• Journal of Information Processing Systems
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• v.16 no.4
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• pp.809-819
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• 2020

Gender prediction accuracy increases as convolutional neural network (CNN) architecture evolves. This paper compares voting and ensemble schemes to utilize the already trained five CNN models to further improve gender prediction accuracy. The majority voting usually requires odd-numbered models while the proposed softmax-based voting can utilize any number of models to improve accuracy. The ensemble of CNN models combined with one more fully-connected layer requires further tuning or training of the models combined. With experiments, it is observed that the voting or ensemble of CNN models leads to further improvement of gender prediction accuracy and that especially softmax-based voters always show better gender prediction accuracy than majority voters. Also, compared with softmax-based voters, ensemble models show a slightly better or similar accuracy with added training of the combined CNN models. Softmax-based voting can be a fast and efficient way to get better accuracy without further training since the selection of the top accuracy models among available CNN pre-trained models usually leads to similar accuracy to that of the corresponding ensemble models.

• KIISE Transactions on Computing Practices
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• v.21 no.11
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• pp.733-738
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• 2015

User demographic information is necessary in order to improve the quality of personalized services such as recommendation systems. Mobile data, especially text data, is known to be effective for prediction of user demographic information. However, mobile text data has privacy issues so that its utilization is limited. In this regard, we introduce an on-device gender prediction framework utilizing mobile text data while minimizing the privacy issue. Discriminative word and emoticon sets of each gender are constructed from web documents written by authors of each gender. After gender prediction is performed by comparing discriminative word and emoticon sets with a user's mobile text data, an ensemble method that combines two prediction results draws a final result. From experiments conducted on real-world mobile text data, the proposed on-device framework shows promising results for gender prediction.

• Journal of Korean Academy of Nursing
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• v.42 no.6
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• pp.810-818
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• 2012

Purpose: This study was done to explore factors relating to number of falls among community-dwelling elders, based on gender. Methods: Participants were 403 older community dwellers (male=206, female=197) aged 60 or above. In this study, 8 variables were identified as predictive factors that can result in an elderly person falling and as such, supports previous studies. The 8 variables were categorized as, exogenous variables; perceived health status, somatization, depression, physical performance, and cognitive state, and endogenous variables; fear of falling, ADL & IADL and frequency of falls. Results: For men, ability to perform ADL & IADL (${\beta}_{32}$=1.84, p<.001) accounted for 16% of the variance in the number of falls. For women, fear of falling (${\beta}_{31}$=0.14, p<.05) and ability to perform ADL & IADL (${\beta}_{32}$=1.01, p<.001) significantly contributed to the number of falls, accounting for 15% of the variance in the number of falls. Conclusion: The findings from this study confirm the gender-based fall prediction model as comprehensive in relation to community-dwelling elders. The fall prediction model can effectively contribute to future studies in developing fall prediction and intervention programs.

• The Journal of Society for e-Business Studies
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• v.21 no.1
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• pp.65-77
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• 2016

As respecting one's privacy becomes an important issue in mobile device data analysis, on-device analysis is getting attention, in which the data analysis is conducted inside a mobile device without sending data from the device to outside. One possible application of the on-device analysis is gender prediction using text data in mobile devices, such as text messages, search keyword, website bookmarks, and contact, which are highly private, and the limited computing power of mobile devices can be addressed by utilizing the word comparison method, where words are selected beforehand and delivered to a mobile device of a user to determine the user's gender by matching mobile text data and the selected words. Moreover, it is known that performing prediction after filtering instances using definite evidences increases accuracy and reduces computational complexity. In this regard, we propose a two-phase approach to on-device gender prediction, where both discriminability and popularity of a word are sequentially considered. The proposed method performs predictions using a few highly discriminative words for all instances and popular words for unclassified instances from the previous prediction. From the experiments conducted on real-world dataset, the proposed method outperformed the compared methods.

• The Journal of Society for e-Business Studies
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• v.21 no.1
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• pp.147-163
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• 2016

Gender information of a smart device user is essential to provide personalized services, and multi-modal data obtained from the device is useful for predicting the gender of the user. However, the method for utilizing each of the multi-modal data for gender prediction differs according to the characteristics of the data. Therefore, in this study, an ensemble method for predicting the gender of a smart device user by using three classifiers that have text, application, and acceleration data as inputs, respectively, is proposed. To alleviate privacy issues that occur when text data generated in a smart device are sent outside, a classification method which scans smart device text data only on the device and classifies the gender of the user by matching text data with predefined sets of word. An application based classifier assigns gender labels to executed applications and predicts gender of the user by comparing the label ratio. Acceleration data is used with Support Vector Machine to classify user gender. The proposed method was evaluated by using the actual smart device log data collected from an Android application. The experimental results showed that the proposed method outperformed the compared methods.

• The Journal of Korean Society for School & Community Health Education
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• v.22 no.3
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• pp.41-54
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• 2021

Objectives: This study aimed to confirm the degree of gender sensitivity, gender role conflict, nursing professionalism of nursing students and the factors that affect nursing professionalism. Methods: During Jan. 19 to Feb. 5 in 2021, the structured questionnaire was used for 187 nursing students by on-line research methods. Data were analyzed by descriptive analysis, mean comparison(t-test, ANOVA), correlation analysis(Pearson's correlation coefficient) and multiple regression using SPSS/WIN 25.0. Results: The gender sensitivity had positive relationship with nursing professionalism, and gender role conflict had negative relationship with nursing professionalism. And the prediction factors influencing nursing professionalism were major satisfaction, gender sensitivity and gender role conflict. The total variance was 8.2% by predictors. Conclusions: In order to improve the nursing professionalism of nursing students, various ways to increase the satisfaction level of major should be sought, and program should be prepared to improve gender sensitivity and reduce gender role conflict.

• The korean journal of orthodontics
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• v.52 no.2
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• pp.102-111
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• 2022

Objective: To develop and explore the usefulness of an artificial intelligence system for the prediction of the need for dental extractions during orthodontic treatments based on gender, model variables, and cephalometric records. Methods: The gender, model variables, and radiographic records of 214 patients were obtained from an anonymized data bank containing 314 cases treated by two experienced orthodontists. The data were processed using an automated machine learning software (Auto-WEKA) and used to predict the need for extractions. Results: By generating and comparing several prediction models, an accuracy of 93.9% was achieved for determining whether extraction is required or not based on the model and radiographic data. When only model variables were used, an accuracy of 87.4% was attained, whereas a 72.7% accuracy was achieved if only cephalometric information was used. Conclusions: The use of an automated machine learning system allows the generation of orthodontic extraction prediction models. The accuracy of the optimal extraction prediction models increases with the combination of model and cephalometric data for the analytical process.

• Child Health Nursing Research
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• v.16 no.3
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• pp.230-238
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• 2010

Purpose: The purpose of this study was to examine gender differences in self-competence, social anxiety and depression in upper level primary school children. Methods: In this cross-sectional study, data were collected from 180 students in grades 5 or 6 (83 boys and 97 girls). The instruments used for this study were a self-report questionnaire, the Self-Perception Profile for Children, the Revised Social Anxiety Scales for Children (SASC-R) and a Depression Instrument. For data analysis, descriptive statistics, t-test, Pearson correlation coefficients, and stepwise multiple regression were used with the SPSS/PC ver 12.0 program. Results: The only gender difference was in depression and girls reported more depression than boys. Social competence showed significantly negative correlations with depression and social anxiety. Gender differences were found in self competence in the prediction of depression and social anxiety. Conclusion: The results of this study indicate that there are gender differences in self competence which influence depression and social anxiety. Thus, enhancing self-competence could prevent social anxiety and depression in children but, differences in gender should be considered when developing programs to enhance self-competence.

• Biomolecules & Therapeutics
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• v.28 no.6
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• pp.491-502
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• 2020

Sex/gender disparity has been shown in the incidence and prognosis of many types of diseases, probably due to differences in genes, physiological conditions such as hormones, and lifestyle between the sexes. The mortality and survival rates of many cancers, especially liver cancer, differ between men and women. Due to the pronounced sex/gender disparity, considering sex/gender may be necessary for the diagnosis and treatment of liver cancer. By analyzing research articles through a PubMed literature search, the present review identified 12 genes which showed practical relevance to cancer and sex disparities. Among the 12 sex-specific genes, 7 genes (BAP1, CTNNB1, FOXA1, GSTO1, GSTP1, IL6, and SRPK1) showed sex-biased function in liver cancer. Here we summarized previous findings of cancer molecular signature including our own analysis, and showed that sex-biased molecular signature CTNNB1^High, IL6^High, RHOA^High and GLIPR1^Low may serve as a female-specific index for prediction and evaluation of OS in liver cancer patients. This review suggests a potential implication of sex-biased molecular signature in liver cancer, providing a useful information on diagnosis and prediction of disease progression based on gender.

• Physical Therapy Korea
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• v.16 no.3
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• pp.60-68
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• 2009

Psychsocial factors appear to play an important role in the maintenance and development of chronic disability from low back pain. Fear of pain may be more disabling than the pain itself in patients with nonacute low back pain. The purpose of this study was to identify the contribution of gender, age, depression and pain-related fear to pain intensity and disability in nonacute low back pain patients. This was a cross-sectional survey study of eighty four patients who had low back pain for at least 4 weeks. More than moderate correlations were found between pain intensity, disability, fear-avoidance beliefs and depression. Regression analyses revealed that disability ratings and fear-avoidance beliefs for work activities significantly contributed to the prediction of pain intensity, even when controlling for age, gender and pain duration. Also, fear-avoidance beliefs for physical activity, pain intensity, age and depression, significantly contributed to the prediction of disability, even when controlling for gender and pain duration. These findings suggest that disability scores and fear-avoidance beliefs for work activities are important determinants of pain intensity. They also suggest that fear-avoidance beliefs for physical activity, pain intensity, age and depression are important determinants of disability.