• Communications of Mathematical Education
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• v.31 no.2
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• pp.167-185
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• 2017

The purpose of this study is to analyze the actual states of the elementary school and the middle school mathematics education as a curriculum quality-management. To this end, this study surveyed the input, process and output phase in the school curriculum to the teachers, students and parents. The results are like these: First, the achievement standards contents in the elementary school and the middle schools are relevant in the input phase. Second, the teachers in the elementary school have more concern on the teaching & learning methods than those in the middle school in the process phase. Third, students and parents' satisfaction on the cognitive and affective domain in the elementary school is higher than that in the middle school in the output phase. This study suggests that these result has to be affected to make ways to apply the new curriculum, and the curriculum revision system has to be established to revise the curriculum as an important method of quality management.

• Journal of Gifted/Talented Education
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• v.20 no.3
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• pp.681-701
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• 2010

This study examined the perception of gifted teachers toward 'conception of creativity personality education,' 'methodology of creativity personality education,' and 'support of creativity personality education' in the future gifted education. As a result, gifted teachers conceptualized creativity personality education as education to foster creative social capitals(social + moral leaders), which showed movement from gifted education focused on cognitive development into future-developmental orientation. Gifted teachers mentioned education to foster social sensitivity, creativity, and leadership as methodology of creativity personality education. More specifically, they recommended inductive curriculum in learning and teaching in order to encourage domain-specific giftedness. As pointing not to separate creativity personality education for gifted from a formal education but to deploy with conjunction with it, gifted teachers mentioned the social recognition for creativity personality education and the development of teacher's professionalism and educational programs.

• Korean Medical Education Review
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• v.18 no.2
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• pp.51-57
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• 2016

This literature review explores artificial intelligence (AI) technology trends and IBM Watson health and medical references. This study explains how healthcare will be changed by the evolution of AI technology, and also summarizes key technologies in AI, specifically the technology of IBM Watson. We look at this issue from the perspective of 'information overload,' in that medical literature doubles every three years, with approximately 700,000 new scientific articles being published every year, in addition to the explosion of patient data. Estimates are also forecasting a shortage of oncologists, with the demand expected to grow by 42%. Due to this projected shortage, physicians won't likely be able to explore the best treatment options for patients in clinical trials. This issue can be addressed by the AI Watson motivation to solve healthcare industry issues. In addition, the Watson Oncology solution is reviewed from the end user interface point of view. This study also investigates global company platform business to explain how AI and machine learning technology are expanding in the market with use cases. It emphasizes ecosystem partner business models that can support startup and venture businesses including healthcare models. Finally, we identify a need for healthcare company partnerships to be reviewed from the aspect of solution transformation. AI and Watson will change a lot in the healthcare business. This study addresses what we need to prepare for AI, Cognitive Era those are understanding of AI innovation, Cloud Platform business, the importance of data sets, and needs for further enhancement in our knowledge base.

• Journal of Korea Multimedia Society
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• v.22 no.10
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• pp.1168-1177
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• 2019

In order to diagnose and prevent Alzheimer's Disease (AD), it is becoming increasingly important to develop a CAD(Computer-aided Diagnosis) system for AD diagnosis, which provides effective treatment for patients by analyzing 3D MRI images. It is essential to apply powerful deep learning algorithms in order to automatically classify stages of Alzheimer's Disease and to develop a Alzheimer's Disease support diagnosis system that has the function of detecting hippocampus and CSF(Cerebrospinal fluid) which are important biomarkers in diagnosis of Alzheimer's Disease. In this paper, for AD diagnosis, we classify a given MRI data into three categories of AD, mild cognitive impairment, and normal control according by applying 3D brain MRI image to the Faster R-CNN model and detect hippocampus and CSF in MRI image. To do this, we use the 2D MRI slice images extracted from the 3D MRI data of the Faster R-CNN, and perform the widely used majority voting algorithm on the resulting bounding box labels for classification. To verify the proposed method, we used the public ADNI data set, which is the standard brain MRI database. Experimental results show that the proposed method achieves impressive classification performance compared with other state-of-the-art methods.

• Journal of Neurogastroenterology and Motility
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• v.24 no.4
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• pp.512-527
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• 2018

This review provides a comprehensive overview of brain imaging studies of the brain-gut interaction in functional gastrointestinal disorders (FGIDs). Functional neuroimaging studies during gut stimulation have shown enhanced brain responses in regions related to sensory processing of the homeostatic condition of the gut (homeostatic afferent) and responses to salience stimuli (salience network), as well as increased and decreased brain activity in the emotional response areas and reduced activation in areas associated with the top-down modulation of visceral afferent signals. Altered central regulation of the endocrine and autonomic nervous responses, the key mediators of the brain-gut axis, has been demonstrated. Studies using resting-state functional magnetic resonance imaging reported abnormal local and global connectivity in the areas related to pain processing and the default mode network (a physiological baseline of brain activity at rest associated with self-awareness and memory) in FGIDs. Structural imaging with brain morphometry and diffusion imaging demonstrated altered gray- and white-matter structures in areas that also showed changes in functional imaging studies, although this requires replication. Molecular imaging by magnetic resonance spectroscopy and positron emission tomography in FGIDs remains relatively sparse. Progress using analytical methods such as machine learning algorithms may shift neuroimaging studies from brain mapping to predicting clinical outcomes. Because several factors contribute to the pathophysiology of FGIDs and because its population is quite heterogeneous, a new model is needed in future studies to assess the importance of the factors and brain functions that are responsible for an optimal homeostatic state.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.188-204
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• 2018

This study examined how early maternal language use was associated with school readiness at kindergarten entry among children of Asian or Hispanic immigrant mothers in the United States. Using a nationally representative sample from the Early Childhood Longitudinal Study-Birth Cohort (ECLS-B; $N{\approx}1,500$), this study estimates multivariate regression models to address each research question. This study finds generally advantages of maternal use of English and bilingualism for children's expressive language in both Asian and Hispanic groups and for children's pro-social behavior in the Asian group. It also finds that longer residency in the U.S. is associated with higher levels of approaches to learning for children of bilingual Asian mothers and lower levels of behavior problems for children of bilingual Hispanic mothers. Based on the findings, social work implications for the healthy development of young children of immigrants were discussed.

• Biomedical Science Letters
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• v.25 no.1
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• pp.99-106
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• 2019

Amyloid positron emission tomography (PET) allows early and accurate diagnosis in suspected cases of Alzheimer's disease (AD) and contributes to future treatment plans. In the present study, a method of implementing a diagnostic system to distinguish ${\beta}$-Amyloid ($A{\beta}$) positive from $A{\beta}$ negative with objectiveness and accuracy was proposed using a machine learning approach, such as the Principal Component Analysis (PCA) and Support Vector Machine (SVM). $^{18}F$-Florbetaben (FBB) brain PET images were arranged in control and patients (total n = 176) with mild cognitive impairment and AD. An SVM was used to classify the slices of registered PET image using PET template, and a system was created to diagnose patients comprehensively from the output of the trained model. To compare the per-slice classification, the PCA-SVM model observing the whole brain (WB) region showed the highest performance (accuracy 92.38, specificity 92.87, sensitivity 92.87), followed by SVM with gray matter masking (GMM) (accuracy 92.22, specificity 92.13, sensitivity 92.28) for $A{\beta}$ positivity. To compare according to per-subject classification, the PCA-SVM with WB also showed the highest performance (accuracy 89.21, specificity 71.67, sensitivity 98.28), followed by PCA-SVM with GMM (accuracy 85.80, specificity 61.67, sensitivity 98.28) for $A{\beta}$ positivity. When comparing the area under curve (AUC), PCA-SVM with WB was the highest for per-slice classifiers (0.992), and the models except for SVM with WM were highest for the per-subject classifier (1.000). We can classify $^{18}F$-Florbetaben amyloid brain PET image for $A{\beta}$ positivity using PCA-SVM model, with no additional effects on GMM.

• Journal of Korean Biological Nursing Science
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• v.21 no.3
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• pp.217-223
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• 2019

Purpose: Caffeine is the most widely consumed psychostimulant of the methylxanthine class. Among adolescents, high-dose of caffeine consumption has increased rapidly over the last few decades due to the introduction of energy drinks. However, little is known about the time-dependent effect of high doses of caffeine consumption in adolescents. The present study aims to examine the short- and long-term influence of high-dose caffeine on behavior of adolescence. Methods: The animals were divided into three groups: a "vehicle" group, which was injected with 1 ml of phosphate-buffered saline for 14 days; a "Day 1" group, which was injected with caffeine (30 mg/kg), 2 h before the behavioral tests; and a "Day 14" group, which was infused with caffeine for 14 days. An open-field test, a Y-maze test, and a passive avoidance test were conducted to assess the rats'activity levels, anxiety, and cognitive function. Results: High-dose caffeine had similar effects in short-and long-term treatment groups. It increased the level of locomotor activity and anxiety-like behavior, as evidenced by the increase in the number of movements and incidences of rearing and grooming in the caffeine-treated groups. No significant differences were observed between the groups in the Y-maze test. However, in the passive avoidance test, the escape latency in the caffeine-treated group was decreased significantly, indicating impaired memory acquisition. Conclusion: These results indicate that high-dose caffeine in adolescents may increase locomotor activity and anxiety-like behavior and impair learning and memory, irrespective of the duration of administration. The findings will be valuable for both evidence-based education and clinical practice.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1574-1580
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• 2020

Traditional pose estimation methods include using special devices or images through image processing. The disadvantage of using a device is that the environment in which the device can be used is limited and costly. The use of cameras and image processing has the advantage of reducing environmental constraints and costs, but the performance is lower. CNN(Convolutional Neural Networks) were studied for pose estimation just using only camera without these disadvantage. Various techniques were proposed to increase cognitive performance. In this paper, the effect of the skip connection on the network was experimented by using various skip connections on the joint recognition of the hand. Experiments have confirmed that the presence of additional skip connections other than the basic skip connections has a better effect on performance, but the network with downward skip connections is the best performance.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.1
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• pp.541-565
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• 2021

The study is aimed at designing an effective edutech platform based on online remote classes, and clarifying the effect of empathy competence of youths through the operation linked with the empathic reading program focusing on reading. To this end, after constructing the environment for education and drawing a class model based on components of edutech for remote classes on the basis of previous studies and elaborating the empathic reading education program, this study has been conducted for one semester for 107 students in 4 classes in their 1st grade at S middle school, Gyeonggi-do in order to apply them to fields. As a result of the study, the empathy reading program that a remote class model was applied has shown a meaningful difference among groups in cognitive empathy and emotional empathy, and it was found that there was a statistically meaningful difference between the two groups in total scores. Besides, the effect and meaning of adolescents' empathy competence have been verified through the remote empathy reading education and the empirical analysis, and the direction to develop the empathy reading program has been suggested for a solution to settle differences in students' learning due to COVID-19.