• Journal of Digital Convergence
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• v.13 no.9
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• pp.269-276
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• 2015

Recently, with the activation of the industry related to the big data, the global security companies have expanded their scopes from structured to unstructured data for the intelligent security threat monitoring and prevention, and they show the trend to utilize the technique of user's tendency analysis for security prevention. This is because the information scope that can be deducted from the existing structured data(Quantify existing available data) analysis is limited. This study is to utilize the analysis of security tendency(Items classified purpose distinction, positive, negative judgment, key analysis of keyword relevance) applying the machine learning algorithm($Na{\ddot{i}}ve$ Bayes, Decision Tree, K-nearest neighbor, Apriori) in the big data environment. Upon the capability analysis, it was confirmed that the security items and specific indexes for the decision of security tendency could be extracted from structured and unstructured data.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.37-44
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• 2020

AI technology has developed in the form of decision support technology in law, patent, finance and national defense and is applied to disease diagnosis and legal judgment. To search real-time information with Deep Learning, Big data Analysis and Deep Learning Algorithm are required. In this paper, we try to predict the entrance rate to high-ranking universities using a Deep Learning model, RNN(Recurrent Neural Network). First, we analyzed the current status of private academies in administrative districts and the number of students by age in administrative districts, and established a socially accepted hypothesis that students residing in areas with a high educational fever have a high rate of enrollment in high-ranking universities. This is to verify based on the data analyzed using the predicted hypothesis and the government's public data. The predictive model uses data from 2015 to 2017 to learn to predict the top enrollment rate, and the trained model predicts the top enrollment rate in 2018. A prediction experiment was performed using RNN, a Deep Learning model, for the high-ranking enrollment rate in the special education zone. In this paper, we define the correlation between the high-ranking enrollment rate by analyzing the household income and the participation rate of private education about the current status of private institutes in regions with high education fever and the effect on the number of students by age.

• Journal of the Korea Society of Computer and Information
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• v.21 no.4
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• pp.39-46
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• 2016

While the difficulty of the music can be classified by a variety of standard, conventional methods are classified by the subjective judgment based on the experience of many musicians or conductors. Music score is difficult to evaluate as there is no quantitative criterion to determine the degree of difficulty. In this paper, we propose a new classification method for determining the degree of difficulty of the music. In order to determine the degree of difficulty, we convert the score, which is expressed as a traditional music score, into electronic music sheet. Moreover, we calculate information about the elements needed to play sheet music by distance of notes, tempo, and quantifying the ease of interpretation. Calculating a degree of difficulty of the entire music via the numerical data, we suggest the difficulty evaluation of the score, and show the difficulty of music through experiments.

• Journal of muscle and joint health
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• v.28 no.1
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• pp.30-40
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• 2021

Purpose: This study aims to present nurses' legal conflicts and legal basis through the precedent analysis of a crime of professional negligence resulting in death and injury for the past 20 years and provide vital references to cultivate the correct and high-level legal consciousness of nurses. Methods: This study was conducted in five stages of the systematic content analysis method. It amalyses the precedents of a crime of nurses' professional negligence resulting in death and injury from 2000 to 2020. The application system for the provision of the written judgment was used to collect precedents. A total of 67 cases were analyzed in this study, and they were classified according to the type of nursing error, and the contents were systematically analyzed. Results: A total of 52 cases (77.5%) of nursing errors were caused by independent nursing practices. They were classified as 38 cases (A1) in the violation of patient supervision obligations, 12 cases in the violation of progress observation obligations (A2), one case in the violation of medical equipment inspection obligations (A3), and one case in the violation of explanation and verification obligations. Among the non-independent nursing practices (code B), B1 was 10 cases related to administrative acts, one blood transfusion accident (B2), and one anesthesia accident (B3). Conclusion: To prevent nurses from being involved in legal confits, the advocation of systematic training such as nurses' legal obligations and judgment grounds through case-based learning from the recent precedent analysis and promote nurses' legal perspective, and preventive activities are essential.

• Journal of information and communication convergence engineering
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• v.19 no.3
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• pp.142-147
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• 2021

In this paper, we propose an intelligent research fund management system that applies artificial intelligence technology to an integrated research fund management system. By defining research fund management rules as work rules, a detection model learned using deep learning is designed, through which the disapproval status is presented for each research fund usage history. The disapproval detection system of the RCMS implemented in this study predicts whether the newly registered usage details are recognized or disapproved using an artificial intelligence model designed based on the use of an 8.87 million research fund registered in the RCMS. In addition, the item-detail recommendation system described herein presents the usage details according to the usage history item newly registered by the artificial intelligence model through a correlation between the research cost usage details and the item itself. The accuracy of the recommendation was shown to be 97.21%.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.449-450
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• 2022

반려견의 행동인식기술은 다양한 센서들에서 입력되는 반려견의 동작과 관련된 정보를 분석하고 해석하여 반려견이 어떤 행동을 취하고 있는지를 인식하는 기술이다. 음성인식기술은 컴퓨터가 청각 자료를 수집, 분석하여 훈련된 데이터와 비교를 통해 소리를 분류하는 기술이다. 본 논문에서는 딥러닝을 기반으로 행동인식기술과 음성인식기술을 적용하여 반려견의 감정을 판단하는 기법을 제안한다. 이러한 기법은 반려견의 감정을 쉽게 파악하여 반려견 보호자가 반려견의 행동과 감정에 대한 이해를 쉽고 빠르게 할 수 있으므로, 보호자에게 즐거운 반려 생활이 가능하도록 도움을 줄 수 있다.

• Proceedings of The KACE
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• 2017.08a
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• pp.135-137
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• 2017

비디오 강의를 활용한 온라인 학습은 교육 효과를 높이는 무한한 가능성을 가지고 있지만 온라인에서 학습자 주도로 이루어지기 때문에 학습 동기를 높이고 온라인 강의에 집중시킬 수 있는 도구의 개발이 필요하다. 또한 교수자는 온라인 환경 안에서 학습자가 실제로 학습을 수행했는지에 대한 여부를 쉽게 파악할 수 있는 도구의 개발이 필요하다. 본 논문은 학습자와 교수자의 요구를 만족 시킬 수 있는 단어게임 기반의 온라인 비디오 학습 자동 판단 시스템을 개발하였고, 시스템 검증을 위해 343명의 학습자를 대상으로 사용자 만족도 설문조사를 수행하였다. 설문 결과, 83%의 시스템 용이성, 73%의 시스템 만족도로 긍정적인 결과를 보였다.

• Journal of Convergence for Information Technology
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• v.10 no.6
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• pp.35-40
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• 2020

As we enter the society of the future, efforts to increase healthy living are a major area of concern for modern people. In particular, the development of technology for a healthy life that combines ICT technology with a competitive healthcare industry environment is becoming the next growth engine. Therefore, in this paper, artificial intelligence-based data analysis of the examination results was applied in the health examination process. Through this, a research was conducted to build a knowledge base modeling that can improve the reliability of the overall judgment. To this end, an algorithm was designed through deep learning analysis to calculate and verify the test result index. Then, the modeling that provides comprehensive examination information through judgment knowledge was studied. Through the application of the proposed modeling, it is possible to analyze and utilize big data on national health, so it can be expected to reduce medical expenses and increase health.

• Hwankyungkyoyuk
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• v.12 no.1
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• pp.64-75
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• 1999

The goals of moral education according to the 7th educational curriculum are (1) to learn the basic life custom and ethical norms necessary to desirable life, (2) to develop the judgment to solve desirably and practically the ethical matters in daily life, (3) to develop the sound citizenship, national identity and consciousness, and the consciousness of world peace and mankind's mutual prosperity, and (4) to develop the ethical propensity to practice the ideal and principle of life systematically Based on the goals in the above, the following can be established as goals of environmental education possible: (1) to learn judgment to solve practically the environmental problems in the society with their ethical understanding, and (2) to recognize that environmental consciousness is the basic necessity of sound citizenship and national identity and consciousness, and mankind's mutual prosperity, and to have attitudes to practice environmental preservation in daily life. Like these, the intellectual aspect, the affective aspect, and the active aspect can be established in the environmental education in the ethics education keeping their balance. In order to achieve its goals, the contents of ethics subject are organized largely with 4 domains: (1) individual life, (2) home life, life with neighbors, and school life, (3) social life, and (4) national life. Among these, environmental education is mainly included in the domain of social life. These contents concerning environmental education take 22 (32.4%) out of the whole 68 teaching factors which are taught in the ethics subject from the 3rd grade to 10th grade. These 22 environmental teaching factors are mainly related to environmental ethics, environmental preservation and measures, and sound consumption life. Classified according to each goal, the environmental contents in the 7th curriculum for ethics subject put emphasis on environmental value and attitudes, action and participation, and information and knowledge. Therefore, the recommendable teaching and learning method for the environmental education in ethics subject is to motivate students' practice or to make them practice in person. For example, role-play model, value-conflict model, group study model can be applied according to the topics of environmental education.

• Journal of Biomedical Engineering Research
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• v.39 no.5
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• pp.213-219
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• 2018

Stomach cancer is the most diagnosed cancer in Korea. When gastric cancer is detected early, the 5-year survival rate is as high as 90%. Gastroscopy is a very useful method for early diagnosis. But the false negative rate of gastric cancer in the gastroscopy was 4.6~25.8% due to the subjective judgment of the physician. Recently, the image classification performance of the image recognition field has been advanced by the convolutional neural network. Convolutional neural networks perform well when diverse and sufficient amounts of data are supported. However, medical data is not easy to access and it is difficult to gather enough high-quality data that includes expert annotations. So This paper evaluates the efficacy of transfer learning in gastroscopy classification and diagnosis. We obtained 787 endoscopic images of gastric endoscopy at Gil Medical Center, Gachon University. The number of normal images was 200, and the number of abnormal images was 587. The image size was reconstructed and normalized. In the case of the ResNet50 structure, the classification accuracy before and after applying the transfer learning was improved from 0.9 to 0.947, and the AUC was also improved from 0.94 to 0.98. In the case of the InceptionV3 structure, the classification accuracy before and after applying the transfer learning was improved from 0.862 to 0.924, and the AUC was also improved from 0.89 to 0.97. In the case of the VGG16 structure, the classification accuracy before and after applying the transfer learning was improved from 0.87 to 0.938, and the AUC was also improved from 0.89 to 0.98. The difference in the performance of the CNN model before and after transfer learning was statistically significant when confirmed by T-test (p < 0.05). As a result, transfer learning is judged to be an effective method of medical data that is difficult to collect good quality data.