• Journal of Korean Home Economics Education Association
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• v.33 no.2
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• pp.153-172
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• 2021

The purpose of this study is to develop the Empathy Education Program by employing the brain-based education principle in home economics education. For this purpose, the study used the "ADDE" (Analysis, Design, Development, and Evaluation) method to develope a Empathy Education Program for middle-school students. Analysis from previous literature derived that four principles of brain-based empathy education in home economics education: 1) understanding through imitation; 2) inference through imagination; 3) interaction through experience, and 4) internalization through practice. Based on these brain-based four principles and three components of empathy, a total of 15 lessons were designed and developed for the educational program for middle-school students, "FEEL" (For Empathy Education & Learning). The contents of this program were selected from the analysis of the 2015-revised technology and home economics education curriculum and textbooks. Results from expert evaluation of the validity and feasibility of the program showed that this program is highly valid with an average validity score of 4.88 and 0.98 for the validity index ("CVI"). This study is meaningful in that it can be effectively employed in the middle school free-year policy system. Also, this study contributes to shedding light on the possibility of combining empathy education with home economics education by presenting newly presented elements of empathy and brain-based education principles in home economics education.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.101-106
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• 2023

In this busy world actually stress is continuously grow up in research and monitoring social websites. The social interaction is a process by which people act and react in relation with each other like play, fight, dance we can find social interactions. In this we find social structure means maintain the relationships among peoples and group of peoples. Its a limit and depends on its behavior. Because relationships established on expectations of every one involve depending on social network. There is lot of difference between emotional pain and physical pain. When you feel stress on physical body we all feel with tensions, stress on physical consequences, physical effects on our health. When we work on social network websites, developments or any research related information retrieving etc. our brain is going into stress. Actually by social network interactions like watching movies, online shopping, online marketing, online business here we observe sentiment analysis of movie reviews and feedback of customers either positive/negative. In movies there we can observe peoples reaction with each other it depends on actions in film like fights, dances, dialogues, content. Here we can analysis of stress on brain different actions of movie reviews. All these movie review analysis and stress on brain can calculated by machine learning techniques. Actually in target oriented business, the persons who are working in marketing always their brain in stress condition their emotional conditions are different at different times. In this paper how does brain deal with stress management. In software industries when developers are work at home, connected with clients in online work they gone under stress. And their emotional levels and stress levels always changes regarding work communication. In this paper we represent emotional intelligence with stress based analysis using machine learning techniques in social networks. It is ability of the person to be aware on your own emotions or feeling as well as feelings or emotions of the others use this awareness to manage self and your relationships. social interactions is not only about you its about every one can interacting and their expectations too. It about maintaining performance. Performance is sociological understanding how people can interact and a key to know analysis of social interactions. It is always to maintain successful interactions and inline expectations. That is to satisfy the audience. So people careful to control all of these and maintain impression management.

• Journal of The Korean Association For Science Education
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• v.26 no.4
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• pp.568-580
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• 2006

The purpose of this study was to investigate the brain activities by 4-types of Generating Process of Scientific Emotion (GPSE) in the hypothesis-generating biological phenomena by using fMRI. Four-types of GPSE were involved in the Basic Generating Process (BGP), Retrospective Generating Process (RGP), Cognitive Generating Process (CGP) and Attributive Generating Process (AGP). For this study, we made an experimental design capable of validating the 4-types of generating process (e.g. BGP, RGP, CGP and AGP), and then measured BOLD signals of 10 pre-service teachers' brain activities by 3.0T fMRI system. Subjects were 10 healthy females majoring in biology education. As a result, there were clear differences among 4-types of GPSE. Brain areas activated by BGP were at right occipital lobe (BA 17), at left thalamus and left parahippocampal gyrus, while in the case of RGP, at left superior parietal lobe (BA 8, 9), at left pulvinar and left globus pallidus were activated. Brain areas activated by CGP were the right posterior cingulate and left medial frontal gyrus (BA 6). In the case of AGP, the most distinctively activated brain areas were the right medial frontal gyrus (BA 8) and left inferior parietal lobule (BA 40). These results would mean that each of the 4-types of GPSE has a specific neural networks in the brain, respectively. Furthermore, it would provide the basis of brain-based learning in science education.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.101-110
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• 2022

Histopathological analysis of biopsy specimens is still used for diagnosis and classifying the brain tumors today. The available procedures are intrusive, time consuming, and inclined to human error. To overcome these disadvantages, need of implementing a fully automated deep learning-based model to classify brain tumor into multiple classes. The proposed CNN model with an accuracy of 92.98 % for categorizing tumors into five classes such as normal tumor, glioma tumor, meningioma tumor, pituitary tumor, and metastatic tumor. Using the grid search optimization approach, all of the critical hyper parameters of suggested CNN framework were instantly assigned. Alex Net, Inception v3, Res Net -50, VGG -16, and Google - Net are all examples of cutting-edge CNN models that are compared to the suggested CNN model. Using huge, publicly available clinical datasets, satisfactory classification results were produced. Physicians and radiologists can use the suggested CNN model to confirm their first screening for brain tumor Multi-classification.

• Investigative Magnetic Resonance Imaging
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• v.26 no.1
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• pp.1-9
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• 2022

Artificial intelligence (AI) powered by deep learning (DL) has shown remarkable progress in image recognition tasks. Over the past decade, AI has proven its feasibility for applications in medical imaging. Various aspects of clinical practice in neuroimaging can be improved with the help of AI. For example, AI can aid in detecting brain metastases, predicting treatment response of brain tumors, generating a parametric map of dynamic contrast-enhanced MRI, and enhancing radiomics research by extracting salient features from input images. In addition, image quality can be improved via AI-based image reconstruction or motion artifact reduction. In this review, we summarize recent clinical applications of DL in various aspects of neuroimaging.

• Electronics and Telecommunications Trends
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• v.36 no.3
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• pp.106-118
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• 2021

The field of brain science (or neuroscience in a broader sense) has inspired researchers in artificial intelligence (AI) for a long time. The outcomes of neuroscience such as Hebb's rule had profound effects on the early AI models, and the models have developed to become the current state-of-the-art artificial neural networks. However, the recent progress in AI led by deep learning architectures is mainly due to elaborate mathematical methods and the rapid growth of computing power rather than neuroscientific inspiration. Meanwhile, major limitations such as opacity, lack of common sense, narrowness, and brittleness have not been thoroughly resolved. To address those problems, many AI researchers turn their attention to neuroscience to get insights and inspirations again. Biologically plausible neural networks, spiking neural networks, and connectome-based networks exemplify such neuroscience-inspired approaches. In addition, the more recent field of brain network analysis is unveiling complex brain mechanisms by handling the brain as dynamic graph models. We argue that the progress toward the human-level AI, which is the goal of AI, can be accelerated by leveraging the novel findings of the human brain network.

• Journal of The Korean Association For Science Education
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• v.29 no.3
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• pp.348-358
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• 2009

The purpose of this study was to investigate brain activation pattern and functional connectivity network during experimental design on the biological phenomena. Twenty six right-handed healthy science teachers volunteered to be in the present study. To investigate participants' brain activities during the tasks, 3.0T fMRI system with the block experimental-design was used to measure BOLD signals of their brain and SPM2 software package was applied to analyze the acquired initial image data from the fMRI system. According to the analyzed data, superior, middle and inferior frontal gyrus, superior and inferior parietal lobule, fusiform gyrus, lingual gyrus, and bilateral cerebellum were significantly activated during participants' carrying-out experimental design. The network model was consisting of six nodes (ROIs) and its six connections. These results suggested the notion that the activation and connections of these regions mean that experimental design process couldn't succeed just a memory retrieval process. These results enable the scientific experimental design process to be examined from the cognitive neuroscience perspective, and may be used as a basis for developing a teaching-learning program for scientific experimental design such as brain-based science education curriculum.

• Journal of The Korean Association For Science Education
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• v.29 no.7
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• pp.751-758
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• 2009

The purpose of this study was to investigate brain activation pattern and functional connectivity network during classification on the biological phenomena. Twenty six right-handed healthy science teachers volunteered to be in the present study. To investigate participants' brain activities during the tasks, 3.0T fMRI system with the block experimental-design was used to measure BOLD signals of their brain. According to the analyzed data, superior, middle and inferior frontal gyrus, superior and inferior parietal lobule, fusiform gyrus, lingual gyrus, and bilateral cerebellum were significantly activated during participants' carrying-out classification. The network model was consisting of six nodes (ROIs) and its fourteen connections. These results suggested the notion that the activation and connections of these regions mean that classification is consist of two sub-network systems (top-down and bottom-up related) and it functioning reciprocally. These results enable the examination of the scientific classification process from the cognitive neuroscience perspective, and may be used as basic materials for developing a teaching-learning program for scientific classification such as brain-based science education curriculum in the science classrooms.

• Korean Journal of Biological Psychiatry
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• v.22 no.1
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• pp.20-27
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• 2015

Objectives It is increasingly thought that the human cerebellum plays an important role in emotion and cognition. Although recent evidence suggests that the cerebellum may also be implicated in fear learning, only a limited number of studies have investigated the cerebellar abnormalities in panic disorder. The aim of this study was to evaluate the cerebellar gray matter deficits and their clinical correlations among patients with panic disorder. Methods Using a voxel-based morphometry approach with a high-resolution spatially unbiased infratentorial template, regional cerebellar gray matter density was compared between 23 patients with panic disorder and 33 healthy individuals. Results The gray matter density in the right posterior-superior (lobule Crus I) and left posterior-inferior (lobules Crus II, VIIb, VIIIa) cerebellum was significantly reduced in the panic disorder group compared to healthy individuals (p < 0.05, false discovery rate corrected, extent threshold = 100 voxels). Additionally, the gray matter reduction in the left posterior-inferior cerebellum (lobule VIIIa) was significantly associated with greater panic symptom severity (r = -0.55, p = 0.007). Conclusions Our findings suggest that the gray matter deficits in the posterior cerebellum may be involved in the pathogenesis of panic disorder. Further studies are needed to provide a comprehensive understanding of the cerebro-cerebellar network in panic disorder.

• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.178-185
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• 2021

Three-dimensional high-resolution magnetic resonance imaging (MRI) provides fine-level anatomical information for disease diagnosis. However, there is a limitation in obtaining high resolution due to the long scan time for wide spatial coverage. Therefore, in order to obtain a clear high-resolution(HR) image in a wide spatial coverage, a super-resolution technology that converts a low-resolution(LR) MRI image into a high-resolution is required. In this paper, we propose a super-resolution technique through filter learning based on information on the surrounding gradient information in 3D space from 3D MRI images. In the learning step, the gradient features of each voxel are computed through eigen-decomposition from 3D patch. Based on these features, we get the learned filters that minimize the difference of intensity between pairs of LR and HR images for similar features. In test step, the gradient feature of the patch is obtained for each voxel, and the filter is applied by selecting a filter corresponding to the feature closest to it. As a result of learning 100 T1 brain MRI images of HCP which is publicly opened, we showed that the performance improved by up to about 11% compared to the traditional interpolation method.