• Culinary science and hospitality research
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• v.12 no.3 s.30
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• pp.49-60
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• 2006

The purpose of this study was to measure the factors influencing on the perception of helpfulness of marking the country of origin in predicting the quality and safety of pork. A total of 239 questionnaires were completed. A multinomial logit model is specified in order to estimate which factors influence the probability that a consumer perceives the country of origin as helpful in assessing food quality and food safety. The estimations were carried out using the logistic procedure of SAS. The results are as follows. The proportional odds assumptions of models were not violated at p<0.05. The effects of age, income, children, occupation and respondents informed on the importance of the country of origin in pork quality model were statistically significant. The effects of age, children, occupation and trust on the importance of the country of origin in pork safety model were statistically significant. The results from this study could be useful in developing marketing and health promotion strategies as well as government trade policies.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.12
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• pp.391-397
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• 2021

One of the sustainable development goals is to promote good health and well-being for all people. Child health is a top priority since their health issues can have a detrimental impact on human capital development, which is a critical input for the growth model. This paper applies the health production model to explore the determinants that influence the health of children under the age of five. The results of a survey of 203 households in Ho Chi Minh City, Vietnam, were examined. Child health is measured using anthropometric indicators such as weight-for-age, height-for-age, and weight-for-height (ZWFH). Three separate multinomial logistic models are regressed to examine the drivers of child health as proxied by z-score weight for age, z-score height for age, and z-score weight for height. The significance of input variables relating to a child's attributes, household, and environment was validated by the findings. The inclusion of overweight besides under-nourished indexes is novel because it reflects the current trend of child over-nutrition. The findings of the study highlight the importance of a wide range of initiatives to enhance child health. Moreover, the genetic effect is found to be crowded out by environmental and household factors. The finding verifies that despite their parents' moderate height, the future generation of Vietnamese can achieve the desired height.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.4
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• pp.424-429
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• 2007

The purpose of this study is to propose types of joint venturesthat can increase the competitivenessof a company in the marketplace. We examine the characteristics of individual venture enterprises based on technology. We considered 16 TEA in order to categorize companies into four groups. Next, we used a multinomial logistic regression model to identify the significant characteristics of a venture company that successfully predicts group membership. Based on this information, we propose various forms of joint venture which complement each other and produce higher overall competence. Our study can provide important feedback information to academics, Policy-makers.

• Korea Trade Review
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• v.47 no.2
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• pp.69-88
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• 2022

This paper aims to predict Busan's regional product and employment using the logistic regression models and machine learning models. The following are the main findings of the empirical analysis. First, the OLS regression model shows that the main industries such as electricity and electronics, machine and transport, and finance and insurance affect the Busan's income positively. Second, the binomial logistic regression models show that the Busan's strategic industries such as the future transport machinery, life-care, and smart marine industries contribute on the Busan's income in large order. Third, the multinomial logistic regression models show that the Korea's main industries such as the precise machinery, transport equipment, and machinery influence the Busan's economy positively. And Korea's exports and the depreciation can affect Busan's economy more positively at the higher employment level. Fourth, the voting ensemble model show the higher predictive power than artificial neural network model and support vector machine models. Furthermore, the gradient boosting model and the random forest show the higher predictive power than the voting model in large order.

• Journal of Korean Society for Quality Management
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• v.26 no.1
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• pp.143-160
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• 1998

This article is concerned with the selection of subsets of predictor variables to be included in bulding the binary response logistic regression model. It is based on a Bayesian aproach, intended to propose and develop a procedure that uses probabilistic considerations for selecting promising subsets. This procedure reformulates the logistic regression setup in a hierarchical normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. It is done by use of the fact that cdf of logistic distribution is a, pp.oximately equivalent to that of $t_{(8)}$/.634 distribution. The a, pp.opriate posterior probability of each subset of predictor variables is obtained by the Gibbs sampler, which samples indirectly from the multinomial posterior distribution on the set of possible subset choices. Thus, in this procedure, the most promising subset of predictors can be identified as that with highest posterior probability. To highlight the merit of this procedure a couple of illustrative numerical examples are given.

• Journal of Environmental Science International
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• v.25 no.4
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• pp.603-613
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• 2016

Fog may have a significant impact on road conditions. In an attempt to improve fog predictability in Jeju, we conducted machine learning with various data mining techniques such as tree models, conditional inference tree, random forest, multinomial logistic regression, neural network and support vector machine. To validate machine learning models, the results from the simulation was compared with the fog data observed over Jeju(184 ASOS site) and Gosan(185 ASOS site). Predictive rates proposed by six data mining methods are all above 92% at two regions. Additionally, we validated the performance of machine learning models with WRF (weather research and forecasting) model meteorological outputs. We found that it is still not good enough for operational fog forecast. According to the model assesment by metrics from confusion matrix, it can be seen that the fog prediction using neural network is the most effective method.

• Journal of Korean Society of Rural Planning
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• v.27 no.1
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• pp.85-94
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• 2021

The purpose of this study is to investigate the factors affecting the types of the 6th industrialization of fishery households. In this study we tried to explain the significance of the demographic and managerial characteristics of fishery households when they choose the types of the 6th industrialization business. Multinomial logistic model was used for this analysis. This study shows that the household and fishery management characteristics, main method of fishing, and regional factors matters for fishery households to choose their business types. Our results implies that it is necessary to reflect the detailed support measures differentiated by business types when implementing the 6th industrialization policy for fishery sector. In addition, the sixth industrialization of fishery should not be limited to marine products, but agricultural products produced in fishing villages should be included.

• Journal of Korean Society of Rural Planning
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• v.30 no.2
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• pp.79-96
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• 2024

The purpose of this study is to analyze the factors affecting residential mobility between urban and rural. After classifying urban and rural region based on discriminatory attributes of the regions, we applied a multinomial logistic model, using the sample data of 2020 Korea Population and Housing Census. The major findings are as follows. The young highly educated in cities avoided rural. The young less educated in rural engaged in 2, 3th industries as well as agricultural industry, but remained in low-paying and unstable jobs. In addition, various classes moved to rural and rising house prices in cities pushed people to rural. Therefore, it is necessary to develop diversified regional industry models and provide opportunities for high quality and stable jobs in rural by linking industrial demand, education and jobs. Also, preserving the rural environment, settlement conditions and residential environment are needed for satisfying various needs of urban residents who migrate to rural areas. While regional policies so far have focused on maintaining the population size and promoting a population influx, rural development and population policies should be established in a way that responds to diverse population classes in an era of population decline.

• 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.

• 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.