• Annual Conference of KIPS
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• 2024.10a
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• pp.461-462
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• 2024

In pediatric healthcare, early detection of cardiovascular diseases in newborns is crucial. Analyzing heart sounds using stethoscopes can be subjective and reliant on physician expertise, potentially leading to delayed diagnosis. There is a need for a simple method that can help even inexperienced doctors detect heart abnormalities without an electrocardiogram or ultrasound. Automated heart sound diagnosis systems can aid clinicians by providing accurate and early detection of abnormal heartbeats. To address this, we developed an intelligent deep-learning model incorporating CNN and LSTM to detect heart abnormalities based on artificial intelligence using heart sound data from stethoscope recordings. Our research achieved a high accuracy rate of 97.8%. Using audio data to introduce advanced models for cardiac abnormalities in children is essential for enhancing early detection and intervention in pediatric cardiovascular healthcare.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.5
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• pp.93-105
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• 2011

This study was performed to figure out the effects of perceived usefulness and ease of use in Technology Acceptance Model(TAM) affecting acceptance attitude and intention to use in smart learning. In addition, the study recognized the need for differentiation of learning time by analyzing the difference of effects influencing acceptance attitude of perceived usefulness and ease of use during learning time, which is at the beginning, midterm, and at the end of the term. As the results of the study, it showed that there were differences between the factors, the learning time of which was considered, affecting acceptance attitude and intention to use. Furthermore, in order to improve the effectiveness of building a smart campus, which is currently under the construction, the study argued that universities need to consider the learning relevance and subjective norm as important factors in perceived usefulness of smart learning. Finally, the need for the design of various smart learning types became accepted considering learning time.

• Journal of Digital Convergence
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• v.6 no.2
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• pp.127-134
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• 2008

The aim of this study is to show that using a Web-Based Instruction system is a better method to help counteract deficient teaching, learning conditions and facilities thus improving the students' interest, understanding, participation and skill development in Physical Education. The key findings of this study are as follows; The first advantage of using this system is that the theory of Softball is explained with the use of pictures rather than text, thus improving the students' understanding. The second is the method in which practical skills are explained and demonstrated. Action image sequences are used as opposed to the old method of using stationary pictures. This means that the learners can clearly see and understand why and, more importantly, how to use these skills. The third advantage is the technique used to teach the application of these skills. Using active rather than passive learning and thus engaging the student encourages improved participation and learning. The fourth advantage comes in evaluation: both subjective and objective questions are asked in theoretical evaluation, and the teachers can evaluate the students' understanding and skill development by filming their actual Softball games. The final advantage is the use of Self-Directed Learning to aid learners' development of understanding of the lesson content as interactions between teachers and learners are constructe

• Journal of the Korea Fashion and Costume Design Association
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• v.25 no.3
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• pp.41-52
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• 2023

This study developed and taught VR content to be used in clothing design and composition practice, which are practical subjects for home education students at the College of Education, and examined the learning effects on students who participated in VR experiences. First, after experiencing classes using VR content, students' perceptions of classes were examined considering participation, class level, expectations, and satisfaction through a survey. As a result of examining the experience of learning sewing machines in classes using VR content and changes in perception of classes, it was found that the class level, class expectations, and satisfaction were affected. As a result of comparative analysis of VR experiences and the perception of VR classes prior to experiencing VR content related to sewing machines developed for practical subjects, VR experiences affected class participation, class level, expectations, but satisfaction was not affected. The advantages of the VR class that students mentioned in the subjective evaluation included interest in the class, the degree of participation, the VR experience, and the use of VR. As for the disadvantages, difficulties in using the device, dizziness, frustration when using the device, and limitations of the program were mentioned.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.441-453
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• 2021

This study seeks to examine the effect of attitude, subjective norm, and perceived behavioral control on the purchase behavior of students enrolled in a private higher education establishment in Tangerang, Indonesia. This is a quantitative study and it employs samples by simple random sampling of 410 university students. The returned and valid questionnaire results totaled 261 samples. Data processing used the SEM method with SmartPLS 3.0 software. The findings of this study reveal that attitude, subjective norm, and perceived behavioral control have a significant effect on purchase intention. Meanwhile, purchase intention has a significant effect on working students' purchase behavior, and halal awareness had a moderating effect of purchase intention on purchase behavior. Purchasing interest has a positive effect on purchasing behavior, and this study proves that halal awareness is able to moderate the effect of purchase intention on purchasing behavior toward halal food products. The higher the awareness of halal products, the greater the relationship between buying interest and buying behavior of halal food. The results of this study also show the importance of paying attention to halal awareness factor in the form of increasing the relationship between buying interest and buying behavior of halal food products.

• Journal of Korean society of Dental Hygiene
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• v.23 no.5
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• pp.379-386
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• 2023

Objectives: This study aimed to prepare basic data to improve the health-related quality of life of cancer survivors by confirming their oral and mental health statuses and identifying factors affecting their health-related quality of life. Methods: Of all participants in the 8th KNHANES (2019-2020), adults aged 19 years or older who responded 'yes' to the diagnosis of cancer and 404 cancer survivors who responded 'none' to the current cancer prevalence item were selected as the final research participants. Multiple regression analysis was conducted to confirm the effect of cancer survivors' oral and mental health on health-related quality of life. Results: Subjective oral health (p<0.01), chewing problems (p<0.05), subjective health (p<0.001), and depression (p<0.01) had an effect on health-related quality of life from multiple regression analyses. Conclusions: Therefore, oral and mental health promotion may improve health-related quality of life. Thus, it is necessary to recognize the importance of oral and mental health and implement preventive education and programs.

• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.485-490
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• 2004

In data representation, the clustering performs a grouping process which combines given data into some similar clusters. The various similarity measures have been used in many researches. But, the validity of clustering results is subjective and ambiguous, because of difficulty and shortage about objective criterion of clustering. The fuzzy clustering provides a good method for subjective clustering problems. It performs clustering through the similarity matrix which has fuzzy membership value for assigning each object. In this paper, for objective fuzzy clustering, the clustering algorithm which joins principal components analysis as a dimension reduction model with bayesian learning as a statistical learning theory. For performance evaluation of proposed algorithm, Iris and Glass identification data from UCI Machine Learning repository are used. The experimental results shows a happy outcome of proposed model.

• The Journal of Korean Association of Computer Education
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• v.8 no.5
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• pp.63-72
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• 2005

The purpose of this study is finding factors affecting the decision of Web-based learning system (Edunet) use and searching for ways to diffuse Edunet use. Based on the diffusion of innovations theory and the results of the previous studies about web-based instruction, seven predictors influencing the decision of Edunet use were extracted. Seven variables as the followings; (1) perceived attributes of innovation (Relative Advantage, Compatibility, Complexity) (2) Innovativeness (3) Self-efficacy (4) Subjective Norm (5) Support. The participants were 315, 5-6th grade elementary school students, and the questionnaire was 20-item with 7-point Likert scales. To analyze the collected data and test the hypothesis, binary logistic regression was employed. The result indicated that the fitness of regression model including seven decision factors was proved. In addition, two factors, subjective norm and support, were identified as the important decision factors of Edunet use.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.741-747
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• 2022

Fatigue lowers an individual's ability and makes it difficult to perform work. As fatigue accumulates, concentration decreases and thus the possibility of causing a safety accident increases. Awareness of fatigue is subjective, but it is necessary to quantitatively measure the level of fatigue in the actual field. In previous studies, it was proposed to measure the level of fatigue by expert judgment by adding objective indicators such as bio-signal analysis to subjective evaluations such as multidisciplinary fatigue scales. However this method is difficult to evaluate fatigue in real time in daily life. This paper is a study on the fatigue classification model that determines the fatigue level of workers in real time using speech data recorded in the field. Machine learning models such as logistic classification, support vector machine, and random forest are trained using speech data collected in the field. The performance evaluation showed good performance with accuracy of 0.677 to 0.758, of which logistic classification showed the best performance. From the experimental results, it can be seen that it is possible to classify the fatigue level using speech signals.

• Journal of Integrative Natural Science
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• v.13 no.3
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• pp.97-104
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• 2020

This study suggests the emotion space modeling and emotion inference methods suitable for personalized services based on psychological and emotional models. For personalized emotion space modeling taking into account the subjective disposition based on the empirical assessment of the personal emotions felt by the personalization process of emotion space was used as a decision support tool, the Analytic Hierarchy Process. This confirmed that the special learning to perform personalized emotion space modeling without considering the subjective tendencies. In particular to check the possible reasoning based on fuzzy emotion space modeling and sensitivity for the quantification and vague human emotion to it based on the inherent human sensitivity.