• International journal of advanced smart convergence
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• v.11 no.2
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• pp.38-43
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• 2022

In this study, there are many difficulties in defining and judging creative people because there is no systematic analysis method using accurate standards or numerical values. Analyze and judge whether In the previous study, A study on the application of rule success cases through machine learning algorithm extraction, a case study was conducted to help verify or confirm the psychological personality test and aptitude test. We proposed a solution to a research problem in psychology using machine learning algorithms, Data Mining's Cross Industry Standard Process for Data Mining, and CRISP-DM, which were used in previous studies. After that, this study proposes a solution that helps to judge creative people by applying the feature selection algorithm. In this study, the accuracy was found by using seven feature selection algorithms, and by selecting the feature group classified by the feature selection algorithms, and the result of deriving the classification result with the highest feature obtained through the support vector machine algorithm was obtained.

• The Korean Journal of Food And Nutrition
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• v.29 no.5
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• pp.643-654
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• 2016

The purpose of this study was to investigate the purchasing behavior and selection attributes for Home Meal Replacement (HMR) and to identify the selection attributes affecting purchasing frequency and purchasing costs of married women living in a city. Among 837 questionnaires distributed to HMR married women consumers, 752 complete questionnaires (89.8%) were analyzed. The younger married women group showed higher frequency of purchasing HMR than the older age group. The 20s and 30s age groups showed higher purchasing costs for HMR than the 40s and older age groups. A higher proportion of employed married women purchased HMR three or more times per week and spent an average of more than 20,000 won per purchase in comparison with unemployed married women. HMR selection attributes were classified into five factors: 'taste and sanitation', 'economic efficiency', 'health and nutrition', 'convenience', and 'reliability and awareness'; mean scores of these factors' importance levels were 4.28, 3.93, 3.59, 3.54, and 3.50 out of 5 points, respectively. The importance level of 'taste and sanitation' factor was significantly greater as married women's age decreased. However, the importance level of 'health and nutrition' factor was significantly greater as married women's age increased. The results of the logistic regression analyses indicate that the 'taste and sanitation' and 'health and nutrition' factors affected frequency of purchasing HMR. The 'reliability and awareness' factor had the most significant impact on cost per purchasing HMR. Therefore, a product differentiation strategy according to married women's age and employment status should be applied. Product qualities and brand value should be improved to enhance competition in the HMR market.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1773-1793
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• 2023

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper-parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.

• Journal of the Korean Society of Food Culture
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• v.26 no.4
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• pp.344-353
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• 2011

The objective of this study was to classify dining-out customers' behaviors at ski resorts based on their restaurant selection factors. Data were collected one-on-one via interview questionnaires of 178 customers at the ski resorts. The mean scores of important attributes (4.12) and satisfactory attributes (3.08) for the sport&leisure purpose group were analyzed. For the date&family trip purpose group, the important attributes (4.13) and satisfactory attributes (3.06) were evaluated, resulting in a significant difference between the two visiting-purpose groups by independent t-test (p<0.05). The recognized important attributes for the sport&leisure purpose group were food taste (4.54), hygiene (4.53), menu variety (4.22), menu price (4.15), and convenience (4.12), and the most recognizable satisfactory attributes were related to convenience (3.52), waiting time (3.95), and employee service (3.90). For the date&family trip purpose group, recognized important attributes were hygiene (4.83), food taste (4.67), menu price (4.40), convenient (4.33), menu variety (4.25), waiting time (4.21), and employee service (4.10), and marked satisfactory attributes were convenience (3.65), hygiene (3.31), atmosphere (3.25), employee service (3.23), waiting time (3.17), and food taste (3.00). These results suggest that restaurant selection attributes would be useful tools to restaurant managers in controlling the quality of foodservice and satisfying service requirements for dinning-out customers at ski resorts.

• Fashion & Textile Research Journal
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• v.16 no.2
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• pp.245-254
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• 2014

The purpose of this study was to describe 20's to 30's men's fashion lifestyle and develop clusters in grooming related variables. We also tried to interpret profile of clusters, and determine the difference between different level of grooming clusters in appearance care behaviors, and clothing behaviors such as clothing selection, preference of clothing image and design in men's wear. Data was obtained using the survey methods by convenience sampling. Frequency analysis, factor analysis, cluster analysis, chi-square test, and t-test were used for analysis using SPSS 18.0. The result of factor analysis of men's lifestyle show that 5 factors are extracted. Two different clusters were formed after the K-means cluster analysis. We realized that the level of grooming activity is significantly associated with the young men's major expenditure item, and beauty/care items, and the reason for exercise. The level of grooming was strongly associated with clothing selection behaviors. In addition, there is a significant difference in preferred image between two different grooming groups. In the feminine image, HG group favored more than LG group. The preferred design was associated with the degree of grooming as well. Unique and stylish top and bottom styles such as cargo, hiphop, and boots cut were favored more by HG group than LG group. We suggest that we can do market segmentation by the degree of the grooming activity, considering the current men's taste and trend to extend market share.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.5
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• pp.3029-3035
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• 2013

Background: Most developing countries have been unable to implement well-organized health care systems, especially comprehensive Pap smear screening-based programs. One of the reasons for this is regional differences in medical services, and a low-cost portable cervical screening system is necessary. To improve regional discrepancies in cervical screening systems, we investigated the usefulness and acceptability of cervical selfsampling by liquid-based cytology (LBC) for 290 volunteers in the Lao PDR. Materials and Methods: Following health education with comprehensive documents, cervical self-sampling kits by LBC were distributed in three provincial, district, and village areas to a total of 290 volunteers, who were asked to take cytology samples by themselves. Subsequently, the acceptability of self-sampling was evaluated using a questionnaire. Results: The documents were well understood in all three regions. Regarding the acceptability of self-sampling, the selections for subsequent screening were 62% self-sampling, 36% gynecologist-sampling, 1% either method, and 1% other methods. The acceptability rates were higher in the district and the village than in the province. For the relationship between acceptability and pregnancy, the self-sampling selection rate was higher in the pregnancy-experienced group (75%) than in the pregnancy-inexperienced group (60%). For the relationship between selection of self-sampling and experience of screening, the self-sampling selection rate was higher in the screening-inexperienced group (62%) than in the screening-experienced group (52%). Conclusions: Our data show that this new way forward, involving a combination of self-sampling and LBC, is highly acceptable regardless of age, educational background, and residence in rural areas in a developing country.

• Imaging Science in Dentistry
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• v.32 no.3
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• pp.147-151
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• 2002

Purpose: To compare the standard deviation of gray levels on digital subtracted images obtained by different dental subtraction programs. Materials and Methods: Paired periapical films were taken at the lower premolar and molar areas of the phantoms involving human mandible. The bite registration group used Rinn XCP equipment and bite registration material, based on polyvinyl siloxane, for standardization. The no bite registration group used only Rinn XCP equipment. The periapical film images were digitized at 1200 dpi resolution and 256 gray levels by a flat bed scanner with transparency unit. Dental digital subtraction programs used for this study were Subtractor (Biomedisys Co., Korea) and Emago (Oral Diagnostic Systems, The Netherlands). To measure the similarities between the subtracted images, the standard deviations of the gray levels were obtained using a histogram of subtracted images, which were then analyzed statistically. Results: Subtracted images obtained by using the Emago program without manual selection of corresponding points showed the lowest standard deviation of gray levels (p<0.01). And the standard deviation of gray levels was lower in subtracted images in the group of a bite registration than in the group of no use of bite registration (p < 0.01). Conclusion: Digital radiographic subtraction without manual selection of reference points was found to be a convenient and superior method.

• Asian-Australasian Journal of Animal Sciences
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• v.27 no.4
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• pp.587-591
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• 2014

Automatic milking systems (AMS) rely upon voluntary cow traffic (the voluntary movement of cattle around a farm) for milk harvesting and feed consumption. Previous research on conventional milking systems has shown differences between dairy cow breeds for intake and milk production, however, the ability to manipulate voluntary cow traffic and milking frequency on AMS farms through breed selection is unknown. This study investigated the effect of breed (Holstein Friesian versus Illawarra) on voluntary cow traffic as determined by gate passes at the Camden AMS research farm dairy facility. Daily data on days in milk, milk yield, gate passes and milking frequency for 158 Holstein Friesian cows and 24 Illawarra cows were collated by month for the 2007 and 2008 years. Illawarra cows had 9% more gate passes/day than Holstein cows over the duration of the study; however, the milking frequency and milk yield of both breeds were similar. Gate passes were greatest for both breeds in early lactation and in the winter (June to August) and summer (December to February) seasons. These findings highlight an opportunity to translate increased voluntary cow movement associated with breed selection into increased milking frequencies, milk production and overall pasture-based AMS performance.

• Journal of the Korean Home Economics Association
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• v.43 no.4 s.206
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• pp.49-63
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• 2005

The Purposes of this study were to segment Chinese consumers by clothing Purchase motive, and then to analyze and compare the clothing purchasing behavior among the segmented groups. The subjects were 655 career women of middle and high class in their 20's and 30's living in Benjing, Shanghai, Shenzhen, and Changchun. A total of 655 questionnaires were analyzed by using frequency, mean, factor analysis, ANOVA, Duncan's multiple range test, cluster analysis, and X^2 _ test. The results were as follows: 1. Chinese consumers were segmented into clothing high-involvement group, fashion pursuing group, practicality pursuing group, and characterless group. 2. The clothing purchase behavior variables such as purchasing motive, using informants, clothing selection standards, store selection standards, purchasing place, satisfaction after purchasing clothes, price of purchase, shopping time, shopping companion, and paying method were significantly different among the 4 segmented groups. 3. The demographic variables such as a city, marriage, total monthly income, and average monthly expenditure on clothing were significantly different among the 4 segmented groups.

• Journal of the Korean Home Economics Association
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• v.40 no.2
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• pp.87-100
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• 2002

The purpose of this study was to investigate the demographics and general clothing buying behavior according to clothing shopping orientation of female workers. A questionnaire was developed to measure clothing shopping orientation, fashion information sources, stores selection criteria, clothing purchasing frequency of a year, purchasing expenditure of clothing, the demographics. The questionnaire was administered to 775 female teacher in Chonnam. The data was analyzed using percentage, frequency, mean, factor analysis, Cluster Analysis, $\chi^2$-_test and ANOVA, Duncan test. The results of the study were as follows: 1. The female teachers were classified into four groups by the cluster analysis; indifferent shopping group, rational shopping group, conspicuous shopping group, recreational shopping group. 2. In the case of fashion information sources, significant differences were found according to shopping orientation subdivision in mass media information, information by consumer, information by marketer. 3. The stores selection criteria were significantly different depending on shopping orientation subdivision in goods and atmosphere of shop, promotion, convenience of shop's location. 4. The clothing purchasing frequency of a year were significantly different depending on shopping orientation subdivision. 5. The significant differences were found according to shopping orientation subdivision in purchasing expenditure of clothing. 7. In the demographic characteristics, significant differences were found according to shopping orientation subdivision in age, marriage, the length of one's work, income.