• 대한원격탐사학회지
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• 제35권1호
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• pp.93-115
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• 2019

The availability of high-resolution laser scanning data and advanced machine learning algorithms has enabled an accurate potential rockfall source identification. However, the presence of other mass movements, such as landslides within the same region of interest, poses additional challenges to this task. Thus, this research presents a method based on an integration of Gaussian mixture model (GMM) and ensemble artificial neural network (bagging ANN [BANN]) for automatic detection of potential rockfall sources at Kinta Valley area, Malaysia. The GMM was utilised to determine slope angle thresholds of various geomorphological units. Different algorithms(ANN, support vector machine [SVM] and k nearest neighbour [kNN]) were individually tested with various ensemble models (bagging, voting and boosting). Grid search method was adopted to optimise the hyperparameters of the investigated base models. The proposed model achieves excellent results with success and prediction accuracies at 95% and 94%, respectively. In addition, this technique has achieved excellent accuracies (ROC = 95%) over other methods used. Moreover, the proposed model has achieved the optimal prediction accuracies (92%) on the basis of testing data, thereby indicating that the model can be generalised and replicated in different regions, and the proposed method can be applied to various landslide studies.

• 대한한의학원전학회지
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• 제35권1호
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• pp.59-79
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• 2022

Objectives : Huangyuanyu's interpretation of the six channel diseases of the Shanghanlun were examined based on contents on the six channel qi metabolism theory in his works, Shanghanxuanjie, Shanghanshuoyi, and Sishengxinyuan. Methods : Contents related to the six channel qi metabolism theory in the Shanghanxuanjie, Shanghanshuoyi, and Sishengxinyuan were extracted and examined to identify a fundamental principle from the perspective of the six channel qi metabolism theory. Characteristics of each of the six channel diseases were organized. Results : Huang's understanding of the six channel diseases in the Shanghanlun could be summarized by the six channel. Its features could be explained as following. First, in examining the principles of the controlling qi[司氣] and constitutionally influenced transformation[從化], the rise and fall of the body's yang qi was emphasized. Second, center qi[中氣] was considered important, the taiyin Spleen being the key to life and death. Third, the pathology of 'earth dampness/water cold/wood stagnation' due to weakness of the center qi was suggested. Fourth, the principle of boosting-yang-suppressing-yin was emphasized in treatment, with criticism of the nurturing-yin-extinguishing-fire method. Conclusions : In understanding the six channel diseases in the Shanghanlun, Huangyuanyu focused on the body's yang qi and center qi based on key theories such as the 'five circuits and six qi' and 'six channel qi metabolism' theories. His perspective could be helpful in understanding Zhangzhongjing's work more comprehensively.

• 대한의용생체공학회:의공학회지
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• 제43권5호
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• pp.353-360
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• 2022

Laryngeal disease harms quality of life, and laryngoscopy is critical in identifying causative lesions. This study extracts and analyzes using radiomics quantitative features from the lesion in laryngoscopy images and will fit and validate a classifier for finding meaningful features. Searching the region of interest for lesions not classified by the YOLOv5 model, features are extracted with radionics. Selected the extracted features are through a combination of three feature selectors, and three estimator models. Through the selected features, trained and verified two classification models, Random Forest and Gradient Boosting, and found meaningful features. The combination of SFS, LASSO, and RF shows the highest performance with an accuracy of 0.90 and AUROC 0.96. Model using features to select by SFM, or RIDGE was low lower performance than other things. Classification of larynx lesions through radiomics looks effective. But it should use various feature selection methods and minimize data loss as losing color data.

• Steel and Composite Structures
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• 제43권4호
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• pp.501-509
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• 2022

Cracks are common defects in concrete structures. Thus far, crack inspection has been manually performed using the contact inspection method. This manpower-dependent method inevitably increases the cost and work hours. Various non-contact studies have been conducted to overcome such difficulties. However, previous studies have focused on developing a methodology for non-contact inspection or local quantitative detection of crack width or length on concrete surfaces. However, crack depth can affect the safety of concrete structures. In particular, although macrocrack depth is structurally fatal, it is difficult to find it with the existing method. Therefore, an experimental investigation based on non-contact infrared thermography and multivariate machine learning was performed in this study to estimate the hidden macrocrack depth. To consider practical applications for inspection, an experiment was conducted that considered the simulated piloting of an unmanned aerial vehicle equipped with infrared thermography equipment. The crack depths (10-60 mm) were comparatively evaluated using linear regression, gradient boosting, and random forest (AI regression methods).

• 운동영양학회지
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• 제24권1호
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• pp.14-18
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• 2020

[Purpose] Functional foods are thought to strongly influence the structure and function of the brain. Previous studies have reported that brain-boosting diets may enhance neuroprotective functions. Certain foods are particularly rich in nutrients like phytochemicals that are known to support brain plasticity; such foods are commonly referred to as brain foods. [Methods] In this review, we briefly explore the scientific evidence supporting the neuroprotective activity of a number of phytochemicals with a focus on phenols and polyunsaturated fatty acids such as flavonoid, olive oil, and omega-3 fatty acid. [Results] The aim of this study was to systematically examine the primary issues related to phytochemicals in the brain. These include (a) the brain-gut-microbiome axis; (b) the effects of phytochemicals on gut microbiome and their potential role in brain plasticity; (c) the role of polyunsaturated fatty acids in brain health; and (d) the effects of nutrition and exercise on brain function. [Conclusion] This review provides evidence supporting the view that phytochemicals from medicinal plants play a vital role in maintaining brain plasticity by influencing the brain-gut-microbiome axis. The consumption of brain foods may have neuroprotective effects, thus protecting against neurodegenerative disorders and promoting brain health.

• 유통과학연구
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• 제21권9호
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• pp.35-42
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• 2023

Purpose: The research aims at examining the impact of marketer capabilities and persistence on marketer performance and distribution of agricultural product facilities. Research design, data, and methodology: The research employs quantitative methods using a cross-sectional design survey by analyzing the marketer of agricultural production facilities. Sampling was done using the purposive sampling technique and data were taken from 235 respondents. The data were then processed using SEM-PLS. Results: The findings reveal that both marketer capabilities and marketer persistence significantly impact the performance of agricultural product facility marketers. Notably, marketer persistence exerts a more dominant influence on marketer performance than marketer capabilities. Effective communication and coordination between the sales team and the distribution center emerge as crucial factors determining the success of distributing agricultural equipment to reach farmers' land at the optimal time. Conclusions: The findings offer valuable managerial insights for agricultural product facility companies seeking to enhance marketer performance. To achieve this, companies should focus on increasing marketer persistence, with an emphasis on nurture-focused persistence rather than closure-focused persistence. Additionally, improving marketer capabilities is crucial, starting with relationship development, followed by trust building, customer retention, responsiveness, and acquisition. These strategies can collectively contribute to boosting marketer performance within the organization.

• Nutrition Research and Practice
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• 제17권2호
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• pp.356-370
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• 2023

BACKGROUND/OBJECTIVES: School food service has played an important role in promoting the health and physical condition of students by providing students with a balanced and nutritious diet. Therefore, boosting the quality of school food service and improving the students' satisfaction is critical. For this purpose, this study examined the structural causal relationship among the quality of school food service factors, emotional responses, and satisfaction in China. SUBJECTS/METHODS: This study was conducted with 4th-6th-grade students from 6 junior high schools in Henan province of China, with 590 questionnaire responses (87.3%) collected and statistically analyzed. RESULTS: The school food service quality factors (including menu management, dietary education, facilities management, price and food distribution management, and personal hygiene during meals) must be enhanced to boost the students' satisfaction. In addition, the study used questionnaire survey data to validate the full mediation of students' emotional responses between school food service quality factors and student satisfaction. CONCLUSIONS: Students' emotions also play an important role in influencing the quality of school food service, all of which affect the emotional responses of students. Therefore, students' positive emotions are an important indicator for improving the quality of school food service. A national support policy is necessary for the ongoing maintenance and development of various programs that drive students' satisfaction and promote the adoption of education guidelines for school food service in China.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• 제49권3호
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• pp.135-141
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• 2023

Objectives: This study aimed to develop and validate machine learning (ML) models using H₂O-AutoML, an automated ML program, for predicting medication-related osteonecrosis of the jaw (MRONJ) in patients with osteoporosis undergoing tooth extraction or implantation. Patients and Methods: We conducted a retrospective chart review of 340 patients who visited Dankook University Dental Hospital between January 2019 and June 2022 who met the following inclusion criteria: female, age ≥55 years, osteoporosis treated with antiresorptive therapy, and recent dental extraction or implantation. We considered medication administration and duration, demographics, and systemic factors (age and medical history). Local factors, such as surgical method, number of operated teeth, and operation area, were also included. Six algorithms were used to generate the MRONJ prediction model. Results: Gradient boosting demonstrated the best diagnostic accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.8283. Validation with the test dataset yielded a stable AUC of 0.7526. Variable importance analysis identified duration of medication as the most important variable, followed by age, number of teeth operated, and operation site. Conclusion: ML models can help predict MRONJ occurrence in patients with osteoporosis undergoing tooth extraction or implantation based on questionnaire data acquired at the first visit.

• Computers and Concrete
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• 제32권4호
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• pp.373-381
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• 2023

Fly ash, granulated blast furnace slag, marble waste powder, etc. are just some of the by-products of other sectors that the construction industry is looking to include into the many types of concrete they produce. This research seeks to use surrogate machine learning methods to forecast the compressive strength of self-compacting concrete. The surrogate models were developed using Gradient Boosting Machine (GBM), Support Vector Machine (SVM), Random Forest (RF), and Gaussian Process Regression (GPR) techniques. Compressive strength is used as the output variable, with nano silica content, cement content, coarse aggregate content, fine aggregate content, superplasticizer, curing duration, and water-binder ratio as input variables. Of the four models, GBM had the highest accuracy in determining the compressive strength of SCC. The concrete's compressive strength is worst predicted by GPR. Compressive strength of SCC with nano silica is found to be most affected by curing time and least by fine aggregate.

• Communications for Statistical Applications and Methods
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• 제30권5호
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• pp.485-499
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• 2023

This study aimed to predict the number of meals served in a group cafeteria using machine learning methodology. Features of the menu were created through the Word2Vec methodology and clustering, and a stacking ensemble model was constructed using Random Forest, Gradient Boosting, and CatBoost as sub-models. Results showed that CatBoost had the best performance with the ensemble model showing an 8% improvement in performance. The study also found that the date variable had the greatest influence on the number of diners in a cafeteria, followed by menu characteristics and other variables. The implications of the study include the potential for machine learning methodology to improve predictive performance and reduce food waste, as well as the removal of subjective elements in menu classification. Limitations of the research include limited data cases and a weak model structure when new menus or foreign words are not included in the learning data. Future studies should aim to address these limitations.