• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.145-156
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• 2022

Children are the foundation and future of this society and understanding their impressions and behaviors is very important and the child's behavioral problems are a burden on the family and society as well as have a bad impact on the development of the child, and the early diagnosis of these problems helps to solve or mitigate them, and in this research project we aim to understand and know the behaviors of children, through artificial intelligence algorithms that helped solve many complex problems in an automated system, By using this technique to read and analyze the behaviors and feelings of the child by reading the features of the child's face, the movement of the child's body, the method of the child's session and nervous emotions, and by analyzing these factors we can predict the feelings and behaviors of children from grief, tension, happiness and anger as well as determine whether this child has the autism spectrum or not. The scarcity of studies and the privacy of data and its scarcity on these behaviors and feelings limited researchers in the process of analysis and training to the model presented in a set of images, videos and audio recordings that can be connected, this model results in understanding the feelings of children and their behaviors and helps doctors and specialists to understand and know these behaviors and feelings.

• Journal of Home Health Care Nursing
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• v.30 no.1
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• pp.96-104
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• 2023

Purpose: This study aimed to investigate the influence of fall risk, fear of falling, and social support on older inpatients' fall prevention behaviors. Methods: The participants were 105 older inpatients. Data were analyzed using descriptive statistics, independent t-test, One-way ANOVA, Pearson's correlation coefficients, and multiple regression analysis using the SPSS program. Results: The mean fall risk, fear of falling, social support, and all prevention behaviors were 58.05±28.27, 2.47±0.67, 3.58±0.63, and 2.93±0.48, respectively. There were significant differences in fall prevention behaviors by sex, and fall related to education experience. Fall prevention behaviors were associated with fall risk, and fear of falling. The factors influencing the fall prevention behaviors of the older inpatients were sex, fall related to educational experience, fall risk, and fear of falling. It was found that 21% could explain fall prevention behaviors. Conclusion: The results indicate a need to develop a fall prevention education program, including intervention, to reduce the fear of falling among older inpatients.

• Korean Journal of Adult Nursing
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• v.27 no.3
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• pp.273-282
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• 2015

Purpose: The objective of this study was to identify the predictors of self-care behaviors among elderly patients with hypertension using quantile regression method. Methods: A total of 253 elderly patients diagnosed with hypertension was recruited via 3 different medical clinics for the study. The quantile regression and a liner regression was conducted using Stata 12.0 program by analyzing predictors of self-care behaviors. Results: In the ordinary least square, self-efficacy, period of disease, and education level explained 42% of the variance in self-care activities. In the quantile regression, affecting predictors of self-care behaviors were self-efficacy for all quantiles, the period of disease for from 60% quantile to 90% quantile, education level for 20%, 30%, and 50% quantiles, economic status for 10%, 50%, and 60% quantiles, age for 10%, 70% quantiles, fatigue for 10% quantile, knowledge about hypertension for 10% and 20% quantiles, and depression for 30% and 40% quantiles. Conclusion: The affecting predictors of self-care behaviors among elderly with hypertension were different from the level of self-care behaviors. These results indicated the significance in assessing predictors according to the level of self-care behaviors when clinical nurses examine the patients' health behaviors and plan any intervention strategies. Specially, education level and knowledge about hypertension were the significant predictors of self-care activities for low quantiles. Clinical nurses may promote self-care activities of the given population though health education programs.

• The Journal of Korean Academic Society of Nursing Education
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• v.27 no.4
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• pp.412-422
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• 2021

Purpose: The purpose of this study was to analyze the factors influencing the health promoting behavior(s) of office worker males in the COVID-19 pandemic by applying Pender's health promotion model. Methods: The participants in this study were 149 male office workers at companies located in S, G and S cities. The collected data were analyzed using descriptive statistics, t-test, ANOVA, Pearson's correlation coefficient and a stepwise multiple regression using the SPSS Window 25.0 program. Results: The subject's health promoting behaviors and prior related behaviors (r=.58, p<.001), perceived benefits of action (r=.41, p<.001), self-efficacy (r=.53, p<.001), social support (r=.39, p<.001), self-esteem (r=.47, p<.001) and commitment to a plan of action (r=.67, p<.001) showed a high positive correlation. The factors affecting the subjects' health promoting behaviors were the commitment to a plan of action (𝛽=.35, p<.001), self-esteem (𝛽=.27, p=.005), prior related behavior (𝛽=.26, p<.001), health status (good) (𝛽=.20, p=.001) and self-efficacy (𝛽=.14, p=.047). These variables explained 63.0% of the subjects' health promoting behaviors. Conclusion: During the COVID-19 pandemic, subjects are more likely to be exposed to disease due to reduced outdoor activity time and irregular eating habits due to the strengthening of social distancing. Health promoting behaviors are an important concept that can maintain health and prevent diseases. To improve the health promoting behaviors of men engaged in office work, it is necessary to develop and operate a health promotion behaviors program considering those variables.

• Nutrition Research and Practice
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• v.4 no.6
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• pp.528-534
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• 2010

This study explores the influence of health behaviors and individual attributes on adolescent overweight and obesity using data from Wave II (Add Health). Structural equation model/ path analysis using maximum likelihood estimation was utilized to analyze the relationships of health behaviors and attributes with obesity. Results of the model reveal that the causal paths (adolescents' attributes and health behaviors) for overweight and obesity were different for African American and Caucasian adolescents. Generally, African Americans were more susceptible to overweight and obesity than Caucasians. Although increasing levels of vigorous physical activities lowers the risk for obesity among African American and Caucasian adolescents alike, low family SES and being sedentary were associated with overweight and obesity among Caucasians. No significant associations were found among African Americans. Increased hours of sleep at night relate positively with obesity among African Americans. These findings suggest important elements in the consideration of race in developing effective intervention and prevention approaches for curbing the obesity epidemic among U.S. adolescents.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.137-140
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• 2003

This paper proposes a pursuing system utilizing the learning method where multiple cooperating agents emulate social behaviors of animals and insects and realize their group behaviors. Each agent contains sensors to perceive other agents in several directions and decides its behavior based on the information obtained by the sensors. In this paper, a neural network is used fir behavior decision controller. The input of the neural network is decided by the existence of other agents and the distance to the other agents. The output determines the directions in which the agent moves. The connection weight values of this neural network are encoded as genes, and the fitness individuals are determined using a genetic algorithm. Here, the fitness values imply how much group behaviors fit adequately to the goal and can express group behaviors. The validity of the system is verified through simulation.

• Journal of the Korean Home Economics Association
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• v.37 no.1
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• pp.79-89
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• 1999

Children's sex, temperament, and empathy were studied as antecedents of preschoolers' prosocial development. Eighty eight 5-, and 6-year-old children, 41 boys and 47 girls, were sampled in two kindergartens. Children's naturally occurring prosocial behaviors were videotaped in their classrooms for 21 minutes in 3 days. Teachers rated the children's prosocial tendency using a questionnaire. Empathy was measured individually with a picture/story measure, and mothers provided information on children's temperament using a questionnaire. The two measures of preschoolers' prosocial behaviors, teacher ratings and free play observations, were moderately related. Girls were more prosocial in terms of helping/sharing/cooperation in both teacher-ratings and free-play observations. Preschoolers' adjustability was moderately related to the prosocial behaviors of children in both teacher-ratings and observations. Boys' verbal empathy was related to the observed prosocial behaviors.

• Research in Community and Public Health Nursing
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• v.26 no.1
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• pp.52-60
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• 2015

Purpose: The study was performed in order to identify the relationship between health behaviors and Internet use among adolescents. Methods: The study used raw data from the Korean Youth Risk Behavior Web-Based Survey that was performed in 2012. Data of a total of 74,186 students were analyzed using SPSS/WIN 18. The data were analyzed using descriptive statistics, t-test, and Pearson's correlation. Results: Internet use and health behaviors were significantly related to gender, depression, suicidal intent, parents' educational level, cohabitation with parents at home, school location, and grade. There was a significant association between Internet use and health behaviors in adolescents. Conclusion: Health promotion and education programs need to consider factors found relevant in this study.

• Journal of Fashion Business
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• v.8 no.6
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• pp.113-122
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• 2004

The purpose of this study was to examine and understand body image, appearance behaviors, and eating disturbances among females in the Diet Center. Subjects for this research were 40 females enrolled at the Diet Center in Seoul. They were interviewed for this study using an interview schedule at September in 2004. As a result, most of the subjects (87%) were dissatisfied with their bodies, especially their lower body such as hip, thighs, legs, stomach. Subjects tended to use dieting and fasting (46.3%) as the most common appearance management behaviors in relation to the body parts. Make-up (32.9%) was also used as routine appearance behaviors, while 37.5% of subjects have had cosmetic surgery on eyes, nose and liposuction. Sixty percent of subjects had participated in Diet center programs more than twice. This may mean that society pressures women to have a slim body, which then will result in more self-confidence. Subjects tend to engage in unhealthy eating behaviors, such as 'fasting', 'inducing vomit', 'using diet pills or laxatives', and 'after chewing, spit out'. Based on these results, socio-cultural body image regarding ideal beauty would be discussed.

• Journal of the Korean Society of Safety
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• v.33 no.6
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• pp.42-49
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• 2018

In this study, we have developed a prediction model for expansion and contraction behaviors of expansion joint in Gwangan Bridge using machine learning techniques and bridge monitoring data. In the development of the prediction model, two famous machine learning techniques, multiple regression analysis (MRA) and artificial neural network (ANN), were employed. Structural monitoring data obtained from bridge monitoring system of Gwangan Bridge were used to train and validate the developed models. From the results, it was found that the expansion and contraction behaviors predicted by the developed models are matched well with actual expansion and contraction behaviors of Gwangan Bridge. Therefore, it can be concluded that both MRA and ANN models can be used to predict the expansion and contraction behaviors of Gwangan Bridge without actual measurements of those behaviors.