• Nutrition Research and Practice
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• v.16 no.5
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• pp.616-627
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• 2022

BACKGROUND/OBJECTIVES: Vitamin E is essential for health, and although vitamin E deficiency seems rare in humans, studies on estimates of dietary intake are lacking. This study aimed to estimate dietary vitamin E intake, evaluate dietary adequacy of vitamin E, and detail major food sources of vitamin E in the Korean population. SUBJECTS/METHODS: This study used data from the Korea National Health and Nutrition Examination Survey (KNHANES) 2016-2019. Individuals aged ≥ 1 year that participated in a nutrition survey (n = 28,418) were included. Dietary intake was assessed by 24-h recall and individual dietary vitamin E intake was estimated using a newly established vitamin E database. Dietary adequacy was evaluated by comparing dietary intake with adequate intake (AI) as defined by Korean Dietary Reference Intakes 2020. RESULTS: For all study subjects, mean daily total vitamin E intake was 7.00 mg α-tocopherol equivalents, which was 61.6% of AI. The proportion of individuals that consumed vitamin E at above the AI was 12.9%. Inadequate intake was observed more in females, older individuals, rural residents, and those with a low income. Mean daily intakes of tocopherol (α-, β-, γ-, and δ-forms) and tocotrienol were 6.02, 0.30, 6.19, 1.63, and 1.61 mg, respectively. The major food groups that contributed to total dietary vitamin E intake were grains (22.3%), seasonings (17.0%), vegetables (15.3%), and fish, and shellfish (7.4%). The top 5 individual food items that contributed to total vitamin E intake were baechu kimchi, red pepper powder, eggs, soybean oil, and rice. CONCLUSIONS: This study shows that mean dietary vitamin E intake by Koreans did not meet the reference adequate intake value. To better understand the status of vitamin E intake, further research is needed that considers intake from dietary supplements.

• Nutrition Research and Practice
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• v.17 no.1
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• pp.48-61
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• 2023

BACKGROUND/OBJECTIVES: Magnesium is an essential nutrient for human health. However, inadequate intake is commonly reported worldwide. Along with reduced consumption of vegetables and fruits and increased consumption of refined or processed foods, inadequate magnesium intake is increasingly reported as a serious problem. This study aimed to assess magnesium intake, its dietary sources, and the adequacy of magnesium intake in Korean populations. SUBJECTS/METHODS: Data was obtained from the Korea National Health and Nutrition Examination Survey 2016-2019 and included individuals aged ≥1 yr who had participated in a nutrition survey (n=28,418). Dietary intake was assessed by 24-h recall, and dietary magnesium intake was estimated using a newly established magnesium database. Diet adequacy was evaluated by comparing dietary intake with the estimated average requirement (EAR) suggested in the Korean Dietary Reference Intakes 2020. RESULTS: The mean dietary magnesium intake of Koreans aged ≥1 yr was 300.4 mg/d, which was equivalent to 119.8% of the EAR. The prevalence of individuals whose magnesium intake met the EAR was 56.8%. Inadequate intake was observed more in females, adolescents and young adults aged 12-29 yrs, elders aged ≥65 yrs, and individuals with low income. About four-fifths of the daily magnesium came from plant-based foods, and the major food groups contributing to magnesium intake were grains (28.3%), vegetables (17.6%), and meats (8.4%). The top 5 individual foods that contributed to magnesium intake were rice, Baechu (Korean cabbage) kimchi, tofu, pork, and milk. However, the contribution of plant foods and individual contributing food items differed slightly by sex and age groups. CONCLUSIONS: This study found that the mean dietary magnesium intake among Koreans was above the recommended intake, whereas nearly one in 2 Koreans had inadequate magnesium intake. To better understand the status of magnesium intake, further research is required, which includes the intake of dietary supplements.

• Imaging Science in Dentistry
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• v.52 no.4
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• pp.383-391
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• 2022

Purpose: Periodontitis, the most prevalent chronic inflammatory condition affecting teeth-supporting tissues, is diagnosed and classified through clinical and radiographic examinations. The staging of periodontitis using panoramic radiographs provides information for designing computer-assisted diagnostic systems. Performing image segmentation in periodontitis is required for image processing in diagnostic applications. This study evaluated image segmentation for periodontitis staging based on deep learning approaches. Materials and Methods: Multi-Label U-Net and Mask R-CNN models were compared for image segmentation to detect periodontitis using 100 digital panoramic radiographs. Normal conditions and 4 stages of periodontitis were annotated on these panoramic radiographs. A total of 1100 original and augmented images were then randomly divided into a training (75%) dataset to produce segmentation models and a testing (25%) dataset to determine the evaluation metrics of the segmentation models. Results: The performance of the segmentation models against the radiographic diagnosis of periodontitis conducted by a dentist was described by evaluation metrics(i.e., dice coefficient and intersection-over-union [IoU] score). MultiLabel U-Net achieved a dice coefficient of 0.96 and an IoU score of 0.97. Meanwhile, Mask R-CNN attained a dice coefficient of 0.87 and an IoU score of 0.74. U-Net showed the characteristic of semantic segmentation, and Mask R-CNN performed instance segmentation with accuracy, precision, recall, and F1-score values of 95%, 85.6%, 88.2%, and 86.6%, respectively. Conclusion: Multi-Label U-Net produced superior image segmentation to that of Mask R-CNN. The authors recommend integrating it with other techniques to develop hybrid models for automatic periodontitis detection.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.17-25
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• 2023

The alarming global prevalence of Type 2 Diabetes Mellitus (T2DM) has catalyzed an urgent need for robust, early diagnostic methodologies. This study unveils a pioneering approach to predicting T2DM, employing the Extreme Gradient Boosting (XGBoost) algorithm, renowned for its predictive accuracy and computational efficiency. The investigation harnesses a meticulously curated dataset of 4303 samples, extracted from a comprehensive Chinese research study, scrupulously aligned with the World Health Organization's indicators and standards. The dataset encapsulates a multifaceted spectrum of clinical, demographic, and lifestyle attributes. Through an intricate process of hyperparameter optimization, the XGBoost model exhibited an unparalleled best score, elucidating a distinctive combination of parameters such as a learning rate of 0.1, max depth of 3, 150 estimators, and specific colsample strategies. The model's validation accuracy of 0.957, coupled with a sensitivity of 0.9898 and specificity of 0.8897, underlines its robustness in classifying T2DM. A detailed analysis of the confusion matrix further substantiated the model's diagnostic prowess, with an F1-score of 0.9308, illustrating its balanced performance in true positive and negative classifications. The precision and recall metrics provided nuanced insights into the model's ability to minimize false predictions, thereby enhancing its clinical applicability. The research findings not only underline the remarkable efficacy of XGBoost in T2DM prediction but also contribute to the burgeoning field of machine learning applications in personalized healthcare. By elucidating a novel paradigm that accentuates the synergistic integration of multifaceted clinical parameters, this study fosters a promising avenue for precise early detection, risk stratification, and patient-centric intervention in diabetes care. The research serves as a beacon, inspiring further exploration and innovation in leveraging advanced analytical techniques for transformative impacts on predictive diagnostics and chronic disease management.

• Nutrition Research and Practice
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• v.17 no.2
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• pp.257-268
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• 2023

BACKGROUND/OBJECTIVES: Zinc is an essential trace mineral which is important for the growth and development of the human body and immunological and neurological functions. Inadequate zinc intake may cause zinc deficiency with its adverse consequences. In this study, we aimed to estimate the dietary zinc intake levels and sources among Koreans. SUBJECTS/METHODS: For this secondary analysis, we obtained data from the Korea National Health and Nutrition Examination Survey (KNHANES) 2016-2019. Individuals aged ≥ 1 yr who had completed a 24-h recall were included. The dietary zinc intake of each individual was calculated by applying data from a newly developed zinc content database to the KNHANES raw data. We also compared the extracted data with the sex-, age-specific reference values suggested in the Korean Dietary Reference Intakes 2020. The prevalence of adequate zinc intake was then evaluated by the proportion of the individuals who met the estimated average requirement (EAR). RESULTS: The mean zinc intake of Koreans aged ≥ 1 yr and adults aged ≥ 19 yrs were 10.2 and 10.4 mg/day, equivalent to 147.4% and 140.8% of the EAR, respectively. Approximately 2 in 3 Koreans met the EAR for zinc, but the zinc intake differed slightly among the different age and sex groups. In children aged 1-2 yrs, 2 out of 5 exceeded the upper level of intake, and nearly half of the younger adults (19-29 yrs) and the elders (≥ 75 yrs) did not meet the EAR. The major contributing food groups were grains (38.9%), meats (20.4%), and vegetables (11.1%). The top 5 food contributors to zinc intake were rice, beef, pork, egg, and baechu kimchi, which accounted for half of the dietary intake. CONCLUSION: The mean zinc intake among Koreans was above the recommended level, but 1 in 3 Koreans had inadequate zinc intake and some children were at risk of excessive zinc intake. Our study included zinc intake from diet only, thus to better understand zinc status, further research to include intake from dietary supplements is needed.

• Nutrition Research and Practice
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• v.17 no.2
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• pp.307-315
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• 2023

BACKGROUND/OBJECTIVES: Previous studies have shown an association between breastfeeding and higher fruit and vegetable consumption and the level of dietary variety in children. However, few studies have reported this association on the feeding characteristics. Therefore, this study examined the association of the feeding characteristics with the consumption of fruit and vegetable and dietary variety in children. SUBJECTS/METHODS: This study recruited 802 participants from their parents with information on their feeding, and 24-h dietary recall. The associations of the feeding characteristics with fruit and vegetable consumption and dietary variety score (DVS) were analyzed using a multiple logistic regression model. RESULTS: Compared to the feeding type of exclusive breastfed children, exclusive formula-fed children had a significant association with a lower DVS (odds ratio [OR], 0.42, 95% confidence interval [CI], 0.23-0.77). Fruit and vegetable consumption was classified into 6 groups: non-salted vegetables (NSV), salted vegetables (SV), fruit (F), total vegetables (TV), non-salted vegetables + fruit (NSVF), and total vegetables + fruit (TVF). According to the mean level of fruit and vegetable consumption, compared to the duration of total breastfeeding for 6 month or less, a greater duration of breastfeeding for 12 mon had a significant association with a higher intake of NSVF and TVF (OR, 1.85, 95% CI, 1.20-2.85 and OR, 1.89, 95% CI, 1.22-2.92). On the other hand, the early introduction of formula feeding for 4 mon had a significant association with a lower intake of F and NSVF (OR, 0.59, 95% CI, 0.38-0.91 and OR, 0.63, 95% CI, 0.40-0.99). CONCLUSIONS: These results confirm that breastfeeding is associated with higher fruit and vegetable consumption and dietary variety, whereas formula feeding is associated with lower fruit and vegetable consumption and dietary variety. Therefore, the feeding characteristics in infants may affect fruit and vegetable consumption and dietary variety in children.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.49-56
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• 2023

Machine learning methods diversely applied to the Internet of Things (IoT) field have been successful due to the enhancement of computer processing power. They offer an effective way of detecting malicious intrusions in IoT because of their high-level feature extraction capabilities. In this paper, we proposed a novel feature selection method for malicious intrusion detection in IoT by using an evolutionary technique - Genetic Algorithm (GA) and Machine Learning (ML) algorithms. The proposed model is performing the classification of BoT-IoT dataset to evaluate its quality through the training and testing with classifiers. The data is reduced and several preprocessing steps are applied such as: unnecessary information removal, null value checking, label encoding, standard scaling and data balancing. GA has applied over the preprocessed data, to select the most relevant features and maintain model optimization. The selected features from GA are given to ML classifiers such as Logistic Regression (LR) and Support Vector Machine (SVM) and the results are evaluated using performance evaluation measures including recall, precision and f1-score. Two sets of experiments are conducted, and it is concluded that hyperparameter tuning has a significant consequence on the performance of both ML classifiers. Overall, SVM still remained the best model in both cases and overall results increased.

• Journal of Korea Entertainment Industry Association
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• v.13 no.8
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• pp.69-76
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• 2019

Backed by the fourth industrial revolution as the background of the research, VR, AR and MR have increased interest and wireless Oculus Quest is releasing, creating hardware recall and continuing virtual reality devices, and game software develop or service VR games using such devices. As a result, it is expected that VR game markets will continue to grow in the future. For this purpose, we understand the technical factors of presence and immersion that appear in virtual reality games and should be able to apply them when we produce VR games. Through the process, we analyzed elements of VR game concept, immersion, and presence and analyzed three games that were commercialized. As a suggestion, we need to take into account presence and immersion characteristics when developing and experiencing virtual reality games in the future.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.6
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• pp.245-254
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• 2022

Proteins are the basic unit of all life activities, and understanding them is essential for studying life phenomena. Since the emergence of the machine learning methodology using artificial neural networks, many researchers have tried to predict the function of proteins using only protein sequences. Many combinations of deep learning models have been reported to academia, but the methods are different and there is no formal methodology, and they are tailored to different data, so there has never been a direct comparative analysis of which algorithms are more suitable for handling protein data. In this paper, the single model performance of each algorithm was compared and evaluated based on accuracy and speed by applying the same data to CNN, LSTM, and GRU models, which are the most frequently used representative algorithms in the convergence research field of predicting protein functions, and the final evaluation scale is presented as Micro Precision, Recall, and F1-score. The combined models CNN-LSTM and CNN-GRU models also were evaluated in the same way. Through this study, it was confirmed that the performance of LSTM as a single model is good in simple classification problems, overlapping CNN was suitable as a single model in complex classification problems, and the CNN-LSTM was relatively better as a combination model.

• Journal of Environmental Impact Assessment
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• v.33 no.2
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• pp.74-83
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• 2024

This study aims to estimate roadkill occurrences and investigate influential factors in Chungcheongnam-do, contributing to the establishment of roadkill prevention measures. By comprehensively considering weather, road, and environmental information, machine learning was utilized to estimate roadkill incidents and analyze the importance of each variable, deriving primary influencing factors. The Gradient Boosting Machine (GBM) exhibited the best performance, achieving an accuracy of 92.0%, a recall of 84.6%, an F1-score of 89.2%, and an AUC of 0.907. The key factors affecting roadkill included average local atmospheric pressure (hPa), average ground temperature (℃), month, average dew point temperature (℃), presence of median barriers, and average wind speed (m/s). These findings are anticipated to contribute to roadkill prevention strategies and enhance traffic safety, playing a crucial role in maintaining a balance between ecosystems and road development.