• Journal of Korea Multimedia Society
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• v.23 no.7
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• pp.862-869
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• 2020

This study attempted to analyze human brain responses toward visual content through EEG signals and intended to measure brain wave reactions of different age groups to determine the sexuality level of the media. The experimental stimuli consist of three different video footage (rated ages 12, 15, and 18) to analyze how subjects react in situations where they actually watch sexual content. For measuring and analyzing brain wave reactions, EEG equipment records alpha, beta, and gamma wave responses of the subjects' left and right frontal lobes, temporal lobes, and occipital lobes. The subjects of this study were 28 total and they are divided into two groups. The experiment configures a sexual content classification scale with age or gender as a discriminating variable and brain region-specific response frequencies (left/right, frontal/temporal/occipital, alpha/beta/gamma waves) as independent variables. The experimental results showed the possibility of distinguishing gender and age differences. The apparent differences in brain wave response areas and bands among high school girls, high school boys, and college students are found. Using these brain wave response data, this study explored the potential of developing algorithm for measurement of age-specific responses to sexual content and apply it as a film rating.

• Journal of information and communication convergence engineering
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• v.21 no.2
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• pp.110-116
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• 2023

Recently, as various examples of machine learning have been applied in the healthcare field, deep learning technology has been applied to various tasks, such as electrocardiogram examination and body composition analysis using wearable devices such as smart watches. To utilize deep learning, securing data is the most important procedure, where human intervention, such as data classification, is required. In this study, we propose a model that uses a clustering algorithm, namely, the K-means clustering, to label body fat according to gender and age considering body size aspects, such as chest circumference and waist circumference, and classifies body fat into five groups from high risk to low risk using a convolutional neural network (CNN). As a result of model validation, accuracy, precision, and recall results of more than 95% were obtained. Thus, rational decision making can be made in the field of healthcare or obesity analysis using the proposed method.

• Journal of Sasang Constitutional Medicine
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• v.22 no.3
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• pp.87-97
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• 2010

1. Objectives: In SCM, a personal Sasang constitution must be determined accurately before any Sasang treatment. The purpose of this study is to develop an objective method for classification of Sasang constitution. 2. Methods: We collected samples from 5 centers where SCM is practiced, and applied two-stage decision tree analysis on these samples. We recruited samples from 5 centers. The collected data were from subjects whose response to herbal medicine was confirmed according to Sasang constitution. 3. Results: The two-stage decision tree model shows higher classification power than a simple decision tree model. This study also suggests that gender must be considered in the first stage to improve the accuracy of classification. 4. Conclusions: We identified important factors for classifying Sasang constitutions through two-stage decision tree analysis. The two-stage decision tree model shows higher classification power than a simple decision tree model.

• The Journal of the Korea Contents Association
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• v.21 no.9
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• pp.105-116
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• 2021

Habruta is a question-based learning that talks, discusses, and argues in pairs. In particular, the famous painting Habruta is being implemented for the purpose of enhancing the appreciation ability of paintings and enriching the expressive power through questions and answers about the famous paintings. In this study, in order to support the famous painting Habruta for oriental paintings, we propose a method of automatically generating questions from the gender perspective of oriental painting characters using the current deep learning technology. Specifically, in this study, based on the pre-trained model, VGG16, we propose a model that can effectively analyze the features of Asian paintings by performing fine-tuning. In addition, we classify the types of questions into three types: fact, imagination, and applied questions used in the famous Habruta, and subdivide each question according to the character to derive a total of 9 question patterns. In order to verify the feasibilityof the proposed methodology, we conducted an experiment that analyzed 300 characters of actual oriental paintings. As a result of the experiment, we confirmed that the gender classification model according to our methodology shows higher accuracy than the existing model.

• Journal of Sasang Constitutional Medicine
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• v.33 no.4
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• pp.1-9
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• 2021

Objectives The purpose of this study was to check whether the classification of the individual's Body Mass Index (BMI) could be predicted by analyzing the voice data constructed at the Korean medicine data center (KDC) using machine learning. Methods In this study, we proposed a convolutional neural network (CNN)-based BMI classification model. The subjects of this study were Korean adults who had completed voice recording and BMI measurement in 2006-2015 among the data established at the Korean Medicine Data Center. Among them, 2,825 data were used for training to build the model, and 566 data were used to assess the performance of the model. As an input feature of CNN, Mel-frequency cepstral coefficient (MFCC) extracted from vowel utterances was used. A model was constructed to predict a total of four groups according to gender and BMI criteria: overweight male, normal male, overweight female, and normal female. Results & Conclusions Performance evaluation was conducted using F1-score and Accuracy. As a result of the prediction for four groups, The average accuracy was 0.6016, and the average F1-score was 0.5922. Although it showed good performance in gender discrimination, it is judged that performance improvement through follow-up studies is necessary for distinguishing BMI within gender. As research on deep learning is active, performance improvement is expected through future research.

• Journal of Fashion Business
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• v.14 no.5
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• pp.78-92
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• 2010

The culture phenomenon, which the desire of self expression is noticeable and the diversity of gender identity is widely accepted, simply makes a difference in the lifestyle of one human being rather than the dichotomous classification of male and female. Now, the delicate and aesthetic sensitivity classified as the feminine characteristics is no longer the exclusive property of female and this refers to substituting it as a social gender from the concept of biological sex. This phenomenon has influenced on the male culture and is creating various codes according to the cultural gender extended from the gender as a social role. Also, the transition into the western lifestyle has extended the aesthetic emotion to accommodate new codes from the diversification and globalization of lifestyle. The mansumer power, which does not care too much about the money for the emotionally attached items, has enabled various fashion styles. After analyzing the diversified clothing behavior conducted by these people in connection with the social phenomenon, First, this shows the phenomenon of emotional value pursuit that finds pleasure over the clothing as the item of augmented reality is added to the concept of play, in which the real space referred to as garment and virtual space of playing the rock, paper and scissors game meet together within the augmented reality. Second, the convergence concept has enabled the coordination of new style by obscuring the area of design concept and this refers to the changes in design from the development of new items and transformation into double-style details. Third, the divergence that intensively provides specific use/convenience and specialized value shows a change in the fashion market from the phenomenon that admits various gene rations of culture and specifically, takes differently about the recognition of middle-aged males. Fourth, the variety seeking tendency receives attention as the value of future design together with the phenomenon of discriminative value pursuit. In the male fashion, it is linked to the collaboration with the design area and this tells that the fashion with the narcissistic cross-dressing and motto of neutral gender without being sided to male/female is rising.

• The Journal of Korean Institute of Information Technology
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• v.16 no.11
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• pp.11-21
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• 2018

Research is carried out to categorize voices using Deep Learning technology. The study examines neural network-based sound classification studies and suggests improved neural networks for voice classification. Related studies studied urban data classification. However, related studies showed poor performance in shallow neural network. Therefore, in this paper the first preprocess voice data and extract feature value. Next, Categorize the voice by entering the feature value into previous sound classification network and proposed neural network. Finally, compare and evaluate classification performance of the two neural networks. The neural network of this paper is organized deeper and wider so that learning is better done. Performance results showed that 84.8 percent of related studies neural networks and 91.4 percent of the proposed neural networks. The proposed neural network was about 6 percent high.

• 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 Medicine
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• v.40 no.4
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• pp.29-40
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• 2019

Objectives: To analyze the prescription frequency of various herbs as either individual or major herbs (in terms of dosage) and their usage patterns in the treatment of different diseases for standardization of traditional Korean medicine. Methods: We analyzed the prescription database of patients at the Pusan National University Korean Medicine Hospital from the date of establishment of the hospital to February 2013. The complete prescription data were extracted from the electronic medical records of patients, and the prescription frequencies of individual herbs, particularly, of major herbs, were analyzed in terms of gender, age, and international classification of diseases (ICD) code. Results: The prescription frequency of individual herbs based on age and gender showed a similar pattern. Herbal mixtures were also distributed in a similar manner. The use of some herbs differed according to age and gender (Table 1.). The herbs that were used at high frequencies for a given ICD code had similar usage patterns in different categories. However, some major herbs in the "Jun (King)" category were used uniquely for a given ICD code (Table 2.). There was significant difference between male and female on ICD code E and N, but the other ICD codes had small differences. The ratio of herbal medicine by gender showed different usage patterns in each gender. Conclusions: The findings of our study provide fundamental data that reflect the real clinical conditions in South Korea, and therefore, can contribute to the standardization of TKM.

• Women's Health Nursing
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• v.15 no.4
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• pp.312-319
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• 2009

Purpose: The purpose of this study was to determine the discriminant factors of attitude pattern toward sexual violence of college women. Methods: A cross-sectional research design with non-probability samples was conducted. A total of 292 college women participated. The instruments were Attitude Pattern toward Sexual Violence, Self-Esteem Scale, Gender Role Scale, and Attitude toward Sexuality. Dependent variable is Attitude Pattern toward Sexual Violence, which is composed of two groups; cases either harmer blame or sufferer blame. Independent variables were self-esteem, attitude toward gender role, and attitude toward sexuality. Data were analyzed by SPSS WIN program and descriptive analysis, $x^2$-test, and discriminant analysis. Results: To assess the adequacy of classification, the overall hit ratio was 68.5%, and the significant predictor variable was attitude toward sexuality. Conclusion: Replication of the study needs to be considered to further enrich the specific knowledge base regarding attitude toward sexual violence among college women.