• Journal of East-West Nursing Research
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• v.28 no.1
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• pp.57-66
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• 2022

Purpose: This study examined the effect of emotional intelligence, job stress, and communication ability on nursing performance of nurses caring for cancer patients. Methods: This is a descriptive study involving 185 nurses with an experience of longer than 6 months at K university hospital in B metropolitan city. The data was collected from March 2nd 2021 to March 31st 2021, and analyzed using the descriptive statistics, independent t-test, one-way ANOVA, Pearson correlation coefficients, and stepwise multiple regression. Results: The factors affect the nursing performance of participants were emotional intelligence, total clinical career, communication ability, job stress and satisfaction of current department. The total explanatory power of those variables on the nursing performance was 43.8%. Conclusion: In order to improve nursing performance, it is necessary to apply a program for improving emotional intelligence and communication ability, and for controlling and coping with job stress, considering the career of a nurse taking care for cancer patients. In addition, efficient manpower management and material support at the hospital organization level are required.

• Journal of the Korean Society of Marine Environment & Safety
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• v.22 no.4
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• pp.328-334
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• 2016

The everyday life of police officers requires them to face a number of criminal acts and enter compromising crime scenes. In this case, officers may be compelled to make a personal expression of negative emotions. Negative emotions of members confidence for effective job processing and contributions duties and achievement of the job within the marine police force. Therefore, to control the emotive dissonance of organization members is a great help to the development of the organization. This study focuses on emotional dissonance among marine police officers to verify the impact of this dissonance on job burnout and consider the mediating effect of emotional intelligence. Research results are as follows: First, the relationship between emotional dissonance sub-factors and job burnout among marine police officers was studied. It was found that marine police did not feel emotionally jarred because they consciously tried to abstain from emotional engagement, but this was found to reduce emotional intelligence as related to desired emotions as well. Second, Emotional intelligence of the Marine Police was found on a significant impact on job burnout. Third, the impact of emotions in relation to emotional dissonance that job burnout of marine police intelligence officials confirmed that there is a statistically significant mediating effect. Finally, in a comparison of direct effects versus mediated effects, marine police were seen to be prone to emotional dissonance and experienced job burnout as a direct result of applying greater emotional intelligence.

• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.21-40
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• 2019

This paper presents a comprehensive survey of various artificial intelligence (AI) techniques implemented in cognitive radio engine to improve cognition capability in cognitive radio networks (CRNs). AI enables systems to solve problems by emulating human biological processes such as learning, reasoning, decision making, self-adaptation, self-organization, and self-stability. The use of AI techniques is studied in applications related to the major tasks of cognitive radio including spectrum sensing, spectrum sharing, spectrum mobility, and decision making regarding dynamic spectrum access, resource allocation, parameter adaptation, and optimization problem. The aim is to provide a single source as a survey paper to help researchers better understand the various implementations of AI approaches to different cognitive radio designs, as well as to refer interested readers to the recent AI research works done in CRNs.

• Electronics and Telecommunications Trends
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• v.38 no.6
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• pp.95-106
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• 2023

As artificial intelligence has become commonplace in various fields, the transparency of AI in its development and implementation has become an important issue. In safety-critical areas, the eXplainable and/or understandable of artificial intelligence is being actively studied. On the other hand, machine learning have been applied to the intelligence of self-organizing network (SON), but transparency in this application has been neglected, despite the critical decision-makings in the operation of mobile communication systems. We describes concepts of eXplainable machine learning (ML), along with research trends, major issues, and research directions. After summarizing the ML research on SON, research directions are analyzed for explainable ML required in intelligent SON of beyond 5G and 6G communication.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.1
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• pp.66-83
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• 2022

Artificial Intelligence (AI) is to assumed to be one of next generation technology which determine technological competitiveness and strategic advantage of a certain country. By using the patent data, this study aims to have a comparative analysis of the technological competitiveness of evolving artificial intelligence at different stages of development among the five largest intellectual property offices in the world (IP5). For the analysis data, all AI technology patent data from 1956 to 2019 were utilized according to the classification system presented in the "WIPO 2019 Technology Trend: Artificial Intelligence" report published by the World Intellectual Property Organization (WIPO) in 2019. The results shows that China has already surpassed the United States in terms of the number of patent applications in the field of artificial intelligence technology. However, in the domains of the United States, Europe, Japan, and Korea, the technology competitiveness of the United States is far ahead of China. Interestingly, the rate of increase of Korea's technology competitiveness is also very fast, and it has been shown that the technology strength is ahead of China in non-Chinese domains. The significance of this study can be found in the fact that the temporal and spatial change process of technological competitiveness of significant countries in the field of artificial intelligence technology artificial intelligence was viewed as a macro-framework using the technology index (TS) the differences were compared.

• Health Policy and Management
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• v.34 no.1
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• pp.4-16
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• 2024

Smart hospitals involve the use of recent ICT (information and communications technology) technologies to improve healthcare access, efficiency, and effectiveness. Standardization in smart hospital technologies is crucial for interoperability, scalability, policy formulation, quality control, and maintenance. This study reviewed relevant international standards for smart hospitals and the organizations that develop them. Specific attention was paid to robotics in smart hospitals and the potential for standardization in this area. The study used online resources and existing standards to analyze technologies, standards, and practices in smart hospitals. Key technologies of smart hospitals were identified. Relevant standards from ISO (International Organization for Standardization) and IEC (International Electrotechnical Commission) were mapped to each core technology. Korea's leadership in smart hospital technology were highlighted. Approaches for standardizing smart hospitals were proposed. Finally, potential new international standard items for robotics in smart hospitals were identified and categorized by function: sampling, remote operation, delivery, disinfection, and movement tracking/contact tracing. Standardization in smart hospital technologies is crucial for ensuring interoperability, scalability, ethical use of artificial intelligence, and quality control. Implementing international standards in smart hospitals is expected to benefit individuals, healthcare institutions, nations, and industry by improving healthcare access, quality, and competitiveness.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.4
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• pp.533-538
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• 2015

Collective intelligence is related to several areas such as sociology, business administration, political science, and computer science. This paper can be classified as a product of social engineering of the era of liberal arts and science convergence, fusion, consilience. Members today have higher need for self-actualization and contribution. As the business is changing fast and getting more complicated, a mechanism of natural science is necessary in social organization. The mechanisms of collective intelligence are composed of divergence process and convergence process. And the seven steps were designed that the first letter of each steps leads to 'PROCESS'. When implemented by applying the procedures that reflect the opinions of members throughout this paper, there are members who participated in the decision-making process will contribute to actively participate in the decision when to run, and specific tools and techniques in online communities are for future studies.

• International journal of advanced smart convergence
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• v.10 no.3
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• pp.122-130
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• 2021

This study is about an Artificial Intelligence-based fake news identification system and its methods to determine the authenticity of content distributed over the Internet. Among the news we encounter is news that an individual or organization intentionally writes something that is not true to achieve a particular purpose, so-called fake news. In this study, we intend to design a system that uses Artificial Intelligence techniques to identify fake content that exists within the news. The proposed identification model will propose a method of extracting multiple unit factors from the target content. Through this, attempts will be made to classify unit factors into different types. In addition, the design of the preprocessing process will be carried out to parse only the necessary information by analyzing the unit factor. Based on these results, we will design the part where the unit fact is analyzed using the deep learning prediction model as a predetermined unit. The model will also include a design for a database that determines the degree of fake news in the target content and stores the information in the identified unit factor through the analyzed unit factor.

• The Korean Journal of Food & Health Convergence
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• v.8 no.6
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• pp.19-29
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• 2022

Business intelligence (BI) is a process for turning data into insights that inform an organization's strategic and tactical decisions. BI aims to give decision-makers the information they need to make better decisions Patient safety analysis, illness surveillance, and fraud identification are just a few healthcare decision-making processes that can be supported by data mining. Thus, the purpose of the current research is to outline the need if BI as an essential factor in the healthcare sector by reviewing various scholarly materials and the findings. The present author conducted one of the most famous qualitative literature approach which has been called as PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) statement. The selecting criteria for eligible prior studies were estimated by whether studies are suitable for the current research, identifying they are peer-reviewed and issued by notable publishers between 2017 and 2022. According to the result based on the PRISMA analysis, BI plays a vital role in the healthcare sector and there are four business intelligence factors (Data, Analytic, Reporting, and Visualization) that will ensure that the healthcare sector provides the right healthcare services to the customers to be addressed in this section include; data, analytics, reporting, and visualization.

• Genomics & Informatics
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• v.19 no.3
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• pp.22.1-22.5
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• 2021

Automatic document classification for highly interrelated classes is a demanding task that becomes more challenging when there is little labeled data for training. Such is the case of the coronavirus disease 2019 (COVID-19) clinical repository-a repository of classified and translated academic articles related to COVID-19 and relevant to the clinical practice-where a 3-way classification scheme is being applied to COVID-19 literature. During the 7th Biomedical Linked Annotation Hackathon (BLAH7) hackathon, we performed experiments to explore the use of named-entity-recognition (NER) to improve the classification. We processed the literature with OntoGene's Biomedical Entity Recogniser (OGER) and used the resulting identified Named Entities (NE) and their links to major biological databases as extra input features for the classifier. We compared the results with a baseline model without the OGER extracted features. In these proof-of-concept experiments, we observed a clear gain on COVID-19 literature classification. In particular, NE's origin was useful to classify document types and NE's type for clinical specialties. Due to the limitations of the small dataset, we can only conclude that our results suggests that NER would benefit this classification task. In order to accurately estimate this benefit, further experiments with a larger dataset would be needed.