• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.191-196
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• 2024

Recently, research on intelligent IoT technology has been actively conducted by various companies and research institutes to analyze various data collected from IoT devices and provide it through actual application services. However, security issues such as personal information leakage may arise in the process of transmitting and receiving data to use data collected from IoT devices for research and development. In addition, as data collected from multiple IoT devices increases, data management difficulties exist, and data movement is costly and time consuming. Therefore, in this paper, we intend to develop an Ethereum network-based federated learning system with guaranteed reliability to improve security issues and inefficiencies in a federated learning environment composed of various devices.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.3
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• pp.319-328
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• 2024

Semi-supervised learning is a good way to train a classification model using a small number of labeled and large number of unlabeled data. We applied semi-supervised learning to a synthetic aperture radar(SAR) image classification model with a limited number of datasets that are difficult to create. To address the previous difficulties, semi-supervised learning uses a model trained with a small amount of labeled data to generate and learn pseudo labels. Besides, a lot of number of papers use a single fixed threshold to create pseudo labels. In this paper, we present a semi-supervised synthetic aperture radar(SAR) image classification method that applies different thresholds for each class instead of all classes sharing a fixed threshold to improve SAR classification performance with a small number of labeled datasets.

• Language Facts and Perspectives
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• v.48
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• pp.195-227
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• 2019

Building upon the state-of-the-art deep learning techniques, the present study classifies the texts written by Korean EFL learners and English native speakers and thereby demonstrates how the two types of texts differ from each other. To this end, the current work makes use of the Yonsei English Learner Corpus (YELC) and Gacheon Learner Corpus (GLC) as the L2 data, and Corpus of Contemporary American English (COCA) as the L1 data. Utilizing the sentence classification methods, the current work implements a system to differentiate the two types of texts, the accuracy of which is about 94%. This indicates that the deep leaning-based system is capable of identifying the well-formedness and felicities of the texts written by Korean EFL learners. Nonetheless, the system-based judgments do not overlap with human judgments largely because the deep learning model exclusively focuses on sequence of words. The present study provides a further analysis to see how the two types of judgments differ with respect to grammatical errors (e.g., word order, voice, etc.) and felicity errors (e.g., semantic prosody, the position of adverbs, etc.).

• International Journal of Computer Science & Network Security
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• v.24 no.10
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• pp.182-190
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• 2024

The use of Short Message Services (SMS) as a mechanism of communication has resulted to loss of sensitive information such as credit card details, medical information and bank account details (user name and password). Several Machine learning-based approaches have been proposed to address this problem, but they are still unable to detect modified SMS spam messages more accurately. Thus, in this research, a stack- ensemble of four machine learning algorithms consisting of Random Forest (RF), Logistic Regression (LR), Multilayer Perceptron (MLP), and Support Vector Machine (SVM), were employed to detect more accurately SMS spams. The simulation was carried out using Python Scikit- learn tools. The performance evaluation of the proposed model was carried out by benchmarking it with an existing model. The evaluation results showed that the proposed model has an increase of 3.03% of accuracy, 8.94% of Recall, 2.17% of F-measure; and a decrease of 4.55% of Precision over the existing model. In conclusion, the ensemble method performed better than any individual algorithms and can be adopted by the Network service providers for better Quality of Service.

• Journal of the Korea Convergence Society
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• v.11 no.8
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• pp.351-360
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• 2020

This study is mixed design to measure the effects of case-based learning and the Cornell notes-taking system in human anatomy course, the basic course in nursing, on self-directed learning ability, critical thinking dispositon, and teamwork of nursing students. Among those who completed anatomy course, 34 underachieving students were targeted and surveyed before and after classes, and interviewed on case-based learning and Cornell notes-taking system. For quantitative analysis, SPSS/WIN 21.0 was used for frequency analysis, paired t-test, and Pearson's correlation coefficients. For qualitative research, content analysis was performed. The results showed significant increases in self-directed learning ability(t=-9.69, p<.001), critical thinking dispositon(t=-7.75, p<.001), and teamwork (t=-12.43, p<.001) in underachieving nursing students. In addition, there was a significant correlation between self-directed learning ability, critical thinking dispositon, and teamwork. In conclusion, case-based and Cornell notes learning methods were effective in helping underachieving students enrolled in human anatomy course. There is a need for continuous research on the use of case-based and Cornell notes in other courses.

• Journal of the Korea Computer Graphics Society
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• v.29 no.5
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• pp.11-20
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• 2023

In this paper, we propose a novel deep learning-based motion reconstruction approach that facilitates the generation of full-body motions, including finger motions, while also enabling the online adjustment of motion generation delays. The proposed method combines the Vive Tracker with a deep learning method to achieve more accurate motion reconstruction while effectively mitigating foot skating issues through the use of an Inverse Kinematics (IK) solver. The proposed method utilizes a trained AutoEncoder to reconstruct character body motions using tracker data in real-time while offering the flexibility to adjust motion generation delays as needed. To generate hand motions suitable for the reconstructed body motion, we employ a Fully Connected Network (FCN). By combining the reconstructed body motion from the AutoEncoder with the hand motions generated by the FCN, we can generate full-body motions of characters that include hand movements. In order to alleviate foot skating issues in motions generated by deep learning-based methods, we use an IK solver. By setting the trackers located near the character's feet as end-effectors for the IK solver, our method precisely controls and corrects the character's foot movements, thereby enhancing the overall accuracy of the generated motions. Through experiments, we validate the accuracy of motion generation in the proposed deep learning-based motion reconstruction scheme, as well as the ability to adjust latency based on user input. Additionally, we assess the correction performance by comparing motions with the IK solver applied to those without it, focusing particularly on how it addresses the foot skating issue in the generated full-body motions.

• Journal of Korea Game Society
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• v.15 no.2
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• pp.53-62
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• 2015

This paper introduces an educational game development method for the purpose of designing game as part of school curriculum activities, base on experimental case of making history learning game for private elementary schools in Korea. Our first approach was to define a game as a educational learning tool like any other media and mediating platform such as smart phones or other applications. We regard user as a player, and students as a end user and decision making-customers. Unlike other game development process we brought service design methode to the development process, making a game platform that is specially designed for teachers' teaching tool, which is easy and effective to use to ther subject of teacher's intention. Based on our research case, we suggest educational games development methodology which can be better suited for games with school curriculum in learning environment.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.131-138
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• 2020

This study was intended to explore the changes in teaching methods when freedom to choose curriculum, instruction, and evaluation methods is allowed and when 'free semesters' are free from entrance examination for high school. For this question, we analyzed free semester education plans of eight sample schools and interviewed 33 teachers and students respectively. The results were as follows. Firstly, all schools planned to use teaching methods for meaningful learning, although they are limited to those exemplified in guidelines for free semester. Secondarily, teaching methods adopted for free semester were characterized as activities enhancing student participation. Thirdly, teaching methods such as career exploration, scientific experimentation, cooperative learning, flipped learning, interdisciplinary learning were used only a couple of times in a semester. Changes in teaching methods were referred to enhance students' interest, confidence, self-regulation, creativity, problem-solving and cooperative learning.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.73-82
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• 2022

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.148-162
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• 2023

Classification systems can significantly assist the medical sector by allowing for the precise and quick diagnosis of diseases. As a result, both doctors and patients will save time. A possible way for identifying risk variables is to use machine learning algorithms. Non-surgical technologies, such as machine learning, are trustworthy and effective in categorizing healthy and heart-disease patients, and they save time and effort. The goal of this study is to create a medical intelligent decision support system based on machine learning for the diagnosis of heart disease. We have used a mixed feature creation (MFC) technique to generate new features from the UCI Cleveland Cardiology dataset. We select the most suitable features by using Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination with Random Forest feature selection (RFE-RF) and the best features of both LASSO RFE-RF (BLR) techniques. Cross-validated and grid-search methods are used to optimize the parameters of the estimator used in applying these algorithms. and classifier performance assessment metrics including classification accuracy, specificity, sensitivity, precision, and F1-Score, of each classification model, along with execution time and RMSE the results are presented independently for comparison. Our proposed work finds the best potential outcome across all available prediction models and improves the system's performance, allowing physicians to diagnose heart patients more accurately.