• Journal of Industrial Convergence
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• v.19 no.6
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• pp.155-164
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• 2021

The purpose of this study was to identify the individual, emotional, parent, and social environmental factors on career decision-making in multicultural youth. To this end, 1,146 multicultural adolescents who were enrolled in high school and whose fathers were Korean were selected as subjects of analysis as data for the 7th year of the Multicultural Youth Panel (MCAPS). As a result, first, it was found that self-esteem, which was an individual factor, and adaptation to school life and multicultural acceptance, which are social and environmental factors, positive effect career decision-making. Second, it was found that depression as an emotional factor and neglect as a parent factor had a negative effect on career decision-making. However, it was confirmed that stress as an individual factor, and parent-child communication as a parent factor did not affect career decision-making. These results are meaningful in that they provided basic data on how to deal with each factor and prevent multicultural youths from wandering in advance without deciding their career paths.

• ETRI Journal
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• v.32 no.5
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• pp.801-809
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• 2010

This paper presents a statistical model-based noise suppression approach for voice recognition in a car environment. In order to alleviate the spectral whitening and signal distortion problem in the traditional decision-directed Wiener filter, we combine a decision-directed method with an original spectrum reconstruction method and develop a new two-stage noise reduction filter estimation scheme. When a tradeoff between the performance and computational efficiency under resource-constrained automotive devices is considered, ETSI standard advance distributed speech recognition font-end (ETSI-AFE) can be an effective solution, and ETSI-AFE is also based on the decision-directed Wiener filter. Thus, a series of voice recognition and computational complexity tests are conducted by comparing the proposed approach with ETSI-AFE. The experimental results show that the proposed approach is superior to the conventional method in terms of speech recognition accuracy, while the computational cost and frame latency are significantly reduced.

• Proceedings of the KSRS Conference
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• v.2
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• pp.706-709
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• 2006

Cancer is one of the major causes of death; however, the survival rate can be increased if discovered at an early stage for timely treatment. According to the statistics of the World Health Organization of 2002, breast cancer was the most prevalent cancer for all cancers occurring in women worldwide, and it account for 16.8% of entire cancers inflicting Korean women today. In order to classify the type of breast cancer whether it is benign or malignant, this study was conducted with the use of the discriminant analysis and the decision tree of data mining with the breast cancer data disclosed on the web. The discriminant analysis is a statistical method to seek certain discriminant criteria and discriminant function to separate the population groups on the basis of observation values obtained from two or more population groups, and use the values obtained to allow the existing observation value to the population group thereto. The decision tree analyzes the record of data collected in the part to show it with the pattern existing in between them, namely, the combination of attribute for the characteristics of each class and make the classification model tree. Through this type of analysis, it may obtain the systematic information on the factors that cause the breast cancer in advance and prevent the risk of recurrence after the surgery.

• Journal of Korean Society for Quality Management
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• v.48 no.3
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• pp.421-431
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• 2020

Purpose: The purpose of this study is to compare and analyze the tokenizer in natural language processing for customer satisfaction in sentiment analysis. Methods: In this study, a supervised learning-based tokenizer Mecab-Ko and an unsupervised learning-based tokenizer SentencePiece were used for comparison. Three algorithms: Naïve Bayes, k-Nearest Neighbor, and Decision Tree were selected to compare the performance of each tokenizer. For performance comparison, three metrics: accuracy, precision, and recall were used in the study. Results: The results of this study are as follows; Through performance evaluation and verification, it was confirmed that SentencePiece shows better classification performance than Mecab-Ko. In order to confirm the robustness of the derived results, independent t-tests were conducted on the evaluation results for the two types of the tokenizer. As a result of the study, it was confirmed that the classification performance of the SentencePiece tokenizer was high in the k-Nearest Neighbor and Decision Tree algorithms. In addition, the Decision Tree showed slightly higher accuracy among the three classification algorithms. Conclusion: The SentencePiece tokenizer can be used to classify and interpret customer sentiment based on online reviews in Korean more accurately. In addition, it seems that it is possible to give a specific meaning to a short word or a jargon, which is often used by users when evaluating products but is not defined in advance.

• Korean Journal of Construction Engineering and Management
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• v.22 no.6
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• pp.76-86
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• 2021

Construction projects have high potentials of claims and disputes due to inherent risks where a variety of stakeholders are involved. Since disputes could cause losses in terms of cost and time, it is a critical issue for contractors to forecast and pro-actively manage disputes in advance in order to secure project efficiency and higher profits. The objective of the study is to develop a decision tree-based predictive analytics model for forecasting dispute types and their probabilities according to construction project conditions. It can be a useful tool to forecast potential disputes and thus provide opportunities for proactive management.

• Asia-Pacific Journal of Business
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• v.6 no.2
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• pp.1-17
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• 2015

This study compares the most widely used parametric and non-parametric techniques to measure cost and profit efficiency of banks, namely the Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA). We formulate the specification form of both stochastic cost and profit frontier models and constant return to scale Cost DEA and Profit DEA models and provide an empirical assessment of the cost and profit frontiers based on a panel dataset of National Commercial Banks (NCBs) and Private Banks (PBs) in Bangladesh over the 2001-2010 period. The cost inefficiency and profit efficiency are slightly higher for PBs than NCBs in case of both SFA and DEA. The coefficients of advance and off-balance sheet items are significant that positively influence the banks in stochastic cost frontier model while the advance, other earning assets, price of borrowed fund are significant and negative effects on the banks in stochastic profit frontier model. The average cost inefficiency and average profit efficiency are recorded with 16.3% and 91% respectively. The highest and lowest cost inefficiency are observed for Janata Bank and United Commercial Bank Limited whilst the highest and lowest profit efficiency are recorded for Eastern Bank Limited and Janata Bank respectively. The average technical and allocative efficiency are 68.8% and 35.9%, respectively in case of CRS cost-DEA model whereas they are 70.3% and 31.8% in case of CRS profit-DEA model. The average cost inefficiency is recorded 6.3% by SFA whereas it is 24.5% by DEA. The average profit efficiency is found 91% by SFA while it is 22.1% by DEA, and SFA method shows better bank efficiency than DEA.

• Informatization Policy
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• v.26 no.3
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• pp.3-35
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• 2019

In the fourth industrial revolution age, because of advance in the intelligence information technologies, the various roles of AI have attracted public attention. Starting with Google's Alphago, AI is now no longer a fantasized technology but a real one that can bring ripple effect in entire society. Already, AI has performed well in the medical service, legal service, and the private sector's business decision making. This study conducted an exploratory analysis on the possibilities and issues of AI-driven policy decision making in the public sector. The three research purposes are i) could AI make a policy decision in public sector?; ii) how different is AI-driven policy decision making compared to the existing methods of decision making?; and iii) what issues would be revealed by AI's policy decision making? AI-driven policy decision making is differentiated from the traditional ways of decision making in that the former is represented by rationality based on sufficient amount of information and alternatives, increased transparency and trust, more objective views for policy issues, and faster decision making process. However, there are several controversial issues regarding superiority of AI, ethics, accountability, changes in democracy, substitution of human labor in the public sector, and data usage problems for AI. Since the adoption of AI for policy decision making will be soon realized, it is necessary to take an integrative approach, considering both the positive and adverse effects, to minimize social impact.

• Journal of muscle and joint health
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• v.20 no.3
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• pp.197-206
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• 2013

Purpose: The purpose of this study was to investigate end-of-life care preferences of employees working in a university hospital. Methods: Of 650 eligible employees that were approached, 607 employees (386 nurses, 93 physicians, and 128 general staff) completed the Korean version of Preferences for Care Near the End of Life (PCEOL-K). Results: Among 5 dimensions of the PECOL-K, "Pain" was the most preferred care dimension and "Decision making by health care professional" was the least preferred care dimension. The item that received the highest mean score was "I want to let nature guide my dying and I do not want my life to be artificially prolonged in any way", and the lowest item was "I want health care providers to make all decisions about my care". As preferred care near the end of life, nurses gave lower scores to the life sustaining treatment and decision making by health care profession than physicians and general staff. Compared to physicians and nurses, general staff preferred the decision making by health care professional and by family. Conclusion: The results show that adequate pain relief is the most preferred care at the end of life among hospital employees and non-medical personnel preferred decision making by others.

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.139-155
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• 2003

This paper presents the new methodology of analyzing and classifying patterns of customers in mobile telecommunication market to enhance the performance of predicting the credit information based on the decision tree and neural network. With the application of variance selection process from decision tree, the systemic process of defining input vector's value and the rule generation were developed. In point of customer management, this research analyzes current customers and produces the patterns of them so that the company can maintain good customer relationship and makes special management on the customer who has huh potential of getting out of contract in advance. The real implementation of proposed method shows that the predicted accuracy is higher than existing methods such as decision tree(CART, C4.5), regression, neural network and combined model(CART and NN).

• Journal of Korean Critical Care Nursing
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• v.13 no.1
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• pp.27-43
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• 2020

Purpose : This integrative review aimed to synthesize studies on intensive care unit (ICU) nurses' attitude, perceptions, and experiences toward end-of-life care decision-making. Methods : Using Whittermore and Knafl (2005)'s methods, we identified and synthesized research articles published in domestic journals between the years 2003 and 2019 and evaluated the quality of selected articles using the Mixed Methods Appraisal Tool. Results : In the 13 studies reviewed, 12 were published prior to enactment of the "The Act for Hospice and Palliative Care and Decision-Making about Life-Sustaining Treatment (2018)." All nine quantitative studies identified were based on cross-sectional descriptive survey. In four qualitative studies, content analysis (n=2) and phenomenology (n=2) were used. Overall, ICU nurses were well-aware of the necessity of communicating and limiting life-sustaining treatments. Many ICU nurses had positive attitude towards limiting life-sustaining treatments to promote patients' comfort and dignity. Although nurses were willing to take active roles, they also reported having experienced high stress in the process of decision-making and implementation. Conclusions : It is important to prepare ICU nurses with proper knowledge and attitude regarding the topic area. It is also equally important to develop systems to support nurses' emotional stress and moral distress during communication, decision-making, and implementation.