• Korean Journal of Occupational Health Nursing
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• v.28 no.3
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• pp.156-165
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• 2019

Purpose: The purpose of this study was to compare working condition and health status between parcel delivery workers (PDW) and food delivery workers (FDW) and to examine the factors influencing their health status. Methods: This was a secondary analysis of data collected from the fifth Korean Working Conditions Survey (KWCS). Based on existing literature, a set of variables was chosen from the KWCS. Results: The proportion of PDW who carryied/moved heavy loads and experienced high job stress and lack of rest time was significantly higher than that of FDW. However, more FDW than their counterparts worked atypical hours. The differences in fatigue and well-being between PDW and FDW were not statistically significant. The multiple logistic regression analysis revealed low temperature, tobacco smoke, standing for long periods, and job stress were significant predictors of fatigue or well-being of FDW. Among PDW, noise, tobacco smoke, sitting for long periods, quantitative demands, hiding emotions, support from colleagues, job stress, no recovery period, and night work were significant predictors of fatigue or well-being. Conclusion: The findings of this study may be useful in developing nursing interventions for disease protection health promotion of delivery workers.

• Journal of Family Resource Management and Policy Review
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• v.23 no.1
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• pp.17-34
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• 2019

Older women who live alone are among society's most vulnerable people, since they experience increased risk of multiple chronic diseases and have limited financial protection. This can lead older women living alone to catastrophic health expenditure(CHE), which is defined as a healthcare expenditure that exceeds a certain portion of a household's ability to pay. Using the Korean Longitudinal Study of Ageing(KLoSA), this study investigated the incidence of CHE among older women living alone and identified the factors related to this incidence. Applying health expenditure thresholds of 10%, 20%, 30% and 40% of ability to pay, the proportions of those with CHE were 41.3%, 22.9%, 14.6%, and 9.4%, respectively. Logistic regression models were used to identify factors related to CHE incidence, which include demographics, income, the number of chronic diseases, perceived health status, and health insurance type. The results show that the health care safety net in South Korea is insufficient for older women living alone. The findings can guide policymakers in improving healthcare and welfare policies to protect people from catastrophic payments. Particularly, welfare policies should be established for poor non-recipients who are not included within the benefits scope of the National Basic Livelihood Security System due to the unrealistic criteria of income recognition and family support obligation.

• Journal of the Korean Society of Safety
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• v.36 no.5
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• pp.52-60
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• 2021

As the prevention of fatal accidents is considered an essential part of social responsibilities, both government and individual have devoted efforts to mitigate the unsafe conditions and behaviors that facilitate accidents. Several studies have analyzed the factors that cause fatal accidents and compared them to those of non-fatal accidents. However, studies on mathematical and systematic analysis techniques for identifying the features of fatal accidents are rare. Recently, various industrial fields have employed machine learning algorithms. This study aimed to apply machine learning algorithms for the classification of fatal and non-fatal accidents based on the features of each accident. These features were obtained by text mining literature on accidents. The classification was performed using four machine learning algorithms, which are widely used in industrial fields, including logistic regression, decision tree, neural network, and support vector machine algorithms. The results revealed that the machine learning algorithms exhibited a high accuracy for the classification of accidents into the two categories. In addition, the importance of comparing similar cases between fatal and non-fatal accidents was discussed. This study presented a method for classifying accidents using machine learning algorithms based on the reports on previous studies on accidents.

• The Korean Journal of Health Service Management
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• v.13 no.1
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• pp.31-42
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• 2019

Objectives: The purpose of this study is to analyze the relationship between the change gap in the perception of subjective hierarchy and medical expenditure and the factors influencing medical expenditure. Methods: An analysis based on the the data extracted from the Panel Study of Korea Health Panel for 2012-2013 (n=9,359) is conducted. Further in this study, data analysis included a chi-square test and logistic regression using SPSS version. 22.0 to analyze the factors influencing the turnover intention of industrial workers. Results: Model I showed decreases in medical expenditure by 1.247, 1.391, and 1.441 times in social classes one, two, and Model II showed an increase in medical expenditure by age, spouse, number of family members living together, insurance type, income class, economic activities, subjective health status, chronic illness and change on subjective recognition of social class. Conclusions: The study concludes that the state and community require psychological, social, and cultural support, in addition to individual efforts, to reduce medical expenditure.

• The Korean Journal of Health Service Management
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• v.13 no.3
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• pp.93-103
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• 2019

Objectives: The purpose of this study was to analyze the association between health status and physical activity levels in older adults (over 65 years) in Korea. Methods: The participants were selected from the database of the Seventh Korea National Health and Nutrition Examination Survey (KNHANES VII), conducted by the Korea Centers for Disease Control and Prevention in 2017. A chi-square test and logistic regression analysis were used to analyze data. Results: The findings showed that health conditions such as hypertension and diabetes were related to physical activity levels of the older adults. In addition to health status, social and economic factors such as gender, age, and geographic region should be considered in order to initiate the practice of aerobic and strength exercise in the older adults. Conclusions: To initiate physical activity levels in the older adults, it is necessary to consider support for costs involved, policies required for the development of integrated exercise programs, expansion of public exercise facilities, and improved facilities.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.10
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• pp.189-200
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• 2022

The gig economy offers many advantages, such as flexibility, variety, independence, and lower cost. However, there are also safety concerns, lack of regulations, uncertainty, and unsatisfactory services, causing people to voice their opinion on social media. This paper aims to explore the sentiments of consumers concerning gig economy services (Grab, Foodpanda and Airbnb) through the analysis of social media. First, Vader Lexicon was used to classify the comments into positive, negative, and neutral sentiments. Then, the comments were further classified into three machine learning algorithms: Support Vector Machine, Light Gradient Boosted Machine, and Logistic Regression. Results suggested that gig economy services in Malaysia received more positive sentiments (52%) than negative sentiments (19%) and neutral sentiments (29%). Based on the three algorithms used in this research, LGBM has been the best model with the highest accuracy of 85%, while SVM has 84% and LR 82%. The results of this study proved the power of text mining and sentiment analysis in extracting business value and providing insight to businesses. Additionally, it aids gig managers and service providers in understanding clients' sentiments about their goods and services and making necessary adjustments to optimize satisfaction.

• 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.

• The Journal of Information Systems
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• v.30 no.4
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• pp.203-226
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• 2021

Purpose The purpose of this study is to enhance the accuracy score of personal credit scoring using the convolutional neural networks and secure the transparency of the deep learning model using eXplainalbe Artifical Inteligence(XAI) technique. Design/methodology/approach This study built a classification model by using the convolutional neural networks(CNN) and applied a methodology that is transformation of numerical data to imaged data to apply CNN on personal credit data. Then layer-wise relevance propagation(LRP) was applied to model we constructed to find what variables are more influenced to the output value. Findings According to the empirical analysis result, this study confirmed that accuracy score by model using CNN is highest among other models using logistic regression, neural networks, and support vector machines. In addition, With the LRP that is one of the technique of XAI, variables that have a great influence on calculating the output value for each observation could be found.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.499-510
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• 2022

This study examines the reporting factors of crime against business in Korea and proposes a corresponding predictive model using machine learning. While many previous studies focused on the individual factors of theft victims, there is a lack of evidence on the reporting factors of crime against a business that serves the public good as opposed to those that protect private property. Therefore, we proposed a crime prevention model for the willingness factor of theft reporting in businesses. This study used data collected through the 2015 Commercial Crime Damage Survey conducted by the Korea Institute for Criminal Policy. It analyzed data from 834 businesses that had experienced theft during a 2016 crime investigation. The data showed a problem with unbalanced classes. To solve this problem, we jointly applied the Synthetic Minority Over Sampling Technique and the Tomek link techniques to the training data. Two prediction models were implemented. One was a statistical model using logistic regression and elastic net. The other involved a support vector machine model, tree-based machine learning models (e.g., random forest, extreme gradient boosting), and a stacking model. As a result, the features of theft price, invasion, and remedy, which are known to have significant effects on reporting theft offences, can be predicted as determinants of such offences in companies. Finally, we verified and compared the proposed predictive models using several popular metrics. Based on our evaluation of the importance of the features used in each model, we suggest a more accurate criterion for predicting var.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.330-334
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• 2022

In light of the decreasing crop production and shortage of food across the world, one of the crucial criteria of agriculture nowadays is selecting the right crop for the right piece of land at the right time. First problem is that How Farmers can predict the right crop for cultivation because famers have no knowledge about prediction of crop. Second problem is that which algorithm is best that provide the maximum accuracy for crop prediction. Therefore, in this research Author proposed a method that would help to select the most suitable crop(s) for a specific land based on the analysis of the affecting parameters (Temperature, Humidity, Soil Moisture) using machine learning. In this work, the author implemented Random Forest Classifier, Support Vector Machine, k-Nearest Neighbor, and Decision Tree for crop selection. The author trained these algorithms with the training dataset and later these algorithms were tested with the test dataset. The author compared the performances of all the tested methods to arrive at the best outcome. In this way best algorithm from the mention above is selected for crop prediction.