• KIPS Transactions on Software and Data Engineering
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• v.5 no.9
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• pp.411-418
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• 2016

Learning analytic has been utilized to helps us to successfully complete the course by using the interaction of the teacher and the learner data generated from the teaching and learning support system. In other words, Learning analytic is a method in order to understand the activities of learners. In the learning analytic, the data model is needed in order to utilize the more useful for teaching and learning activities data. Therefore, in this study, we propose a user centric data model of learning styles and learning objects. This model is expressed by aggregating of user learning style, learning objects, and learning activities. The proposed model is significant that laid the foundation for analyzing the activities of the learners in course units.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.41-63
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• 2023

As the online e-commerce market growing, the need for a recommender system that can provide suitable products or services to customer is emerging. Recently, many studies using the sentiment score of online review have been proposed to improve the limitations of study on recommender systems that utilize only quantitative information. However, this methodology has limitation in extracting specific preference information related to customer within online reviews, making it difficult to improve recommendation performance. To address the limitation of previous studies, this study proposes a novel recommendation methodology that applies deep learning technique and uses various linguistic factors within online reviews to elaborately learn customer preferences. First, the interaction was learned nonlinearly using deep learning technique for the purpose to extract complex interactions between customer and product. And to effectively utilize online review, cognitive contents, affective contents, and linguistic style matching that have an important influence on customer's purchasing decisions among linguistic factors were used. To verify the proposed methodology, an experiment was conducted using online review data in Amazon.com, and the experimental results confirmed the superiority of the proposed model. This study contributed to the theoretical and methodological aspects of recommender system study by proposing a methodology that effectively utilizes characteristics of customer's preferences in online reviews.

• Industrial Engineering and Management Systems
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• v.13 no.4
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• pp.421-431
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• 2014

The market basket data in the form of a binary user-item matrix or a binary item-user matrix can be modelled as a binary classification problem. The binary logistic regression approach tackles the binary classification problem, where principal components are predictor variables. If users or items are sparse in the training data, the binary classification problem can be considered as a cold-start problem. The binary logistic regression approach may not function appropriately if the principal components are inefficient for the cold-start problem. Assuming that the market basket data can also be considered as a special regression problem whose response is either 0 or 1, we propose three supervised learning approaches: random forest regression, random forest classification, and elastic net to tackle the cold-start problem, comparing the performance in a variety of experimental settings. The experimental results show that the proposed supervised learning approaches outperform the conventional approaches.

• The Journal of Information Systems
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• v.28 no.4
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• pp.271-291
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• 2019

Purpose The purpose of this study is to propose a methodology to derive positive keywords and negative keywords through deep learning to classify reviews into positive reviews and negative ones, and then refine the results of topic modeling using these keywords. Design/methodology/approach In this study, we extracted topic keywords by performing LDA-based topic modeling. At the same time, we performed attention-based deep learning to identify positive and negative keywords. Finally, we refined the topic keywords using these keywords as filters. Findings We collected and analyzed about 6,000 English reviews of Gyeongbokgung, a representative tourist attraction in Korea, from Tripadvisor, a representative travel site. Experimental results show that the proposed methodology properly identifies positive and negative keywords describing major topics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3169-3181
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• 2015

Nowadays, online word-of-mouth has become a powerful influencer to marketing and sales in business. Opinion mining and sentiment analysis is frequently adopted at market research and business analytics field for analyzing word-of-mouth content. However, there still remain several challengeable areas for 1) sentiment analysis aiming for Korean word-of-mouth content in film market, 2) availability of machine learning models only using linguistic features, 3) effect of the size of the feature set. This study took a sample of 10,000 movie reviews which had posted extremely negative/positive rating in a movie portal site, and conducted sentiment analysis with four machine learning algorithms: naïve Bayesian, decision tree, neural network, and support vector machines. We found neural network and support vector machine produced better accuracy than naïve Bayesian and decision tree on every size of the feature set. Besides, the performance of them was boosting with increasing of the feature set size.

• Electronics and Telecommunications Trends
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• v.34 no.6
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• pp.71-86
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• 2019

The unprecedented demands of mobile communication networks by the rapid rising popularity of mobile applications and services require future networks to support the exploding mobile traffic volumes, the real time extraction of fine-rained analytics, and the agile management of network resources, so as to maximize user experience. To fulfill these needs, research on the use of emerging deep learning techniques in future mobile systems has recently emerged; as such, this study deals with deep learning based mobile communication research activities. A thorough survey of the literature, conference, and workshops on deep learning for mobile communication networks is conducted. Finally, concluding remarks describe the major future research directions in this field.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.4_2
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• pp.645-653
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• 2022

Recently, deep reinforcement learning has been applied to a variety of industries, such as games, robotics, autonomous vehicles, and data cooling systems. An algorithm called reinforcement learning allows for automated asset allocation without the requirement for ongoing monitoring. It is free to choose its own policies. The purpose of this paper is to carry out an empirical analysis of the performance of asset allocation strategies. Among the strategies considered were the conventional Mean- Variance Optimization (MVO) and the Proximal Policy Optimization (PPO). According to the findings, the PPO outperformed both its benchmark index and the MVO. This paper demonstrates how dynamic asset allocation can benefit from the development of a reinforcement learning algorithm.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.245-247
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• 2021

최근 코로나 19로 인하여 온라인 강의가 증가하고 있으며 이를 활용한 학습 분석에 대한 연구가 활발히 진행되고 있다. 본 논문에서는 학습 분석 중 학습 결과에 영향을 미칠 수 있는 학습 활동 데이터를 수집하여 학습 결과를 예측하는 모델을 설계하고자 한다. 예측 모델은 기계학습을 이용하며 이전 학기의 학습 결과 데이터를 학습시켜 학습 결과에 영향을 미치는 학습 활동 데이터를 도출한다. 도출된 데이터를 이용하여 차후 학습자의 학습 결과를 예측한다. 학습 결과를 예측하기 위한 모델로 딥러닝의 DNN을 활용한다. 향후 연구로는 예측한 결과를 바탕으로 학습자의 학습 동기 부여와 학습 지도 방향을 정하는 것이다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.337-339
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• 2021

본 연구는 최근 펜데믹 위기에서 교육의 변화하는 모습을 점검하고 미래의 학습에 대한 모습들을 예측하기 위해 이러닝과 학습분석에 대한 신흥기술의 동향을 살펴보고자 한다. 연구방법으로 신흥기술의 '하이프 사이클'과 '이러닝 예측 하이프 커버'를 기반으로 하여 각 단계별 기술들을 점검하고 펜데믹 위기에서 더 공고히 된 이러닝과 학습 관련 기술들이 무엇인지 살펴본다. 또한 하이프 사이클의 5단계인 기술촉발 단계, 부풀려진 기대의 정점 단계, 환멸 단계, 계몽 단계, 생산성 안정 단계인 각 단계별 학습과 관련된 기술들은 어떤 것이 있으며, 그 기술들이 이러닝과 학습분석에 어떠한 영향을 미칠 것인지 예측해 본다. 향후 연구로는 본 연구를 기반으로 인공지능이 이러닝과 학습분석에서의 역할을 알아보고자 한다.

• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.65-77
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• 2021

COVID-19 has recently impacted the world with the large numbers of infected and deaths. The development of effective COVID-19 vaccine has not been successful. Hence, people have a high concern on the infection of this disease. The infection information from the governmantal public organizations are mainly based on simple summary statistics. Consequently, it is hard to assess the infection risks of individual person and the current location of the person. In this paper, we present a machine learning-based software system that analyzes COVID-19 infection risks and guidelines for safe activities.This paper proposes a suite of risk factors regarding COVID-19 infection and deaths and methods to quantitatively measure the individual and group risks using the proposed metrics. The proposed system utilizes a clustering algorithms and various software approaches that reflect the information and features of inviduals and their geograpical locations.