinicians to analyze patients' disease information so that they can collect and treat diseases associated with their eating habits through IoT devices. The analyzed disease information minimizes hospital work to facilitate the handling of prescriptions and care according to the patient's degree of illness.

• Journal of the Korean Society for information Management
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• v.40 no.4
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• pp.1-31
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• 2023

This study conducted user experience evaluation by introducing various text mining techniques along with topic modeling techniques for mobile menstrual cycle measurement applications that are closely related to women's health and analyzed the results by combining them with a honeycomb model. To evaluate the user experience revealed in the menstrual cycle measurement application review, 47,117 Korean reviews of the menstrual cycle measurement application were collected. Topic modeling analysis was conducted to confirm the overall discourse on the user experience revealed in the review, and text network analysis was conducted to confirm the specific experience of each topic. In addition, sentimental analysis was conducted to understand the emotional experience of users. Based on this, the development strategy of the menstrual cycle measurement application was presented in terms of accuracy, design, monitoring, data management, and user management. As a result of the study, it was confirmed that the accuracy and monitoring function of the menstrual cycle measurement of the application should be improved, and it was observed that various design attempts were required. In addition, the necessity of supplementing personal information and the user's biometric data management method was also confirmed. By exploring the user experience (UX) of the menstrual cycle measurement application in-depth, this study revealed various factors experienced by users and suggested practical improvements to provide a better experience. It is also significant in that it presents a methodology by combines topic modeling and text network analysis techniques so that researchers can closely grasp vast amounts of review data in the process of evaluating user experiences.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.217-242
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• 2022

With the launch of Artificial Intelligence(AI)-based intelligent products on the market, innovative changes are taking place not only in business but also in consumers' daily lives. Intelligent products have the potential to realize technology differentiation and increase market competitiveness through advanced functions of artificial intelligence. However, there is no new product development methodology that can sufficiently reflect the characteristics of artificial intelligence for the purpose of developing intelligent products with high market acceptance. This study proposes a KANO-QFD integrated model as a methodology for intelligent product development. As a specific example of the empirical analysis, the types of consumer requirements for hair loss prediction and treatment device were classified, and the relative importance and priority of engineering characteristics were derived to suggest the direction of intelligent medical product development. As a result of a survey of 130 consumers, accurate prediction of future hair loss progress, future hair loss and improved future after treatment realized and viewed on a smartphone, sophisticated design, and treatment using laser and LED combined light energy were realized as attractive quality factors among the KANO categories. As a result of the analysis based on House of Quality of QFD, learning data for hair loss diagnosis and prediction, micro camera resolution for scalp scan, hair loss type classification model, customized personal account management, and hair loss progress diagnosis model were derived. This study is significant in that it presented directions for the development of artificial intelligence-based intelligent medical product that were not previously preceded.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.57-64
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• 2022

In this paper, the definition and overview of digital health care that has emerged recently, core technology, and We would like to propose a plan to establish a next-generation information protection system that can protect digital healthcare devices and data from cyber attacks. Various vulnerabilities exist for digital healthcare devices and data, and cyber attacks are possible for those vulnerabilities. Through an attack on digital health care devices and information and communication networks, it can directly adversely affect human life and health, Since digital healthcare data contains sensitive and personal information, it is essential to safely protect it from cyber attacks. In the case of this proposal, for continuous safe management of data and cyber attacks on equipment and communication networks for digital health devices, It is expected to be able to respond more effectively and continuously through the establishment of the next-generation information protection system.

• Journal of Advanced Navigation Technology
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• v.13 no.4
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• pp.577-585
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• 2009

Various studies on individual health-care application services have been lively going on due to recent development of information and communication technology and improvement of life quality. Moreover, U-health care area is emerging as a new growth industry as the demand of U-health care service for a quality life grows with the development of medical technology and ubiquitous environment. U-health care system for crew members on ships, who need to spend much time on sea far from their families, is especially needed because they find their job much more stressful not only physically but also mentally than any other people in different jobs do and have less chance to get proper medical services in time. In this paper, we suggested how to get more accurate and objective U-health wellbeing index by complementing SF-36, the general heath care index model in order to managing health of crew. Also we designed the U-health wellbeing index service system which can provide appropriate sports programs or diet contents according to health index.

• Journal of Korean Society of Archives and Records Management
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• v.24 no.3
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• pp.65-84
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• 2024

Since the 2000s, there has been growing interest in community archival research in the West, and in Korea, projects that support citizen or resident participation in archival activities have also been increasing. With the role of community members as producers of records having gained importance in Korea, it has become necessary to examine the affective approach currently discussed in archival studies, focusing on the impact of "archival activities" on individual citizens. Unlike emotion, which is a personal and subjective experience, affect is characterized by "a sense shared based on relationships" and involves the concept of transformation of being (affection). This study aims to explore a method for analyzing the "affective impact applicable to citizen archival activities," an area that has not been previously addressed. To this end, the study reviews the meaning and concept of citizen archival activities and their development in Korea, focusing on the UCLA study (2018) and Brophy's (2005) approach to analyzing the affective impact of community archives to explore methodologies. It also explores the integration of the concept of "partyhood" to better reflect the characteristics of citizen archival activities. Based on these findings, this study proposes a conceptual model for analyzing the affective impact of citizen archival activities on recorders in Korea.

• Journal of health informatics and statistics
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• v.42 no.4
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• pp.309-316
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• 2017

Objectives: Radon and its progeny pose environmental risks as a carcinogen, especially to the lungs. Investigating factors affecting indoor radon concentrations and models thereof are needed to prevent exposure to radon and to reduce indoor radon concentrations. The purpose of this study was to identify factors affecting indoor radon concentration and to construct a comprehensive model thereof. Methods: Questionnaires were administered to obtain data on residential environments, including building materials and life style. Decision tree and structural equation modeling were applied to predict residences at risk for higher radon concentrations and to develop the comprehensive model. Results: Greenery ratio, impermeable layer ratio, residence at ground level, daily ventilation, long-term heating, crack around the measuring device, and bedroom were significantly shown to be predictive factors of higher indoor radon concentrations. Daily ventilation reduced the probability of homes having indoor radon concentrations ${\geq}200Bq/m^3$ by 11.6%. Meanwhile, a greenery ratio ${\geq}65%$ without daily ventilation increased this probability by 15.3% compared to daily ventilation. The constructed model indicated greenery ratio and ventilation rate directly affecting indoor radon concentrations. Conclusions: Our model highlights the combined influences of geographical properties, groundwater, and lifestyle factors of an individual resident on indoor radon concentrations in Korea.

• The Transactions of the Korea Information Processing Society
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• v.13 no.9
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• pp.395-403
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• 2024

Recently, with the advancement of technology, the automotive industry has seen an increase in network connectivity. CAN (Controller Area Network) bus technology enables fast and efficient data communication between various electronic devices and systems within a vehicle, providing a platform that integrates and manages a wide range of functions, from core systems to auxiliary features. However, this increased connectivity raises concerns about network security, as external attackers could potentially gain access to the automotive network, taking control of the vehicle or stealing personal information. This paper analyzed abnormal messages occurring in CAN and confirmed that message occurrence periodicity, frequency, and data changes are important factors in the detection of abnormal messages. Through DBC decoding, the specific meanings of CAN messages were interpreted. Based on this, a model for classifying abnormalities was proposed using the GRU model to analyze the periodicity and trend of message occurrences by measuring the difference (residual) between the predicted and actual messages occurring within a certain period as an abnormality metric. Additionally, for multi-class classification of attack techniques on abnormal messages, a Random Forest model was introduced as a multi-classifier using message occurrence frequency, periodicity, and residuals, achieving improved performance. This model achieved a high accuracy of over 99% in detecting abnormal messages and demonstrated superior performance compared to other existing models.