• The Journal of the Convergence on Culture Technology
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• v.8 no.2
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• pp.1-6
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• 2022

Lifelog is a digital record of an individual collected from various digital sensors, and includes activity amount, sleep information, weight change, body mass, muscle mass, fat mass, etc. Recently, as wearable devices have become common, a lot of high-quality lifelog data is being produced. Lifelog data shows the state of an individual's body, and can be used not only for individual health care, but also for causes and treatment of diseases. However, at present, AI/ML-based correlation analysis and personalization are not reflected. It is only at the level of presenting simple records or fragmentary statistics. Therefore, in this paper, the correlation/relationship between lifelog data and disease, and AI/ML technology inside lifelog data are examined, and furthermore, a lifelog data analysis process based on AI/ML is proposed. The analysis process is demonstrated with the data collected in the actual Galaxy Watch. Finally, we propose a future convergence service roadmap including lifelog data, diet, health information, and disease information.

• Nutrition Research and Practice
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• v.16 no.6
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• pp.801-812
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• 2022

BACKGROUND/OBJECTIVES: Diet planning in childcare centers is difficult because of the required knowledge of nutrition and development as well as the high design complexity associated with large numbers of food items. Artificial intelligence (AI) is expected to provide diet-planning solutions via automatic and effective application of professional knowledge, addressing the complexity of optimal diet design. This study presents the results of the evaluation of the utility of AI-generated diets for children and provides related implications. MATERIALS/METHODS: We developed 2 AI solutions for children aged 3-5 yrs using a generative adversarial network (GAN) model and a reinforcement learning (RL) framework. After training these solutions to produce daily diet plans, experts evaluated the human- and AI-generated diets in 2 steps. RESULTS: In the evaluation of adequacy of nutrition, where experts were provided only with nutrient information and no food names, the proportion of strong positive responses to RL-generated diets was higher than that of the human- and GAN-generated diets (P < 0.001). In contrast, in terms of diet composition, the experts' responses to human-designed diets were more positive when experts were provided with food name information (i.e., composition information). CONCLUSIONS: To the best of our knowledge, this is the first study to demonstrate the development and evaluation of AI to support dietary planning for children. This study demonstrates the possibility of developing AI-assisted diet planning methods for children and highlights the importance of composition compliance in diet planning. Further integrative cooperation in the fields of nutrition, engineering, and medicine is needed to improve the suitability of our proposed AI solutions and benefit children's well-being by providing high-quality diet planning in terms of both compositional and nutritional criteria.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.49-54
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• 2021

With the recent development of smart healthcare technology, interest in daily diseases is increasing. However, healthcare data has an imbalance between positive and negative data. This is caused by the difficulty of collecting data because there are relatively many people who are not patients compared to patients with certain diseases. Data imbalances need to be adjusted because they affect performance in ongoing learning during disease prediction and analysis. Therefore, in this paper, We replace missing values through multiple imputation in detection models to determine whether they are prevalent or not, and resolve data imbalances through over-sampling. Based on AutoML using preprocessed data, We generate several models and select top 3 models to generate ensemble models.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.68-71
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• 2022

항로표지는 해상에 독립적으로 암초 위나 줄에 의해 떠 있는 형태로 존재하며, 선박들의 안전 운행에 필요한 다양한 정보를 제공하는 역할을 수행한다. 이러한 항로표지의 설치 및 동작 형태는 풍랑에 따라 기기의 위치가 가변적으로 변하게 된다. 따라서 기기의 위치가 급격하게 변했을 때, 항로표지 기기 내에도 영향을 받는다면 지방청의 항로표지 데이터 수신이 낮아질 것이라고 가설 설정했다. 본 논문에서는 기상특보에 따른 시간적 기준으로 구간을 나누어 풍랑과 항로표지 데이터 수신 간의 상관관계가 있는지 연구를 진행하였다. 연구 결과 풍랑이 거세질수록 평균 데이터 수집량이 감소하는 것으로 데이터 수신 강도의 영향을 줄 수 있음을 확인하였다. 이번 연구를 통해 풍랑에 대비한 항로표지 데이터의 개선이 필요하며, 선박의 안전과 관련된 만큼 정밀한 개선을 요한다.

• 대한생식의학회:학술대회논문집
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• 2006.11a
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• pp.158-159
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• 2006

• Journal of Animal Environmental Science
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• v.18 no.2
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• pp.85-90
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• 2012

In Korea, livestock has grown into the most value-added business in the agricultural and forest industry. But due to the recent outbreak of deadly infectious diseases such as foot-and-mount disease and avian influenza (AI), the demand for IT-enabled cutting-edge management system is getting stronger. As for humans, pilot projects and researches concerning U-healthcare have been carried out since early 2000. So this study explored the current progress of U-healthcare introduction, and suggested the strategies to develop technologies of collecting, processing, and utilizing information; to apply elements for a service model development and prioritization; to provide policy and institutional support. Therefore it is expected to vitalize the livestock U-healthcare in the future through continuous study based on these results.

• Korean Journal of Radiology
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• v.25 no.7
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• pp.603-612
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• 2024

Artificial intelligence (AI) is rapidly gaining recognition in the radiology domain as a greater number of radiologists are becoming AI-literate. However, the adoption and implementation of AI solutions in clinical settings have been slow, with points of contention. A group of AI users comprising mainly clinical radiologists across various Asian countries, including India, Japan, Malaysia, Singapore, Taiwan, Thailand, and Uzbekistan, formed the working group. This study aimed to draft position statements regarding the application and clinical deployment of AI in radiology. The primary aim is to raise awareness among the general public, promote professional interest and discussion, clarify ethical considerations when implementing AI technology, and engage the radiology profession in the ever-changing clinical practice. These position statements highlight pertinent issues that need to be addressed between care providers and care recipients. More importantly, this will help legalize the use of non-human instruments in clinical deployment without compromising ethical considerations, decision-making precision, and clinical professional standards. We base our study on four main principles of medical care-respect for patient autonomy, beneficence, non-maleficence, and justice.

• International Journal of Advanced Culture Technology
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• v.11 no.1
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• pp.283-289
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• 2023

AI is bringing about drastic changes not only in the aspect of technologies but also in society and culture. Medical AI based on deep learning have developed rapidly. Especially, the field of medical image analysis has been proven that AI can identify the characteristics of medical images more accurately and quickly than clinicians. Evaluating the latest results of the AI-based medical image processing is important for the implication for the development direction of medical AI. In this paper, we analyze and evaluate the latest trends in AI-based medical image analysis, which is showing great achievements in the field of medical AI in the healthcare industry. We analyze deep learning models for medical image analysis and AI-based medical image segmentation for quantitative analysis. Also, we evaluate the future development direction in terms of marketability as well as the size and characteristics of the medical AI market and the restrictions to market growth. For evaluating the latest trend in the deep learning-based medical image processing, we analyze the latest research results on the deep learning-based medical image processing and data of medical AI market. The analyzed trends provide the overall views and implication for the developing deep learning in the medical fields.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1518-1539
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• 2024

Artificial intelligence-enabled business models aim to improve decision-making, operational efficiency, innovation, and productivity. The presented systematic literature review is conducted to highlight elucidating the utilization of artificial intelligence (AI) methods and techniques within AI-enabled businesses, the significance and functions of AI-enabled organizational models and frameworks, and the design parameters employed in academic research studies within the AI-enabled business domain. We reviewed 39 empirical studies that were published between 2010 and 2023. The studies that were chosen are classified based on the artificial intelligence business technique, empirical research design, and SLR search protocol criteria. According to the findings, machine learning and artificial intelligence were reported as popular methods used for business process modelling in 19% of the studies. Healthcare was the most experimented business domain used for empirical evaluation in 28% of the primary research. The most common reason for using artificial intelligence in businesses was to improve business intelligence. 51% of main studies claimed to have been carried out as experiments. 53% of the research followed experimental guidelines and were repeatable. For the design of business process modelling, eighteen AI mythology were discovered, as well as seven types of AI modelling goals and principles for organisations. For AI-enabled business models, safety, security, and privacy are key concerns in society. The growth of AI is influencing novel forms of business.

• Communications of the Korean Institute of Information Scientists and Engineers
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• v.35 no.7
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• pp.9-25
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• 2017

In this paper, we surveyed state-of-the-art health and wellness platforms. The motivation of this paper is to review the state-of-the-art health and wellness platforms and their maturity with respect to adoption of latest enabling technologies. The is review is classified into four categories: healthcare systems, AI-assisted healthcare, wellness platforms, and open source health and wellness initiatives. From this comprehensive review, it can be stated that the contemporary healthcare systems are well-adopting wellness due to the concentration shift towards prevention. Thus, the gap between health and wellness is slowly yet carefully entering gray area. Where both the domains can freely invoke each other's services, and supporting enabling technologies. Furthermore, the biomedical researchers and physicians are no longer carrying the myopic views of trusting their knowledge for diagnosis. AI-assisted technologies based on machine learning and big data are influencing today's prognosis with trust and confidence.