• Journal of Information Technology Applications and Management
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• 제29권2호
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• pp.27-37
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• 2022

This study was attempted to identify the effects of physical and mental health on quality of life in middle-aged adults by gender. The Data were analyzed for 4,511 adults (2,260 men, 2,251 women) aged 45 to 65 who had no missing values in major variables based on the data of the 2016 Korea Health Panel. According to the data, the quality of life in middle-aged adults was .92 (±.08) for men and .91 (±.10) for women, which was significantly higher than that of women (t=3.54, p<.001). Factors affecting the quality of life in middle-aged men were subjective health status (β=.40, p<.001), stress (β=-.17, p<.001) and education level (β=.10, p<.001), and these variables explained 23% of the quality of life (F=227.28, p<.001). Factors affecting the quality of life in middle-aged women were subjective health status (β=.40, p<.001), stress (β=-.11, p<.001), education level (β=.05, p=.011) and anxiety (β=-.05, p=.022), and these variables explained 21% of the quality of life (F=145.42, p<.001). Based on the results of this study, the group with low level of education in middle-aged adults needs health management, education on how to relieve stress, and intensive management to improve the quality of life. In addition, the differentiated approach should be required to reduce anxiety in middle-aged women.

• ETRI Journal
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• 제45권3호
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• pp.448-461
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• 2023

This article performs a detailed data scrutiny on a chronic kidney disease (CKD) dataset to select efficient instances and relevant features. Data relevancy is investigated using feature extraction, hybrid outlier detection, and handling of missing values. Data instances that do not influence the target are removed using data envelopment analysis to enable reduction of rows. Column reduction is achieved by ranking the attributes through feature selection methodologies, namely, extra-trees classifier, recursive feature elimination, chi-squared test, analysis of variance, and mutual information. These methodologies are ranked via Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) using weight optimization to identify the optimal features for model building from the CKD dataset to facilitate better prediction while diagnosing the severity of the disease. An efficient hybrid ensemble and novel similarity-based classifiers are built using the pruned dataset, and the results are thereafter compared with random forest, AdaBoost, naive Bayes, k-nearest neighbors, and support vector machines. The hybrid ensemble classifier yields a better prediction accuracy of 98.31% for the features selected by extra tree classifier (ETC), which is ranked as the best by TOPSIS.

• 공학교육연구
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• 제26권3호
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• pp.12-19
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• 2023

The purpose of this study is to develop the Artificial Intelligence thinking-based education program for improving AI literacy and verify its effectiveness for beginner. This program consists of 17 sessions, was designed according to the "ABCDE" model and is a project-based program. This program was conducted on 51 first-year middle school students and 36 respondents excluding missing values were analyzed in R language. The effect of this program on ethics, understanding, social competency, execution plan, data literacy, and problem solving of AI literacy is statistically significant and has very large practical significance. According to the result of this study, this program provided learners experiencing Artificial Intelligence education for the first time with Artificial Intelligence concepts and principles, collection and analysis of information, and problem-solving processes through application in real life, and served as an opportunity to enhance AI literacy. In addition, education program to enhance AI literacy should be designed based on AI thinking.

• International journal of advanced smart convergence
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• 제11권4호
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• pp.247-252
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• 2022

The purpose of the domestic physician scientist support program is to promote the development of various biotechnology. Therefore, it can be said that examining whether the purpose of support is being faithfully implemented has an important meaning for the future domestic biotechnology development ecosystem. Therefore, this study limited the subject of analysis to 79 MD-PhD experts who participated or participated in doctor scientist programs at major universities in Korea. Among them, a total of 25 researchers, one researcher from each classroom in parasitology, microbiology, pharmacology, biochemistry, physiology, and anatomy, which had the highest paper citations in the last five years (2016-2021), were selected to examine the relationship between joint research. It was selected as the subject of review. As a result, 25 selected pseudo-scientists(MD-PhD) identified domestic and foreign researchers who participated as co-researchers when publishing in overseas academic journals for the last 5 years(2016-2021), and identified the affiliation and name of the top 5 among them, as well as the pseudo-scientist(MD-PhD), it was possible to identify the relationship of a total of 123 co-researchers(excluding 2 missing values) of the top 5 co-researchers with a high degree of cooperation with respect to the researcher(25 in total), and the collaboration of pseudo-scientists. Relationships, major researchers, and research institutes were examined. Nodexl Basic 2018 ver. (Microsof) was used for the analysis, and the relationship between researchers could be visualized by applying network analysis techniques.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 추계학술발표대회
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• pp.55-56
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• 2022

Hypertension is a severe health problem and increases the risk of other health issues, such as heart disease, heart attack, and stroke. In this research, we propose a machine learning-based prediction method for the risk of chronic hypertension. The proposed method consists of four main modules. In the first module, the linear interpolation method fills missing values of the integration of gas and meteorological datasets. In the second module, the OrdinalEncoder-based normalization is followed by the Decision tree algorithm to select important features. The prediction analysis module builds three models based on k-Nearest Neighbors, Decision Tree, and Random Forest to predict hypertension levels. Finally, the features used in the prediction model are explained by the DeepSHAP approach. The proposed method is evaluated by integrating the Korean meteorological agency dataset, natural gas leakage dataset, and Korean National Health and Nutrition Examination Survey dataset. The experimental results showed important global features for the hypertension of the entire population and local components for particular patients. Based on the local explanation results for a randomly selected 65-year-old male, the effect of hypertension increased from 0.694 to 1.249 when age increased by 0.37 and gas loss increased by 0.17. Therefore, it is concluded that gas loss is the cause of high blood pressure.

• ETRI Journal
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• 제45권4호
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• pp.650-665
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• 2023

A novel smart metering technique capable of anomaly detection was proposed for real-time home power management system. Smart meter data generated in real-time were obtained from 900 households of single apartments. To detect outliers and missing values in smart meter data, a deep learning model, the autoencoder, consisting of a graph convolutional network and bidirectional long short-term memory network, was applied to the smart metering technique. Power management based on the smart metering technique was executed by multi-objective optimization in the presence of a battery storage system and an electric vehicle. The results of the power management employing the proposed smart metering technique indicate a reduction in electricity cost and amount of power supplied by the grid compared to the results of power management without anomaly detection.

• 산경연구논집
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• 제14권10호
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• pp.35-43
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• 2023

Purpose: The purpose of this study is to analyze the structural relationship between the possibility of socioeconomic class elevation of wage earners, happiness and organizational commitment, and life satisfaction. Research design, data and methodology: Data from the 24th fiscal year (2021) of the Korea Labor Panel data were used for analysis. Only wage earners who measured job satisfaction and organizational engagement were analyzed, and a sample of 9,138 respondents was finally used, excluding missing values. Structural Equation Modeling was performed using AMOS 23.0, and Maximum Likelihood Estimation (MLE) was used as a model estimation method. Results: First, the hypothetical structural model set up for the study was found to be suitable. Second, the Possibility of Socioeconomic Class Elevation of wage earners, happiness, and organizational commitment were found to have a direct impact on life satisfaction. Third, the possibility of improving the socio-economic status of wage earners affects life satisfaction, and happiness and organizational commitment appear to have a partially mediating effect. Conclusions: This study is significant in that it has increased interest in organizational participation and life satisfaction, which were not covered in previous studies on the possibility of wage workers moving up the socioeconomic class.

• 한국해양공학회지
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• 제37권5호
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• pp.198-204
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• 2023

In response to the complexity and time demands of conventional methods for estimating the hydrodynamic coefficients, this study aims to revolutionize ship maneuvering analysis by utilizing automatic identification system (AIS) data and the Support Vector Regression (SVR) algorithm. The AIS data were collected and processed to remove outliers and impute missing values. The rate of turn (ROT), speed over ground (SOG), course over ground (COG) and heading (HDG) in AIS data were used to calculate the rudder angle and ship velocity components, which were then used as training data for a regression model. The accuracy and efficiency of the algorithm were validated by comparing SVR-based estimated hydrodynamic coefficients and the original hydrodynamic coefficients of the Mariner class vessel. The validated SVR algorithm was then applied to estimate the hydrodynamic coefficients for real ships using AIS data. The turning circle test wassimulated from calculated hydrodynamic coefficients and compared with the AIS data. The research results demonstrate the effectiveness of the SVR model in accurately estimating the hydrodynamic coefficients from the AIS data. In conclusion, this study proposes the viability of employing SVR model and AIS data for accurately estimating the hydrodynamic coefficients. It offers a practical approach to ship maneuvering prediction and control in the maritime industry.

• 유통과학연구
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• 제22권2호
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• pp.95-102
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• 2024

Purpose: This study systematically examined the concept of digital selling readiness of salespeople. Additionally, this study empirically confirms the sequential mediating roles of digital literacy and salesperson self-efficacy in the impact of digital selling readiness on customer-oriented behavior. Research design, data, and methodology: We collected data from salespeople at a Bank and Financial Service firm in South Korea. A total of 254 salespeople were invited to participate, with 154 surveys returned. After removing the questionnaires with missing values, 150 complete surveys were employed for the analysis. Results: The empirical analysis indicates that digital selling readiness positively affects digital literacy. Digital literacy, in turn, is positively associated with self-efficacy, leading to increased customer-oriented behaviors among salespersons. This study also confirms the sequential mediating effects of digital literacy and self-efficacy in the impact of salespeople's digital selling readiness on customer-oriented behavior. Conclusions: Our research deepens the understanding of how digital selling readiness fosters customer-oriented behavior through the sequential mediating effects of digital literacy and self-efficacy. This study extends the previous model by sequentially involving digital literacy and self-efficacy to better understand the psychological processes of digital selling. The results highlight the role of digital selling readiness in preparing salespeople for digital sales.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권11호
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• pp.3050-3063
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• 2023

Recommendation systems provide personalized products or services to online users by mining their past preferences. Collaborative filtering is a popular recommendation technique because it is easy to implement. However, with the rapid growth of the number of users in recommendation systems, collaborative filtering suffers from serious scalability and sparsity problems. To address these problems, a novel collaborative filtering recommendation algorithm is proposed. The proposed algorithm partitions the users using affinity propagation clustering, and searches for k nearest neighbors in the partition where active user belongs, which can reduce the range of searching and improve real-time performance. When predicting the ratings of active user's unrated items, mean deviation method is used to impute values for neighbors' missing ratings, thus the sparsity can be decreased and the recommendation quality can be ensured. Experiments based on two different datasets show that the proposed algorithm is excellent both in terms of real-time performance and recommendation quality.