• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.59-66
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• 2022

This research conducted a study classifying gender of speakers by analyzing feature vectors extracted from the voice data. The study provides convenience in automatically recognizing gender of customers without manual classification process when they request any service via voice such as phone call. Furthermore, it is significant that this study can analyze frequently requested services for each gender after gender classification using a learning model and offer customized recommendation services according to the analysis. Based on the voice data of males and females excluding blank spaces, the study extracts feature vectors from each data using MFCC(Mel Frequency Cepstral Coefficient) and utilizes SVM(Support Vector Machine) models to conduct machine learning. As a result of gender classification of voice data using a learning model, the gender recognition rate was 94%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.653-654
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• 2022

Recently, technology development using artificial intelligence (AI) is being conducted in various fields. It is being used in many areas, from a personalized recommendation system for general personal taste to the development of application technology that meets a specific purpose. In this study, for adult detection, we propose a method for detecting adults in elementary schools where many elementary school students live. Clothing color, pattern, style, or physical size are used as factors to differentiate between adults and children, and through this, it will be possible to quickly detect adults or unauthorized adults who break into elementary schools and use them in the pre-recognition system.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.353-354
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• 2021

본 연구에서는 빠르게 발전하는 남성 화장품 시장 트렌드에 맞춰서 남성 고객과 남성 화장품을 타겟으로 하는 기업에 유용한 정보를 제공할 수 있는 데이터베이스 시스템을 구축하고자 한다. 아직까지는 여성에 비해 남성 고객과 남성 화장품에 대한 데이터 분석 및 연구가 현저히 적은 편이다. 본 연구는 남성 고객의 데이터와 빅데이터 자료를 바탕으로 구매율이 높은 상위 10개 제품명과 브랜드명, 소비자가 원하는 가격대의 유명하고 인기있는 제품, 특정 피부고민을 가진 고객이 구매한 제품 중 알레르기 유발 물질이 포함된 제품의 정보와 같은 유용한 정보들을 데이터베이스 시스템을 활용하여 산출해냈다. 이를 통해, 남성 화장품 시장이 앞으로 나아갈 방향에 대해 파악하고 국내 남성 화장품 시장의 발전에도 이바지할 수 있을 것으로 예측된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.368-369
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• 2023

대화 데이터 기반 광고 추천은 광고 마케팅에서 고객 맞춤형 광고 제공, 마케팅 효과 극대화 등을 위한 중요한 기술로 주목받고 있다. 본 논문에서는 모바일 인스턴스 메신저인 카카오톡 대화창에서 발생한 텍스트 데이터를 기반으로 대화 내용을 분석하여 대화 주제별 적절한 광고 키워드를 제안한다. 이를 위해 주제별 대화 내용을 미용, 식음료, 상거래로 세분하고 KoNLPy 의 Okt 를 이용하여 텍스트 전처리를 수행하고 키워드별로 빈도수를 뽑아 워드 클라우드를 제시한다. 또한, 잠재 디리클레 할당(Latent Dirichlet Allocation, LDA)을 기반으로 대화 주제를 세분화한 뒤 라벨링을 통해 주제별 대화 키워드를 분석한다. 실험 결과, 대화 주제를 온라인 쇼핑, 헤어, 뷰티 관리, 음식으로 나눌 수 있었으며, 토픽별 상위 키워드를 Word2Vec 을 통해 특정 단어와 유사한 키워드를 도출하여 적절한 광고 키워드를 제시할 수 있었다.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.633-640
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• 2024

This study develops AI-based 3D body shape modeling technology that can be utilized in the healthcare sector, proposing a system that enables monitoring of users' body shape changes and health status. Utilizing data from Size Korea, the study developed a model to generate 3D body shape images from 2D images, and compared various models to select the one with the best performance. Ultimately, by proposing a system process through the developed technology, including personalized health management, exercise recommendations, and dietary suggestions, the study aims to contribute to disease prevention and health promotion.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.1250-1253
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• 2023

최근 정보 기술의 급격한 발전으로 스마트폰이 우리의 일상 생활에 점점 더 많이 들어오고 있으며, 사용자들은 많은 서비스들을 누릴 수 있게 되었다. 위치 기반 서비스(LBS)의 경우 스마트폰에 탑재된 위치 확인 기능을 통해 음식점 추천, 길찾기 등 개인형 맞춤 서비스를 제공하며, 사용자는 간단한 동의를 통해 자신의 위치를 LBS 서버에 전송하게 된다. 이는 사용자의 개인정보 침해의 요소가 될 수 있으며, 사용자의 민감한 정보가 공개될 수 있다. 따라서 본 논문에서는 사용자의 경로 데이터의 민감 정점을 보호하고, 통계적 질의를 할 때, 절대적으로 개인정보를 보호할 수 있는 방법을 제시한다.

• The Journal of the Korea Contents Association
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• v.10 no.2
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• pp.111-119
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• 2010

U-Healthcare provides healthcare and medical services, such as prevention, diagnosis, treatment, and follow-up services whenever and wherever it is needed, and its ultimate goal is to improve quality of life. This study defines the figure of U-Healthcare personalized services for providing U-Healthcare personalized services and proposes a healthcare model. A diet prescription system for personalized services can draw customized calories and rates of nutrition factors and represent a personalized diet through analyzing the personal preference in foods. This system changes the personal preference by monitoring the diet selection behavior of users. Also, this system is designed to be interactively operated with some sensors and devices in various environments using Java-based OSGi middleware.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.259-268
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• 2018

The purpose of this study is to propose a training method customized to the level of cognitive abilities to increase users' interest and engagement while using cognitive function training contents. The level of cognitive ability of the users was based on the clustering based on the users' information and Mini-Mental Statue Examination-Korea Child test score using the K-means algorithm applied collaborative filtering. The results were applied to the integrated cognitive function training system, and the contents order and difficulty level of the cognitive function training area were recommended to the user's cognitive ability level. Particularly, the contents difficulty control was designed to give a high immersion feeling by applying the 'flow theory' method that users can repeatedly feel tension and comfort. In conclusion, the user-customized cognitive function training method proposed in this paper can be expected to be more effective and rehabilitative results than existing therapists' subjective setting of contents order and difficulty level.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.167-183
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• 2023

In the recent field of recommendation systems, various studies have been conducted to model sparse data effectively. Among these, GLocal-K(Global and Local Kernels for Recommender Systems) is a research endeavor combining global and local kernels to provide personalized recommendations by considering global data patterns and individual user characteristics. However, due to its utilization of kernel tricks, GLocal-K exhibits diminished performance on highly sparse data and struggles to offer recommendations for new users or items due to the absence of side information. In this paper, to address these limitations of GLocal-K, we propose the GEase-K (Global and EASE kernels for Recommender Systems) model, incorporating the EASE(Embarrassingly Shallow Autoencoders for Sparse Data) model and leveraging side information. Initially, we substitute EASE for the local kernel in GLocal-K to enhance recommendation performance on highly sparse data. EASE, functioning as a simple linear operational structure, is an autoencoder that performs highly on extremely sparse data through regularization and learning item similarity. Additionally, we utilize side information to alleviate the cold-start problem. We enhance the understanding of user-item similarities by employing a conditional autoencoder structure during the training process to incorporate side information. In conclusion, GEase-K demonstrates resilience in highly sparse data and cold-start situations by combining linear and nonlinear structures and utilizing side information. Experimental results show that GEase-K outperforms GLocal-K based on the RMSE and MAE metrics on the highly sparse GoodReads and ModCloth datasets. Furthermore, in cold-start experiments divided into four groups using the GoodReads and ModCloth datasets, GEase-K denotes superior performance compared to GLocal-K.

• The Journal of the Korea Contents Association
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• v.13 no.2
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• pp.341-347
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• 2013

There are insufficient cadaver-used practice programs for paramedic student education. To provide the basic data for the effective cadaver practice program, the study interviewed 255 students in department of EMT, who attended cadaver practicum. The results indicated that the average satisfaction level in education was 4.5 out of 5 and in relation to allotted time was 3.61 out of 5. The average understanding level of was 4.5 out of 5. In conclusion, senior students who have already taken clinical education & clinical procedure are recommended to focus on clinical anatomy practice and lower grade students are recommended to focus on understanding human body structure in cadaver-used practice program.