• Korean Journal of Radiology
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• v.21 no.10
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• pp.1126-1137
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• 2020

Imaging plays a key role in the management of brain tumors, including the diagnosis, prognosis, and treatment response assessment. Radiomics and deep learning approaches, along with various advanced physiologic imaging parameters, hold great potential for aiding radiological assessments in neuro-oncology. The ongoing development of new technology needs to be validated in clinical trials and incorporated into the clinical workflow. However, none of the potential neuro-oncological applications for radiomics and deep learning has yet been realized in clinical practice. In this review, we summarize the current applications of radiomics and deep learning in neuro-oncology and discuss challenges in relation to evidence-based medicine and reporting guidelines, as well as potential applications in clinical workflows and routine clinical practice.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.11
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• pp.1022-1028
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• 2013

It is well knowns that based on the CSP (Common Spatial Pattern) algorithm, the linear projection of an EEG (Electroencephalography) signal can be made to spaces that optimize the discriminant between two patterns. Sharing disadvantages from linear time invariant systems, CSP suffers from the non-stationary nature of EEGs causing the performance of the classification in a BCI (Brain-Computer Interface) system to drop significantly when comparing the training data and test data. The author has suggested a simple idea based on the parallel model of CSP filters to improve the performance of BCI systems. The model was tested with a simple CSP algorithm (without any elaborate regularizing methods) and a perceptron learning algorithm as a classifier to determine the improvement of the system. The simulation showed that the parallel model could improve classification performance by over 10% compared to conventional CSP methods.

• Electronics and Telecommunications Trends
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• v.33 no.1
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• pp.1-10
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• 2018

Artificial intelligence, which is based on deep learning, is emerging as a fundamental technology that will bring about future social changes. Artificial intelligence technology in IT is an essential intelligent system, and will overcome the performance limit of computing systems, and is expected to be the foundation for the development of computing environment destructively. The development of artificial intelligence technology in developed countries is a direction toward convergence with brain science. In this article, we will look at the prospect of artificial intelligence as the manifestation of imagination, as well as the technology and policy trends of artificial intelligence both at home and abroad, and discuss the direction of future technology development in terms of fusion with brain science.

• Journal of Biomedical Engineering Research
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• v.42 no.6
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• pp.268-276
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• 2021

Functional near-infrared spectroscopy-based brain-computer interface (fNIRS-based BCI) has been receiving much attention. However, we are practically constrained to obtain a lot of fNIRS data by inherent hemodynamic delay. For this reason, when employing machine learning techniques, a problem due to the high-dimensional feature vector may be encountered, such as deteriorated classification accuracy. In this study, we employ an elastic net-based feature selection which is one of the embedded methods and demonstrate the utility of which by analyzing the results. Using the fNIRS dataset obtained from 18 participants for classifying brain activation induced by mental arithmetic and idle state, we calculated classification accuracies after performing feature selection while changing the parameter α (weight of lasso vs. ridge regularization). Grand averages of classification accuracy are 80.0 ± 9.4%, 79.3 ± 9.6%, 79.0 ± 9.2%, 79.7 ± 10.1%, 77.6 ± 10.3%, 79.2 ± 8.9%, and 80.0 ± 7.8% for the various values of α = 0.001, 0.005, 0.01, 0.05, 0.1, 0.2, and 0.5, respectively, and are not statistically different from the grand average of classification accuracy estimated with all features (80.1 ± 9.5%). As a result, no difference in classification accuracy is revealed for all considered parameter α values. Especially for α = 0.5, we are able to achieve the statistically same level of classification accuracy with even 16.4% features of the total features. Since elastic net-based feature selection can be easily applied to other cases without complicated initialization and parameter fine-tuning, we can be looking forward to seeing that the elastic-based feature selection can be actively applied to fNIRS data.

• Journal of The Korean Association of Information Education
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• v.24 no.6
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• pp.539-550
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• 2020

This study establishes the scientific basis for the use of digital learning materials through the education-neurological research method and derives implications for education based on education-neurological understandings. The main findings of the education-neurological analysis of digital learning materials are as follows: First, various sensory stimuli go through multiple sensory neurons and deep sections of the upper sphere and make possible the cooperative processing of information. Second, indirect experience from digital learning materials helps students understand the learning contents vividly through the mirror neuron system. Third, positive emotions originating from digital learning materials promote functions of dopamine, the reticular activating system, frontal-striatal circuit, cerebrum cortex. Based on the findings, the study suggests the following educational implications. First of all, when selecting digital learning materials, teachers should consider expression forms, learning contents, the flow of classes, and the adverse effects of digital learning materials. Next, it is effective to utilize digital learning materials in the lecture for provoking curiosity and enjoyment, maintaining interest and effort, and reviewing what students learned.

• 대한공업교육학회지
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• v.35 no.2
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• pp.124-139
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• 2010

This study has its purpose of researching on the relevant variables which affect the attitude toward engineering science and brain dominance for the department of engineering students. The results of this study are as follows: First, the department of engineering students' attitude toward engineering science has shown the order of cognitive element (3.73), definitional element (3.05) and behavioral element (2.86), and in the actual context it is considered that it is necessary to establish a teaching-learning strategy which can reinforce the behavioral elements such as experiments and practices as well as can improve engineering-related cognitive ability. Second, the attitudes toward engineering science according to their brain dominance thinking (Type A: analyst, Type B: Administrator, Type C: Cooperator, and Type D: Jointer) have no significant difference, but the students of Type A who have the characteristics of 7 analyzing thinking have shown high academic accomplishment. Based on these results of study, it is necessary to make a change of the current teaching-learning stratery in accordance with the types of thinking of the students from the teaching-learning perspective. In particular, in order to develop the weak dominance properties and thinking type of individual learners, the change in teacher's recognition that the teacher's teaching-learning strategy and practice is important has to take precedence.

• Journal of Korean Elementary Science Education
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• v.35 no.3
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• pp.336-347
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• 2016

The purpose of this study is to analyze the effects of elementary science instruction applying a Brain-Based Evolutionary (ABC-DEF) approach using Rapid-cycling Brassica rapa (RcBr) on the interests of elementary school students. For this study, two elementary school classes in Seoul and one elementary school class in Gyeonggi-do were selected. Comparison group received instruction using textbook and teacher's guidebook. A class taught using only brain-based evolutionary approach is experimental group A, and a class taught through brain-based evolutionary approach using RcBr is experimental group B. In order to analyze the quantitative differences about the interests of students, three kinds of test were administered to the students: 'Applied Unit-Related Interests', 'Follow-up Interests' and 'Interests in the observation material'. To get more information, qualitative data such as portfolios and interviews were analyzed. The major findings are as follows. First, for the test of applied unit-related interests, a statistically significant difference was found between comparison group and experimental group A, and between comparison group and experimental group B. As the results of interviews, the students have shown that the intensified exploration activities on plant in Brain-Based Evolutionary approach applied to experimental groups A and B had a positive effect. Second, for test of follow-up interests, we classified the students' follow-up interests into three types: extended-developed-deepened (EDD) type, simply expanded-maintained (SEM) type, and stopped or decreased (SD) type. Both experimental group A and experimental group B showed the highest percentage of EDD. Also, observation journal applying the evolutionary process (DEF) showed a positive effect on the students' interest. Comparison group showed the highest percentage of SEM. Third, for test of applied interests in the observation material, a statistically significant difference was found between comparison group and experimental group A, and comparison group and experimental group B. Experimental group B using RcBr showed the highest average score, while experimental group A showed a higher score than comparison group. Based on these findings, educational implications of Brain-Based Evolutionary approach and using RcBr are discussed.

• Journal of KIISE
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• v.43 no.1
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• pp.54-60
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• 2016

Brain gene expression information is closely related to the structural and functional characteristics of the brain. Thus, extensive research has been carried out on the relationship between gene expression patterns and the brain's structural organization. In this study, Principal Component Analysis was used to extract features of gene expression patterns, and genes were automatically classified by spatial distribution. Voxels were then clustered with classified specific region expressed genes. Finally, we visualized the clustering results for mouse hippocampal region gene expression with the Allen Brain Atlas. This experiment allowed us to classify the region-specific gene expression of the mouse hippocampal region and provided visualization of clustering results and a brain atlas in an integrated manner. This study has the potential to allow neuroscientists to search for experimental groups of genes more quickly and design an effective test according to the new form of data. It is also expected that it will enable the discovery of a more specific sub-region beyond the current known anatomical regions of the brain.

• Journal of Korean Elementary Science Education
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• v.34 no.1
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• pp.109-122
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• 2015

In National Curriculum of Science revised in 2007, free inquiry was newly introduced to increase student's interest in science and to foster creativity by having students make their own curiosity questions and find answers by themselves. The purpose of this study is to analyze elementary school students' perceptions on free science inquiry activities applying a brain-based evolutionary approach. For this study, 106 the fifth grade students participated, and then completed a questionnaire on free inquiry activities according to a brain-based evolutionary science teaching and learning principles. The students performed a series of steps of the Diversifying, Estimating-Evaluating-Executing, and Furthering activities in each of Affective, Behavioral, and Cognitive domains (ABC-DEF approach) and constructed their own free inquiry diary, then the observations by the researcher and interviews with the students were analyzed both quantitatively and qualitatively. The major results of the study were as follows: First, the majority of the students perceived the each domain and step positively although a few of them perceived negatively. The reasons perceived as negatively were categorized into two; preference dimension of like or dislike and ability dimension of metacognitive or self-reflective capacity. Also, they perceived the free inquiry experience in the form of ABC-DEF as helpful to understand the nature of scientists' scientific activities. Based on these findings, implications for supporting authentic inquiry in school science are discussed.

• Journal of Broadcast Engineering
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• v.22 no.2
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• pp.253-256
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• 2017

This paper deals with the implementation of speech disorder detection system based on machine learning classification. Problems with speech are a common early symptom of a stroke or other brain injuries. Therefore, detection of speech disorder may lead to correction and fast medical treatment of strokes or cerebrovascular accidents. The speech disorder system can be implemented by extracting features from the input speech and classifying the features using machine learning algorithms. Ten machine learning algorithms with various scaling methods were used to discriminate speech disorder from normal speech. The detection system was evaluated by the TORGO database which contains dysarthric speech collected from speakers with either cerebral palsy or amyotrophic lateral sclerosis.