• Journal of Digital Convergence
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• v.17 no.12
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• pp.345-352
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• 2019

This study aims to analyze the factors affecting the length of stay in elderly pediatric inpatients in traffic accidents. We used Korean National Hospital Discharge In-depth Injury data on the discharged from 2012 to 2016. Statistically significant factors affecting the length of stay are admission route, Charlson Comorbidity Index(CCI), injury parts, operation, results, hospital area, and beds for hospitals. The length of stay was shorter in the case of the admission route of the outpatient department than the emergency room, the results were not improved or death rather than improved, and the bed size was 500-999 beds or over 1000 beds rather than 100-299 beds. However, the length of stay was longer in the case of CCI score was 1-2 or over 3 rather than 0, injury parts were other parts rather than head/neck, when the operation was yes, and when the hospital area was a province, metropolitan rather than Seoul. This study intends to understand the medical characteristics of inpatient to prevent pedestrian traffic accidents in accordance with the population aging. Based on this finding, we wish to be used as the basic data for the establishment of policies to effectively manage traffic safety and medical resources in consideration of the characteristics of the elderly people.

• Korean Journal of Construction Engineering and Management
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• v.23 no.3
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• pp.13-24
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• 2022

Construction projects are exposed to various types of risks, which tend to increase. The increasing risks call for contractors' more attentions to forecasting and dealing with these risks. One of the measures to deal with contractors' risks is to forecast or estimate risk cost and include it in the construction budget. Although various researches in relation to risk cost have been observed, little attention has been paid to general contractors' perceptions and current practices in estimating risk cost of construction budget. The objective of the study is to identify and discuss key characteristics and implications based on the survey and analysis of general contractors' perceptions and current practices in estimating risk cost of construction budget. The study shows that there is a gap between the perception and the practice of estimating risk cost, that is, high perception of the importance of risk cost and a relatively low level of practice. It suggests that historical cost data, guidelines and corporate-level standard procedures are required to improve the current practice in addition to sufficient time allocations for risk cost estimating. It discusses that there is a need for using sophisticated estimating techniques including bid data analytics despite a low level of the current adoption, and also proposes that research and development in the field of the sophisticated estimating techniques should be further implemented in order to increase their practicality.

• 한국노년학
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• v.39 no.2
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• pp.231-240
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• 2019

The study aimed to identify risk factors for falls as well as hospitalization status according to disease and demographic characteristics of demented inpatients by investigating the in-depth Injury Patient Surveillance System data collected by Korea Centers for Disease Control and Prevention(KCDC). Older adults over 60 years old who were diagnosed with dementia were included(n=1,732). Their data were analyzed after being assigned to either a fall group or a non-fall group. STATA was used for statistical analyses, such as frequency analysis, chi-square (χ²) test, and logistics regression. It was found that 8.0% of the demented inpatients experienced falls. According to the analysis on category of fall and non-fall group were statistically significant difference in age and Charlson Comorbidity Index(CCI) and bone density deficiency. Based on the logistic regression analysis of factors affecting falls, older adults over 80 are 2.386 times more likely to fall and based on a target with a CCI of 0, the risk of falls is 0.421 times lower, finally based on those without bone density disorder, the fall risk for those with bone density disorder was 3.581 times higher. Therefore, we expect that the important about the factors relating to falls identified in this can not only be found valuable for educating inpatients with dementia and care-givers, but also be used as reference that supports clinical professionals to make decisions on falls management for patients with dementia.

• Information Systems Review
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• v.20 no.2
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• pp.39-62
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• 2018

Thanks to the growth of computing power and the recent development of data analytics, researchers have started to work on the data produced by users through the Internet or social media. This study is in line with these recent research trends and attempts to adopt data analytical techniques. We focus on the impact of "internal marketing" factors on firm performance, which is typically studied through survey methodologies. We looked into the job review platform Jobplanet (www.jobplanet.co.kr), which is a website where employees and former employees anonymously review companies and their management. With web crawling processes, we collected over 40K data points and performed morphological analysis to classify employees' reviews for internal marketing data. We then implemented econometric analysis to see the relationship between internal marketing and market capitalization. Contrary to the findings of extant survey studies, internal marketing is positively related to a firm's market capitalization only within a limited area. In most of the areas, the relationships are negative. Particularly, female-friendly environment and human resource development (HRD) are the areas exhibiting positive relations with market capitalization in the manufacturing industry. In the service industry, most of the areas, such as employ welfare and work-life balance, are negatively related with market capitalization. When firm size is small (or the history is short), female-friendly environment positively affect firm performance. On the contrary, when firm size is big (or the history is long), most of the internal marketing factors are either negative or insignificant. We explain the theoretical contributions and managerial implications with these results.

• Journal of Navigation and Port Research
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• v.43 no.1
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• pp.72-78
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• 2019

The second-hand ship market provides immediate access to the freight market for shipping investors. When introducing second-hand vessels, the precise estimate of the price is crucial to the decision-making process because it directly affects the burden of capital cost to investors in the future. Previous studies on the second-hand market have mainly focused on the market efficiency. The number of papers on the estimation of second-hand vessel values is very limited. This study proposes an artificial neural network model that has not been attempted in previous studies. Six factors, freight, new-building price, orderbook, scrap price, age and vessel size, that affect the second-hand ship price were identified through literature review. The employed data is 366 real trading records of Panamax second-hand vessels reported to Clarkson between January 2016 and December 2018. Statistical filtering was carried out through correlation analysis and stepwise regression analysis, and three parameters, which are freight, age and size, were selected. Ten-fold cross validation was used to estimate the hyper-parameters of the artificial neural network model. The result of this study confirmed that the performance of the artificial neural network model is better than that of simple stepwise regression analysis. The application of the statistical verification process and artificial neural network model differentiates this paper from others. In addition, it is expected that a scientific model that satisfies both statistical rationality and accuracy of the results will make a contribution to real-life practices.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.91-98
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• 2024

With the advancement of ICT (Information and Communication Technology), searching for judgments through the internet has become increasingly convenient. However, predicting sentencing based on judgments remains a challenging task for individuals. This is because sentencing involves a complex process of applying aggravating and mitigating factors within the framework of legal provisions, and it often depends on the subjective judgment of the judge. Therefore, this research aimed to develop a model for predicting sentencing using artificial intelligence by focusing on structuring the data from judgments, making it suitable for AI applications. Through theoretical and statistical analysis of previous studies, we identified variables with high explanatory power for predicting sentencing. Additionally, by analyzing 50 legal judgments related to serious crimes that are publicly available, we presented a framework for extracting essential information from judgments. This framework encompasses basic case information, sentencing details, reasons for sentencing, the reasons for the determination of the sentence, as well as information about offenders, victims, and accomplices evident within the specific content of the judgments. This research is expected to contribute to the development of artificial intelligence technologies in the field of law in the future.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.37-57
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• 2018

With the spread of smart phones, interest in mobile media is on the increase as useful media recently. Mobile media is assessed as having differentiated advantages from existing media in that not only can they provide consumers with desired information anytime and anywhere but also real-time interaction is possible in them. So far, studies on mobile advertising were mostly researches analyzing satisfaction with, and acceptance of, mobile advertising based on survey, researches focusing on the factors affecting acceptance of mobile advertising messages and researches verifying the effect of mobile advertising on brand recall, advertising attitude and brand attitude through experiments. Most of the domestic mobile advertising studies related to advertisement effect and advertisement attitude have been conducted through experiments and surveys. The advertising effectiveness measure of the mobile ad used the attitude of the advertisement, purchase intention, etc. To date, there have been few studies on the effects of mobile advertising on actual advertising data to prove the characteristics of the advertising platform and to prove the relationship between the factors influencing the advertising effect and the factors. In order to explore advertising effect of mobile advertising platform currently commercialized, this study defined advertising characteristics and media characteristics from the perspective of advertiser, advertising platform and publisher and analyzed the influence of each characteristic on advertising effect. As the advertisement characteristics, we classified advertisement format classified by bar type and floating type, and advertisement material classified by image and text. We defined advertisement characteristics of advertisement platform as Hedonic and Utilitarian media characteristics. As a dependent variable, we use CTR, which is the ratio of response (click) to ad exposure. The theoretical background and the analysis of the mobile advertising business, the hypothesis that the advertisement effect is different according to the advertisement specification, the advertisement material, In the ad standard, bar ads are classified as static framing, Floating ads can be categorized as dynamic framing, and the hypothetical definition of floating advertisements, which are high-profile dynamic framing ads, is highly responsive. In advertising, images with high salience are defined to have higher ad response than text. In the media characteristics classified as practical / hedonic type, it is defined that the hedonic type media has a more relaxed tendency than the practical media, and there is a high possibility of receiving various information because there is no clear target. In addition, image material and hedonic media are defined to be highly effective in the interaction between advertisement specification and advertisement material, advertisement specifications and media characteristics, and advertisement material and media characteristics. As the result of regression analysis on each characteristic, material standard, which is a characteristic of mobile advertisement, and media characteristics separated into 'Hedonic' and 'Utilitarian' had significant influence on advertisement effect and mutual interaction effect was also confirmed. In the mobile advertising standard, the advertising effect of the floating advertisement is higher than that of the bar advertisement, Floating ads were more effective than text ads for image ads. In addition, it was confirmed that the advertising effect is higher in the practical media than the hedonic media. The research was carried out with the big data collected from the mobile advertising platform, and it was possible to grasp the advertising effect of the measure index standard which is used in the practical work which could not be grasped in the previous research. In other words, the study was conducted using the CTR, which is a measure of the effectiveness of the advertisement used in the online advertisement and the mobile advertisement, which are not dependent on the attitude of the ad, the attitude of the brand, and the purchase intention. This study suggests that CTR is used as a dependent variable of advertising effect based on actual data of mobile ad platform accumulated over a long period of time. The results of this study is expected to contribute to establishment of optimum advertisement strategy such as creation of advertising materials and planning of media which suit advertised products at the time of mobile advertisement.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.157-173
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• 2021

With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Journal of Navigation and Port Research
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• v.42 no.6
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• pp.539-545
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• 2018

In recent years, research on shipping market forecasting with the employment of non-linear AI models has attracted significant interest. In previous studies, input variables were selected with reference to past papers or by relying on the intuitions of the researchers. This paper attempts to address this issue by applying the stepwise regression model and the random forest model to the Cape-size bulk carrier market. The Cape market was selected due to the simplicity of its supply and demand structure. The preliminary selection of the determinants resulted in 16 variables. In the next stage, 8 features from the stepwise regression model and 10 features from the random forest model were screened as important determinants. The chosen variables were used to test both models. Based on the analysis of the models, it was observed that the random forest model outperforms the stepwise regression model. This research is significant because it provides a scientific basis which can be used to find the determinants in shipping market forecasting, and utilize a machine-learning model in the process. The results of this research can be used to enhance the decisions of chartering desks by offering a guideline for market analysis.