• Steel and Composite Structures
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• v.44 no.5
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• pp.691-705
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• 2022

Shear failure in reinforced concrete (RC) structures is very hazardous. This failure is rarely predicted and may occur without any prior signs. Accurate shear strength prediction of the RC members is challenging, and traditional methods have difficulty solving it. This study develops a JAYA-GBRT model based on the JAYA algorithm and the gradient boosting regression tree (GBRT) to predict the shear strength of RC slender beams without stirrups. Firstly, 484 tests are carefully collected and divided into training and test sets. Then, the hyperparameters of the GBRT model are determined using the JAYA algorithm and 10-fold cross-validation. The performance of the JAYA-GBRT model is compared with five well-known empirical models. The comparative results show that the JAYA-GBRT model (R² = 0.982, RMSE = 9.466 kN, MAE = 6.299 kN, µ = 1.018, and Cov = 0.116) outperforms the other models. Moreover, the predictions of the JAYA-GBRT model are globally and locally explained using the Shapley Additive exPlanation (SHAP) method. The effective depth is determined as the most crucial parameter influencing the shear strength through the SHAP method. Finally, a Graphic User Interface (GUI) tool and a web application (WA) are developed to apply the JAYA-GBRT model for rapidly predicting the shear strength of RC slender beams without stirrups.

• Journal of Korea Multimedia Society
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• v.9 no.10
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• pp.1266-1275
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• 2006

In this paper, we propose a real-lime face detection and tracking algorithm using AdaBoost(Adaptive Boosting) algorithm. The proposed algorithm consists of two levels such as the face detection and the face tracking. First, the face detection used the eight-wavelet feature models which ate very simple. Each feature model applied to variable size and position, and then create initial feature set. The intial feature set and the training images which were consisted of face images, non-face images used the AdaBoost algorithm. The basic principal of the AdaBoost algorithm is to create final strong classifier joining linearly weak classifiers. In the training of the AdaBoost algorithm, we propose SAT(Summed-Area Table) method. Face tracking becomes accomplished at real-time using the position information and the size information of detected face, and it is extended view region dynamically using the fan-Tilt camera. We are setting to move center of the detected face to center of the Image. The experiment results were amply satisfied with the computational efficiency and the detection rates. In real-time application using Pan-Tilt camera, the detecter runs at about 12 frames per second.

• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.279-286
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• 2021

The purpose of this study is to compare the predictive power of the Bagging and Boosting algorithm of ensemble method based on the motion information that occurs in woman handball matches and to analyze the availability of motion information. To this end, this study analyzed the predictive power of the result of 15 practice matches based on inertial motion by analyzing the predictive power of Random Forest and Adaboost algorithms. The results of the study are as follows. First, the prediction rate of the Random Forest algorithm was 66.9 ± 0.1%, and the prediction rate of the Adaboost algorithm was 65.6 ± 1.6%. Second, Random Forest predicted all of the winning results, but none of the losing results. On the other hand, the Adaboost algorithm shows 91.4% prediction of winning and 10.4% prediction of losing. Third, in the verification of the suitability of the algorithm, the Random Forest had no overfitting error, but Adaboost showed an overfitting error. Based on the results of this study, the availability of motion information is high when predicting sports events, and it was confirmed that the Random Forest algorithm was superior to the Adaboost algorithm.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.3
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• pp.617-623
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• 2018

This study focuses on detection of malicious code through AndroidManifest permissoion feature extracted based on Android static analysis. Features are built on the permissions of AndroidManifest, which can save resources and time for analysis. Malicious app detection model consisted of SVM (support vector machine), NB (Naive Bayes), Gradient Boosting Classifier (GBC) and Logistic Regression model which learned 1,500 normal apps and 500 malicious apps and 98% detection rate. In addition, malicious app family identification is implemented by multi-classifiers model using algorithm SVM, GPC (Gaussian Process Classifier) and GBC (Gradient Boosting Classifier). The learned family identification machine learning model identified 92% of malicious app families.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.3
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• pp.97-102
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• 2010

The purpose of medical data mining for efficient algorithms and techniques throughout the various diseases is to increase the reliability of estimates to classify. Previous studies, an algorithm based on a single model, and even the existence of the model to better predict the classification accuracy of multi-model ensemble-based research techniques are being applied. In this paper, the higher the medical data to predict the reliability of the existing scope of the ensemble technique applied to the I-ENSEMBLE offers. Data for the diagnosis of hypothyroidism is the result of applying the experimental technique, a representative ensemble Bagging, Boosting, Stacking technique significantly improved accuracy compared to all existing, respectively. In addition, compared to traditional single-model techniques and ensemble techniques Multi modeling when applied to represent the effects were more pronounced.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1975-1988
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• 2019

Short-term traffic flow prediction plays an important role in intelligent transportation systems (ITS) in areas such as transportation management, traffic control and guidance. For short-term traffic flow regression predictions, the main challenge stems from the non-stationary property of traffic flow data. In this paper, we design an ensemble cascading prediction framework based on extremely randomized trees (extra-trees) using a boosting technique called EET to predict the short-term traffic flow under non-stationary environments. Extra-trees is a tree-based ensemble method. It essentially consists of strongly randomizing both the attribute and cut-point choices while splitting a tree node. This mechanism reduces the variance of the model and is, therefore, more suitable for traffic flow regression prediction in non-stationary environments. Moreover, the extra-trees algorithm uses boosting ensemble technique averaging to improve the predictive accuracy and control overfitting. To the best of our knowledge, this is the first time that extra-trees have been used as fundamental building blocks in boosting committee machines. The proposed approach involves predicting 5 min in advance using real-time traffic flow data in the context of inherently considering temporal and spatial correlations. Experiments demonstrate that the proposed method achieves higher accuracy and lower variance and computational complexity when compared to the existing methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.545-550
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• 2013

In pattern recognition field, data classification is an essential process for extracting meaningful information from data. Adaptive boosting algorithm, known as AdaBoost algorithm, is a kind of improved boosting algorithm for applying to real data analysis. It consists of weak classifiers, such as random guessing or random forest, which performance is slightly more than 50% and weights for combining the classifiers. And a strong classifier is created with the weak classifiers and the weights. In this paper, a research is performed using AdaBoost algorithm for detecting chaff echo which has similar characteristics to precipitation echo and interrupts weather forecasting. The entire process for implementing chaff echo classifier starts spatial and temporal clustering based on similarity with weather radar data. With them, learning data set is prepared that separated chaff echo and non-chaff echo, and the AdaBoost classifier is generated as a result. For verifying the classifier, actual chaff echo appearance case is applied, and it is confirmed that the classifier can distinguish chaff echo efficiently.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.147-155
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• 2022

In this study, we developed a system to dynamically balance a daily stock portfolio and performed trading simulations using gradient boosting and genetic algorithms. We collected various stock market data from stocks listed on the KOSPI and KOSDAQ markets, including investor-specific transaction data. Subsequently, we indexed the data as a preprocessing step, and used feature engineering to modify and generate variables for training. First, we experimentally compared the performance of three popular gradient boosting algorithms in terms of accuracy, precision, recall, and F1-score, including XGBoost, LightGBM, and CatBoost. Based on the results, in a second experiment, we used a LightGBM model trained on the collected data along with genetic algorithms to predict and select stocks with a high daily probability of profit. We also conducted simulations of trading during the period of the testing data to analyze the performance of the proposed approach compared with the KOSPI and KOSDAQ indices in terms of the CAGR (Compound Annual Growth Rate), MDD (Maximum Draw Down), Sharpe ratio, and volatility. The results showed that the proposed strategies outperformed those employed by the Korean stock market in terms of all performance metrics. Moreover, our proposed LightGBM model with a genetic algorithm exhibited competitive performance in predicting stock price movements.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.29-36
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• 2022

In this paper, we propose an efficient agricultural products price prediction model based on dataset which provided in DACON. This model is XGBoost and CatBoost, and as an algorithm of the Gradient Boosting series, the average accuracy and execution time are superior to the existing Logistic Regression and Random Forest. Based on these advantages, we design a machine learning model that predicts prices 1 week, 2 weeks, and 4 weeks from the previous prices of agricultural products. The XGBoost model can derive the best performance by adjusting hyperparameters using the XGBoost Regressor library, which is a regression model. The implemented model is verified using the API provided by DACON, and performance evaluation is performed for each model. Because XGBoost conducts its own overfitting regulation, it derives excellent performance despite a small dataset, but it was found that the performance was lower than LGBM in terms of temporal performance such as learning time and prediction time.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.07a
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• pp.214-214
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• 2004