• 한국작물학회:학술대회논문집
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• 한국작물학회 2022년도 추계학술대회
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• pp.233-233
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• 2022

As the world's population grows and food needs diversify, the demand for horticultural crops for beneficial traits is increasing. In order to meet this demand, it is necessary to develop suitable cultivars and breeding methods accordingly. Breeding methods have changed over time. With the recent development of sequencing technology, the concept of genomic selection (GS) has emerged as large-scale genome information can be used. GS shows good predictive ability even for quantitative traits by using various markers, breaking away from the limitations of Marker Assisted Selection (MAS). Moreover, GS using machine learning (ML) and deep learning (DL) has been studied recently. In this study, we aim to build a system that selects phenotype-related markers using the genomic information of the pepper population and trains a genomic selection model to select individuals from the validation population. We plan to establish an optimal genome wide association analysis model by comparing and analyzing five models. Validation of molecular markers by applying linkage markers discovered through genome wide association analysis to breeding populations. Finally, we plan to establish an optimal genome selection model by comparing and analyzing 12 genome selection models. Then We will use the genome selection model of the learning group in the breeding group to verify the prediction accuracy and discover a prediction model.

• 응용통계연구
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• 제33권4호
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• pp.439-449
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• 2020

메타 분석은 여러 연구 결과들을 종합시켜주는 분석 방법 중 하나이다. 이 때 수집되는 연구 문헌들은 소규모 연구인 경우 통계적으로 유의한 결과를 보이는 연구가 출간될 확률이 높기 때문에, 선택 편향의 특수한 경우인 출판 편향이 종종 발생한다. 선택 편향을 보정하는 방법에는 조건부 가능도와 가중 추정 방정식이 있는데 이 방법들은 실제 얻기 힘든 정확한 선택 확률 모형을 필요로한다. 반면 쌍별 유사가능도 접근법은 선택 확률 모형을 정확히 알 수 없는 경우에도 선택 편향을 보정할 수 있는 방법으로 제안되었다. 본 논문은 메타분석에서 쌍별 유사가능도 접근법의 성능과 문제점을 수치적으로 연구한다.

• 대한안전경영과학회지
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• 제11권2호
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• pp.153-158
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• 2009

An optimal selection problem of SCM system is one of the critical issues for the company's competitiveness and performance under global economy. This paper presents a hierarchy model consisted of characteristic factors for introducing SCM system and an AHP (Analytic Hierarchy Process) based decision-making model for SCM system evaluation and selection. The proposed model can systematically construct the objectives of SCM system selection to meet the business goals. This paper focuses on selecting an optimal SCM system considering both all decision factors and sub-decision factors of a hierarchy model. Especially, the benefit/cost analysis is applied to choose SCM system. A case study shows the feasibility of the proposed model and the model can help a company to make better decision-making in the SCM system selection problem.

• 대한가정학회지
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• 제47권10호
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• pp.1-11
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• 2009

The purpose of this study was to explore differences in style preference, information source, and store selection behaviors of Korean and Chinese wedding dress shoppers. Data obtained from 141 Chinese and 143 Korean females were analyzed through descriptive analysis, t-test, cross-tabulation and factor analysis. Results for Chinese and Korean consumers depended on the marketer-driven information source as well as on the consumer-driven information source. Both groups considered the aesthetics of the wedding dress as the most important criterion for store selection. There were some differences between Chinese and Korean consumers in style preferences and store selection criteria. Chinese consumers liked a wider range of wedding dress styles compared to Korean consumers. Also, Chinese consumers regarded fashionability as more significant whereas Koreans considered economic benefits more importantly when selecting stores for a wedding dress.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권4호
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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.

• Journal of Communications and Networks
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• 제18권3호
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• pp.288-298
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• 2016

Proactive relay selection in cognitive radio networks has recently received considerable attention. However, its outage probability analysis is limited to partially-identical fading distributions, uncorrelation among received signal-to-noise ratios (SNRs), and no direct channel. This paper completes this literature deficiency by generalizing the existing analysis for non-identical fading distributions, correlation among received SNRs, and with direct channel. Numerous results demonstrate that relay selection with a direct channel achieves a higher diversity order and superior performance than that without a direct channel at virtually no cost of power and bandwidth. Further, proactive relay selection suffers an error floor at either a large maximum transmit power or large maximum interference power; however, the error floor level can be significantly remedied with an increase in the number of relays.

• 한국조리학회지
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• 제23권3호
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• pp.111-122
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• 2017

The purpose of this study was to examine the market segments of Chinese dining-out customers based on their lifestyle. This study focused on the selection and consumption behavior of dining-out customers. The subjects of this study were 20 years old or older diners in Luoyang, China, and the data were collected for 11 days from April 5, 2016. 400 questionnaires were distributed, and 390 copies were collected. After excluding 9 inadequate questionnaires, 381 responses were used for data analysis by using IBM SPSS 23.0, and Data analysis included frequency analysis, cluster analysis, one-way ANOVA, and cross tabulation. The results of empirical analysis showed that there was a significant difference in selection attributes, consumption behavior and demographic characteristics in terms of lifestyle market segments.

• 스마트미디어저널
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• 제12권3호
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• pp.30-37
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• 2023

Alzheimer's disease (AD) symptoms are being treated by early diagnosis, where we can only slow the symptoms and research is still undergoing. In consideration, using T1-weighted images several classification models are proposed in Machine learning to identify AD. In this paper, we consider the improvised feature selection, to reduce the complexity by using wrapping techniques and Restricted Boltzmann Machine (RBM). This present work used the subcortical and cortical features of 278 subjects from the ADNI dataset to identify AD and sMRI. Multi-class classification is used for the experiment i.e., AD, EMCI, LMCI, HC. The proposed feature selection consists of Forward feature selection, Backward feature selection, and Combined PCA & RBM. Forward and backward feature selection methods use an iterative method starting being no features in the forward feature selection and backward feature selection with all features included in the technique. PCA is used to reduce the dimensions and RBM is used to select the best feature without interpreting the features. We have compared the three models with PCA to analysis. The following experiment shows that combined PCA &RBM, and backward feature selection give the best accuracy with respective classification model RF i.e., 88.65, 88.56% respectively.

• Human Ecology Research
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• 제60권4호
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• pp.625-643
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• 2022

The purpose of this study was to explore the trends and core keywords of the secondary school home economics teacher selection test using content analysis and text network analysis. The sample comprised texts of the secondary school home economics teacher 1st selection test for the 2017-2022 school years. Determination of frequency of occurrence, generation of word clouds, centrality analysis, and topic modeling were performed using NetMiner 4.4. The key results were as follows. First, content analysis revealed that the number of questions and scores for each subject (field) has remained constant since 2020, unlike before 2020. In terms of subjects, most questions focused on 'theory of home economics education', and among the evaluation content elements, the highest percentage of questions asked was for 'home economics teaching·learning methods and practice'. Second, the network of the secondary school home economics teacher selection test covering the 2017-2022 school years has an extremely weak density. For the 2017-2019 school years, 'learning', 'evaluation', 'instruction', and 'method' appeared as important keywords, and 7 topics were extracted. For the 2020-2022 school years, 'evaluation', 'class', 'learning', 'cycle', and 'model' were influential keywords, and five topics were extracted. This study is meaningful in that it attempted a new research method combining content analysis and text network analysis and prepared basic data for the revision of the evaluation area and evaluation content elements of the secondary school home economics teacher selection test.

• ETRI Journal
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• 제34권6호
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• pp.847-857
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• 2012

Intrusion detection systems (IDSs) have an important effect on system defense and security. Recently, most IDS methods have used transformed features, selected features, or original features. Both feature transformation and feature selection have their advantages. Neighborhood component analysis feature transformation and genetic feature selection (NCAGAFS) is proposed in this research. NCAGAFS is based on soft computing and data mining and uses the advantages of both transformation and selection. This method transforms features via neighborhood component analysis and chooses the best features with a classifier based on a genetic feature selection method. This novel approach is verified using the KDD Cup99 dataset, demonstrating higher performances than other well-known methods under various classifiers have demonstrated.