• Journal of Information Management
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• v.38 no.4
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• pp.88-118
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• 2007

The purpose of this research is to analyze the problems of regional culture and tourism websites and to seek for the ways to improve those websites since the culture and tourism information websites of local government do not provide enough contents and service. This research is based on following 4 indicators, content evaluation, usability evaluation, design evaluation and interactivity evaluation which are used to evaluate tourism homepages of municipality by Korea Tourism Organization. The result of this research came out with the issue that regional cultural tourism websites lack user-oriented contents. Therefore, regional culture and tourism websites need to provide the custom-made information such as setting a free tourist route, complementing reservation system and diversifying the range of search.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.1321-1322
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• 2012

With the advance of Information Technology (IT) and dynamic requirements, diverse application services have been provided for end users. With huge volume of these services and information, users are required to acquire customized services that provide personalized information and decision at particular extent of time. The case is more appealing in healthcare, where patients wish to have access to their medical record where they have control and provided with recommendation on the medical information. PHR (Personal Health Record) is most prevailing initiative that gives secure access on patient record at anytime and anywhere. PHR should also incorporate decision support to help patients in self-management of their diseases. Available PHR system incorporates basic recommendations based on patient routine data. We have proposed decision support service called "Smart CDSS" that provides recommendations on PHR data for diabetic patients. Smart CDSS follows HL7 vMR (Virtual Medical Record) to help in integration with diverse application including PHR. PHR shares patient data with Smart CDSS through standard interfaces that pass through Adaptability Engine (AE). AE transforms the PHR CCR/CCD (Continuity of Care Record/Document) into standard HL7 vMR format. Smart CDSS produces recommendation on PHR datasets based on diabetic knowledge base represented in shareable HL7 Arden Syntax format. The Smart CDSS service is deployed on public cloud over MS Azure environment and PHR is maintaining on private cloud. The system has been evaluated for recommendation for 100 diabetic patients from Saint's Mary Hospital. The recommendations were compared with physicians' guidelines which complement the self-management of the patient.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.53-60
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• 2021

LOD-based recommender systems usually leverage the data available within LOD datasets, such as DBpedia, in order to recommend items(movies, books, music) to the end users. These systems use a semantic similarity algorithm that calculates the degree of matching between pairs of Linked Data resources. In this paper, we proposed a new approach to measuring semantic distance in an LOD-based recommender system by assigning weights converted from user ratings to links in the LOD graph. The semantic distance measurement model proposed in this paper is based on a processing step in which a graph is personalized to a user through weight calculation and a method of applying these weights to LDSD. The Experimental results showed that the proposed method showed higher accuracy compared to other similar methods, and it contributed to the improvement of similarity by expanding the range of semantic distance measurement of the recommender system. As future work, we aim to analyze the impact on the model using different methods of LOD-based similarity measurement.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.730-737
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• 2018

With the increasing number of health consumers aiming for a happy quality of life, the O2O medical marketing market is activated by choosing reliable health care facilities and receiving high quality medical services based on the medical information distributed on web's blog. Because unstructured text data used on the Internet, mobile, and social networks directly or indirectly reflects authors' interests, preferences, and expectations in addition to their expertise, it is difficult to guarantee credibility of medical information. In this study, we propose a blog reading system that provides users with a higher quality medical information service by classifying medical information blogs (medical blog, ad blog) using bigdata and MLP processing. We collect and analyze many domestic medical information blogs on the Internet based on the proposed big data and machine learning technology, and develop a personalized health information recommendation system for each disease. It is expected that the user will be able to maintain his / her health condition by continuously checking his / her health problems and taking the most appropriate measures.

• The Journal of Korean Association of Computer Education
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• v.22 no.3
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• pp.123-133
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• 2019

The purpose of this study is to establish Smart Education Environment for children's personalized learning based on the data are accumulated by Smart Device which is one of element on Smart Education. In this study, we propose the operational improvement plan for the free choice activity in the 5-year-old kindergarten and also implement the Free Choice Activity(FCA) management system for children to select and to evaluate the play plans for themselves. Children participating in this study have fun the whole time for the process of self-planning, the playing activities and the self-assessment of playing. As a result, it is confirmed that children participate actively in decision-making of interesting areas through the smart device than the traditional education environment before. A single teacher using FCA management system with smart device in this study can get useful information without difficulty of individual child's interests, learning and the statistics of children in the classroom.

• Fashion & Textile Research Journal
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• v.24 no.4
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• pp.460-470
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• 2022

Customized cosmetics are emerging as a consumer product that companies should pay attention to in the beauty industry due to the combination of market trends and institutional introduction of customized cosmetics. In this study, six offline service brands and online service brands currently in Korea were selected to understand the current status of domestic customized cosmetics online and offline services and to derive detailed characteristics, and the cases of each brand were analyzed. The results are as follows. First, customized cosmetics services could be classified online and offline. Second, customized cosmetics brands could be divided into general brand types and brand extension types. Third, skin data measurements could be classified into genetic analysis, big data-based surveys, and device measurements. Fourth, customized cosmetics manufacturing could be classified into a device manufacturing system, a consultant manufacturing system, and an individual production process system. Fifth, customized cosmetics distribution and delivery could be classified into same-day sales, general delivery, and regular delivery. The results of this study are meaningful in that they have identified and analyzed the current status of personalized cosmetics on-line and offline systems in recent trends, and it was confirmed that creative attempts in the domestic customized cosmetics market continue to change. It is hoped that this study will provide information and ideas to the beauty industry and related experts in the future and be used as basic data for customized cosmetics marketing

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.4
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• pp.11-19
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• 2023

The paper proposes a smart mirror system that recommends fragrances based on user emotion analysis. This paper combines natural language processing techniques such as embedding techniques (CounterVectorizer and TF-IDF) and machine learning classification models (DecisionTree, SVM, RandomForest, SGD Classifier) to build a model and compares the results. After the comparison, the paper constructs a personal emotion-based fragrance recommendation mirror model based on the SVM and word embedding pipeline-based emotion classifier model with the highest performance. The proposed system implements a personalized fragrance recommendation mirror based on emotion analysis, providing web services using the Flask web framework. This paper uses the Google Speech Cloud API to recognize users' voices and use speech-to-text (STT) to convert voice-transcribed text data. The proposed system provides users with information about weather, humidity, location, quotes, time, and schedule management.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.91-96
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• 2022

At the current camera-based technology level, sensor-based basic life pattern recognition technology has to suffer inconvenience to obtain accurate data, and commercial band products are difficult to collect accurate data, and cannot take into account the motive, cause, and psychological effect of behavior. the current situation. In this paper, radar technology for life pattern recognition is a technology that measures the distance, speed, and angle with an object by transmitting a waveform designed to detect nearby people or objects in daily life and processing the reflected received signal. It was designed to supplement issues such as privacy protection in the existing image-based service by applying it. For the implementation of the proposed system, based on TI IWR1642 chip, RF chipset control for 60GHz band millimeter wave FMCW transmission/reception, module development for distance/speed/angle detection, and technology including signal processing software were implemented. It is expected that analysis of individual life patterns will be possible by calculating self-management and behavior sequences by extracting personalized life patterns through quantitative analysis of life patterns as meta-analysis of living information in security and safe guards application.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.91-98
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• 2023

This study is an exploratory research on smart healthcare services, specifically focusing on a multi-channel CCR based healthcare service system. The study examines the overview and operational principles of the service system, highlighting the significance of user communication and its role in the system's functionality. Furthermore, the study investigates the impact of service quality on user satisfaction and intention to continue using the service. To achieve this, a structural equation modeling(SEM) analysis was conducted with a sample of 188 users who owned and utilized healthcare devices and apps. The results indicate that service quality dimensions (reliability, responsiveness, empathy, assurance, and tangibility) all had a positive influence on user satisfaction. Additionally, user satisfaction was found to have a significant impact on intention to continue using the service. The findings of this study contribute to exploring the effectiveness and potential value of CCR-based smart healthcare services. It was also provided insights for the future development of smart healthcare systems that offer accurate health information and personalized services for individual health management and prevention.

• Information Systems Review
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• v.23 no.4
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• pp.69-86
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• 2021

As artificial intelligence technology advances, personalized recommendation systems using big data have attracted huge attention. In the case of beauty products, product preferences are clearly divided depending on customers' skin types and sensitivity along with individual tastes, so it is necessary to provide customized recommendation services based on accumulated customer data. Therefore, by employing deep learning methods, this study proposes a neural network-based recommendation model utilizing both product search history and context information such as gender, skin types and skin worries of customers. The results show that our model with context information outperforms collaborative filtering-based recommender system models using customer search history.