• Child Health Nursing Research
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• v.24 no.2
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• pp.128-137
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• 2018

Purpose: To investigate factors that may affect the method of feeding among preterm infants at 4 weeks after discharge. Methods: This study included 222 mother-infant dyads born before a gestational age of 37 weeks. The feeding method and general medical characteristics of the participants were assessed at 4 weeks after discharge using a structured questionnaire. Multinomial logistic regression analysis was used to examine which factors were associated with breastfeeding at home. Results: Of the 222 infants who qualified for the study, 71 (32.9%) continued to receive breastmilk at 4 weeks post-discharge. Multinomial logistic regression analysis showed that breastfeeding at 4 weeks post-discharge was associated with higher breastfeeding self-efficacy, vaginal delivery (experience), direct breastfeeding in the neonatal intensive care unit (NICU), gestational age between 30 and 34 weeks, and breastmilk consumption in the NICU. The following factors were associated with mixed feeding at 4 weeks post-discharge: being employed, having higher breastfeeding self-efficacy, and direct breastfeeding in the NICU. Conclusion: NICU nurses should provide opportunities for direct breastfeeding during hospitalization and support breastfeeding to enhance breastfeeding self-efficacy. These factors may help to ensure the continuation of breastfeeding after discharge. Moreover, factors that affect breastfeeding should be considered when providing interventions.

• The Korean Journal of Food And Nutrition
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• v.36 no.4
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• pp.222-235
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• 2023

In this research, we examined the shifts in determinants influencing the frequency of eco-friendly food purchases pre- and post-COVID-19. Our analysis utilized filtered 2019-2021 Consumption Behavior Survey data from the Korea Rural Economic Institute Food, excluding any irrational responses. Given the nature of the dependent variable, a multinomial logistic regression model was employed with demographic factors, variables pertaining to food consumption behavior, and variables concerning food consumption awareness as predictors. Following the onset of the COVID-19 pandemic, an individual's level of education was observed to positively influence the frequency of eco-friendly food purchases. In contrast, income level and fluctuations in food consumption expenditure did not appear to have a discernible impact on the purchasing frequency of such eco-friendly products. Irrespective of the advent of COVID-19, variables such as the frequency of online food purchases, the utilization of early morning delivery services, dining out frequency, and the intake of health-functional foods consistently demonstrated a positive correlation with the propensity to purchase eco-friendly foods. Overall, consumers prioritizing safety, quality, and nutrition over price, taste, and convenience in their procurement decisions for rice, vegetables, meat, and processed foods exhibit an increased inclination toward the acquisition of eco-friendly food products.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.1-5
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• 2021

Cyberbullying is a problem that is faced in many cultures. Due to their popularity and interactive nature, social media platforms have also been affected by cyberbullying. Social media users from Arab countries have also reported being a target of cyberbullying. Machine learning techniques have been a prominent approach used by scientists to detect and battle this phenomenon. In this paper, we compare different machine learning algorithms for their performance in cyberbullying detection based on a labeled dataset of Arabic YouTube comments. Three machine learning models are considered, namely: Multinomial Naïve Bayes (MNB), Complement Naïve Bayes (CNB), and Linear Regression (LR). In addition, we experiment with two feature extraction methods, namely: Count Vectorizer and Tfidf Vectorizer. Our results show that, using count vectroizer feature extraction, the Logistic Regression model can outperform both Multinomial and Complement Naïve Bayes models. However, when using Tfidf vectorizer feature extraction, Complement Naive Bayes model can outperform the other two models.

• Communications for Statistical Applications and Methods
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• v.30 no.6
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• pp.577-587
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• 2023

Conventional categorical data imputation techniques, such as mode imputation, often encounter issues related to overestimation. If the variable has too many categories, multinomial logistic regression imputation method may be impossible due to computational limitations. To rectify these limitations, we propose a two-stage imputation method. During the first stage, we utilize the Boruta variable selection method on the complete dataset to identify significant variables for the target categorical variable. Then, in the second stage, we use the important variables for the target categorical variable for logistic regression to impute missing data in binary variables, polytomous regression to impute missing data in categorical variables, and predictive mean matching to impute missing data in quantitative variables. Through analysis of both asymmetric and non-normal simulated and real data, we demonstrate that the two-stage imputation method outperforms imputation methods lacking variable selection, as evidenced by accuracy measures. During the analysis of real survey data, we also demonstrate that our suggested two-stage imputation method surpasses the current imputation approach in terms of accuracy.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1465-1475
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• 2013

The chi-square type test statistic is the most commonly used test in terms of measuring testing goodness-of-fit for multinomial logistic regression model, which has its grouped data (binomial data) and ungrouped (binary) data classified by a covariate pattern. Chi-square type statistic is not a satisfactory gauge, however, because the ungrouped Pearson chi-square statistic does not adhere well to the chi-square statistic and the ungrouped Pearson chi-square statistic is also not a satisfactory form of measurement in itself. Currently, goodness-of-fit in the ordinal setting is often assessed using the Pearson chi-square statistic and deviance tests. These tests involve creating a contingency table in which rows consist of all possible cross-classifications of the model covariates, and columns consist of the levels of the ordinal response. I examined goodness-of-fit tests for a proportional odds logistic regression model-the most commonly used regression model for an ordinal response variable. Using a simulation study, I investigated the distribution and power properties of this test and compared these with those of three other goodness-of-fit tests. The new test had lower power than the existing tests; however, it was able to detect a greater number of the different types of lack of fit considered in this study. I illustrated the ability of the tests to detect lack of fit using a study of aftercare decisions for psychiatrically hospitalized adolescents.

• Journal of the Korean Society of School Health
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• v.35 no.3
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• pp.133-142
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• 2022

Purpose: The purpose of the study was to investigate the effect of the environment on adolescent binge drinking. Methods: The study was designed as a cross-sectional study. Using statistics from the 17th (20201) Korea Youth Risk Behavior Web-based Survey, the raw data target population was 2,629,588 people, and the sample group used for analysis as the final data was 54,848 people. A Rao-scott 𝑥² test and univariate multinomial logistic regression analysis were performed using IBM SPSS 27.0. Results: In the results of univariate logistic regression analysis and multivariate logistic regression analysis, common related variables were gender, school level, academic achievement, sleep satisfaction, current smoking, daily smoking, and alcohol education experience. Conclusion: As a result of confirming the factors influencing binge drinking in Korean adolescents, some variables that increase the possibility of problematic drinking behavior in the socio-environmental areas such as individuals, communities, and national policies were identified. For effective prevention and intervention, it is necessary to develop programs to build a healthy environmental support system with support from national policies, including individuals, peer groups, and communities.

• Safety and Health at Work
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• v.9 no.3
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• pp.347-351
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• 2018

Background: The success of an injury intervention program can be measured by the proportion of successful return to work (RTW). This study examined factors of successful return to employment among workers suffering from work-related injuries. Methods: Data were obtained from the Social Security Organization, Malaysia database consisting of 10,049 RTW program participants in 2010-2014. The dependent variable was the RTW outcome which consisted of RTW with same employer, RTW with new employer or unsuccessful return. Multinomial logistic regression was performed to test the likelihood of successful return with same employer and new employer against unsuccessful return. Results: Overall, 65.3% of injured workers were successfully returned to employment, 52.8% to the same employer and 12.5% to new employer. Employer interest; motivation; age 30-49 years; intervention less than 9 months; occupational disease; injuries in the lower limbs, upper limbs, and general injuries; and working in the manufacturing, services, and electrical/electronics were associated with returning to work with the same employer against unsuccessful return. Male, employer interest, motivation, age 49 years or younger, intervention less than 6 months, occupational disease, injuries in the upper limbs and services sector of employment were associated with returning to new employer against unsuccessful return. Conclusion: There is a need to strengthen employer commitment for early and intensified intervention that will lead to improvement in the RTW outcome.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.39-46
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• 2022

The spread of Omicron, a mutated version of COVID-19 across several countries is leading to the discussion of lockdown once again for curbing the spread of the new virus. In this context, this research is showing the impact of lockdown for the successful control of the COVID-19 pandemic in Saudi Arabia. The outbreak of the COVID-19 pandemic around the globe has affected Saudi Arabia with around 2,37,803 confirmed cases within the initial 4 months of transmission. Saudi Arabia has announced a 21-day lockdown from March 23, 2020, to reduce the transmission of the COVID-19 pandemic. Machine Learning-based, Multinomial logistic regression was applied to understand the relationship between daily COVID-19 confirmed cases and lockdown in the 17 most-affected cities of KSA. We used secondary published data from the Ministry of Health, KSA daily dataset of COVID-19 confirmed case counts. These 17 cities were categorized into 4 classes based on lockdown dates. A total of three scenarios such as night lockdown, full lockdown, and no lockdown have been analyzed with the total number of confirmed cases with 4 classes. 15 out of 17 cities have shown a strong correlation with a confidence interval of 95%. These findings provide evidence that the COVID-19 pandemic may be partially suppressed with lockdown measures.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.4
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• pp.428-436
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• 2014

This paper proposes a statistical regression method for classifying pillars and vehicles in parking area using a single ultrasonic sensor. There are three types of information provided by the ultrasonic sensor: TOF, the peak and the width of a pulse, from which 67 different features are extracted through segmentation and data preprocessing. The classification using the multiple SVM and the multinomial logistic regression are applied to the set of extracted features, and has achieved the accuracy of 85% and 89.67%, respectively, over a set of real-world data. The experimental result proves that the proposed feature extraction and classification scheme is applicable to the object classification using an ultrasonic sensor.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.229-237
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• 2021

Our modern 'information-hungry' age demands delivery of information at unprecedented fast rates. Timely delivery of noteworthy information about recent events can help people from different segments of life in number of ways. As world has become global village, the flow of news in terms of volume and speed demands involvement of machines to help humans to handle the enormous data. News are presented to public in forms of video, audio, image and text. News text available on internet is a source of knowledge for billions of internet users. Urdu language is spoken and understood by millions of people from Indian subcontinent. Availability of online Urdu news enable this branch of humanity to improve their understandings of the world and make their decisions. This paper uses available online Urdu news data to train machines to automatically categorize provided news. Various machine learning algorithms were used on news headline for training purpose and the results demonstrate that Bernoulli Naïve Bayes (Bernoulli NB) and Multinomial Naïve Bayes (Multinomial NB) algorithm outperformed other algorithms in terms of all performance parameters. The maximum level of accuracy achieved for the dataset was 94.278% by multinomial NB classifier followed by Bernoulli NB classifier with accuracy of 94.274% when Urdu stop words were removed from dataset. The results suggest that short text of headlines of news can be used as an input for text categorization process.