• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.4
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• pp.277-281
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• 2006

Cyber counseling, one of the most compatible type of consultation for the information society, enables people to reveal their mental agonies and private problems anonymously, since it does not require face-to-face interview between a counsellor and a client. However, there are few cyber counseling centers which provide high quality and trustworthy service, although the number of cyber counseling center has highly increased. Therefore, this paper is intended to enable an appropriate consultation for each client by analyzing client propensity using Bayesian variable selection. Bayesian variable selection is superior to stepwise regression analysis method in finding out a regression model. Stepwise regression analysis method, which has been generally used to analyze individual propensity in linear regression model, is not efficient since it is hard to select a proper model for its own defects. In this paper, based on the case database of current cyber counseling centers in the web, we will analyze clients' propensities using Bayesian variable selection to enable individually target counseling and to activate cyber counseling programs.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2016.05a
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• pp.217-218
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• 2016

Domestic distribution warehouse has been developed as a strategic strategic point for inventory management and shipping of goods for companies. Warehouse is classified as distribution center, transport center, complex center depending on the role. Currently, at the same time it is considering the price factor such as rent price or land price and and logistical capabilities when considering the location of domestic warehouses. The price evaluation factors has specific methods for the evaluation but logistical elements is not currently clear. In this study, we have studied location selection of distribution center considering the logistical elements through expert surveys.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.170-173
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• 2009

In this paper, we propose a fast intra prediction mode selection method in Scalable Video Coding(SVC) which is an emerging video coding standard as an extension of H.264/Advanced Video Coding(H.264/AVC). The proposed method decides a candidate intra prediction mode based on the characteristic of macroblock smoothness. Statistical analysis is applied to computing that smoothness in spatial enhancement layer. We also propose an early termination scheme for Intra_BL mode decision where the RD cost value of Intra_BL is utilized. Compared with JSVM software, our scheme can reduce about 55% of the computation complexity of intra prediction on average, while the performance degradation is negligible; For low QP values, the average PSNR loss is very negligible, equivalently the bit rate increases by 0.01%. For high QP values, the average PSNR loss is less than 0.01dB, which equals to 0.25% increase in bitrate on average.

• Journal of Korean Clinical Nursing Research
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• v.23 no.3
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• pp.361-375
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• 2017

Purpose: This study was conducted to update the existing nursing practice guideline for intravenous infusion guidelines according to the evidence-based practice guideline in South Korea. Methods: Guideline update process was performed using 22 steps according to the manuals developed by NICE and SIGN. Results: Updated nursing practice guidelines for the intravenous infusion were consisted of 23 domains and 322 recommendations. The number of recommendations in each domain were 4 for general instruction, 12 for vascular access device selection, 20 for site selection, 9 for insertion, 54 for stabilization, 21 for maintaining patency, 4 for blood sampling, 33 for exchange and removal, 28 for add-on device selection, 28, 72 for infusion related complications, 56 for infusion therapies, 7 for education, and 2 for documentation and report. There were 15.9% of A, 30.2% of B, 53.9% of C in terms of grade recommendations. A total of 178 (51.6%) recommendations were newly developed and 24 previous recommendations have been deleted. Conclusion: Updated nursing practice guideline for intravenous infusion was expected to be an evidence-based practice guideline for intravenous infusion in South Korea. This guideline is suggested to be disseminated to clinical nursing settings nationwide to improve the efficiency of intravenous infusion practice.

• Journal of Korean Society for Quality Management
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• v.48 no.4
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• pp.567-580
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• 2020

Purpose: The purpose of this study is to present a preferred parts list, establish a search tool for common parts, and propose a development plan for parts management. Methods: When selecting the preferred parts list, the weight of the selection of the military parts and the online site of the civilian parts were taken into account. By establishing a database of selected preferred parts list, we have established a system for searching common parts. Results: The results of this study are as follows; In order to establish parts management work in Korea's defense, it is necessary to improve the Defense Acquisition Program Act, Enforcement Decree (Rules), and the Regulations for Defense Acquisition Program Administration. Development of performance indicators for parts management is required, and establishment of procedures and standards for selection of preferred parts lists is required. Considerations for the selection of preferred parts list include whether they are used a lot in civilian products, whether they are used a lot, whether they are certified suppliers, whether they are national and industrial standard products, whether they perform well considering reliability, durability, and quality, and whether they can be supplied continuously. Conclusion: It is necessary to establish policies and systems related to parts management and to establish criteria for selecting preferred parts list for the development of domestic parts management tasks.

• Journal of Biomedical Engineering Research
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• v.44 no.1
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• pp.25-32
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• 2023

Recognizing the size and location of prostate cancer is critical for prostate cancer diagnosis, treatment, and predicting prognosis. This paper proposes a model to classify the tumor region and normal tissue with cross-sectional visual images of prostatectomy tissue. We used specimen images of 44 prostate cancer patients who received prostatectomy at Gachon University Gil Hospital. A total of 289 prostate slice images consist of 200 slices including tumor region and 89 slices not including tumor region. Images were divided based on the presence or absence of tumor, and a total of 93 features from each slice image were extracted using Radiomics: 18 first order, 24 GLCM, 16 GLRLM, 16 GLSZM, 5 NGTDM, and 14 GLDM. We compared feature selection techniques such as LASSO, ANOVA, SFS, Ridge and RF, LR, SVM classifiers for the model's high performances. We evaluated the model's performance with AUC of the ROC curve. The results showed that the combination of feature selection techniques LASSO, Ridge, and classifier RF could be best with an AUC of 0.99±0.005.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.108-117
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• 2024

The selection and recommendation of a suitable job applicant from the pool of thousands of applications are often daunting jobs for an employer. The recommendation and selection process significantly increases the workload of the concerned department of an employer. Thus, Resume Classification System using the Natural Language Processing (NLP) and Machine Learning (ML) techniques could automate this tedious process and ease the job of an employer. Moreover, the automation of this process can significantly expedite and transparent the applicants' selection process with mere human involvement. Nevertheless, various Machine Learning approaches have been proposed to develop Resume Classification Systems. However, this study presents an automated NLP and ML-based system that classifies the Resumes according to job categories with performance guarantees. This study employs various ML algorithms and NLP techniques to measure the accuracy of Resume Classification Systems and proposes a solution with better accuracy and reliability in different settings. To demonstrate the significance of NLP & ML techniques for processing & classification of Resumes, the extracted features were tested on nine machine learning models Support Vector Machine - SVM (Linear, SGD, SVC & NuSVC), Naïve Bayes (Bernoulli, Multinomial & Gaussian), K-Nearest Neighbor (KNN) and Logistic Regression (LR). The Term-Frequency Inverse Document (TF-IDF) feature representation scheme proven suitable for Resume Classification Task. The developed models were evaluated using F-Score_M, Recall_M, Precission_M, and overall Accuracy. The experimental results indicate that using the One-Vs-Rest-Classification strategy for this multi-class Resume Classification task, the SVM class of Machine Learning algorithms performed better on the study dataset with over 96% overall accuracy. The promising results suggest that NLP & ML techniques employed in this study could be used for the Resume Classification task.

• KNOM Review
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• v.21 no.2
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• pp.35-45
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• 2018

As Network Slicing functionality gets applied to mobile networking, a mechanism that enables the selection of network slices becomes indispensable. Following the 3GPP Technical Specification for the 5G Architecture, the inclusion of the Network Slice Selection Function (NSSF) in order to leverage the process of slice selection is apparent. However, actual implementation of this network function needs to deal with the dynamic changes of network instances, due to this, a platform that supports the orchestration of Virtual Network Functions (VNF) is required. Our proposed solution include the use of the Central Office Rearchitected as a Data Center (CORD) platform, with the specified profile for mobile networks (M-CORD) that integrates a service orchestrator (XOS) alongside solutions oriented to Software Defined Networking (SDN), Network Function Virtualization (VNF) and virtual machine management through OpenStack, in order to provide the right ecosystem where our implementation of NSSF can obtain slice information dynamically by relying on synchronization between back-end services and network function instances.

• Proceedings of the Korean Society of Plant Biotechnology Conference
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• 2005.11a
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• pp.403-403
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• 2005

• Korea Journal of Hospital Management
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• v.12 no.4
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• pp.1-21
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• 2007

The relaxation of the regulation in selection of medical institution allows patients to use their own judgement in choosing proper institution for their diseases. Since the change of the regulation, there should have been many changes in medical institution selection behavior. The analysis of the change in disease specific selection pattern is critical because there be an optimal selection criteria that ensure the efficient and effective utilization of medical resources. This study analysis the institution selection factors by comparing the choice among the cases of acute diseases, the cases of chronic diseases, inpatient services, outpatient services, and emergency medical service. The comparisons performed in terms of size, class and other characteristics of medical institutions. For the study the nationally surveyed database was used and the data were analyzed using logistic regression procedure. The results indicates that the primary care facilities were not properly utilized. This study speculates that the reason for the undesirable pattern of utilization is that the roles of primary care facilities in the healthcare delivery system was not clearly defined. Based on the results, the medical policy implications are discussed.