Keun Ho Ryu

Keun Ho Ryu
Chungbuk National University · Department of Computer Science

PhD

About

503
Publications
77,033
Reads
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5,220
Citations
Citations since 2017
114 Research Items
3114 Citations
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20172018201920202021202220230100200300400500600
20172018201920202021202220230100200300400500600
20172018201920202021202220230100200300400500600

Publications

Publications (503)
Article
Full-text available
Music therapy is increasingly being used to promote physical health. Emotion semantic recognition is more objective and provides direct awareness of the real emotional state based on electroencephalogram (EEG) signals. Therefore, we proposed a music therapy method to carry out emotion semantic matching between the EEG signal and music audio signal,...
Article
Full-text available
Creating an interpretable model with high predictive performance is crucial in eXplainable AI (XAI) field. We introduce an interpretable neural network-based regression model for tabular data in this study. Our proposed model uses ordinary least squares (OLS) regression as a base-learner, and we re-update the parameters of our base-learner by using...
Article
Full-text available
With the increasing volume of the published biomedical literature, the fast and effective retrieval of the literature on the sequence, structure, and function of biological entities is an essential task for the rapid development of biology and medicine. To capture the semantic information in biomedical literature more effectively when biomedical do...
Chapter
Coronary heart disease (CHD) is one of the top causes of global mortality. Most patients cannot be diagnosed at the early stage because it does not give any symptoms for many years. If CHD gets worse, it will require advanced treatments, such as heart transplant and stent surgery. Therefore, it is useful in preventing CHD by predicting high-risk pe...
Article
Full-text available
The increasing expansion of biomedical documents has increased the number of natural language textual resources related to the current applications. Meanwhile, there has been a great interest in extracting useful information from meaningful coherent groupings of textual content documents in the last decade. However, it is challenging to discover in...
Article
The sheer amount of open source codes made available in code repositories and code search engines along with the rapidly increasing releases of Application Programming Interfaces (APIs) have made code devel- opment process easier for programmers. However, learning how to use the elements of an API properly is both challenging and requires learning...
Article
Full-text available
Protein-protein interaction (PPI) prediction is meaningful work for deciphering cellular behaviors. Although many kinds of data and machine learning algorithms have been used in PPI prediction, the performance still needs to be improved. In this paper, we propose InferSentPPI, a sentence embedding based text mining method with gene ontology (GO) in...
Chapter
In the field of distant supervision relation extraction, PCNN (piecewise convolution neural network) is normally involved to trap local features of sentences, and has achieved good results. However, the existing PCNN-based methods are unable to capture the features of long-distance interdependence in sentences and cannot distinguish the influence o...
Chapter
The large volumes of biomedical documents have been generating exponentially in modern applications. Document clustering methods play an important role in gathering textual content documents into a few meaningful coherent groups. However, clustering unstructured and unlabeled text is challenging to extract informative representations and find the r...
Article
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Explaining dynamic relationships between input and output variables is one of the most important issues in time dependent domains such as economic, finance and so on. In this work, we propose a novel locally adaptive interpretable deep learning architecture that is augmented by recurrent neural networks to provide model explainability and high pred...
Article
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Semantic mining is always a challenge for big biomedical text data. Ontology has been widely proved and used to extract semantic information. However, the process of ontology-based semantic similarity calculation is so complex that it cannot measure the similarity for big text data. To solve this problem, we propose a parallelized semantic similari...
Article
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This study proposes an efficient prediction method for coronary heart disease risk based on two deep neural networks trained on well-ordered training datasets. Most real datasets include an irregular subset with higher variance than most data, and predictive models do not learn well from these datasets. While most existing prediction models learned...
Article
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The rapid rise of non-communicable diseases (NCDs) becomes one of the serious health issues and the leading cause of death worldwide. In recent years, artificial intelligence-based systems have been developed to assist clinicians in decision-making to reduce morbidity and mortality. However, a common drawback of these modern studies is related to e...
Article
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Background: With advances in next-generation sequencing technologies, the bisulfite conversion of genomic DNA followed by sequencing has become the predominant technique for quantifying genome-wide DNA methylation at single-base resolution. A large number of computational approaches are available in literature for identifying differentially methyla...
Chapter
Hematopoietic cancer is the malignant transformation in immune system cells. This cancer usually occurs in areas such as bone marrow and lymph nodes, the hematopoietic organ, and is a frightening disease that collapses the immune system with its own mobile characteristics. Hematopoietic cancer is characterized by the cells that are expressed, which...
Chapter
One of the tremendous topics in the music industry is an automatic music composition. In this study, we aim to build an architecture that shows how LSTM models compose music using the four emotional piano datasets. The architecture consists of four steps: data collection, data preprocessing, training the models with one and two hundred epochs, and...
Article
Full-text available
Credit scoring is a process of determining whether a borrower is successful or unsuccessful in repaying a loan using borrowers’ qualitative and quantitative characteristics. In recent years, machine learning algorithms have become widely studied in the development of credit scoring models. Although efficiently classifying good and bad borrowers is...
Chapter
Since the Bluetooth Low Energy (BLE) with version 4.0 of the Bluetooth Core Specification was introduced by Bluetooth Special Interest Group in 2010, the BLE technology has been developing in a wide variety of applications such as the Internet of Things, sports and fitness equipment, home automation, health care, and mobile payment for bringing a c...
Chapter
Atrial Fibrillation AF reported as the most occurring heart arrhythmia. Steadfast detection of AF in ECG monitoring systems is considerable for early treatment and health risks reduction. Various ECG mining and analysis efforts have addressed a wide variety of technical issues. However, the morphological descriptors are changing along the time with...
Chapter
As the elderly population increases, the number of patients with chronic diseases, which usually occur in the elderly population, continues to increase. Many studies have been conducted to predict chronic disease using various medical data. Chronic diseases are mainly caused by complex factors rather than independent factors. In this study, the dis...
Chapter
The World Health Organization (WHO) reported that diabetes is now one of the top ten causes of global mortality, and it is also highly ranked in Korea. The poor lifestyle, such as lack of physical activity, unhealthy diet, overweight, and tobacco usage, directly increases the risk of diabetes. It is asymptomatic at the early stage, and most patient...
Chapter
Full-text available
Hypertension is a serious medical condition that significantly increases the risk of chronic diseases. Early detection of individuals at risk for hypertension, it allows to prevent and delay the incidence of related diseases and strokes. In recent years, numerous researches have been focused on the decision support system for predicting hypertensio...
Chapter
The integration of multi-omics data is suitable for early detection and is also significant to a wide variety of cancer detection and treatment fields. Accurate prediction of survival in cancer patients remains a challenge due to the ever-increasing heterogeneity and complexity of cancer. The latest developments in high-throughput sequencing techno...
Article
Full-text available
Hematopoietic cancer is a malignant transformation in immune system cells. Hematopoietic cancer is characterized by the cells that are expressed, so it is usually difficult to distinguish its heterogeneities in the hematopoiesis process. Traditional approaches for cancer subtyping use statistical techniques. Furthermore, due to the overfitting prob...
Chapter
Full-text available
Hypertension is one of the chronic medical conditions and the major risk factor for multiple diseases and strokes. Prevention of hypertension is significant to delay the incidence of disease progression and decrease the severe health complications. In recent years, predictive models have been developed to recognize hypertensive and normotensive ind...
Book
This book presents selected papers from the Sixteenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, in conjunction with the Thirteenth International Conference on Frontiers of Information Technology, Applications and Tools, held on November 5–7, 2020, in Ho Chi Minh City, Vietnam. It is divided into tw...
Article
Full-text available
Smoking-induced noncommunicable diseases (SiNCDs) have become a significant threat to public health and cause of death globally. In the last decade, numerous studies have been proposed using artificial intelligence techniques to predict the risk of developing SiNCDs. However, determining the most significant features and developing interpretable mo...
Article
Full-text available
Smoking is one of the significant avoidable risk factors for premature death. Most smokers make multiple quit attempts during their lifetime but smoking dependence is not easy and many people eventually failed quit attempts. Predicting the likelihood of success in smoking cessation program is necessary for public health. In recent years, they have...
Article
Full-text available
Background For a genome-wide association study in humans, genotype imputation is an essential analysis tool for improving association mapping power. When IMPUTE software is used for imputation analysis, an imputation output (GEN format) should be converted to variant call format (VCF) with imputed genotype dosage for association analysis. However,...
Article
Full-text available
Automatic anomaly detection for time-series is critical in a variety of real-world domains such as fraud detection, fault diagnosis, and patient monitoring. Current anomaly detection methods detect the remarkably low proportion of the actual abnormalities correctly. Furthermore, most of the datasets do not provide data labels, and require unsupervi...
Article
Full-text available
Smoking is one of the major public health issues, which has a significant impact on premature death. In recent years, numerous decision support systems have been developed to deal with smoking cessation based on machine learning methods. However, the inevitable class imbalance is considered a major challenge in deploying such systems. In this paper...
Preprint
Full-text available
Machine learning models with both good predictability and high interpretability are crucial for decision support systems. Linear regression is one of the most interpretable prediction models. However, the linearity in a simple linear regression worsens its predictability. In this work, we introduce a locally adaptive interpretable regression (LoAIR...
Chapter
With all the advanced technology nowadays, the availability of time-series data is being increased. Outlier detection is an identification of abnormal patterns that provide useful information for many kinds of applications such as fraud detection, fault diagnosis, and disease detection. However, it will require an expensive domain and professional...
Chapter
A determining the most relevant variables and proper lag length are the most challenging steps in multivariate time series analysis. In this paper, we propose a hybrid Vector Autoregressive and Gated Recurrent Unit (VAR-GRU) model to find the contextual variables and suitable lag length to improve the predictive performance for financial multivaria...
Article
Full-text available
Class imbalance is a common issue in many applications such as medical diagnosis, fraud detection, web advertising, etc. Although standard deep learning method has achieved remarkably high-performance on datasets with balanced classes, its ability to classify imbalanced dataset is still limited. This paper proposes a novel end-to-end deep neural ne...
Chapter
With all the advanced technology nowadays, new data is being generated every minute. For example, the average size of the computer’s hard disk is 10 gigabytes in 2000, today on the Facebook website has increased 500 terabytes of new data per day [1]. Data is growing rapidly, but it is not enough valuable. Thus, it is important to extract informatio...
Chapter
In this paper, we developed mobile application framework which manages the hydrogen skin moisturizing device. We use react native framework for developing the mobile application. It allows us to manage and control the hydrogen skin device. Our hydrogen skin moisturizing device is based on PCB boards. We were connecting to PCB boards by Bluetooth co...
Chapter
Full-text available
Recommendation system is a subclass of information filtering system to help users find relevant items of interest from a large set of possible selections. Model-based collaborative filtering utilized the ratings of the user–item matrix dataset to generate a prediction. Essentially, this type of intelligent system plays a critical role in e-commerce...
Chapter
Credit scoring is one of important issues in banking to control a loss due to debtors who fail to meet their credit payment. Hence, the banks aim to develop their credit scoring model for accurately detecting their bad borrowers. In this study, we propose a hybrid credit scoring model using deep neural networks and logistic regression to improve it...
Chapter
Coronary heart disease (CHD) is one of the top causes of death globally; if suffering from CHD, long time permanent treatments are required. Furthermore, the early detection of CHD is not easy; doctors diagnose it based on many kinds of clinical tests. Therefore, it is effective to reduce the risks of developing CHD by predicting high-risk people w...
Chapter
Full-text available
The diabetes mellitus is a kind of growing epidemic which leads to several complications in recent years. Therefore, the prevention of diabetes mellitus is an important public health topic in medical domain. In this study, we try to find the risk factors of diabetes mellitus for the prediction of diabetes mellitus by using the association rule mini...
Article
Full-text available
Developing lifelong learning algorithms are mandatory for computational systems biology. Recently, many studies have shown how to extract biologically relevant information from high-dimensional data to understand the complexity of cancer by taking the benefit of deep learning (DL). Unfortunately, new cancer growing up into the hundred types that ma...
Article
Full-text available
Proper demand forecasting for postal delivery service can be used for optimal logistic management, staff scheduling and topology planning. More especially, during special holidays, such as the Lunar New Year and the Chuseok (Mid-autumn day), the demand for delivery service increases sharply in South Korea. It makes a challenge to forecast demand to...
Preprint
In this paper, we propose an algorithm that extracts spatial frequent patterns to explain the relative characteristics of a specific location from the available social data. This paper proposes a spatial social data model which includes spatial social data, spatial support, spatial frequent patterns, spatial partition, and spatial clustering; these...
Article
Full-text available
An accurate exchange rate forecasting and its decision-making to buy or sell are critical issues in the Forex market. Short-term currency rate forecasting is a challenging task due to its inherent characteristics, which include high volatility, trend, noise, and market shocks. We propose a novel deep learning architecture consisting of an adaptive...
Article
Full-text available
Coronary heart disease (CHD) is one of the leading causes of death worldwide; if suffering from CHD and being in its end-stage, the most advanced treatments are required, such as heart surgery and heart transplant. Moreover, it is not easy to diagnose CHD at the earlier stage; hospitals diagnose it based on various types of medical tests. Thus, by...
Chapter
The World Health Organization (WHO) reported that coronary heart disease (CHD) is one of the top causes of global mortality, and it is also highly ranked in Korea. The wrong lifestyle such as alcohol, tobacco, and high fatty food is directly involved in the main risk factors for CHD. In the early stage, it is possible to prevent suffering from CHD...
Conference Paper
Full-text available
In medical domain, the prediction of chronic disease is a very crucial topic. Hypertension is one of the most popular and representative chronic disease in the world. In this paper, we proposed a majority voting ensemble classifier for hypertension prediction to the KNHANES dataset from 2013 to 2015. We first combined the complex sampling-based fea...
Article
Full-text available
Named Entity Recognition (NER) in the healthcare domain involves identifying and categorizing disease, drugs, and symptoms for biosurveillance, extracting their related properties and activities, and identifying adverse drug events appearing in texts. These tasks are important challenges in healthcare. Analyzing user messages in social media networ...
Article
Full-text available
Emotion detection and recognition from text is a recent essential research area in Natural Language Processing (NLP) which may reveal some valuable input to a variety of purposes. Nowadays, writings take many forms of social media posts, micro-blogs, news articles, customer review, etc., and the content of these short-texts can be a useful resource...
Article
Full-text available
A multivariate time series forecasting is critical in many applications such as signal processing, finance, air quality forecasting, pattern recognition, etc. In particular, determining the most relevant variables and proper lag length from multivariate time series are challenging. This paper proposes an end-to-end recurrent neural network framewor...
Article
Multivessel disease (MVD) is an independent risk factor for poor prognosis in acute myocardial infarction patients. Although several global risk scoring systems (RSS) are in use in clinical practice, there is no dedicated RSS for MVD in ST-segment elevation myocardial infarction (STEMI). The primary objective of this study is to develop a novel RSS...
Article
Defective die on a wafer map tend to cluster in distinguishable patterns, and such defect patterns can provide crucial information to identify equipment problems or process failures in the semiconductor manufacturing. Therefore, it is important to accurately and efficiently classify the defect patterns. In this research, we propose a novel clusteri...
Article
Full-text available
Background Little is known about epigenetic silencing of genes by promoter hypermethylation in renal cell carcinoma (RCC). The aim of this study was to identify prognostic methylation markers in surgically treated clear cell RCC (ccRCC). Methods Methylation patterns were assayed using the Infinium HumanMethylation450 BeadChip array on pairs of ccR...
Article
The present study aimed to identify novel methylation markers of clear cell renal cell carcinoma (ccRCC) using microarray methylation analysis and evaluate their prognostic relevance in patient samples. To identify cancer‑specific methylated biomarkers, microarray profiling of ccRCC samples from our institute (n=12) and The Cancer Genome Atlas (TCG...
Article
Full-text available
Cigarette smoking is the leading cause of preventable death in a general population and it seems a significant topic in health research. The primary aim of this study determines the significant risk factors and investigates the prediction of 6 months smoking cessation program among women in Korea. In this regard, we examined real-world dataset abou...
Article
Full-text available
Machine learning and artificial intelligence have achieved a human-level performance in many application domains, including image classification, speech recognition and machine translation. However, in the financial domain expert-based credit risk models have still been dominating. Establishing meaningful benchmark and comparisons on machine-learni...
Article
Full-text available
With the rapid increase of publishable research articles and manuscripts, the pressure to find reviewers often overwhelms journal editors. This paper incorporates the major entity level metrics found in the heterogeneous publication networks into a pattern mining process in order to recommend academic reviewers and potential research collaborators....
Conference Paper
Full-text available
Recommender system is the type of intelligent systems, which utilizes user ratings of items or additional information of satisfaction scale, then to recommend items to the similar users. Particularly, it plays critical role in e-commerce, social network and popular domains increasingly. In this paper, we explore a model based collaborative filterin...