• Journal of the Korea Convergence Society
• /
• v.8 no.10
• /
• pp.215-223
• /
• 2017

This article has tried to analyse the effect of the corporate earning return tax empirically through analysis on the impact of previous internal reservation on the dividends rate of the current year. In addition to this, this article has tried to the effectiveness of government policies with leverage ratio as a moderating variable. Moreover, DRF and GBM model were used to see the effect again. As a result of the actual proof analysis, OCF, ROE, FOR have a significance level of 99% in model1, model2, model3. However, ADV and MSE has appeared not to be meaningful in all models. In the result of DRF and GBM model for convergence was higher than GBM in depth and leaves. However, when it comes to a model explaining capability, GBM high than DRF. The further study will be required to examine the effect of government policy by time series analysis in the period of enforcement of the reflux tax, from 2015 to 2017.

• Journal of the Korea Convergence Society
• /
• v.13 no.2
• /
• pp.249-255
• /
• 2022

This study proposed an analysis framework for real-time prediction of CNC processing defects using machine learning-based models that are recently attracting attention as processing defect prediction methods, and applied it to CNC machines. Analysis shows that the XGBoost, CatBoost, and LightGBM models have the same best accuracy, precision, recall, F1 score, and AUC, of which the LightGBM model took the shortest execution time. This short run time has practical advantages such as reducing actual system deployment costs, reducing the probability of CNC machine damage due to rapid prediction of defects, and increasing overall CNC machine utilization, confirming that the LightGBM model is the most effective machine learning model for CNC machines with only basic sensors installed. In addition, it was confirmed that classification performance was maximized when an ensemble model consisting of LightGBM, ExtraTrees, k-Nearest Neighbors, and logistic regression models was applied in situations where there are no restrictions on execution time and computing power.

• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.8
• /
• pp.3629-3633
• /
• 2014

Background: Glioblastoma (GBM) is an immunosuppressive tumor whose median survival time is only 12-15 months, and patients with GBM have a uniformly poor prognosis. It is known that heredity contributes to formation of glioma, but there are few genetic studies concerning GBM. Materials and Methods: We genotyped six tagging SNPs (tSNP) in Han Chinese GBM and control patients. We used Microsoft Excel and SPSS 16.0 statistical package for statistical analysis and SNP Stats to test for associations between certain tSNPs and risk of GBM in five different models. ORs and 95%CIs were calculated for unconditional logistic-regression analysis with adjustment for age and gender. The SHEsis software platform was applied for analysis of linkage disequilibrium, haplotype construction, and genetic associations at polymorphism loci. Results: We found rs891835 in CCDC26 to be associated with GBM susceptibility at a level of p=0.009. The following genotypes of rs891835 were found to be associated with GBM risk in four different models of gene action: i) genotype GT (OR=2.26; 95%CI, 1.29-3.97; p=0.019) or GG (OR=1.33; 95%CI, 0.23-7.81; p=0.019) in the codominant model; ii) genotypes GT and GG (OR=2.18; 95%CI, 1.26-3.78; p=0.0061) in the dominant model; iii) GT (OR=2.24; 95%CI, 1.28-3.92; p=0.0053) in the overdominant model; iv) the allele G of rs891835 (OR=1.85; 95%CI, 1.14-3.00; p=0.015) in the additive model. In addition, "CG" and "CGGAG" were found by haplotype analysis to be associated with increased GBM risk. In contrast, genotype GG of CCDC26 rs6470745 was associated with decreased GBM risk (OR=0.34; 95%CI, 0.12-1.01; p=0.029) in the recessive model. Conclusions: Our results, combined with those from previous studies, suggest a potential genetic contribution of CCDC26 to GBM progression among Han Chinese.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.150-150
• /
• 2022

This study investigates the roles of decomposition methods on high accuracy in daily rainfall prediction from light gradient boosting machine (LightGBM) algorithm. Here, empirical mode decomposition (EMD) and singular spectrum analysis (SSA) methods were considered to decompose and reconstruct input time series into trend terms, fluctuating terms, and noise components. The decomposed time series from EMD and SSA methods were used as input data for LightGBM algorithm in two hybrid models, including empirical mode-based light gradient boosting machine (EMDGBM) and singular spectrum analysis-based light gradient boosting machine (SSAGBM), respectively. A total of four parameters (i.e., temperature, humidity, wind speed, and rainfall) at a daily scale from 2003 to 2017 is used as input data for daily rainfall prediction. As results from statistical performance indicators, it indicates that the SSAGBM model shows a better performance than the EMDGBM model and the original LightGBM algorithm with no decomposition methods. It represents that the accuracy of LightGBM algorithm in rainfall prediction was improved with the SSA method when using multivariate dataset.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.15 no.6
• /
• pp.1123-1130
• /
• 2020

This study aims to classify the severity in car crashes using five classification learning models. The dataset used in this study contains 21,013 vehicle crashes, obtained from Korea Expressway Corporation, between the year of 2015-2017 and the LightGBM(Light Gradient Boosting Model) performed well with the highest accuracy. LightGBM, the number of involved vehicles, type of accident, incident location, incident lane type, types of accidents, types of vehicles involved in accidents were shown as priority factors. Based on the results of this model, the establishment of a management strategy for response of highway traffic accident should be presented through a consistent prediction process of accident severity level. This study identifies applicability of Machine Learning Models for Predicting of the Severity of Car Traffic Accidents on a Highway and suggests that various machine learning techniques based on big data that can be used in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.8
• /
• pp.2016-2029
• /
• 2023

Accurate prediction of critical illness is significant for ensuring the lives and health of patients. The selection of indicators affects the real-time capability and accuracy of the prediction for critical illness. However, the diversity and complexity of these indicators make it difficult to find potential connections between them and critical illnesses. For the first time, this study proposes an indicator analysis model to extract key indicators from the preoperative and intraoperative clinical indicators and laboratory results of critical illnesses. In this study, preoperative and intraoperative data of heart failure and respiratory failure are used to verify the model. The proposed model processes the datum and extracts key indicators through four parts. To test the effectiveness of the proposed model, the key indicators are used to predict the two critical illnesses. The classifiers used in the prediction are light gradient boosting machine (LightGBM) and eXtreme Gradient Boosting (XGBoost). The predictive performance using key indicators is better than that using all indicators. In the prediction of heart failure, LightGBM and XGBoost have sensitivities of 0.889 and 0.892, and specificities of 0.939 and 0.937, respectively. For respiratory failure, LightGBM and XGBoost have sensitivities of 0.709 and 0.689, and specificity of 0.936 and 0.940, respectively. The proposed model can effectively analyze the correlation between indicators and postoperative critical illness. The analytical results make it possible to find the key indicators for postoperative critical illnesses. This model is meaningful to assist doctors in extracting key indicators in time and improving the reliability and efficiency of prediction.

• Computers and Concrete
• /
• v.33 no.2
• /
• pp.137-145
• /
• 2024

The technique of experimentally determining concrete's compressive strength for a given mix design is time-consuming and difficult. The goal of the current work is to propose a best working predictive model based on different machine learning algorithms such as Gradient Boosting Machine (GBM), Stacked Ensemble (SE), Distributed Random Forest (DRF), Extremely Randomized Trees (XRT), Generalized Linear Model (GLM), and Deep Learning (DL) that can forecast the compressive strength of ternary geopolymer concrete mix without carrying out any experimental procedure. A geopolymer mix uses supplementary cementitious materials obtained as industrial by-products instead of cement. The input variables used for assessing the best machine learning algorithm not only include individual ingredient quantities, but molarity of the alkali activator and age of testing as well. Myriad statistical parameters used to measure the effectiveness of the models in forecasting the compressive strength of ternary geopolymer concrete mix, it has been found that GBM performs better than all other algorithms. A sensitivity analysis carried out towards the end of the study suggests that GBM model predicts results close to the experimental conditions with an accuracy between 95.6 % to 98.2 % for testing and training datasets.

• Journal of the Korea Convergence Society
• /
• v.9 no.1
• /
• pp.9-20
• /
• 2018

The purpose of this paper is to analyze the effect of corporation internal reservation on R&D investment. It is to find how much effect the reflux tax has achieved through empirical analysis. In addition, education training expense was taken as a moderating variable to find the effectiveness of government policy. Furthermore, the study looked through the effect once again by using GMB model. According to the result counted by regression analysis, it could be concluded that the effect of both moderation and intervention had a significant effect and the variable of interest cost and welfare & benefit cost in model 1, 2 and 3 had a meaningful impact at the level of 99%. On the other hand, the previous corporate internal reservation failed to show any significant result in all types of models. Even in GBM model of convergence level applied to additional analysis, similar results came out.

• Molecules and Cells
• /
• v.43 no.6
• /
• pp.539-550
• /
• 2020

Glioblastoma multiforme (GBM) is a fatal malignant tumor that is characterized by diffusive growth of tumor cells into the surrounding brain parenchyma. However, the diffusive nature of GBM and its relationship with the tumor microenvironment (TME) is still unknown. Here, we investigated the interactions of GBM with the surrounding microenvironment in orthotopic xenograft animal models using two human glioma cell lines, U87 and LN229. The GBM cells in our model showed different features on the aspects of cell growth rate during their development, dispersive nature of glioma tumor cells along blood vessels, and invasion into the brain parenchyma. Our results indicated that these differences in the two models are in part due to differences in the expression of CXCR4 and STAT3, both of which play an important role in tumor progression. In addition, the GBM shows considerable accumulation of resident microglia and peripheral macrophages, but polarizes differently into tumor-supporting cells. These results suggest that the intrinsic factors of GBM and their interaction with the TME determine the diffusive nature and probably the responsiveness to non-cancer cells in the TME.

• Korean Journal of Applied Biomechanics
• /
• v.32 no.1
• /
• pp.1-8
• /
• 2022

Objective: This study proposes a methodology to analyze important variables that have a significant impact on the putting direction prediction using a machine learning-based putting direction prediction model trained with IMU sensor data. Method: Putting data were collected using an IMU sensor measuring 12 variables from 6 adult males in their 20s at K University who had no golf experience. The data was preprocessed so that it could be applied to machine learning, and a model was built using five machine learning algorithms. Finally, by comparing the performance of the built models, the model with the highest performance was selected as the proposed model, and then 12 variables of the IMU sensor were applied one by one to analyze important variables affecting the learning performance. Results: As a result of comparing the performance of five machine learning algorithms (K-NN, Naive Bayes, Decision Tree, Random Forest, and Light GBM), the prediction accuracy of the Light GBM-based prediction model was higher than that of other algorithms. Using the Light GBM algorithm, which had excellent performance, an experiment was performed to rank the importance of variables that affect the direction prediction of the model. Conclusion: Among the five machine learning algorithms, the algorithm that best predicts the putting direction was the Light GBM algorithm. When the model predicted the putting direction, the variable that had the greatest influence was the left-right inclination (Roll).