• Journal of Software Assessment and Valuation
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• v.14 no.2
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• pp.1-9
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• 2018

The proportion of users and companies using open source continues to grow. The size of open source software market is growing rapidly not only in foreign countries but also in Korea. However, compared to the continuous development of open source software, there is little research on open source software subject classification, and the classification system of software is not specified either. At present, the user uses a method of directly inputting or tagging the subject, and there is a misclassification and hassle as a result. Research on open source software classification can also be used as a basis for open source software evaluation, recommendation, and filtering. Therefore, in this study, we propose a method to classify open source software by using machine learning model and propose performance comparison by machine learning model.

• Advanced Industrial SCIence
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• v.3 no.1
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• pp.1-16
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• 2024

The purpose of this study is to develop a machine learning-based AI convergence class model and class design principles that can foster data literacy in high school students, and to develop detailed guidelines accordingly. We developed a machine learning-based teaching model, design principles, and detailed guidelines through research on prior literature, and applied them to 15 students at a specialized high school in Seoul. As a result of the study, students' data literacy improved statistically significantly (p<.001), so we confirmed that the model of this study has a positive effect on improving learners' data literacy, and it is expected that it will lead to related research in the future.

• Journal of the Korea Convergence Society
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• v.13 no.4
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• pp.39-44
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• 2022

Mathematics franchise education companies are developing various online learning systems to provide on-off integrated education to learners. Most online learning systems deliver one-way lecture content to learners and perform quantitative problem-solving learning for learning results. However, each learner has different academic achievement competencies, and it is impossible to determine exactly where the level of understanding fell when solving a math method. and based on this, establish an online learning system to discover the weak points of learners and propose an effective learner management method. Through the developed learning method and system, it is expected to cultivate balanced problem-solving ability for learners and provide differentiated brand image and counseling service to franchise companies.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.369-372
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• 2018

최근 지식경영에 있어 특허를 통한 지식재산권 확보는 기업 운영에 큰 영향을 주는 요소이다. 성공적인 특허 확보를 위해서, 먼저 변화하는 특허 분류 제계를 이해하고, 방대한 특허 정보 데이터를 빠르고 신속하게 특허 분류 체계에 따라 분류화 시킬 필요가 있다. 본 연구에서는 머신 러닝 기술 중에서도 계층적 주의 네트워크를 활용하여 특허 자료의 초록을 학습시켜 분류를 할 수 있는 방법을 제안한다. 그리고 본 연구에서는 제안된 계층적 주의 네트워크의 성능을 검증하기 위해 수정된 입력데이터와 다른 워드 임베딩을 활용하여 진행하였다. 이를 통하여 특허 문서 분류에 활용하려는 계층적 주의 네트워크의 성능과 특허 문서 분류 활용화 방안을 보여주고자 한다. 본 연구의 결과는 많은 기업 지식경영에서 실용적으로 활용할 수 있도록 지식경영 연구자, 기업의 관리자 및 실무자에게 유용한 특허분류기법에 관한 이론적 실무적 활용 방안을 제시한다.

• Information Systems Review
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• v.22 no.4
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• pp.21-39
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• 2020

Recently, deep learning has shown high performance in various applications such as pattern analysis and image classification. Especially known as a difficult task in the field of machine learning research, stock market forecasting is an area where the effectiveness of deep learning techniques is being verified by many researchers. This study proposed a deep learning Convolutional Neural Network (CNN) model to predict the direction of stock prices. We then used the feature selection method to improve the performance of the model. We compared the performance of machine learning classifiers against CNN. The classifiers used in this study are as follows: Logistic Regression, Decision Tree, Neural Network, Support Vector Machine, Adaboost, Bagging, and Random Forest. The results of this study confirmed that the CNN showed higher performancecompared with other classifiers in the case of feature selection. The results show that the CNN model effectively predicted the stock price direction by analyzing the embedded values of the financial data

• Journal of Digital Convergence
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• v.17 no.11
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• pp.233-240
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• 2019

In order to predict and respond to cyber attacks, a number of security companies quickly identify the methods, types and characteristics of attack techniques and are publishing Security Intelligence Reports(SIRs) on them. However, the SIRs distributed by each company are huge and unstructured. In this paper, we propose a framework that uses five analytic techniques to formulate a report and extract key information in order to reduce the time required to extract information on large unstructured SIRs efficiently. Since the SIRs data do not have the correct answer label, we propose four analysis techniques, Keyword Extraction, Topic Modeling, Summarization, and Document Similarity, through Unsupervised Learning. Finally, has built the data to extract threat information from SIRs, analysis applies to the Named Entity Recognition (NER) technology to recognize the words belonging to the IP, Domain/URL, Hash, Malware and determine if the word belongs to which type We propose a framework that applies a total of five analysis techniques, including technology.

• Journal of Korea Game Society
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• v.17 no.3
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• pp.33-44
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• 2017

With the development of the Internet environment, various types of online reviews are being generated and exchanged among consumers to share their opinions. In line with this trend, companies are making efforts to analyze online reviews and use the results in various business activities such as marketing, sales, and product development. However, research on online review in industry related to 'Video Game' which is representative experience goods has not been performed enough. Therefore, this study analyzed STEAM community review data using machine learning techniques. We analyzed the factors affecting the opinion of other users' game review. We also propose managerial implications to incease user loyalty and usability.

• Journal of Korea Game Society
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• v.21 no.6
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• pp.75-86
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• 2021

Brand image refers to how customers, stakeholders and the market see and recognize the brand. A positive brand image leads to continuous purchases, but a negative brand image is directly linked to consumers' buying behavior, such as stopping purchases, so from the corporate perspective, it needs to be quickly and accurately identified. Currently, methods of investigating brand images include surveys and SNS surveys, which have limited number of samples and are time-consuming and costly. Therefore, in this study, we are going to conduct an emotional analysis of text data on social media by utilizing the machine learning based KoBERT model, and then suggest how to use it for game corporate brand image analysis and verify its performance. The result has proved some degree of usability showing the same ranking within five brands when compared with the BRI Korea's brand reputation ranking.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.448-451
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• 2022

주식과 암호화폐 거래는 매매방식에 있어서 유사한 점이 있지만 기업의 사업분야, 자본금, 순이익 등의 경영현황과 미래가치에 영향을 많이 받는 주식과는 다르게 암호화폐는 실물 실체가 없으며 탈중앙화, 전산화된 데이터를 기반으로 하며 심리적인 요소가 크게 작용하여 단기적인 변동이 클 수 있다. 본 연구에서는 이러한 암호화폐 거래의 특성을 활용하여 특정 암호화폐에 관련된 뉴스기사들을 수집하고 그 암호화폐의 가격 변화 데이터와 연관되어 가격예측 딥러닝 모델을 생성하고 해당 암호화폐에 대한 신규 뉴스기사가 발생되었을 때 이를 이용하여 매수, 매도, 관망 등과 같은 매매 정보를 예측 적용할 수 있게 하였다. 첫째, 뉴스 기사에서 언급한 암호화폐를 매수, 매도, 관망 중 어느 편이 좋을 것인지 추천하는 알고리즘을 구현하였고, 둘째, 매수 이후 매매 차익을 위한 매도 시점이나 매도 이후 저가매수에 유리한 시점을 제안하는 알고리즘을 구현하였다. 또한, 실시간 뉴스기사 수집 및 예측한 매매 판단에 따라 매매 자동화 시스템을 구현하여 수익률을 직접 확인함으로써 그 유효성을 검증하였다.

• The Korean Journal of Franchise Management
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• v.5 no.1
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• pp.49-64
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• 2014

The purpose of this work is to analyze successful business strategy of Daekyo Noonnoppi. Daekyo Noonnoppi, a franchise company of educational service, activated education business by establishing new way of providing education opportunity: self-directed learning center. They introduced not only the concept of learning center but also sustainable business strategies, which leads to remarkable success in the education business field. Daekyo Noonnoppi deployed three managerial concepts for study achievement: goal management, study management, and environment management. This Franchise company has three advantages of its success: Goal, Study and environment management: First, the goal management helps students to develop self-directed attitudes by making(appropriate) atmosphere which is able to build study goal and plan. In addition, this company provides information to their students to searches ways of study through the test reflecting their tendency. Furthermore, this company offers a variety of events for motivating study. Second, study management is helpful for students to develop holistic fundamental knowledge through its textbooks of this company and provides solutions and time management for study through 1 on 1 study advice. Third, environment management is used to making atmosphere to develop self-directed learning way for its students and provides spaces for students equipped with multimedia systems and cyber learning infrastructures.