• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.441-447
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• 2023

Intravenous infusion therapy is a standard nursing procedure in medical institutions that provides patients with drugs, fluids, blood, and nutrients into the patient's mucus. It is mainly performed and managed by nurses. Additionally, it is an injection method that injects drugs directly into the blood vessels, and is used to achieve rapid results in emergency situations, and quick and accurate effects can be expected. Even experienced nurses through education and training often make mistakes, which can not only cause discomfort to patients but also cause various problems that threaten patient safety. Various studies are being conducted to reduce the pain caused by these mistakes. This paper acquired images of veins on the back of the hands of three subjects through an image detection device and conducted a study to derive an algorithm to provide clear vein images through image processing of the collected images. To sharpen the acquired vein images, existing algorithms Histogram Equalization, CLAHE, and Unsharp Masking were selected and combined. A histogram graph was used to compare images derived by applying individual algorithms and algorithm combinations to images. The histogram graph was checked by calculating the difference between the minimum and maximum values of distributed pixels and averaging them. The algorithm combination presented in this paper was 209.1, which was higher than the average values of individual algorithms of 138.7, 132.3, and 126.2, and it was confirmed that visibility was good even in actual images.

• Information Systems Review
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• v.21 no.1
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• pp.39-59
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• 2019

E-learning has improved the educational effect by making it possible to learn anytime and anywhere by escaping the traditional infusion education. As the use of e-learning system increases with the increasing popularity of e-learning, it has become important to measure e-learning satisfaction. In this study, we used the mixed research method to identify satisfaction factors of e-learning. The mixed research method is to perform both qualitative research and quantitative research at the same time. As a quantitative research, we collected reviews in Udemy.com by text mining. Then we classified high and low rated lectures and applied topic modeling technique to derive factors from reviews. Also, this study conducted an in-depth 1:1 interview on e-learning learners as a qualitative research. By combining these results, we were able to derive factors of e-learning satisfaction and dissatisfaction. Based on these factors, we suggested ways to improve e-learning satisfaction. In contrast to the fact that survey-based research was mainly conducted in the past, this study collects actual data by text mining. The academic significance of this study is that the results of the topic modeling are combined with the factor based on the information system success model.