• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.17-21
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• 2023

The primary objective of this research is to utilize machine learning models to analyze factors tailored to each dataset for predicting mental health conditions. The study aims to develop appropriate models based on specific datasets, with the goal of accurately predicting mental health states through the analysis of distinct factors present in each dataset. This approach seeks to design more effective strategies for the prevention and intervention of depression, enhancing the quality of mental health services by providing personalized services tailored to individual circumstances. Overall, the research endeavors to advance the development of personalized mental health prediction models through data-driven factor analysis, contributing to the improvement of mental health services on an individualized basis.

• The Journal of Society for e-Business Studies
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• v.15 no.2
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• pp.1-18
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• 2010

Mobile Communication Companies are trying to increase data services rather than telephone communication services that already became saturated as the competition of mobile communication market gets intensified. However, it is hard and time-consuming for customers to find desired mobile phone wireless services because of the limitation of screen and speed of the mobile phone. Therefore, the market does not grow rapidly as mobile communication companies expected. In our research, we suggest a personalized wireless service recommendation system that considers each individual context by using geographic information and wireless internet usage logs to overcome the mentioned problems. In order to design and implement the system, we conducted clustering analysis on base stations and real service usage logs of each base station, and suggested a personalized recommendation system based on collaborative filtering that uses the clustering results. Moreover, we verified the performances of our system with experiments.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.2
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• pp.206-218
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• 2002

In this paper, we design and implement a system that presents a method for selecting and providing personalized services independently without unifying the existing system platform with shopping malls joined in the hub site. This system provides a mechanism for gathering information left behind by many clients visiting Web sites for analysis of customers property, vector model for selecting personalized services, and mechanism for providing them to customers who visited in a shopping mall joined to the hub site. In a position of shopping mall site, this kind of personalization system can provide target advertisement, point marketing, and point share service etc. without changing existing shopping mall's environment through wrapper web server. Hub site customers can get personalized services from many shopping mall sites with only once registration for the hub site.

• Journal of Digital Contents Society
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• v.11 no.1
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• pp.105-115
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• 2010

Search engines provide web documents that are related to user's query. However, using only the query terms that user provided, it is hard for search engines to know user's exact intention and provide the very matching web documents. To remedy this problem, search systems are needed to exploit personalized search technologies. In this paper, we propose not only a novel personalized query recommendation scheme based on folksonomy but also a new personalized search service architecture which reduces the risk of privacy violation while enabling search service providers to provide other various personalized services such as personalized advertisement.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.576-580
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• 2004

In this paper, we present a design and implementation of a TV-Anytime compliant STB to provide personalized content consumption according to user preferences and various terminal/network conditions. This paper mainly details with a metadata engine which consists of meta data de-multiplexing, metadata decoding, and metadata-based content browsing. For personalized content consumption, the proposed metadata engine provides the following key functionalities: advanced EPG, non-linear segment navigation wirh Tables-of-Content and/or event-based summary, automatic recommendation of user-preferred programs, and etc. The implemented STB employing the metadata engine is successfully tested with a set of service scenarios in an end-to-end broadcasting test-bed.

• Convergence Security Journal
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• v.15 no.5
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• pp.23-28
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• 2015

This study was carried out the exercise monitoring utilizing mobile device which is easily accessible and personalized exercise prescription based on it. For this, a variety of exercise monitoring and status of those were investigated and suggested the potential of personalized exercise prescription. If individual users send their body and vital informations using a mobile device, all informations are collected in u-Fitness center. After then exercise expert provide a customized prescription based on the collected information and feed data into database of u-Fitness center. System of U-Fitness center will provide the best personalized exercise prescription by automatically connecting to the content providers. In the future, a variety of mobile devices and services will work together and it can be evolved as an open platform that can be used for multiple services according to the needs of individual users on a single platform.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.164-175
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• 2022

The OTT (Over The Top) platform promotes itself as a distinctive competitive advantage in that it allows users to stay on the platform longer and visit more often through a Personalized Recommendation Service. In this study, the characteristics of the Personalized Recommendation Service are divided into three categories: recommendation accuracy, recommendation diversity, and recommendation novelty. Then proposed a research model which affects the usefulness of users to recognize recommendation services by each characteristics and leads to satisfaction of expectations. The result of conducting an online survey of 300 people in their 20s and 30s who subscribe Netflix shows that the perceived usefulness increased when the accuracy, variety, and novelty of Netflix's Recommendation Service were high. It was also confirmed that high perceived usefulness leads to satisfaction of expectations before and after Netflix use. The derived research results can confirm the importance of evaluating the personalized recommendation service in terms of user experience and provide implications for ways to improve the quality of recommendation services.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.289-291
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• 2007

The Digital Multimedia Broadcasting(DMB) system is developed to offer high quality audio-visual multimedia contents to the uses by the various portable terminals in the mobile environment. Integrated complex reception platform is required to receive multimedia broadcasting services transmitted from various transmission media. In this paper, we present the design and implementation technic for providing the both of terrestrial and satellite DMB services simultaneously using the same hardware platform. The implemented complex receiving terminal to accommodate these DMB services simultaneously need composed of it RF module. it baseband module, it complex control module and the complex de-multiplexer module. The complex control module is designed using uClinux operating system. The complex de-multiplexer, which perform the functions of the address decoder and each DMB stream de-multiplexer, is implemented. with FPGA device. The implemented platform is tested in a real environment and its performance is satisfied with required performance criteria.

• Journal of Information Technology Services
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• v.16 no.4
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• pp.213-222
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• 2017

The advancement of ICT is changing every field of life. It becomes possible with the penetration of personalized devices, that is, smartphone. The boom of IoT will come when there exist diversified and personalized services. In general, we might admit that it is needed that the more privatized services than the overall serviced. Jeju Island is the only one special self-governing province in Republic of Korea and deserves to be proud of the unique culture from its long historical background. One of the very regional culture which performs by women divers, Haenyeo activity or culture, was registered as Intangible Cultural Heritage. When authors were researched Jeju Haenyeo as a worthy reserving service, we recognized that it has never considered to use any point of ICT/IoT yet. Because IoT holds the high potentiality to create any service scenario between interesting groups. We will design a few services for Haenyeo which covers their job territory or daily life, adopts up-to-date technology or method such as sensored network, smart contract and App/Web. In this paper, we intent to show the simplicity and easiness of the application of IoT not to much inconspicuous target. So, we suggest a specialized IoT service for the reservation and promotion of Haenyeo Culture. This service would be composed of sensors, IoT network and App/Web at home and office. This service can be used among interesting groups : Haenyeo, policy maker, manufacturer, service provider and culture consumer.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.