• Steel and Composite Structures
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• v.49 no.6
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• pp.645-666
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• 2023

Due to the steadily declining supply of natural coarse aggregates, the concrete industry has shifted to substituting coarse aggregates generated from byproducts and industrial waste. Oil palm shell is a substantial waste product created during the production of palm oil (OPS). When considering the usage of OPSC, building engineers must consider its uniaxial compressive strength (UCS). Obtaining UCS is expensive and time-consuming, machine learning may help. This research established five innovative hybrid AI algorithms to predict UCS. Aquila optimizer (AO) is used with methods to discover optimum model parameters. Considered models are artificial neural network (AO - ANN), adaptive neuro-fuzzy inference system (AO - ANFIS), support vector regression (AO - SVR), random forest (AO - RF), and extreme gradient boosting (AO - XGB). To achieve this goal, a dataset of OPS-produced concrete specimens was compiled. The outputs depict that all five developed models have justifiable accuracy in UCS estimation process, showing the remarkable correlation between measured and estimated UCS and models' usefulness. All in all, findings depict that the proposed AO - XGB model performed more suitable than others in predicting UCS of OPSC (with R², RMSE, MAE, VAF and A_15-index at 0.9678, 1.4595, 1.1527, 97.6469, and 0.9077). The proposed model could be utilized in construction engineering to ensure enough mechanical workability of lightweight concrete and permit its safe usage for construction aims.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.48-60
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• 2024

With the exponential growth of satellite data utilization, machine learning has become pivotal in enhancing innovation and cybersecurity in satellite systems. This paper investigates the role of machine learning techniques in identifying and mitigating vulnerabilities and code smells within satellite software. We explore satellite system architecture and survey applications like vulnerability analysis, source code refactoring, and security flaw detection, emphasizing feature extraction methodologies such as Abstract Syntax Trees (AST) and Control Flow Graphs (CFG). We present practical examples of feature extraction and training models using machine learning techniques like Random Forests, Support Vector Machines, and Gradient Boosting. Additionally, we review open-access satellite datasets and address prevalent code smells through systematic refactoring solutions. By integrating continuous code review and refactoring into satellite software development, this research aims to improve maintainability, scalability, and cybersecurity, providing novel insights for the advancement of satellite software development and security. The value of this paper lies in its focus on addressing the identification of vulnerabilities and resolution of code smells in satellite software. In terms of the authors' contributions, we detail methods for applying machine learning to identify potential vulnerabilities and code smells in satellite software. Furthermore, the study presents techniques for feature extraction and model training, utilizing Abstract Syntax Trees (AST) and Control Flow Graphs (CFG) to extract relevant features for machine learning training. Regarding the results, we discuss the analysis of vulnerabilities, the identification of code smells, maintenance, and security enhancement through practical examples. This underscores the significant improvement in the maintainability and scalability of satellite software through continuous code review and refactoring.

• The Journal of Economics, Marketing and Management
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• v.12 no.3
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• pp.35-42
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• 2024

Purpose This study aims to investigate the correlation of social skills and metacognition among university students participating in service-learning programs. Also by evaluating the satisfaction of college students participating in service learning, this research seeks to understand the impact of this program on learning experiences. Research design, data and methodology: The research period spans two semesters, each comprising 15 weeks, from March 2, 2023, to December 20, 2023. Detailed procedures, including planning, preparation, data collection, analysis, and organization, cover activities conducted over the course of 30 weeks. These activities encompass various stages, from initial classroom planning with designated English storybooks to reflection and feedback sessions aimed at continuous development. Data collection methods include surveys, interviews, and observations, allowing for a comprehensive examination of social skills and metacognition among participating students. Results: The results show significant correlations between social skills and metacognition, such as the correlation between knowledge and statistics (r = 0.759, p < .01), the moderate correlation between cooperation and knowledge (r = 0.532, p < .01), the moderate correlation between statistics and cooperation (r = 0.539, p < .01), and the correlation between self-regulation and assertion (r = 0.278, p < .001). The average score of the satisfaction of college students participating in service learning was 4.8 out of 5. Conclusions: This study highlights the significant role of service-learning in boosting social skills and metacognition among university students. This study enhances the academic understanding of the relationships between social skills, metacognition, and service-learning programs, contributing to the expansion of both theoretical and practical knowledge in the field.

• Safety and Health at Work
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• v.10 no.4
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• pp.452-460
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• 2019

Background: Monitoring and control of PM_2.5 are being recognized as key to address health issues attributed to PM_2.5. Availability of low-cost PM_2.5 sensors made it possible to introduce a number of portable PM_2.5 monitors based on light scattering to the consumer market at an affordable price. Accuracy of light scatteringe-based PM_2.5 monitors significantly depends on the method of calibration. Static calibration curve is used as the most popular calibration method for low-cost PM_2.5 sensors particularly because of ease of application. Drawback in this approach is, however, the lack of accuracy. Methods: This study discussed the calibration of a low-cost PM_2.5-monitoring device (PMD) to improve the accuracy and reliability for practical use. The proposed method is based on construction of the PM_2.5 sensor network using Message Queuing Telemetry Transport (MQTT) protocol and web query of reference measurement data available at government-authorized PM monitoring station (GAMS) in the republic of Korea. Four machine learning (ML) algorithms such as support vector machine, k-nearest neighbors, random forest, and extreme gradient boosting were used as regression models to calibrate the PMD measurements of PM_2.5. Performance of each ML algorithm was evaluated using stratified K-fold cross-validation, and a linear regression model was used as a reference. Results: Based on the performance of ML algorithms used, regression of the output of the PMD to PM_2.5 concentrations data available from the GAMS through web query was effective. The extreme gradient boosting algorithm showed the best performance with a mean coefficient of determination (R²) of 0.78 and standard error of 5.0 ㎍/㎥, corresponding to 8% increase in R² and 12% decrease in root mean square error in comparison with the linear regression model. Minimum 100 hours of calibration period was found required to calibrate the PMD to its full capacity. Calibration method proposed poses a limitation on the location of the PMD being in the vicinity of the GAMS. As the number of the PMD participating in the sensor network increases, however, calibrated PMDs can be used as reference devices to nearby PMDs that require calibration, forming a calibration chain through MQTT protocol. Conclusions: Calibration of a low-cost PMD, which is based on construction of PM_2.5 sensor network using MQTT protocol and web query of reference measurement data available at a GAMS, significantly improves the accuracy and reliability of a PMD, thereby making practical use of the low-cost PMD possible.

• Korean Journal of Animal Reproduction
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• v.20 no.3
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• pp.271-278
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• 1996

The present study was carried out to sex effectively mouse and rabbit embryos using rat H-Y antiserum and to improve the rate of rat producing H-Y antiserum with H-Y antibody. The H-Y antiserum was prepared from inbred SD strain female rat immunized by intrasplenic injection and subsequent intraperitioneal booster of testis cell of syngenic male newborn rat. the titer of antiserum was identified by in vitro cytotoxicity test of mouse embryos. The rabbit embryos exposed to the H-Y antiserum was classified to the developed (H-Y negative) or delayed (H-Y positive) group. The H-Y negative rabbit embryos were transferred to recipients and sex of offspring was examined. 1. When mouse embryos were exposed to the rat H-Y antisera, the ratio of embryos developed vs delayed was various. The H-Y antisera where the ratio of embryos developed vs delayed showed the range of 40~60% were recognized as having titer of H-Y antibody. 2. When the subsequent intraperitioneal boosters were followed after priming of intrasplenic injection of H-Y antigen, the rate of rat producting the H-Y antiserum with H-U antibody was 13, 27, 70 and 73% in control, 1st B, 2nd B and 3rd B, respectively. The rate in 2nd B and 3rd B was significantly(P<0.05) higher than that in control and 1st B. 3. When the rabbit morulae were exposed to the rat H-Y antiserum with H-Y antibody, the ratio of morulae developed versus delayed was 42:58% and it was close to the natural sex ratio 50:50%. It was confirmed that the rat H-Y antiserum was cross reactive to the rabbit morulae. 4. When the H-Y negative rabbit embryos were transferred to the recipients, the pregnancy rate was 50% and 83% of the newborns were females. In conclusion, the rat H-Y antiserum with high titer of H-Y antibody was able to be obtained from the female rat immunized by the intrasplenic injection followed by the second intrapent oneal booster of testis cells at a week interval. When the rabbit embryos negative to the rat H-Y antiserum were transferred, 83% of the newborns were females.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.977-990
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• 2015

Food delivery services are well developed in the Republic of Korea, The increase of one person households and the success of app applications influence delivery services these days. We consider a prediction model for the food delivery service based on weather and dates to predict the number of food delivery services in 2014 using various data mining techniques. We use linear regression, random forest, gradient boosting, support vector machines, neural networks, and logistic regression to find the best prediction model. There are four categories of food delivery services and we consider two methods. For the first method, we estimate the total number of delivery services and the posterior probabilities of each delivery service. For the second method, we use different models for each category and combine them to estimate the total number of delivery services. The neural network and linear regression model perform best in the first method, this is followed by the neural network which is the best for the second method. The result shows that we can estimate the number of deliveries accurately based on dates and weather information.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.41-55
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• 2017

This study predicts the average scores of top 150 PGA golf players on 132 PGA Tour tournaments (2013-2015) using data mining techniques and statistical analysis. This study also aims to predict the Top 10 and Top 25 best players in 4 different playoffs. Linear and nonlinear regression methods were used to predict average scores. Stepwise regression, all best subset, LASSO, ridge regression and principal component regression were used for the linear regression method. Tree, bagging, gradient boosting, neural network, random forests and KNN were used for nonlinear regression method. We found that the average score increases as fairway firmness or green height or average maximum wind speed increases. We also found that the average score decreases as the number of one-putts or scrambling variable or longest driving distance increases. All 11 different models have low prediction error when predicting the average scores of PGA Tournaments in 2015 which is not included in the training set. However, the performances of Bagging and Random Forest models are the best among all models and these two models have the highest prediction accuracy when predicting the Top 10 and Top 25 best players in 4 different playoffs.

• Tuberculosis and Respiratory Diseases
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• v.86 no.3
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• pp.203-215
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• 2023

Background: Surgical resection is the standard treatment for early-stage lung cancer. Since postoperative lung function is related to mortality, predicted postoperative lung function is used to determine the treatment modality. The aim of this study was to evaluate the predictive performance of linear regression and machine learning models. Methods: We extracted data from the Clinical Data Warehouse and developed three sets: set I, the linear regression model; set II, machine learning models omitting the missing data: and set III, machine learning models imputing the missing data. Six machine learning models, the least absolute shrinkage and selection operator (LASSO), Ridge regression, ElasticNet, Random Forest, eXtreme gradient boosting (XGBoost), and the light gradient boosting machine (LightGBM) were implemented. The forced expiratory volume in 1 second measured 6 months after surgery was defined as the outcome. Five-fold cross-validation was performed for hyperparameter tuning of the machine learning models. The dataset was split into training and test datasets at a 70:30 ratio. Implementation was done after dataset splitting in set III. Predictive performance was evaluated by R² and mean squared error (MSE) in the three sets. Results: A total of 1,487 patients were included in sets I and III and 896 patients were included in set II. In set I, the R² value was 0.27 and in set II, LightGBM was the best model with the highest R² value of 0.5 and the lowest MSE of 154.95. In set III, LightGBM was the best model with the highest R² value of 0.56 and the lowest MSE of 174.07. Conclusion: The LightGBM model showed the best performance in predicting postoperative lung function.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1721-1730
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• 2023

As awareness of the problem of global warming emerges around the world, the role of carbon sinks in settlement is increasingly emphasized to achieve carbon neutrality in urban areas. In order to manage carbon sinks in settlement, it is necessary to identify the current status of carbon sinks. Identifying the status of carbon sinks requires a lot of manpower and time and a corresponding budget. Therefore, in this study, a map predicting the location of trees was created using already established tree location information and Sentinel-2 satellite images targeting Seoul. To this end, after constructing a tree presence/absence dataset, structured data was generated using 16 types of vegetation indices information constructed from satellite images. After learning this by applying the Extreme Gradient Boosting (XGBoost) model, a tree prediction map was created. Afterward, the correlation between independent and dependent variables was investigated in model learning using the Shapely value of Shapley Additive exPlanations(SHAP). A comparative analysis was performed between maps produced for local parts of Seoul and sub-categorized land cover maps. In the case of the tree prediction model produced in this study, it was confirmed that even hard-to-detect street trees around the main street were predicted as trees.

• Journal of Distribution Science
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• v.15 no.8
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• pp.65-73
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• 2017

Purpose - Although e-learning has this advantage, currently many organizations have failed to recognize the necessity for basic e-learning educational training. It follows that practitioners working in the above organizations face the difficulty of having to find educational training processes of boosting their capabilities by themselves, rather than being able to utilize the educational training processes offered by e-learning. So of their own accord, learners have considered the necessity of information relating to being able to choose between high quality educational training processes. The purpose of this study is to propose opening e-learning content information for enabling an efficient choice of learning processes related to e-learning. Research design, data, and methodology - To pinpoint the items of e-learning content information, the study was initiated according to the following process. First, information relating to e-learning content (offered on e-learning websites) was researched. Second, based on the items of information which emerged from the research, selection and validity verification took place with 5 e-learning specialists as the subjects. Third, the opinions of adult learners at K University were collated relating to the items of information which emerged from the research. Results - The e-learning content information was comprised of 16 items in order to improve the choosing process for learner's e-learning contents. The analysis results showed that when learners were choosing e-learning processes, the most highly considered item was 'mobile support' (4.35). Following this (in order) were 'tuition fees' (4.30), 'certificate issuing' (4.23), and 'awareness of educational institution' (4.18). The least considered items were 'recruiting learners' (3.01) and 'tutor support' (3.18). Conclusions - The 16 items of e-learning content information in this study, were deemed to be helpful to learners in providing them with a choice of desirable e-learning process when this process was offered to them. Following this, there is a need for service institutions offering e-learning processes to make public the information suggested by this study. Research into educational methods additionally points to a necessity for not only e-learning forms, but also offline educational methods and a combination of blended learning to be offered and run parallel to e-learning.