• Journal of Digestive Cancer Research
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• v.10 no.2
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• pp.99-106
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• 2022

Clinical research ultimately aimed to promptly diagnose and prevent diseases through precise biomarker development. Finding the optimal cut-off point of a regularly measured biomarker can help its interpretation and ultimately help in disease investigation and diagnosis, more specifically in determining the presence of diseases. Therefore, this study aimed to use the characteristics of outcome variables in clinical research to explain how to determine the optimal cutoff point. The outcome variables can be divided into dichotomous, ordinal, and survival types. The optimal cut-off point can be determined by finding points that maximize the Youden index, extended Youden index, and log-rank statistics. This study will enable clinical researchers to accurately determine the optimal cut-off points for regularly measured biomarkers, thereby enabling prompt disease diagnosis for effective treatment.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.1-11
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• 2022

As data-based research activities and outcomes increase and ontact or non-face-to-face activities become common, the demand for easy utilization of resources, tools, functions, and easily accessible information required for research in the R&D sector has increased accordingly. With the rapid increase in the demand for collaborative research based on online platforms, research support institutions strive to provide venues for research activities that merge various information and functions. ScienceON, an integrated science & technology (S&T) knowledge infrastructure service developed and operated by the Korea Institute of S&T Information (KISTI), supports open collaboration by connecting and merging all the information, functions, and infrastructure required for research activities. This paper describes the online research activity support tool provided by ScienceON and the remarkable results achieved through this activity. Specifically, the excellent creation of the following flow of meta-material research activities in the ontact space is elucidated. First, the papers required for a meta-material analysis are retrieved, virtual simulation is conducted with the experimental data extracted from the papers, and research data are accumulated. ScienceON's tools for supporting ontact research activity will play a role as an important service in the era of digital transformation and open science.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.32 no.2
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• pp.149-169
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• 2021

As collaboration of research is increasing in social science, this study aims to investigate the changes in patterns of research collaboration in the field of library and information science, over the years from 2002 to 2020. The data used for this study were collected from four major journals in the field to analyze the frequency of co-authored research articles by journals and by institutions that all authors were associated with. Also, the institutions data were used to build a co-authorship network, which produced various indices including TBC (Triangle Betweenness Centrality) that showed which institutions were more central than others in the network. The result shows the number of co-authored articles were constantly increasing in all journals, and some institutions, mostly universities, showed the higher centrality scores than others and the range of collaboration were also expanded.

• Proceedings of the Materials Research Society of Korea Conference
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• 2000.05a
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• pp.37-51
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• 2000

This paper presented the trend of CIS solar cell development and the current status of CIS cell research conducted by the collaboration of KAIST and KIER.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.558-566
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• 2020

New Product Development (NPD) is essential for companies' survival, and continuous growth in the rapidly changing, convergence and business environment, and companies need to collaborate with their internal and external partners to improve performance in NPD. This research examined the integrated relationship between NPD (Cross-functional collaboration, and supplier collaboration) and collaborative communication (formality and reciprocal feedback) on corporate performance. The employees who worked in manufacturing for more than one year with experience in NPD were tested. The reliability and feasibility were assessed using the sample 272 data. The results are summarized as follows. First, cross-functional collaboration and supplier collaboration, which are the factors of NPD collaboration, had a significant positive effect on formality. Second, formality and reciprocal feedback had a significant positive effect on reciprocal feedback and corporate performance. Therefore, for successful NPD, manufacturing companies need to establish efficient management strategies and communicate officially and reciprocally to maximize productivity and efficiency based on the reciprocal partnership between suppliers. To accomplish this, companies need to plan effective communication strategies to respond quickly to internal and external partners' needs.

• Journal of the Korean association of regional geographers
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• v.23 no.3
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• pp.459-469
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• 2017

Recently, the third mission as the new role of the university is being emphasized as contributing to the local community through active participation in local and regional problems as well as an economic contribution as an entrepreneurial university. Thus, overseas universities started various university-industry collaboration activities targeting sustainable development based on local community and improve their roles for regional regeneration and innovation. Universities in Korea also tend to set up a cooperative governance with various agents in the local community via university financial support projects by the government and started to promote the university-industry collaboration project for solving the problem of the local community. Therefore, this research tries to find implications in order to expand the role and responsibility as local university and reinforce substantiality and enhancement of university-industry collaboration through a case analysis of university-industry collaboration to solve the problem in local communities in foreign countries. In order to solve the problem of local communities based on local agents-led small-sized projects, it is requested to improve the more active role of the university, local governments and university students.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.61.2-61.2
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• 2021

We present the current focus issues on biosignature and life in the Solar System and exoplanters considering the possible research items at KASI in collaboration with other fields and institutes. We also suggest possible KASI research projects that can be conducted in the next decade.

• Journal of Distribution Research
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• v.10 no.3
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• pp.1-14
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• 2005

It is important to control performance and a Sustainable Collaboration (SC) for the successful Supply Chain Management (SCM). This research developed a control model which analyzed SCM performances based on a Balanced Scorecard (ESC) and an SC using Support Vector Machine (SVM). 108 specialists of an SCM completed the questionnaires. We analyzed experimental data set using SVM. This research compared the forecasting accuracy of an SCMSC through four types of SVM kernels: (1) linear, (2) polynomial (3) Radial Basis Function (REF), and (4) sigmoid kernel (linear > RBF > Sigmoid > Polynomial). Then, this study compares the prediction performance of SVM linear kernel with Artificial Neural Network. (ANN). The research findings show that using SVM linear kernel to forecast an SCMSC is the most outstanding. Thus SVM linear kernel provides a promising alternative to an SC control level. A company which pursues an SCM can use the information of an SC in the SVM model.

• Journal of Engineering Education Research
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• v.7 no.3
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• pp.32-38
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• 2004

We present a new collaboration model between industry and university through the case study of LG Electronics and Handong Global University. The aim of this education-oriented model is providing highly competitive engineers to the fields instead of accomplishing research projects or developing a new high-end technology. The proposed cooperation model is a win-win strategy for both academia and industry to have not only short-term but also long term benefits. We discuss how to develope and manage the demand-driven course which is the core program of our model. And we also evaluate the pros and cons of this collaboration model.