• Journal of the Korea Convergence Society
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• v.11 no.3
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• pp.201-210
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• 2020

The study was conducted to predict usage attitudes and behaviors by combining usage motivation and sensitivity, gender, and age in aviation-related social media. Specifically, the purpose of this study was to examine the effects of social media information acceptance, motivation and sensitivity on gender acceptance by gender and age. To this end, we collected data in an even distribution to prevent gender and age bias among adults aged 20 or older online from April 19 to May 3, 2018. As a result, the lower the female and age in the gender and age had a positive effect on the acceptance of social media information. Motivation for use has a positive effect on information acceptance and sensitivity has a negative effect on information acceptance. Through this, it was found that user class such as gender and age, motivation to use, and sensitivity affect information acceptance. In addition, the negative aspects of the sensitivity factor can be identified, and it is expected to be used as basic data in aviation-related social media marketing strategies.

• Journal of Digital Convergence
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• v.11 no.12
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• pp.725-734
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• 2013

This paper aimed to verify the current status of adolescents' donations and predictors influencing their donation behaviors. First, the result revealed that adolescents' donations were higher in the order of time, material, and talent donation. Second, donation motives, attitude towards donation, self-esteem, a sense of community, and trust in charitable organization were positively correlated. Third, the predictors of material donation behaviors of adolescents were gender, age, extrinsic motives, attitude towards donation, sense of community, and trust in charitable organization. And the predictors of time donation behaviors were gender and sense of community, and those of talent donation were age, intrinsic motives and sense of community. The strategies for the activation of adolescents' donations were suggested.

• The Monthly Diabetes
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• s.256
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• pp.10-13
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• 2011

최근에 발표한 '성별, 사망원인별, 연령별로 조정한 인구예측' 보고서(2011)에 따르면 심장병은 한국인 최대 사망원인 2위에 해당해 2030년이 되면 5명 중 1명은 심장병 때문에 숨을 거둘 것으로 전문가들은 예측한다. 문제는 많은 사람이 이런 시한 폭탄과 함께 살고 있으면서도 여전히 부족하거나 잘못된 정보를 갖고 있다. 이에 분당21세기의원 김한수 박사가 말하는 정확한 심장 이야기를 들어본다.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.553-556
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• 2022

이상지질혈증의 발병에 대한 조기 진단 및 관리하는 것은 중요한 문제이다. 이상지질혈증의 진단은 혈액계측 정보 중에서 네 가지 LDL, HDL, TG, 그리고 TC를 이용하여 진단하며, 이상지질혈증 관리를 위해서는 LDL을 추정하는 것이 중요하다. 본 논문에서는 나이, 성별, 그리고 BMI와 같은 신체계측 정보를 학습하여 LDL-콜레스테롤을 예측하기 위한 준지도학습(Semi-supervised learning) 기반 기계학습 방법을 제안한다. 제안 방법은 얕은 학습(Shallow Learning)기반의 MLP(Multi-Layer Perceptron)을 이용하고, 이상지질혈증 진단인자간의 상관관계를 고려하여 신체계측 정보로 예측된 HDL, TG, 그리고 TC을 이용하여 일반적인 기계학습을 이용한 예측방법의 정확도를 개선한다. 즉, 제안방법은 신체계측 정보를 이용하여 혈액계측 정보의 LDL, HDL, TG, 그리고 TC을 각각 예측하고, 신체계측에 혈액계측의 예측 정보를 추가하여 학습한 준지도학습 기반 얕은 네트워크를 설계한다. 실험결과, HDL, TG, 그리고 TC의 혈액예측 정보를 이용한 준지도학습 기반 LDL 예측 정확도는 71.4%로 신체계측 정보만을 이용한 예측 방법의 67.0% 보다 약 4.4% 개선할 수 있음을 확인한다.

• Journal of Digital Convergence
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• v.12 no.8
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• pp.19-27
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• 2014

Traditionally, Anderson model is recognized as suitable for analysis of predictive factors for the use of medical and social services. Therefore, the present study was aimed to investigate the predictors of the elderly's use of the social service based on previous studies by configuring Anderson model's predisposing factors(gender, age, education level, place of residence, marital status), enabling factors(economic status, health literacy, use of welfare center or not), and need factors(whether held chronic disease, IADL and depression). To this aim, SPSS 18.0 was used for the subject of 329 elderly living in Chungbuk region. The main findings of this study are as follows. The most influential factor on the social service use of the elderly turned out to be whether to use the welfare centers and health literacy of enabling factors. Next, the depressed levels showed the most significant impact among the need factors, and gender was the most influential among the predisposing factors. Based on the results of these studies, some measures were suggested to activate the elderly's use of social services.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.2
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• pp.359-365
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• 2020

Sasang Constitution data that were classified by the QSCCII (Questionnaire for the Sasang Constitution Classification II) and artery pulse waves of Chon, Guan, and Chuck data measured using an electronic manometer, were obtained from 732 subjects who visited an oriental hospital. The pulse width, peak height, and number of peaks were extracted from the pulse waves as feature variables. Validity and reliability analyses were performed to obtain the feature variables. The feature variables with high validity and reliability were selected as the discriminant variables. The pulse wave data were divided into training and predicting samples by applying a fivefold cross-validation technique. Discriminant analysis was performed for the training sample, and discriminant functions were obtained. The discriminant functions were applied to the predicting sample and the Sasang Constitution was predicted. The accuracy of prediction was estimated by comparing the predicted Sasang Constitution and that obtained by QSCCII. The accuracy of the predicted Sasang Constitution before (after) age and sex calibration was 73.6 % (70.4 %), 68.4 % (84.2 %), and 74.2 % (67.7 %) for Taeumin, Soumin, and Soyangin, respectively, and 72.5 % (73.8 %) in total.

• Journal of Digital Convergence
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• v.15 no.5
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• pp.487-494
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• 2017

The purpose of this study was to identify factors which predict problem drinking on adults. Using the data on the Korea Welfare Panel Study for the 7th year, 3,915 people responded to the demographic factor, psychosocial factors and drinking behavior. And the logistic regression analysis was conducted to identify predictors of problem drinking. As a result, 36 percent of those surveyed showed that the problem drinking group. Gender, age, education, occupation, economic status, self-esteem, depression, and satisfaction of family and social relationships were correlated to alcohol use. In addition, the results of logistic regression, gender, age, education, job, self-esteem, depression were predicted problem drinking. Based on these findings, it is recommended practical counterplan that prevention of the problem drinking.

• Proceedings of the KOR-KST Conference
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• 1998.10a
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• pp.73-82
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• 1998

본 연구의 목적은 일주일간 조사된 개인통행행태를 고려한 각 특성별 통행발생예측 방법을 제시하는데 있다. 이를 위하여 일주일간 통행빈도수의 차이를 고려한 집단간 차이를 검정하고, 그 원인을 분석하여 이에 따른 특성별 개인 통행발생예측 모형을 정립하였다. 전체 표본의 각 특성별 개인 내부 변이성을 분석해 본 결과 기간의 차이에 따른 개인 통행행태의 변화는 직업별, 나이별, 성별, 차량소유 유무, 주택소유 형태, 통행목적, 통행수단, 가구원수에 따라 집단간 차이를 보여주었다. 이러한 변수를 이용한 통행발생 예측모형의 분석결과 개인소득이 높을수록, 주책을 자가로 소유한 경우, 자동차를 소유한 경우, 학생일수록, 유직일수록 개인 통행발생량이 많은 것으로 분석되었다. 반면, 아니는 연령대가 높아질수록 통행수가 적어졌다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.256-262
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• 2017

This study examined the peer and gender effects in knowledge-sharing behavior. More specifically, this paper examined how coworker social loafing is related to knowledge sharing and how gender differences moderate the relationship between coworker social loafing and knowledge-sharing behavior. Drawing on economic and social exchange theory, this study predicts that coworker social loafing will decrease the knowledge-sharing behavior. In addition, this paper hypothesized that men will be more likely to withdraw knowledge-sharing behavior than women when they faced coworker social loafing. To test these hypotheses, this paper conducted a hierarchical regression test with the supervisor-employee dyad samples. The empirical results showed that in the relationship between coworker social loafing and knowledge-sharing behavior, coworker social loafing decreased the knowledge-sharing behavior, and the negative effect was larger in the case of men rather than women. In the discussion section, this paper proposes the theoretical and practical implications based on theoretical arguments and empirical findings.

• The Journal of Bigdata
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• v.1 no.1
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• pp.61-66
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• 2016

With big data analysis, companies use the customized marketing strategy based on customer's information. However, because of the concerns about privacy issue and identity theft, people start erasing their personal information or changing the privacy settings on social network site. Facebook, the most used social networking site, has the feature called 'Likes' which can be used as a tool to predict user's demographic profiles, such as sex and age range. To make accurate analysis model for the study, 'Likes' data has been processed by using Gaussian RBF and nFactors for dimensionality reduction. With random Forest and 5-fold cross-validation, the result shows that sex has 75% and age has 97.85% accuracy rate. From this study, we expect to provide an useful guideline for companies and marketers who are suffering to collect customers' data.