• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.6
• /
• pp.1562-1582
• /
• 2024

The prediction of pedestrian trajectory is conducive to reducing traffic accidents and protecting pedestrian safety, which is crucial to the task of intelligent driving. The existing methods mainly use the past pedestrian trajectory to predict the future deterministic pedestrian trajectory, ignoring pedestrian intention and trajectory diversity. This paper proposes a multi-modal trajectory prediction model that introduces pedestrian intention. Unlike previous work, our model makes multi-modal goal-conditioned trajectory pedestrian prediction based on the past pedestrian trajectory and pedestrian intention. At the same time, we propose a novel Gate Recurrent Unit (GRU) to process intention information dynamically. Compared with traditional GRU, our GRU adds an intention unit and an intention gate, in which the intention unit is used to dynamically process pedestrian intention, and the intention gate is used to control the intensity of intention information. The experimental results on two first-person traffic datasets (JAAD and PIE) show that our model is superior to the most advanced methods (Improved by 30.4% on MSE_0.5s and 9.8% on MSE_1.5s for the PIE dataset; Improved by 15.8% on MSE_0.5s and 13.5% on MSE_1.5s for the JAAD dataset). Our multi-modal trajectory prediction model combines pedestrian intention that varies at each prediction time step and can more comprehensively consider the diversity of pedestrian trajectories. Our method, validated through experiments, proves to be highly effective in pedestrian trajectory prediction tasks, contributing to improving traffic safety and the reliability of intelligent driving systems.

• Research in Community and Public Health Nursing
• /
• v.13 no.3
• /
• pp.456-470
• /
• 2002

Objectives: The purpose of this study was to test the Theory of Planned Behavior (TPB) in the prediction of smoking cessation intention and smoking cessation behavior among adolescent smokers, in order to provide basic data to develop a future smoking cessation program as a nursing intervention. Method: The study subjects were 80 adolescent smokers who had smoked one cigarette and attended a five-day school smoking cessation program. The data were collected from October 24 to December 21, 1999. The instruments used in this study were the tools developed by Jee (1994) to measure TPB variables such as attitude toward smoking cessation behavior, subjective norm, perceived behavioral control, smoking cessation intention, and smoking cessation behavior. The data were analyzed with the SAS/PC program using descriptive statistics, hierarchical multiple regression, and logistic multiple regression. Results: 1. Attitude toward smoking cessation behavior, subjective norm, and perceived behavioral control were partially significant in predicting smoking cessation intention. 2. Smoking cessation intention and perceived behavioral control toward smoking cessation behavior did not significantly predict smoking cessation behavior. 3. There were partial interaction effects among the attitude toward smoking cessation behavior, subjective norm, and perceived behavioral control in the prediction of smoking cessation intention. 4. There were partial interaction effects between smoking cessation intention and perceiver behavioral control toward smoking cessation behavior in the prediction of smoking cessation behavior. Conclusion: This study partially demonstrated support for the TPB model that was partially useful in predicting smoking cessation intention and smoking cessation behavior among adolescent smokers. Therefore, it is recommended that attitude toward smoking cessation behavior and perceived behavioral control should be considered in developing smoking cessation programs and implementing nursing interventions to change the smoking behavior of adolescent smokers.

• Korean Journal of Childcare and Education
• /
• v.20 no.2
• /
• pp.127-143
• /
• 2024

Objective: The purpose of this study is to develop a machine learning model to predict the subsequent childbirth intention of married women with one child, aiming to address the low birth rate problem in Korea, This will be achieved by utilizing data from the 2021 Family and Childbirth Survey conducted by the Korea Institute for Health and Social Affairs. Methods: A prediction model was developed using the Random Forest algorithm to predict the subsequent childbirth intention of married women with one child. This algorithm was chosen for its advantages in prediction and generalization, and its performance was evaluated. Results: The significance of variables influencing the Random Forest prediction model was confirmed. With the exception of the presence or absence of leave before and after childbirth, most variables contributed to predicting the intention to have subsequent childbirth. Notably, variables such as the mother's age, number of children planned at the time of marriage, average monthly household income, spouse's share of childcare burden, mother's weekday housework hours, and presence or absence of spouse's maternity leave emerged as relatively important predictors of subsequent childbirth intention.

• Journal of Korean Academy of Nursing
• /
• v.28 no.3
• /
• pp.550-562
• /
• 1998

The purpose of this study was to test the Theory of Planned Behavior in the prediction of contraceptive behavior among married women. This study used a descriptive correlational design to examine the relationships among the study variables. Eighty married women in Seoul and Kyungki-do participated in this study, Research instruments used were the tool for measuring TPB variables search as attitude toward contraception, subjective norm, perceived behavioral control, and intention ; and the tool for measuring contraceptive behavior. The former was modified by the researcher according to Ajzen & Fishbein(1980)'s guidelines for tool development and Jee (1993)'s tool. The latter was developed by the researcher Data was collected from July 20, 1996 to October 25, 1996. The results are as follows ; The three factors, attitude, subjective norm and perceived behavioral control of contraception can explain 30% of the variance in contraceptive intention. Inspection of path coefficient for each of the three predictor variables revealed that subjective norm and perceived behavioral control were the predictor variables on intention, while attitude was not. ; and intention and percevied behavioral control factors can explain 42% of the variance in contraceptive behavior. Inspection of path coefficient for each of the two predictor variables revealed that intention and perceived behavioral control were the predictor variables on behavior. In conclusion, this study identified that Theory of Planned Behavior was a useful model in the prediction of contraceptive behavior, and the contraceptive service program based on the TPB variables would be an effective nursing intervention for the change in contraceptive behavior.

• International journal of advanced smart convergence
• /
• v.12 no.1
• /
• pp.157-163
• /
• 2023

The current research aims to examine the moderating effect of consumers' mindset on their product purchase intention in the multi-promotion offers containing both a bonus pack and a price discount (i.e., BP + PD offers). That is, this research investigateswhether consumers' product purchase intention in the BP + PD offers variesdepending on their mindset (growth mindset vs. fixed mindset). Specifically, it is predicted that consumers with a fixed mindset will have higher product purchase intention in the offers containing the high PD but low extra amount of BP (LBP HPD) than in the offers with a high extra amount of BP but low PD (HBP LPD), whereas consumers with a growth mindset will have higher product purchase intention in the HBP LPD offers than in the LBP HPD offers. An experiment wasconducted to test the prediction. Consistent with the prediction, it was found that participants' mindset moderates their product purchase intention in multi-promotion offers. The findings imply that marketers can evoke more positive consumer purchase intention toward BP and PD offers, considering consumer mindset.

• Journal of Korean Academy of Nursing Administration
• /
• v.10 no.2
• /
• pp.185-193
• /
• 2004

Purpose: The purpose of this study was to analyze the effects of job satisfaction and organizational commitment on the nurse's turnover intention working in the social welfare facilities. Methods: The subjects of this study were 319 nurses who were working in the 238 social welfare facilities. The data were collected by self-reporting questionnaires. The data were analyzed using descriptive statistics, Pearson correlation coefficient and multiple regression. Results: It was found that the key predictor of turnover intention was organizational commitment Organizational commitment explained 41.2% of the total variance of turnover intention. In case of sub categories of job satisfaction, organizational commitment had 37.2% prediction and then payment and supervision added 6.2% prediction. Conclusion: These results suggest that the key predict factor of nurse's turnover intention working in social welfare facilities is organizational commitment. Therefore, the findings of this study can be used to develop effective strategies to decrease nurse's turnover intention.

• The Journal of Information Systems
• /
• v.30 no.4
• /
• pp.21-42
• /
• 2021

Purpose In order for companies to continue to grow, they should properly manage human resources, which are the core of corporate competitiveness. Employee turnover means the loss of talent in the workforce. When an employee voluntarily leaves his or her company, it will lose hiring and training cost and lead to the withdrawal of key personnel and new costs to train a new employee. From an employee's viewpoint, moving to another company is also risky because it can be time consuming and costly. Therefore, in order to reduce the social and economic costs caused by employee turnover, it is necessary to accurately predict employee turnover intention, identify the factors affecting employee turnover, and manage them appropriately in the company. Design/methodology/approach Prior studies have mainly used logistic regression and decision trees, which have explanatory power but poor predictive accuracy. In order to develop a more accurate prediction model, XGBoost is proposed as the classification technique. Then, to compensate for the lack of explainability, SHAP, one of the XAI techniques, is applied. As a result, the prediction accuracy of the proposed model is improved compared to the conventional methods such as LOGIT and Decision Trees. By applying SHAP to the proposed model, the factors affecting the overall employee turnover intention as well as a specific sample's turnover intention are identified. Findings Experimental results show that the prediction accuracy of XGBoost is superior to that of logistic regression and decision trees. Using SHAP, we find that jobseeking, annuity, eng_test, comm_temp, seti_dev, seti_money, equl_ablt, and sati_safe significantly affect overall employee turnover intention. In addition, it is confirmed that the factors affecting an individual's turnover intention are more diverse. Our research findings imply that companies should adopt a personalized approach for each employee in order to effectively prevent his or her turnover.

• The Journal of the Acoustical Society of Korea
• /
• v.15 no.1
• /
• pp.61-70
• /
• 1996

According to topics or speaker's intentions in a dialogue, utterance spoken by speaker has a different sentence structure of word combinations. Based on these facts, we have proposed the statistical approach. IDT(intention decision table), which is modeling the correlations between sentence patterns and the intention. In a IDT, the sentence is splitted into 5 different factors, and the intention of a sentence is determined by the similarity between and intention and 5 factors that have represent a sentence. From the experimental results, the IDT has indicated that the prediction rate of an intention is improved 10~18% over the word-intention correlations and is enhanced 3~12% compared with the MIG(Markov intention graph) that models the intention with a transition graph for word categories in a sentence. Based on these facts, we have found that the IDT is effective method for the prediction of an intention.

• Journal of Korean Academy of Nursing
• /
• v.27 no.4
• /
• pp.796-806
• /
• 1997

The majority of studies on breastfeeding consists of descriptive correlational studies identifying the incidence and correlates of breastfeeding. The theory of planned behavior has been shown to yield great predictive power for behavioral goals over which individuals have only limited control such as improving school grades and weight loss. The purpose of this study was to test the "theory of planned behavior" in the prediction of breastfeeding of mothers who delivered vaginally, One hundred mothers who delivered vaginally in one general hospital in Seoul and one general hospital and three private hospitals in Taejeon participated in this study. The instruments used for data collection in this study were developed by the researchers following the guidelines suggested by Ajzen & Fishbein(1980) and Ajzen & Madden(1986). The instruments included measurement of attitude, subjective norm, perceived behavioral control and intention. The collected data were analyzed using descriptive statistics, Pearson product moment correlation, hierachical multiple regression and logistic regression. The results are as follows ; 1. Intention to breastfeed correlated significantly with attitude, subjective norm and perceived behavioral control. Both attitude and subjective norm did not make a significant contribution to the prediction of intention, but the addition of perceived behavioral control to the regression equation greatly improved the model's predictive power, increasing the R²from .05 to .52. 2. Intention to breastfeed alone had a significant predictive effect on actual breastfeeding, resulting in a regression coefficient of .16(X²=8 60, p<.01), but when perceived behavioral control was added to the equation, intention was not a significant predictive variable and only perceived behavioral control showed significant predictive power on actual breastfeeding, resulting in a regression coefficient of .12(X²=4.69, p<.05). In sum, breastfeeding behavior lent only partial support to the second version of the theory of planned behavior, and because perceived behavioral control had a strong effect on intention to breastfeed and actual breastfeeding, It would be desirable to develop nursing intervention programs which focus on strengthening the perceived behavioral control for the promotion of breastfeeding.

• Journal of KIISE:Software and Applications
• /
• v.35 no.9
• /
• pp.554-561
• /
• 2008

Prediction technique of user's intention can be used as a post-processing method for reducing the search space of an automatic speech recognizer. Prediction technique of system's intention can be used as a pre-processing method for generating a flexible sentence. To satisfy these practical needs, we propose a statistical model to predict speakers' intentions that are generalized into pairs of a speech act and a concept sequence. Contrary to the previous model using simple n-gram statistic of speech acts, the proposed model represents a dialogue history of a current utterance to a feature set with various linguistic levels (i.e. n-grams of speech act and a concept sequence pairs, clue words, and state information of a domain frame). Then, the proposed model predicts the intention of the next utterance by using the feature set as inputs of CRFs (Conditional Random Fields). In the experiment in a schedule management domain, The proposed model showed the precision of 76.25% on prediction of user's speech act and the precision of 64.21% on prediction of user's concept sequence. The proposed model also showed the precision of 88.11% on prediction of system's speech act and the Precision of 87.19% on prediction of system's concept sequence. In addition, the proposed model showed 29.32% higher average precision than the previous model.