• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.107-119
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• 2022

This study was a descriptive research study to identify the effects of related knowledge to COVID-19, infection control performance, and job stress in emergency department nurses due to COVID-19 on nursing performance, to improve emergency department nurses' ability to cope with emerging infectious diseases and to prepare basic data for effective nursing work. This study was collected data from August, 10 until September, 10, 2021, for 165 emergency department nurses in 26 hospitals, which were located in G province and designated as regional emergency medical institutions, and total 150 copies were finally analyzed. Data were analyzed using descriptive statistics, t-test, ANOVA, correlation, and multiple regression. As a result of analyzing the variables affecting the subject's nursing job performance by multiple regression using the hierarchical selection method, the higher the infection control performance and the higher the job stress, the more higher the nursing job performance, and the explanatory power was 18.4%. The study results showed that infection control performance, job stress, and non-shift work had an effect on nursing performance. It was thought that various plans to protect them, reduced tasks to efficiently perform and the nursing ability to cope with emerging infectious disease should be prepared to improve and reduce the job stress of emergency department nurses.

• Journal of Service Research and Studies
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• v.13 no.1
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• pp.76-95
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• 2023

As information and communications technology-based "smart hospitals" and "digital healthcare" have become a hot topic in the healthcare field, hospital mobile apps are gaining attention; but, the utilization rate is low due to lack of publicity, unstable systems, and late updates. In this situation, systematic research is needed to increase the utilization rate of hospital mobile apps, but related research has been rare. Therefore, this study integrates the information systems success model (ISSM) from the technical perspective and the expectation-confirmation model (ECM) from the cognitive perspective to demonstrate the influence mechanism on the continuous use intention of hospital mobile apps. For this purpose, an online survey was conducted among 181 Korean adults who have used hospital mobile apps. The results of the structual equation modeling showed that most of the quality factors have significant effects on expectation confirmation, perceived usefulness, and satisfaction. Additionally, expectation confirmation significantly affects perceived usefulness and satisfaction, and both perceived usefulness and satisfaction significantly affect the continuous use intention of hospital mobile apps. This study is of importance in that it integrates the ISSM and ECM and applies them to the context of using hospital mobile apps, which are underutilized in the healthcare field, and provides practical implications for increasing the utilization rate of hospital mobile apps and operating effective and efficient services through the findings.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.355-363
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• 2023

The purpose of this study was attempted to confirm the relationship between medical workers' knowledge of the elderly, burn out, job satisfaction and awareness of elder abuse. The study was conducted on 371 doctors, nurses, and nurse's aides working at eight health centers, 15 nursing hospitals, and 30 university hospital institutions. Looking at the relationships between variables, this study found that knowledge of the elderly was a significant positive correlation with awareness of elder abuse(r=.14, p<.01), and burn out was a significant negetive correlation with job satisfaction(r=-.55, p<.01) and awareness of elder abuse(r=-.10, p<.05). Job satisfaction was a significant positive correlation with awareness of elder abuse(r=.13, p<.01). Awareness of elder abuse was a significant positive correlation with knowledge of the elderly(r=.14, p<.01) and was a significant negetive correlation with burn out(r=-.10, p<.05). As a result of this study, it is expected that Hospital workers can have a positive perception and attitude toward the elderly by reducing their burnout and improving their job satisfaction.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.861-866
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• 2023

Individuals with visual impairments face difficulties in accessing accurate information about medical services and medications, making it challenging for them to ensure proper medication intake. While there are healthcare laws addressing this issue, there is a lack of standardized solutions, and not all over-the-counter medications are covered. Therefore, we have undertaken the design of a mobile application that utilizes image recognition technology, barcode scanning, and QR code recognition to provide guidance on how to take over-the-counter medications, filling the existing gaps in the knowledge of visually impaired individuals. Currently available applications for individuals with visual impairments allow them to access information about medications. However, they still require the user to remember which specific medication they are taking, posing a significant challenge. In this research, we are optimizing the camera capture environment, user interface (UI), and user experience (UX) screens for image recognition, ensuring greater accessibility and convenience for visually impaired individuals. By implementing the findings from our research into the application, we aim to assist visually impaired individuals in acquiring the correct methods for taking over-the-counter medications.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.121-132
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• 2024

This study attempted to determine the effects of job stress, nursing problem-solving ability, nursing work performance, and job stress and nursing problem-solving ability on nursing work performance according to the MBTI type of nurses. The study subjects were 141 nurses working at a medical institution in G Province, and data collection was conducted from March 01 to March 31, 2024. The collected data were analyzed using correlation and multiple regression analysis. Among the psychological function types of MBTI, the SF type (sympathetic and friendly type) was the most common, and among the psychological temperament types, the SP type (sensuous and open type) was the most common. Nursing work performance had a negative correlation with job resource stress, a positive correlation with nursing problem-solving ability, and a positive correlation with problem recognition, information collection, planning ability, and evaluation. The variable that had a significant impact on nursing work performance was job resources, and problem recognition, a subfactor of nursing problem-solving ability, was found to be the best predictor of nursing work performance, followed by planning ability. The explanatory power was 17.8%. The results of this study are expected to be used as basic data to develop efficient nursing management guidelines by not only improving understanding of the personality of nurses but also investigating factors related to nurses' work performance. Through the development of programs and measures to improve nursing performance, it is necessary to revitalize programs, provide educational opportunities, and provide institutional support from hospital organizations to enable high-quality nursing care through skilled nursing work.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.1-13
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• 2015

As opinion mining in big data applications has been highlighted, a lot of research on unstructured data has made. Lots of social media on the Internet generate unstructured or semi-structured data every second and they are often made by natural or human languages we use in daily life. Many words in human languages have multiple meanings or senses. In this result, it is very difficult for computers to extract useful information from these datasets. Traditional web search engines are usually based on keyword search, resulting in incorrect search results which are far from users' intentions. Even though a lot of progress in enhancing the performance of search engines has made over the last years in order to provide users with appropriate results, there is still so much to improve it. Word sense disambiguation can play a very important role in dealing with natural language processing and is considered as one of the most difficult problems in this area. Major approaches to word sense disambiguation can be classified as knowledge-base, supervised corpus-based, and unsupervised corpus-based approaches. This paper presents a method which automatically generates a corpus for word sense disambiguation by taking advantage of examples in existing dictionaries and avoids expensive sense tagging processes. It experiments the effectiveness of the method based on Naïve Bayes Model, which is one of supervised learning algorithms, by using Korean standard unabridged dictionary and Sejong Corpus. Korean standard unabridged dictionary has approximately 57,000 sentences. Sejong Corpus has about 790,000 sentences tagged with part-of-speech and senses all together. For the experiment of this study, Korean standard unabridged dictionary and Sejong Corpus were experimented as a combination and separate entities using cross validation. Only nouns, target subjects in word sense disambiguation, were selected. 93,522 word senses among 265,655 nouns and 56,914 sentences from related proverbs and examples were additionally combined in the corpus. Sejong Corpus was easily merged with Korean standard unabridged dictionary because Sejong Corpus was tagged based on sense indices defined by Korean standard unabridged dictionary. Sense vectors were formed after the merged corpus was created. Terms used in creating sense vectors were added in the named entity dictionary of Korean morphological analyzer. By using the extended named entity dictionary, term vectors were extracted from the input sentences and then term vectors for the sentences were created. Given the extracted term vector and the sense vector model made during the pre-processing stage, the sense-tagged terms were determined by the vector space model based word sense disambiguation. In addition, this study shows the effectiveness of merged corpus from examples in Korean standard unabridged dictionary and Sejong Corpus. The experiment shows the better results in precision and recall are found with the merged corpus. This study suggests it can practically enhance the performance of internet search engines and help us to understand more accurate meaning of a sentence in natural language processing pertinent to search engines, opinion mining, and text mining. Naïve Bayes classifier used in this study represents a supervised learning algorithm and uses Bayes theorem. Naïve Bayes classifier has an assumption that all senses are independent. Even though the assumption of Naïve Bayes classifier is not realistic and ignores the correlation between attributes, Naïve Bayes classifier is widely used because of its simplicity and in practice it is known to be very effective in many applications such as text classification and medical diagnosis. However, further research need to be carried out to consider all possible combinations and/or partial combinations of all senses in a sentence. Also, the effectiveness of word sense disambiguation may be improved if rhetorical structures or morphological dependencies between words are analyzed through syntactic analysis.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.57-75
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• 2019

In this paper, we conducted a study focusing on the innovation direction of the documentary model on the Internet of Things industry, which is the most actively industrialized among the core technologies of the 4th Industrial Revolution. Policy, economic, social, and technical issues were derived using PEST analysis for global trend analysis. It also presented future prospects for the Internet of Things industry of ICT-related global research institutes such as Gartner and International Data Corporation. Global research institutes predicted that competition in network technologies will be an issue for industrial Internet (IIoST) and IoT (Internet of Things) based on infrastructure and platforms. As a result of the PEST analysis, developed countries are pushing policies to respond to the fourth industrial revolution through cooperation of private (business/ research institutes) led by the government. It was also in the process of expanding related R&D budgets and establishing related policies in South Korea. On the economic side, the growth tax of the related industries (based on the aggregate value of the market) and the performance of the entity were reviewed. The growth of industries related to the fourth industrial revolution in advanced countries overseas was found to be faster than other industries, while in Korea, the growth of the "technical hardware and equipment" and "communication service" sectors was relatively low among industries related to the fourth industrial revolution. On the social side, it is expected to cause enormous ripple effects across society, largely due to changes in technology and industrial structure, changes in employment structure, changes in job volume, etc. On the technical side, changes were taking place in each industry, representing the health and medical sectors and manufacturing sectors, which were rapidly changing as they merged with the technology of the Fourth Industrial Revolution. In this paper, various management methodologies for innovation of existing business model were reviewed to cope with rapidly changing industrial environment due to the fourth industrial revolution. In addition, four criteria were established to select a management model to cope with the new business environment: 'Applicability', 'Agility', 'Diversity' and 'Connectivity'. The expert survey results in an AHP analysis showing that Business Model Canvas is best suited for business model innovation methodology. The results showed very high importance, 42.5 percent in terms of "Applicability", 48.1 percent in terms of "Agility", 47.6 percent in terms of "diversity" and 42.9 percent in terms of "connectivity." Thus, it was selected as a model that could be diversely applied according to the industrial ecology and paradigm shift. Business Model Canvas is a relatively recent management strategy that identifies the value of a business model through a nine-block approach as a methodology for business model innovation. It identifies the value of a business model through nine block approaches and covers the four key areas of business: customer, order, infrastructure, and business feasibility analysis. In the paper, the expansion and application direction of the nine blocks were presented from the perspective of the IoT company (ICT). In conclusion, the discussion of which Business Model Canvas models will be applied in the ICT convergence industry is described. Based on the nine blocks, if appropriate applications are carried out to suit the characteristics of the target company, various applications are possible, such as integration and removal of five blocks, seven blocks and so on, and segmentation of blocks that fit the characteristics. Future research needs to develop customized business innovation methodologies for Internet of Things companies, or those that are performing Internet-based services. In addition, in this study, the Business Model Canvas model was derived from expert opinion as a useful tool for innovation. For the expansion and demonstration of the research, a study on the usability of presenting detailed implementation strategies, such as various model application cases and application models for actual companies, is needed.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.89-106
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• 2022

Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object's departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will investigate the problem that the IoF algorithm cannot solve to develop an additional complementary algorithm. In addition, we plan to conduct additional research to apply this model to various fields' dataset related to intelligent video analysis.

• Progress in Medical Physics
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• v.25 no.4
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• pp.298-303
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• 2014

In this study, we examine the trends and types of incidents frequently occur during radiation therapy by using the data from the radiation oncology safety information system (ROSIS), according to discovery method explores the development direction of future research accident cause factor control method. This study was carried out analysis of incident data in ROSIS nearly 1163 cases in last 11 years from 2003 to 2013. We categorized into treatment methods, found the time, discoverer of occupations and finding ways to analyze the data. Then, we calculate the percentage and the classification for each item. About 1163 cases of incident cases including the near miss cases, external radiation therapy, brachytherapy and other were 97%, 2% and 1%. In the case was improperly planned dose delivery was 44% (497 cases) which 429 cases (86%) was found before 3 fractions and 13 cases were found after 11 fractions. The investigation was found to be distributed in various a found times. Approximately 42% of found time was during treatment and 29% of patients were found the problem during inspection chart. Occupation to discover the most radiation accidents was the radiation therapist (53%) who works in treatment room. Among 1163 incidence cases, 24% cases were found the accident before the treatment, therefore most of accident were found after of during the treatment (70%, 813 cases). This trend is acquired through ROSIS analysis, is expected to be not significantly different in the case of Korea, so it is necessary more diverse and systematic research for the prevention and early detection by using the ROSIS data.