• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.305-307
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• 2021

Alzheimer's Disease (AD) is a cognitive disorder characterized by memory impairment that can be assessed at early stages based on administering clinical tests. However, the AD pathophysiological mechanism is still poorly understood due to the difficulty of distinguishing different levels of AD severity, even using a variety of brain modalities. Therefore, in this study, we present a hybrid EEG-fNIRS modalities to compensate for each other's weaknesses with the help of Machine Learning (ML) techniques for classifying four subject groups, including healthy controls (HC) and three distinguishable groups of AD levels. A concurrent EEF-fNIRS setup was used to record the data from 41 subjects during Oddball and 1-back tasks. We employed both a traditional neural network (NN) and a CNN-LSTM hybrid model for fNIRS and EEG, respectively. The final prediction was then obtained by using majority voting of those models. Classification results indicated that the hybrid EEG-fNIRS feature set achieved a higher accuracy (71.4%) by combining their complementary properties, compared to using EEG (67.9%) or fNIRS alone (68.9%). These findings demonstrate the potential of an EEG-fNIRS hybridization technique coupled with ML-based approaches for further AD studies.

• Journal of Korean Library and Information Science Society
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• v.33 no.1
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• pp.35-55
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• 2002

The author reviewed the school library and information skills instruction. In order to fit the goal that is the enhancement of student's creativity, the instruction the author introduced is based on the learning model such as The big Six. She insisted that students can be creative, if they solve their classroom tasks by using such learning model for their entire school days because the teaming models are structured similar to human brain's thinking process and it can help the function of human information processing system. The author suggested the improvement of the conditions of school library media centers for school library and information skills instruction to meet the goal of school, that Is the creativity of students.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.23 no.2
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• pp.49-56
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• 2012

Methylphenidate is a widely used stimulant for treatment of attention-deficit hyperactivity disorder (ADHD). In addition to core symptoms of attention and behavioral symptoms, methylphenidate is even effective for executive functions. However, abuse and misuse of stimulants, including methylphenidate, for the purpose of cognitive enhancement is an issue of concern worldwide. Some prejudices and misunderstandings against methylphenidate are popular ; however, little attention has been given to the neuropsychiatric evidence of methylphenidate for enhancement of cognitive function among healthy populations. In this article, our aim was to conduct a review of previous studies investigating the effect of methylphenidate in healthy populations. Findings from several recent studies have demonstrated the effectiveness of methylphenidate for enhancement of cognitive function in healthy populations. The mechanisms of cognitive enhancement are thought to be associated with motivation and the reward circuit in the brain. However, when considering the risk to benefit, an official discussion of the use of methylphenidate among healthy members of the population would be premature. Instead, investigation of epidemiological studies of the prevalence of misuse of stimulants among healthy members of the population, particularly adolescents and college students, is needed. In addition, based on achievements reported in previous studies, investigation of the effect of an approach using non-pharmacological enhancing motivation, which will in turn result in increased cognitive function would be helpful.

• Journal of Digital Convergence
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• v.19 no.12
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• pp.353-358
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• 2021

Parkinson's disease is the second most common degenerative brain disease after Alzheimer's in old age. Symptoms of Parkinson's disease are factors that reduce the quality of life in daily life, such as shaking hands, slowing behavior and cognitive function. Parkinson's disease that can slow the progression of the disease through early diagnosis. To diagnoze Parkinson's disease early, an algorithm was implemented to extract features using wav2vec and to diagnose the presence or absence of Parkinson's disease with deep learning(ANN). As a results of the experiment, the accuracy was 97.47%. It was better than the results of diagnosing Parkinson's disease using the existing neural network. The audio voice file could simply reduce the experiment process and obtain improved results.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.971-974
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• 2020

Infants express their physical and emotional needs to the outside world mainly through crying. However, most of parents find it challenging to understand the reason behind their babies' cries. Failure to correctly understand the cause of a baby' cry and take appropriate actions can affect the cognitive and motor development of newborns undergoing rapid brain development. In this paper, we propose an infant cry recognition system based on deep transfer learning to help parents identify crying babies' needs the same way a specialist would. The proposed system works by transforming the waveform of the cry signal into log-mel spectrogram, then uses the VGGish model pre-trained on AudioSet to extract a 128-dimensional feature vector from the spectrogram. Finally, a softmax function is used to classify the extracted feature vector and recognize the corresponding type of cry. The experimental results show that our method achieves a good performance exceeding 0.96 in precision and recall, and f1-score.

• Clinical Psychopharmacology and Neuroscience
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• v.16 no.4
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• pp.449-460
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• 2018

Objective: Prior functional magnetic resonance imaging (fMRI) work has revealed that children/adolescents with disruptive behavior disorders (DBDs) show dysfunctional reward/non-reward processing of non-social reinforcements in the context of instrumental learning tasks. Neural responsiveness to social reinforcements during instrumental learning, despite the importance of this for socialization, has not yet been previously investigated. Methods: Twenty-nine healthy children/adolescents and 19 children/adolescents with DBDs performed the fMRI social/non-social reinforcement learning task. Participants responded to random fractal image stimuli and received social and non-social rewards/non-rewards according to their accuracy. Results: Children/adolescents with DBDs showed significantly reduced responses within the caudate and posterior cingulate cortex (PCC) to non-social (financial) rewards and social non-rewards (the distress of others). Connectivity analyses revealed that children/adolescents with DBDs have decreased positive functional connectivity between the ventral striatum (VST) and the ventromedial prefrontal cortex (vmPFC) seeds and the lateral frontal cortex in response to reward relative to non-reward, irrespective of its sociality. In addition, they showed decreased positive connectivity between the vmPFC seed and the amygdala in response to non-reward relative to reward. Conclusion: These data indicate compromised reinforcement processing of both non-social rewards and social non-rewards in children/adolescents with DBDs within core regions for instrumental learning and reinforcement-based decision-making (caudate and PCC). In addition, children/adolescents with DBDs show dysfunctional interactions between the VST, vmPFC, and lateral frontal cortex in response to rewarded instrumental actions potentially reflecting disruptions in attention to rewarded stimuli.

• Journal of the Korean Institute of Educational Facilities
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• v.26 no.3
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• pp.15-23
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• 2019

This study aims to suggest a Biophilic classroom design of high school to obtain attention restoration for students. The learning space for youth in the modern society is composed of dry artificial structures. This space is considered to be a space that can not relieve stress caused by learning. "The Attention Restoration Theory" is divided into "Directed Attention" of humans, which is the cause of fatigue and stress, and "Involuntary Attention" as a solution to it. "Involuntary Attention" takes place in a rest state and helps the brain recover when exposed to nature. And the core of "Biophilic-Design Theory" is that humans can recover physical and mental conditions when exposed to nature. The purpose of this study is to apply "The Biophilic-Design Theory" that emphasizes the importance of exposure to nature to the educational space and plan the space where the 'Attention Restoration' can be achieved. The research method is as follows. First, we review previous studies related to "The Biophilic-Design Theory" and "The Attention Restoration Theory". Second, we analyze the application examples of "The Biophilic-Design Theory" and "The Attention Restoration Theory" in domestic and foreign educational spaces. Third, the concept of educational space is set up based on the elements derived from previous studies. Finally, we propose the planning direction of classroom design based on Biophilic-Design. The following conclusions were drawn. First, The creation of the education space to restore the learner's attention requires a visual space plan that utilizes natural elements such as natural light, artificial light, plants, and natural materials that can directly experience nature. Second, the direction in which students in the classroom can be "The Attention Restoration Theory" should consider the use of indirect natural elements that bring the surrounding natural landscape into the interior. This study will be used as the baseline data for the spatial design and planning of education facilities based on Biophilic-Design.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.92-99
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• 2019

This study empirically analyzed the effects of elderly people participating in lifelong learning on their psychological well-being. For this purpose, 160 elderly people who participated in lifelong education programs in hospitals C and D located in Cheonan city were selected as the subjects. A survey was conducted for 10 days, from May 8, 2019 to May 17, 2019. First, does the goal-oriented factor, which is part of the lifelong learning motivation of the elderly, have a positive effect on their psychological well-being? Second, does the activity-oriented factor of the elderly have a positive effect on their psychological well-being? Third, does the learning-oriented factor of elderly people's motivation for participating in lifelong learning have a positive effect on their psychological well-being? To answer these research questions, variables such as gender, age, marital status, and education level were controlled through correlation analysis, and simple regression analysis was also performed. The results of the direct effect analysis showed that goal-orientation (${\beta}=.244$; p<.001) and activity-orientation (${\beta}=.321$, p<.001) have statistically significant positive effects on psychological well-being. However, a learning-orientation (${\beta}=.284$, p>.001) was found to have no statistically significant effect on psychological well-being. Based on these findings, lifelong learning programs for the elderly should be more goal-oriented and activity-oriented than learning-oriented to promote the psychological well-being of the elderly.

• Korean Journal of Radiology
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• v.23 no.1
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• pp.77-88
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• 2022

Objective: Our study aimed to evaluate the quality of radiomics studies on brain metastases based on the radiomics quality score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist, and the Image Biomarker Standardization Initiative (IBSI) guidelines. Materials and Methods: PubMed MEDLINE, and EMBASE were searched for articles on radiomics for evaluating brain metastases, published until February 2021. Of the 572 articles, 29 relevant original research articles were included and evaluated according to the RQS, TRIPOD checklist, and IBSI guidelines. Results: External validation was performed in only three studies (10.3%). The median RQS was 3.0 (range, -6 to 12), with a low basic adherence rate of 50.0%. The adherence rate was low in comparison to the "gold standard" (10.3%), stating the potential clinical utility (10.3%), performing the cut-off analysis (3.4%), reporting calibration statistics (6.9%), and providing open science and data (3.4%). None of the studies involved test-retest or phantom studies, prospective studies, or cost-effectiveness analyses. The overall rate of adherence to the TRIPOD checklist was 60.3% and low for reporting title (3.4%), blind assessment of outcome (0%), description of the handling of missing data (0%), and presentation of the full prediction model (0%). The majority of studies lacked pre-processing steps, with bias-field correction, isovoxel resampling, skull stripping, and gray-level discretization performed in only six (20.7%), nine (31.0%), four (3.8%), and four (13.8%) studies, respectively. Conclusion: The overall scientific and reporting quality of radiomics studies on brain metastases published during the study period was insufficient. Radiomics studies should adhere to the RQS, TRIPOD, and IBSI guidelines to facilitate the translation of radiomics into the clinical field.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.802-805
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• 2002

In the three-dimensional domain image expressed with two-dimensional slice images, such as fMRI images and multi-slice CT images, we propose the three-dimensional domain automatic segmentation for the purpose of extracting region. In this paper, we segmented each domain from the fMRI images of the head of people and monkey. We used the neural network "Pulse-Coupled Neural Network" which is one of the models of visual cortex of the brain based on the knowledge from neurophysiology as the technique. By using this technique, we can segment the region without any learning. Then, we reported the result of division of each domain and extraction to the fMRI slice images of human's head using "three-dimensional Pulse-Coupled Neural Network" which is arranged and created the neuron in the shape of a three-dimensional lattice.