• Journal of the Korea Society of Computer and Information
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• v.27 no.4
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• pp.193-203
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• 2022

The purpose of this study was to analyze the effect of home training with app on metabolic syndrome risk factors and atherogenic index in on obese middle aged women. It was carried out to present as an intervention method for improving obesity in the pandemic era of COVID-19. The subjects of this study were 33 obese middle aged women, AHTG(n=15) and CG(n=18). Home training using the app for 8 weeks was conducted 3 times a week. The results of this study as follow, metabolic syndrome risk factors was WC(p<.001) significantly decreased, HDL-C(p<.05) significantly increased and atherogenic index was LDL-C/HDL-C(p<.01) significantly increased in the AHTG. In the era of the COVID-19 pandemic, PA plays an important role in alleviating the severe COVID-19 pandemic, in addition to its ameliorating effects on several chronic diseases. The possibility of home training using an app is an effective intervention method for preventing obesity and metabolic syndrome.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.91-98
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• 2022

The purpose of this study is to investigate the effect of online basic clinical training on critical thinking disposition, self determination motivation and learning satisfaction for nursing student in the COVID-19 situation. This study is one group pre-test and post-test design. The subjects of this study were collected using an online questionnaire for third-year nursing students located in G city, and 41 students participated in the final. The results of this study showed that critical thinking disposition(p=.013) and self determination motivation(p=.007) increased statistically significantly after the online basic clinical training. This findings indicate that online basic clinical training is effective in improving critical thinking disposition, self determination motivation. As it is difficult to perform direct nursing in the COVID-19 medical field recently, it is expected that the limitations of online clinical practice can be overcome by using various online contents.

• Journal of The Korean Association of Information Education
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• v.25 no.6
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• pp.1015-1024
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• 2021

There are various educational programming environments in which students can train artificial intelligence (AI) using block-based programming languages, such as Entry, Machine Learning for Kids, and Teachable Machine. However, these programming environments are designed so that students can train AI through a separate menu, and then use the trained model in the code editor. These approaches have the advantage that students can check the training process more intuitively, but there is also the disadvantage that both the training menu and the code editor must be used. In this paper, we present a novel artificial intelligence block that can perform both AI training and programming in the code editor. While this AI block is presented as a Scratch block, the training process is performed through a Python server. We describe the blocks in detail through the process of training a model to classify a blue pen and a red pen, and a model to classify a dental mask and a KF94 mask. Also, we experimentally show that our approach is not significantly different from Teachable Machine in terms of performance.

• Journal of the Korea Society of Computer and Information
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• v.14 no.3
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• pp.139-147
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• 2009

The memory based reasoning just stores in the memory in the form of the training pattern of the representative pattern. And it classifies through the distance calculation with the test pattern. Because it uses the techniques which stores the training pattern whole in the memory or in which it replaces training patterns with the representative pattern. Due to this, the memory in which it is a lot for the other machine learning techniques is required. And as the moreover stored training pattern increases, the time required for a classification is very much required. In this paper, We propose the EAS(Evaluation And Selection) algorithm in order to minimize memory usage and to improve classification performance. After partitioning the training space, this evaluates each partitioned space as MDL and PM method. The partitioned space in which the evaluation result is most excellent makes into the representative pattern. Remainder partitioned spaces again partitions and repeat the evaluation. We verify the performance of Proposed algorithm using benchmark data sets from UCI Machine Learning Repository.

• Journal of the Korea Society of Computer and Information
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• v.28 no.7
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• pp.95-102
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• 2023

The purpose of this study was to investigate the effect of 8 weeks of core training on dynamic posture control, lower extremity injury and proprioceptive joint position sensory in ski athletes. Twenty subjects participated in this study and were randomly divided into two groups : exercise group (Ex=10) and control group (Con=10). The core training program consisted of a bench, a sideways bench, a plank, a side bridge, and a supine bridge, and was conducted three times a week for 8 week. The dynamic posture control had a significant effect on the left and right postero-medial reach, and the lower extremity criterion test had a significant effect on the left and right composite scores. In addition, there was a significant decrease in the proprioceptive joint position sense at 15°of the left leg and 45°. In conclusion, 8 weeks a core training have been shown to improve skiers' dynamic posture control, lower extremity injury and proprioceptive joint position sensory.

• Journal of Practical Engineering Education
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• v.5 no.2
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• pp.169-176
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• 2013

Recently the construction of simulation environment is an important issue in all fields. In case of the training for operating machines such as airplanes or spaceships which cause a huge cost, simulators could be helpful to reduce the costs and training efforts by simulating real situations. When people get a driver's license, too many trainees have to wait for their turns because of the limited number of cars and the small space of training sites. To solve this problem, we have designed and developed the basic design for the simulators. We suggest the Computer 3D Simulation Model for a driver's practice. The concept of this simulator is from a 3D Racing-game which suits for a driving exercise. We provide users with handle-controlled simulation settings to let users feel reality as if they drive in real through this simulator. We also use a 'force-feedback' system which gives handle vibration when users collide against obstacles or exceed lanes. Users can be absorbed in the simulation program and feel the sense of the real. This paper is the study about modeling the driving exercise model of 'computer 3D simulation', and producing and utilizing the simulator through this modeling.

• Journal of the Korean Society of Physical Medicine
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• v.7 no.1
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• pp.87-94
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• 2012

Purpose : The purpose of this study was to examine the feasibility and efficiency of balance training program through an interactive video game regimen in people with chronic stroke. Methods : Thirty patients with chronic hemiparetic stroke were recruited. Participants were randomly assigned to either a control group (n=15) or an experimental group (n=15). The control group received the general physical therapy including of strengthening and balance exercise five times a week whereas the experimental group received a program of balance exercise with video game play based on virtual reality as well as the same typical physical therapy. The experimental group received 6 sessions for four weeks. Each session was given 5 minutes. An interactive computer game exercise regimen lasted 30 minutes without rest periods. Outcome measures for weight transfer to paretic side, non-paretic side and sit-squat-speed, sit-squat-length, sit-to-standspeed and sit-to-stand-area for the control group (n=15) and experimental group (n=15) before and after treatment were obtained by using the biorescure. Results: Outcomes demonstrated significant improvement in the experimental group compared with the control group in weight transfer to paretic side, non-paretic side and sit-squat-speed, sit-squat-length, sit-to-stand-speed. No significant training effect was showed in sit-to-stand-area between pretraining and post-training. Conclusion : An interactive computer game exercise based on task oriented approach for balance in chronic stroke were feasible. In other words, This regimen resulted in a greater improvement in dynamic balance for people with chronic stroke.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.59-66
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• 2023

Text mining (TM) is most widely used to find patterns from various text documents. Cyber-bullying is the term that is used to abuse a person online or offline platform. Nowadays cyber-bullying becomes more dangerous to people who are using social networking sites (SNS). Cyber-bullying is of many types such as text messaging, morphed images, morphed videos, etc. It is a very difficult task to prevent this type of abuse of the person in online SNS. Finding accurate text mining patterns gives better results in detecting cyber-bullying on any platform. Cyber-bullying is developed with the online SNS to send defamatory statements or orally bully other persons or by using the online platform to abuse in front of SNS users. Deep Learning (DL) is one of the significant domains which are used to extract and learn the quality features dynamically from the low-level text inclusions. In this scenario, Convolutional neural networks (CNN) are used for training the text data, images, and videos. CNN is a very powerful approach to training on these types of data and achieved better text classification. In this paper, an Ensemble model is introduced with the integration of Term Frequency (TF)-Inverse document frequency (IDF) and Deep Neural Network (DNN) with advanced feature-extracting techniques to classify the bullying text, images, and videos. The proposed approach also focused on reducing the training time and memory usage which helps the classification improvement.

• Journal of Information Processing Systems
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• v.2 no.1
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• pp.23-27
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• 2006

This paper presents a feature selection technique based on distributional differences for efficient machine learning. Initial training data consists of data including many features and a target value. We classified them into positive and negative data based on the target value. We then divided the range of the feature values into 10 intervals and calculated the distribution of the intervals in each positive and negative data. Then, we selected the features and the intervals of the features for which the distributional differences are over a certain threshold. Using the selected intervals and features, we could obtain the reduced training data. In the experiments, we will show that the reduced training data can reduce the training time of the neural network by about 40%, and we can obtain more profit on simulated stock trading using the trained functions as well.

• ETRI Journal
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• v.32 no.5
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• pp.810-818
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• 2010

This paper presents a new framework for integrating untranscribed spoken content into the acoustic training of an automatic speech recognition system. Untranscribed spoken content plays a very important role for under-resourced languages because the production of manually transcribed speech databases still represents a very expensive and time-consuming task. We proposed two new methods as part of the training framework. The first method focuses on combining initial acoustic models using a data-driven metric. The second method proposes an improved acoustic training procedure based on unsupervised transcriptions, in which word endings were modified by broad phonetic classes. The training framework was applied to baseline acoustic models using untranscribed spoken content from parliamentary debates. We include three types of acoustic models in the evaluation: baseline, reference content, and framework content models. The best overall result of 18.02% word error rate was achieved with the third type. This result demonstrates statistically significant improvement over the baseline and reference acoustic models.