Hyeon-Ju Jeon

AI-assisted ultrasonic system for non-invasive glucose classification in whole blood

Jeong Eun Lee, Hyeon-Ju Jeon, Min-Seo Kim, O‐Joun Lee et autres

Abstract Diabetes mellitus is a chronic disorder characterized by persistent hyperglycemia that damages multiple organs. With global prevalence rising, accurate, convenient, and non-invasive glucose monitoring is urgently needed. However, current diagnostic methods—such as point-sample tests and continuous glucose monitoring (CGM)—remain limited by …

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Mitigating Degree Bias in Graph Representation Learning With Learnable Structural Augmentation and Structural Self-Attention

Van Thuy Hoang, Hyeon-Ju Jeon, O‐Joun Lee

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Halal or Not: Knowledge Graph Completion for Predicting Cultural Appropriateness of Daily Products

Van Thuy Hoang, Jinho Seo, S. Kim, Luong Vuong Nguyen et autres

The growing demand for halal cosmetic products has exposed significant challenges, especially in Muslim-majority countries. Recently, various machine learning-based strategies, e.g., image-based methods, have shown remarkable success in predicting the halal status of cosmetics. However, these methods mainly focus on analyzing the …

Halal or Not: Knowledge Graph Completion for Predicting Cultural Appropriateness of Daily Products

Van Thuy Hoang, Jinho Seo, S. Kim, Luong Vuong Nguyen et autres

The growing demand for halal cosmetic products has exposed significant challenges, especially in Muslim-majority countries. Recently, various machine learning-based strategies, e.g., image-based methods, have shown remarkable success in predicting the halal status of cosmetics. However, these methods mainly focus on analyzing the …

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Observation impact explanation in atmospheric state estimation using hierarchical message-passing graph neural networks*

Hyeon-Ju Jeon, J. S. Kang, In‐Hyuk Kwon, O‐Joun Lee

Abstract The impact of meteorological observations on weather forecasting varies with the sensor type, location, time, and other environmental factors. Thus, the quantitative analysis of observation impacts is crucial for the effective and efficient development of weather forecasting systems. However, existing impact …

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Internal pipe corrosion assessment method in water distribution system using ultrasound and convolutional neural networks

Yeongho Sung, Hyeon-Ju Jeon, Daehun Kim, Min-Seo Kim et autres

Abstract Internal pipe corrosion within water distribution systems leads to iron oxide deposits on pipe walls, potentially contaminating the water supply. Consuming iron oxide-contaminated water can cause significant health issues such as gastrointestinal infections, dermatological problems, and lymph node complications. Therefore, non-destructive …

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Predicting the daily number of patients for allergic diseases using PM10 concentration based on spatiotemporal graph convolutional networks

Hyeon-Ju Jeon, Hyeon-Jin Jeon, Seung Ho Jeon

Air pollution causes and exacerbates allergic diseases including asthma, allergic rhinitis, and atopic dermatitis. Precise prediction of the number of patients afflicted with these diseases and analysis of the environmental conditions that contribute to disease outbreaks play crucial roles in the effective …

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Explainable Graph Neural Networks for Observation Impact Analysis in Atmospheric State Estimation

Hyeon-Ju Jeon, Jeon‐Ho Kang, In‐Hyuk Kwon, O‐Joun Lee

This paper investigates the impact of observations on atmospheric state estimation in weather forecasting systems using graph neural networks (GNNs) and explainability methods. We integrate observation and Numerical Weather Prediction (NWP) points into a meteorological graph, extracting $k$-hop subgraphs centered on NWP …

CloudNine: Analyzing Meteorological Observation Impact on Weather Prediction Using Explainable Graph Neural Networks

Hyeon-Ju Jeon, Jeon‐Ho Kang, In‐Hyuk Kwon, O‐Joun Lee

The impact of meteorological observations on weather forecasting varies with sensor type, location, time, and other environmental factors. Thus, quantitative analysis of observation impacts is crucial for effective and efficient development of weather forecasting systems. However, the existing impact analysis methods are …

Quantification of Dysnatremia Using Single-Beam Acoustic Microbeam and Convolutional Neural Networks

Ji Won Nam, Hyeon-Ju Jeon, Jeong Eun Lee, O‐Joun Lee et autres

Recently, the use of artificial intelligence (AI) in cell analysis has gained significant attention, with a particular focus on ultrasound-based AI for single-cell analysis. One application is diagnosing diseases by using ultrasound signals to analyze the physical properties contained in the signals. …

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Diagnosis of diabetes mellitus using high frequency ultrasound and convolutional neural network

Jeong Eun Lee, Hyeon-Ju Jeon, O‐Joun Lee, Hae Gyun Lim

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Graph Representation Learning and Its Applications: A Survey

Van Thuy Hoang, Hyeon-Ju Jeon, Eun-Soon You, Yoewon Yoon et autres

Graphs are data structures that effectively represent relational data in the real world. Graph representation learning is a significant task since it could facilitate various downstream tasks, such as node classification, link prediction, etc. Graph representation learning aims to map graph entities …

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