• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.206-211
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• 2021

Internet users are increasingly invited to express their opinions on various subjects in social networks, e-commerce sites, news sites, forums, etc. Much of this information, which describes feelings, becomes the subject of study in several areas of research such as: "Sensing opinions and analyzing feelings". It is the process of identifying the polarity of the feelings held in the opinions found in the interactions of Internet users on the web and classifying them as positive, negative, or neutral. In this article, we suggest the implementation of a sentiment analysis tool that has the role of detecting the polarity of opinions from people about COVID-19 extracted from social media (tweeter) in the Arabic language and to know the impact of the pre-processing phase on the opinions classification. The results show gaps in this area of research, first of all, the lack of resources when collecting data. Second, Arabic language is more complexes in pre-processing step, especially the dialects in the pre-treatment phase. But ultimately the results obtained are promising.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.151-152
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• 2019

In this paper, we propose an improved learning method based on a small data set for animal image classification. First, CNN creates a training model for a small data set and uses the data set to expand the data set of the training set Second, a bottleneck of a small data set is extracted using a pre-trained network for a large data set such as VGG16 and stored in two NumPy files as a new training data set and a test data set, finally, learn the fully connected network as a new data set.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2904-2926
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• 2022

Currently, diabetes is the most common chronic disease in the world, affecting 23.7% of the population in the Kingdom of Saudi Arabia. Diabetes may be the cause of lower-limb amputations, kidney failure and blindness among adults. Therefore, diagnosing the disease in its early stages is essential in order to save human lives. With the revolution in technology, Artificial Intelligence (AI) could play a central role in the early prediction of diabetes by employing Machine Learning (ML) technology. In this paper, we developed a diagnosis system using machine learning models for the detection of type 2 diabetes among adults, through the adoption of two different diabetes datasets: one for training and the other for the testing, to analyze and enhance the prediction accuracy. This work offers an enhanced classification accuracy as a result of employing several pre-processing methods before applying the ML models. According to the obtained results, the implemented Random Forest (RF) classifier offers the best classification accuracy with a classification score of 98.95%.

• Proceedings of the KSLP Conference
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• 2017.04a
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• pp.100-100
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• 2017

• Explosives and Blasting
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• v.9 no.2
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• pp.13-18
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• 1991

It was described Several Tuneling method for applying petrolum oil storage. The most Important factor of Tunneling is not only reinforcing works such as pre-grouting and after grouting but also rock bolting and shotcrete. The efficent works should be done by professional skillman, and also the classification of rock should be decided by professional engineers.

• Journal of Convergence for Information Technology
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• v.12 no.5
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• pp.139-149
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• 2022

The purpose of this study is to develop and analyze the effects of an educational program that can cultivate artificial intelligence(AI) convergence education competency for future education and enhance students' understanding of pre-service teachers. For this end, an AI convergence education program using Machine Learning for Kids and Scratch 3 was developed for 15 weeks under the theme of classifying the state of matter. The developed program were treated by K University pre-service teachers who participated voluntarily. As a result, pre-service teachers were able to metaphorically understand the learning process of students through understanding of machine learning training process. In addition, the pre-post t-test result of AI teaching efficacy showed a statistically significant improvement with t=-7.137 (p<.000). Therefore, it is suggested that the AI convergence education program developed in this study can help to increase the understanding of the pre-service teacher's students in an indirect way other than practice teaching, and can contribute to foster AI education competency.

• Journal of The Korea Institute of Healthcare Architecture
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• v.6 no.10
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• pp.77-94
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• 2000

The purpose of this thesis is to lay groundwork for the development of emergency care system in metropolitan area. It compares the performance and outcome of the current system with foreign counterparts and investigates the changing aspects of future medical environment. Emergency medical system can be divided into two parts of both pre-hospital care, which refers to the emergency measures taken before arriving at a hospital, and hospital care that is given within a hospital. Pre-hospital care includes on-the-spot expedients, information system and delivery system, whereas hospital care is related to the classification and specialization of medical care facilities. This research focuses on the evaluation of the performance of a rescue party, which is part of pre-historical care system. As a result, it provides valuable material for the development of the emergency medical system in Seoul.

• Journal of Sensor Science and Technology
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• v.30 no.5
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• pp.290-294
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• 2021

Experts have designed popular and successful model architectures, which, however, were not the optimal option for different scenarios. Despite the remarkable performances achieved by deep neural networks, manually designed networks for classification tasks are the backbone of object detection. One major challenge is the ImageNet pre-training of the search space representation; moreover, the searched network incurs huge computational cost. Therefore, to overcome the obstacle of the pre-training process, we introduce a network adaptation technique using a pre-trained backbone model tested on ImageNet. The adaptation method can efficiently adapt the manually designed network on ImageNet to the new object-detection task. Neural architecture search (NAS) is adopted to adapt the architecture of the network. The adaptation is conducted on the MobileNetV2 network. The proposed NAS is tested using SSDLite detector. The results demonstrate increased performance compared to existing network architecture in terms of search cost, total number of adder arithmetics (Madds), and mean Average Precision(mAP). The total computational cost of the proposed NAS is much less than that of the State Of The Art (SOTA) NAS method.

• Journal of Sasang Constitutional Medicine
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• v.34 no.1
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• pp.37-45
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• 2022

Objectives The purpose of this study was to reveal that Sasang constitution(SC) was associated with hypertension and pre-hypertension and could be a risk factor. Methods We introduced this study to educational personnel in D university in Daejeon, and 275 subjects joined this study. The SC classification was conducted with KS 15 questionnaire. The subjected measured the blood pressure with Jawon medical device automatically after 10 minute rest. The hypertension and pre-hypertension was classified by the guide of the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. The frequency analysis and T-test was used in general characteristics, and chi-square test was also used between SC and pre-hypertension and hypertension. Logistic regression was used to calculate the odds ratios (ORs) and 95% confidence interval (95% CI) for pre-hypertension and hypertension. Results The number of Taeeumin(TE), Soeumin(SE), and Soyangin(SY) was 142, 71, and 61 respectively. There was significantly different in systolic and diastolic blood pressure among SC types(p<.001). The distribution of the normal group, pre-hypertension and hypertension group by SC types was significantly different (p<.001). The ORs of TE was significantly increased (ORs 4.039, 95% CI=2.019-8.082 in pre-hypertension and ORs 4.235, 95% CI=1.581-11.348 in hypertension) compared with SE(p<.001), and after adjusting gender and smoking habit, it was still significantly different(p<.001). Conclusions It is possible that SC, especially TE could be a risk factor both pre-hypertension and hypertension.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.209-216
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• 2019

Over the past decade, researchers were able to solve complex medical problems as well as acquire deeper understanding of entire issue due to the availability of machine learning techniques, particularly predictive algorithms and automatic recognition of patterns in medical imaging. In this study, a technique called transfer learning has been utilized to classify Magnetic Resonance (MR) images by a pre-trained Convolutional Neural Network (CNN). Rather than training an entire model from scratch, transfer learning approach uses the CNN model by fine-tuning them, to classify MR images into Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal control (NC). The performance of this method has been evaluated over Alzheimer's Disease Neuroimaging (ADNI) dataset by changing the learning rate of the model. Moreover, in this study, in order to demonstrate the transfer learning approach we utilize different pre-trained deep learning models such as GoogLeNet, VGG-16, AlexNet and ResNet-18, and compare their efficiency to classify AD. The overall classification accuracy resulted by GoogLeNet for training and testing was 99.84% and 98.25% respectively, which was exceptionally more than other models training and testing accuracies.