• International Journal of Heart Failure
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• v.6 no.1
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• pp.11-19
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• 2024

The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.39-57
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• 2012

Recently, due to the introduction of high-tech equipment in interactive exhibits, many people's attention has been concentrated on Interactive exhibits that can double the exhibition effect through the interaction with the audience. In addition, it is also possible to measure a variety of audience reaction in the interactive exhibition. Among various audience reactions, this research uses the change of the facial features that can be collected in an interactive exhibition space. This research develops an artificial neural network-based prediction model to predict the response of the audience by measuring the change of the facial features when the audience is given stimulation from the non-excited state. To present the emotion state of the audience, this research uses a Valence-Arousal model. So, this research suggests an overall framework composed of the following six steps. The first step is a step of collecting data for modeling. The data was collected from people participated in the 2012 Seoul DMC Culture Open, and the collected data was used for the experiments. The second step extracts 64 facial features from the collected data and compensates the facial feature values. The third step generates independent and dependent variables of an artificial neural network model. The fourth step extracts the independent variable that affects the dependent variable using the statistical technique. The fifth step builds an artificial neural network model and performs a learning process using train set and test set. Finally the last sixth step is to validate the prediction performance of artificial neural network model using the validation data set. The proposed model is compared with statistical predictive model to see whether it had better performance or not. As a result, although the data set in this experiment had much noise, the proposed model showed better results when the model was compared with multiple regression analysis model. If the prediction model of audience reaction was used in the real exhibition, it will be able to provide countermeasures and services appropriate to the audience's reaction viewing the exhibits. Specifically, if the arousal of audience about Exhibits is low, Action to increase arousal of the audience will be taken. For instance, we recommend the audience another preferred contents or using a light or sound to focus on these exhibits. In other words, when planning future exhibitions, planning the exhibition to satisfy various audience preferences would be possible. And it is expected to foster a personalized environment to concentrate on the exhibits. But, the proposed model in this research still shows the low prediction accuracy. The cause is in some parts as follows : First, the data covers diverse visitors of real exhibitions, so it was difficult to control the optimized experimental environment. So, the collected data has much noise, and it would results a lower accuracy. In further research, the data collection will be conducted in a more optimized experimental environment. The further research to increase the accuracy of the predictions of the model will be conducted. Second, using changes of facial expression only is thought to be not enough to extract audience emotions. If facial expression is combined with other responses, such as the sound, audience behavior, it would result a better result.

• Asia pacific journal of information systems
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• v.23 no.3
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• pp.131-154
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• 2013

New internet technologies have created a revolutionary new platform which allows consumers to make decision about product price and quality quickly and provides information about themselves through the transcript of online reviews. By expressing their feelings toward products or services on virtual opinion platforms, users extend their influence into cyberspace as electronic word-of-mouth (e-WOM). Existing research indicates that an impact of eWOM on the consumer decision process is influential. For both academic researchers and practitioners, investigating this phenomenon of information sharing in online website is essential given the increasing number of consumers using them as sources of purchase decisions. It is worthwhile to examine the extent to which opinion seekers are willing to accept and adopt online reviews and which factors encourage adoption. Discerning the most motivating aspects of information adoption in particular, could help electronic marketers better promote their brand and presence on the internet. The objectives of this study are to investigate how online WOM influences a persons' purchase decision by discovering which factors encourage information adoption. Especially focused on the self-efficacy, this research investigates how self-efficacy affects on information usefulness and adoption of online information. Although people are exposed to same review or comment about product or service, some accept the reviews while others do not. We notice that accepting online reviews mainly depends on the person's preference or personal characteristics. This study empirically examines this issue by using cognitive dissonance theory. Specifically, in the movie industry, we address few questions-is always positive WOM generating positive effect? What if the movie isn't the person's favorite genre? What if the person who is very self-assertive so doesn't take other's opinion easily? In these cases of cognitive dissonance, is always WOM generating same result? While many studies have focused on one direct of WOM which indicates positive (or negative) informative reviews or comments generate positive (or negative) results and more (or less) profits, this study investigates not only directional properties of WOM but also how people change their opinion towards product or service positive to negative, negative to positive through the online WOM. An experiment was conducted quantitatively by using a sample of 168 users who have experience within the online movie review site, 'Naver Movie'. Users were required to complete a survey regarding reviews and comments taken from the real movie page. The data reflected user's perceptions of online WOM information that determined users' adoption level. Analysis results provide empirical support for the proposed theoretical perspective. When user can't agree with the opinion of online WOM information, in other words, when cognitive dissonance between online WOM information and users' preference occurs, perceived self-efficacy significantly decreases customers' perception of usefulness. And this perception of usefulness plays an important role in determining users' intention to adopt online WOM information. Most of researches have been concentrated on characteristics of online WOM itself such as quality or vividness of information, credibility of source and direction of online WOM, etc. for describing effect of online WOM, but our results suggest that users' personal character (e.g., self-efficacy) plays decisive role for acceptance of online WOM information. Higher self-efficacy means lower possibility to accept the information that represents counter opinion because of cognitive dissonance, whereas the people that have lower self-efficacy are willing to accept the online WOM information as true and refer to purchase decision. This study suggests a model for understanding role of direction of online WOM information. Also, our result implicates the importance of online review supervision and personalized information service by confirming switching opinion negative to positive is more difficult than positive to negative through the online WOM information. This implication would help marketers to manage online reviews of their products or services.

• Journal of Intelligence and Information Systems
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• v.14 no.2
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• pp.137-156
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• 2008

The purpose of this study is to implement evaluation on domestic cyber University web site to analyze web usability and web accessibility in order to present how much cyber University provides various personalized services and qualitative contents to users on web site. And also with this result, I hope to contribute in qualitative development of web site and reliability of remote education. For this purpose, I developed checklist suitable to remote University by applying SM-ABCDE assessment method of Moonhyeongnam, the professor in Sookmyung Women's University, and implemented evaluation on web sites of 17 cyber University over the five aspects of attraction, business, contents, design, and engineering. As the result of this study, it was found that Busan Digital University was best in the respect of attraction, both Kyunghee Cyber University and Cyber University of foreign studies were best in the respect of business, Kyunghee University was best in the respect of contents, and Hanyang Cyber University was best in the respect of design. It was also evaluated in the comprehensive view and the order was found that Kyunghee Cyber University, Busan Cyber University, and Hanyang Cyber University. However in the respect of engineering, it came out that all the sites did not observe the regulation on web accessibility. If domestic Cyber Universities are to observe the regulation on web accessibility with the long term view, on the base of this study, web usability would be increasing. Then they can secure various classes of customers as well as ordinary people so that they will be able to take the real place of educational institution orienting the most advanced educational method in the society of knowledge and information in addition to advance in themselves.

• The Journal of Korean Association of Computer Education
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• v.20 no.6
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• pp.1-15
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• 2017

The purpose of this study is to design a LMS(Learning Management System) dashboard for instructor feedback support based on learning analytics and to apply a LMS dashboard incorporating such taxonomy which allows an instructor to give a student personalized feedback according to the class content and a student's traits. In the dashboard design phase, usable instructional data were selected from LMS based on feedback taxonomy in terms of learning analytics. Two validity tests were conducted with 8 instructional technologists over 8 years of experience, and were revised accordingly. The final dashboard screen has three parts: A comprehensive analysis screen to provide appropriate feedback based on instructor feedback taxonomy analysis, a summary screen for learner analysis, and a recommended feedback guide screen. Detailed analysis information are provided through other dashboards that are displayed in eight screens: login analysis, learning information confirmation analysis, teaching materials learning analysis, assignment/tests, and posts analysis. All of these dashboards were represented by analysis information and data based on learner analytics through visualization methods including graphs and tables. The implications of educational utilization of the dashboard for instructor feedback support based on learning analytics and the future researches were suggested based on these results.

• Journal of Broadcast Engineering
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• v.23 no.4
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• pp.536-548
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• 2018

Public data has been transformed from provider-oriented information disclosure to a form of personalized information sharing centered on individual citizens since government 3.0. As a result, the government is implementing policies and projects to maximize the value of public data and increase reuse. This study analyzes the issues related to public data in the news and seeks the status of government agencies and government projects by issue. We conducted semantic analysis on domestic online news and public agency bidding information including public data and conducted the work of linking major key words derived with social and economic values inherent in public data. As a result, major issues related to public data were divided into broader access to public data, growth of new technology, cooperation and conflict among stakeholders, and utilization of the private sector, which were closely related to transparency, efficiency, participation, and innovation mechanisms. Also major agencies of four issues include the Ministry of Strategy and Finance and Seoul, Ministry of Culture, Sports and Tourism and Gyeonggi-do, Ministry of Trade, Industry and Energy and Incheon, and Ministry of Land, Infrastructure and Transport and Gyeongsangbuk-do. Most of the issues are being led by the government.

• Journal of the Korea Convergence Society
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• v.11 no.8
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• pp.23-31
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• 2020

The need for personalized recommendation is growing due to convenient access and various types of items due to the development of information communication and smartphones. Weather and weather conditions have a great influence on the decision-making of users' places and activities. This weather information can increase users' satisfaction with recommendations. In this paper, we propose a collaborative filtering-based place recommendation system using living index by utilizing living index of users' location information on mobile platform to find users with similar propensity and to recommend places by predicting preferences for places. The proposed system consists of a weather module for analyzing and classifying users' weather, a recommendation module using collaborative filtering for place recommendations, and a management module for user preferences and post-management. Experiments have shown that the proposed system is valid in terms of the convergence of collaborative filtering algorithms and living indices and reflecting individual propensity.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2008.10a
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• pp.106-112
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• 2008

Personalization is user oriented dynamic method based on user preferences for easy access to what users want to view or get. It has become more important in mobile domain with rapid growth of wireless Internet and mobile phone market after success of web based market and therefore, it can be applied to service of spatial analysis result. In this paper, spatial analysis using user profile and notification service methods are proposed as one of personalized spatial data service methods for mobile users. A service system for spatial analysis with user profile is designed to prove possibility of spatial analysis based on user preferences and notification service is also designedto show generated output can be sent to user's mobile devices efficiently to make users informed of preferred information. Prototype system is implemented and it is applied to real estate data that has many selectable conditions by users. Information service based on user preferences can be applied to spatial data by using proposed system and it is efficient when cache module is used to shorten response time. Various user models for application domains and performance evaluation methods need to be developed in the future.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.1
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• pp.47-62
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• 2012

To propagate clinical disease management service, there should be built a ecosystem where service developers, service providers, device suppliers closely cooperate for u-Health platform. However, most u-Health platform is difficult to build an effective ecosystem due to the lack of secure and effective PHR(Personal Health Record) management, the lack of personalized and intelligent service, difficulties of N-screen service. To solve these problems we suggest the CDMP(Chronic Disease Management Platform) architecture. The CDMP is a software platform that provides the core functions to develop the chronic disease management services and performs a hub function for the link and integration rbetween various services and systems. CDMP is SOA based platform that enables a provision of reusability, expansibility and it provides open API where everybody can share information, contents and services easily. CDMP supports the multi platform system foN-screen service and the self management functions via SNS. In this paper, we design and implement the CDMP including PHR service based on hybrid data model for privacy preservation. Experiment results prove the effectiveness of hybrid model-based PHR service.

• Journal of Broadcast Engineering
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• v.25 no.5
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• pp.758-769
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• 2020

Hybrid broadcasts utilizing heterogeneous networks can provide not only uniform broadcasting services but also various services using broadcast networks and communication networks. In particular, as content is consumed in various countries and regions, demands for personalized services continue to increase, and research on content insertion technology utilizing heterogeneous networks has been actively conducted. The most important technical challenge when inserting content based on heterogeneous networks is that the start of the inserted content, which replaces the original broadcast content at the time of content insertion, should proceed smoothly, and it must be able to accurately return to the original broadcast content. Currently, UHD broadcasting is converted to digital. However, since there is a system that supports the frame rate used in the analog method, when content insertion occurs in a conventional UHD broadcasting service, there is a problem in decoding the broadcast and inserted content. Since the replacement cost of the broadcasting system is astronomical, this paper proposes a content insertion method using by frame control that can support analog methods without replacing transmission equipment.