• Management & Information Systems Review
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• v.22
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• pp.25-59
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• 2007

These days, a lot of information and knowledge is being produced and accumulated constantly, which makes it difficult for a person to get the exact information or knowledge in simple way that he or she wants to get. It is also true in college and university. A lot of data is increasing so fast that a student cannot achieve his or her goal in learning with the text. This means that it is necessary to bring a change in the way of teaching and learning from only simple lectures. So in this treatise, we try to develop the method of the introduction and operation of center for teaching and learning. In order to accomplish the purposes, this research has examined the questionare and domestic colleges and universities.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.6
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• pp.307-316
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• 2020

Machine learning model data is highly affected by performance. preprocessing is needed to enable analysis of various types of data, such as letters, numbers, and special characters. This paper proposes a development environment that aims to process categorical and continuous data according to the type of missing values in stage 1, implementing the function of selecting the best performing algorithm in stage 2 and automating the process of checking model performance in stage 3. Using this model, machine learning models can be created without prior knowledge of data preprocessing.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.285-294
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• 2023

Autonomous radiation source detection has long been studied for radiation emergencies. Compared to conventional data-driven or path planning methods, deep reinforcement learning shows a strong capacity in source detection while still lacking the generalized ability to the geometry in unknown environments. In this work, the detection task is decomposed into two subtasks: exploration and localization. A hierarchical control policy (HC) is proposed to perform the subtasks at different stages. The low-level controller learns how to execute the individual subtasks by deep reinforcement learning, and the high-level controller determines which subtasks should be executed at the current stage. In experimental tests under different geometrical conditions, HC achieves the best performance among the autonomous decision policies. The robustness and generalized ability of the hierarchy have been demonstrated.

• 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.

• The Journal of Korean Association of Computer Education
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• v.11 no.6
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• pp.77-84
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• 2008

The Teaching & Learning Center plays an important role in increasing the expertise of instructors and for directing the diffusion of innovation not only in primary & secondary education but also in education at the university level. In this study, the Performance Evaluation Model is devised and developed for improving the competency of the Teaching & Learning Center. It consists of three domains - (a) the planning domain, (b) the process domain, and (c) the performance domain - and 11 external indices and 8 internal indices. The Performance Evaluation Index and Guidelines are proposed based on this model.

• Psychiatry investigation
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• v.15 no.11
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• pp.1030-1036
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• 2018

Objective In this study, we aimed to develop a model predicting individuals with suicide ideation within a general population using a machine learning algorithm. Methods Among 35,116 individuals aged over 19 years from the Korea National Health & Nutrition Examination Survey, we selected 11,628 individuals via random down-sampling. This included 5,814 suicide ideators and the same number of non-suicide ideators. We randomly assigned the subjects to a training set (n=10,466) and a test set (n=1,162). In the training set, a random forest model was trained with 15 features selected with recursive feature elimination via 10-fold cross validation. Subsequently, the fitted model was used to predict suicide ideators in the test set and among the total of 35,116 subjects. All analyses were conducted in R. Results The prediction model achieved a good performance [area under receiver operating characteristic curve (AUC)=0.85] in the test set and predicted suicide ideators among the total samples with an accuracy of 0.821, sensitivity of 0.836, and specificity of 0.807. Conclusion This study shows the possibility that a machine learning approach can enable screening for suicide risk in the general population. Further work is warranted to increase the accuracy of prediction.

• Journal of Multimedia Information System
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• v.8 no.2
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• pp.79-84
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• 2021

Detecting brain tumors of different sizes is a challenging task. This study aimed to identify brain tumors using detection algorithms. Most studies in this area use segmentation; however, we utilized detection owing to its advantages. Data were obtained from 64 patients and 11,200 MR images. The deep learning model used was RetinaNet, which is based on ResNet152. The model learned three different types of pre-processing images: normal, general histogram equalization, and contrast-limited adaptive histogram equalization (CLAHE). The three types of images were compared to determine the pre-processing technique that exhibits the best performance in the deep learning algorithms. During pre-processing, we converted the MR images from DICOM to JPG format. Additionally, we regulated the window level and width. The model compared the pre-processed images to determine which images showed adequate performance; CLAHE showed the best performance, with a sensitivity of 81.79%. The RetinaNet model for detecting brain tumors through deep learning algorithms demonstrated satisfactory performance in finding lesions. In future, we plan to develop a new model for improving the detection performance using well-processed data. This study lays the groundwork for future detection technologies that can help doctors find lesions more easily in clinical tasks.

• International Journal of Internet, Broadcasting and Communication
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• v.4 no.1
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• pp.8-12
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• 2012

The purpose of this study is to review the possible effect of the learning paradigm shift from traditional method to ubiquitous learning. What are the societal issues that need to be address in order to design a new pedagogical platform trending from e-learning to m-learning and now the u-learning? That without the proper study of how learning environment may affect the learning process of an individual will lead to poor quality of education. This new era of learning environment offer a big opportunity for "anytime, anywhere" learning. Thus, Lifelong learning is at hand of everyone. Maximizing the benefit of new trend will be a great help and addressing the limitations will lead to quality education.

• International Journal of Internet, Broadcasting and Communication
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• v.4 no.2
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• pp.7-11
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• 2012

In this paper, we defined constructivism and ontology theory and associate it in ubiquitous learning. The typical ubiquitous learning involving the Context Aware Intelligent system was presented. Also the Architecture for learning environment including the key idea and technical concept is being presented in this paper. Guided with these principles and with the advancement of information and communication technology the context-awareness based on Artificial intelligence for Location based Service for ubiquitous Learning was conceptualized.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.835-838
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• 2005

본 논문에서는 국내에 산재한 생물다양성정보를 e-Learning에 활용하기 위하여 KISTI에서 구축한 생물다양성 데이터베이스 현황과 e-Learning의 기술요소 등을 조사하였으며, 기존에 구축된 생물다양성정보 데이터베이스를 활용하여 일반인과 학생을 위한 e-Learning 생물다양성 학습 콘텐트를 기획하고 설계하였다. 본 설계를 바탕으로 생물다양성 콘텐트를 개발한다면, 국토가 좁고, 네트워크 인프라가 잘 갖추어져 있는 우리나라의 실정에 맞는 사이버공간상의 학습의 장으로서 일반인과 학생들에게도 양질의 e-Learning 학습 콘텐트를 제공할 수 있으리라 기대한다.