• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.212-219
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• 2020

We conducted this study to identify the accommodation applications users' lifestyle types and the composition factors for consumers' accommodation applications selection attributes and to identify the difference in the selection attributes perception of accommodation applications between groups classified by user's lifestyle types. According to the study, 6 factors were derived as the accommodation applications users' lifestyle types and were named social/leisure-oriented type, fashion-seeking type, culture-seeking type, self-examining type, self-centered type, family-oriented type. Also 6 factors were derived as the accommodation applications selection attributes and were named convenience, interactivity, economic efficiency, transaction reliability, product reliability and informativeness. Valid clusters were divided into four groups and were named culture/tourism group, self-examining group, passive and cautious group and Social and practicality-seeking group. Most of the selection attributes perception of accommodation applications between groups had statistically significant differences(p<.05), except for some items of transaction reliability. Based on the results of this study, we should strive to establish effective marketing strategies that reflect differences in the selection attributes perception of the accommodation application between groups classified by users' lifestyle types.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.38-59
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• 2022

Web services instances can be classified into two categories, namely trusted and untrusted from users. A web service with high throughput (TP) and low response time (RT) instance values is a trusted web service. Web services are not trustworthy due to the mismatch in the guaranteed instance values and the actual values achieved by users. To perform web services selection from users' attained TP and RT values, we need to verify the correct prediction of trusted and untrusted instances from invoked web services. This accurate prediction of web services instances is used to perform the selection of web services. We propose to construct fuzzy rules to label web services instances correctly. This paper presents web services selection using a well-known machine learning algorithm, namely REPTree, for the correct prediction of trusted and untrusted instances. Performance comparison of REPTree with five machine learning models is conducted on web services datasets. We have performed experiments on web services datasets using a ten k-fold cross-validation method. To evaluate the performance of the REPTree classifier, we used accuracy metrics (Sensitivity and Specificity). Experimental results showed that web service (WS1) gained top selection score with the (47.0588%) trusted instances, and web service (WS2) was selected the least with (25.00%) trusted instances. Evaluation results of the proposed web services selection approach were found as (asymptotic sig. = 0.019), demonstrating the relationship between final selection and recommended trust score of web services.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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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.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.5
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• pp.712-721
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• 2020

Objective: The Wannan Black pig is a typical Chinese indigenous, disease-resistant pig breed with high fertility, and a crude-feed tolerance that has been bred by artificial selection in the south of Anhui province for a long time. However, genome variation, genetic relationships with other pig breeds, and domestication, remain poorly understood. Here, we focus on elucidating the genetic characteristics of the Wannan Black pig and identifying selection signatures during domestication and breeding. Methods: We identified the whole-genome variation in the Wannan Black pig and performed population admixture analyses to determine genetic relationships with other domesticated pig breeds and wild boars. Then, we identified the selection signatures between the Wannan Black pig and Asian wild boars in 100-kb windows sliding in 10 kb steps by using two approaches: the fixation index (F_ST) and π ratios. Results: Resequencing the Wannan Black pig genome yielded 501.52 G of raw data. After calling single-nucleotide variants (SNVs) and insertions/deletions (InDels), we identified 21,316,754 SNVs and 5,067,206 InDels (2,898,582 inserts and 2,168,624 deletions). Additionally, we found genes associated with growth, immunity, and digestive functions. Conclusion: Our findings help in explaining the unique genetic and phenotypic characteristics of Wannan Black pigs, which in turn can be informative for future breeding programs of Wannan Black pigs.

• The Journal of the Korea Contents Association
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• v.13 no.6
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• pp.401-411
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• 2013

Selection of profitable research and development (R&D) projects is one of the major factors affecting sustained growth of firms and countries. This paper analyze what influences the knowledge on the business model exerted on selection of a R&D project. A business model converts the technology value to the customer value, and comprehensively describes the target customers for commercializing a new technology, core values, behaviors within organizations, resources, and external partners. Thus, understanding a business model would make R&D project evaluators place the feasibility and profitability of the business above the merits of the proposed technology in evaluating the technology development. To verify this hypothesis, we had 78 R&D project evaluators acquire the knowledge on the business model and measured how their criteria for R&D project selection have changed using the AHP method. The results shows that feasibility and profitability are more important than the merit of proposed technology, especially capability of company and business development are more important than the levels of technology innovation.

• Journal of Microbiology and Biotechnology
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• v.29 no.2
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• pp.230-234
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• 2019

Currently, the genetic modification of Aspergillus oryzae is mainly dependent on protoplast-mediated transformation (PMT). In this study, we established a dual selection marker system in an industrial A. oryzae 3.042 strain by using Agrobacterium tumefaciens-mediated transformation (ATMT). We first constructed a uridine/uracil auxotrophic A. oryzae 3.042 strain and a pyrithiamine (PT)-resistance binary vector. Then, we established the ATMT system by using uridine/uracil auxotrophy and PT-resistance genes as selection markers. Finally, a dual selection marker ATMT system was developed. This study demonstrates a useful dual selection marker transformation system for genetic manipulations of A. oryzae 3.042.

• Asian-Australasian Journal of Animal Sciences
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• v.20 no.3
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• pp.453-460
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• 2007

An overview of developments important in the future of animal breeding is discussed. Examples from the application of quantitative genetic principles to selection in chickens and mice are given. Lessons to be learned from these species are that selection for production traits in livestock must also consider selection for reproduction and other fitness-related traits and inbreeding should be minimized. Short-term selection benefits of best linear unbiased predictor methodology must be weighed against long-term risks of increased rate of inbreeding. Different options have been developed to minimize inbreeding rates while maximizing selection response. Development of molecular genetic methods to search for quantitative trait loci provides the opportunity for incorporating marker-assisted selection and introgression as new tools for increasing efficiency of genetic improvement. Theoretical and computer simulation studies indicate that these methods hold great promise once genotyping costs are reduced to make the technology economically feasible. Cloning and transgenesis are not likely to contribute significantly to genetic improvement of livestock production in the near future.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.652-662
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• 2023

Identifying highly discriminating genes is a critical step in tumor recognition tasks based on microarray gene expression profile data and machine learning. Gene selection based on tree models has been the subject of several studies. However, these methods are based on a single-tree model, often not robust to ultra-highdimensional microarray datasets, resulting in the loss of useful information and unsatisfactory classification accuracy. Motivated by the limitations of single-tree-based gene selection, in this study, ensemble gene selection methods based on multiple-tree models were studied to improve the classification performance of tumor identification. Specifically, we selected the three most representative tree models: ID3, random forest, and gradient boosting decision tree. Each tree model selects top-n genes from the microarray dataset based on its intrinsic mechanism. Subsequently, three ensemble gene selection methods were investigated, namely multipletree model intersection, multiple-tree module union, and multiple-tree module cross-union, were investigated. Experimental results on five benchmark public microarray gene expression datasets proved that the multiple tree module union is significantly superior to gene selection based on a single tree model and other competitive gene selection methods in classification accuracy.

• ETRI Journal
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• v.33 no.2
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• pp.210-218
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• 2011

Battiti's mutual information feature selector (MIFS) and its variant algorithms are used for many classification applications. Since they ignore feature synergy, MIFS and its variants may cause a big bias when features are combined to cooperate together. Besides, MIFS and its variants estimate feature redundancy regardless of the corresponding classification task. In this paper, we propose an automated greedy feature selection algorithm called conditional mutual information-based feature selection (CMIFS). Based on the link between interaction information and conditional mutual information, CMIFS takes account of both redundancy and synergy interactions of features and identifies discriminative features. In addition, CMIFS combines feature redundancy evaluation with classification tasks. It can decrease the probability of mistaking important features as redundant features in searching process. The experimental results show that CMIFS can achieve higher best-classification-accuracy than MIFS and its variants, with the same or less (nearly 50%) number of features.

• Journal of Technologic Dentistry
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• v.42 no.4
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• pp.362-371
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• 2020

Purpose: The following study seeks to ascertain the motivations behind students' academic major selection and to identify the obstacles they encounter in the transition to university life, with the objective of providing information necessary to adapt well to the university and the course. Thereby, we aim to supply basic resources needed in the development of a university adaptation program to prevent academic dropout. Methods: Between October 1, 2019 and November 29, 2019, a self-administered questionnaire was distributed to a study sample consisting of students currently attending dental technology courses in Gangwondo and Gyeonggido. A total of 474 (94.8%) responses to the questionnaire were received and used for the final analysis. Results: Factors including major selection motivation, intrinsic motivation (p<0.001), academic adjustment (p<0.001), social adjustment (p<0.01), and institutional adjustment (p<0.05) all had negative relationships with academic dropout intention. Personal-emotional adjustment (p<0.001), however, showed a positive relationship with dropout intention. The explanatory power of the model was found to be 50.0%. Conclusion: This research shows that intrinsic motivation and personal-emotional adjustment diminish academic dropout intention. Therefore, it is recommended that diverse postenrolment course-adjustment programs should be developed to improve students' confidence in their choice of study, their adjustment to the course, and their level of satisfaction.