• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권2호
• /
• pp.632-657
• /
• 2022

Detecting web attacks is a major challenge, and it is observed that the use of simple models leads to low sensitivity or high false positive problems. In this study, we aim to develop a robust two-stage deep learning based stacked ensemble web application firewall. Normal and abnormal classification is carried out in the first stage of the proposed WAF model. The classification process of the types of abnormal traffics is postponed to the second stage and carried out using an integrated stacked ensemble model. By this way, clients' requests can be served without time delay, and attack types can be detected with high sensitivity. In addition to the high accuracy of the proposed model, by using the statistical similarity and diversity analyses in the study, high generalization for the ensemble model is achieved. Within the study, a comprehensive, up-to-date, and robust multi-class web anomaly dataset named GAZI-HTTP is created in accordance with the real-world situations. The performance of the proposed WAF model is compared to state-of-the-art deep learning models and previous studies using the benchmark dataset. The proposed two-stage model achieved multi-class detection rates of 97.43% and 94.77% for GAZI-HTTP and ECML-PKDD, respectively.

• Computers and Concrete
• /
• 제32권3호
• /
• pp.233-246
• /
• 2023

Accurate prediction of concrete compressive strength can minimize the need for extensive, time-consuming, and costly mixture optimization testing and analysis. This study attempts to enhance the prediction accuracy of compressive strength using stacking ensemble machine learning (ML) with feature engineering techniques. Seven alternative ML models of increasing complexity were implemented and compared, including linear regression, SVM, decision tree, multiple layer perceptron, random forest, Xgboost and Adaboost. To further improve the prediction accuracy, a ML pipeline was proposed in which the feature engineering technique was implemented, and a two-layer stacked model was developed. The k-fold cross-validation approach was employed to optimize model parameters and train the stacked model. The stacked model showed superior performance in predicting concrete compressive strength with a correlation of determination (R²) of 0.985. Feature (i.e., variable) importance was determined to demonstrate how useful the synthetic features are in prediction and provide better interpretability of the data and the model. The methodology in this study promotes a more thorough assessment of alternative ML algorithms and rather than focusing on any single ML model type for concrete compressive strength prediction.

• 자원환경지질
• /
• 제57권3호
• /
• pp.329-342
• /
• 2024

The exponential increase in nitrate pollution of river water poses an immediate threat to public health and the environment. This contamination is primarily due to various human activities, which include the overuse of nitrogenous fertilizers in agriculture and the discharge of nitrate-rich industrial effluents into rivers. As a result, the accurate prediction and identification of contaminated areas has become a crucial and challenging task for researchers. To solve these problems, this work leads to the prediction of nitrate contamination using machine learning approaches. This paper presents a novel approach known as Grey Wolf Optimizer (GWO) based on the Stacked Ensemble approach for predicting nitrate pollution in the Cauvery Delta region of Tamilnadu, India. The proposed method is evaluated using a Cauvery River dataset from the Tamilnadu Pollution Control Board. The proposed method shows excellent performance, achieving an accuracy of 93.31%, a precision of 93%, a sensitivity of 97.53%, a specificity of 94.28%, an F1-score of 95.23%, and an ROC score of 95%. These impressive results underline the demonstration of the proposed method in accurately predicting nitrate pollution in river water and ultimately help to make informed decisions to tackle these critical environmental problems.

• Communications for Statistical Applications and Methods
• /
• 제28권3호
• /
• pp.217-232
• /
• 2021

The Korea film industry has matured and the number of movie-watching per capita has reached the highest level in the world. Since then, movie industry growth rate is decreasing and even the total sales of movies per year slightly decreased in 2018. The number of moviegoers is the first factor of sales in movie industry and also an important factor influencing additional sales. Thus it is important to predict the number of movie audiences. In this study, we predict the cumulative number of audiences of films using stacking, an ensemble method. Stacking is a kind of ensemble method that combines all the algorithms used in the prediction. We use box office data from Korea Film Council and web comment data from Daum Movie (www.movie.daum.net). This paper describes the process of collecting and preprocessing of explanatory variables and explains regression models used in stacking. Final stacking model outperforms in the prediction of test set in terms of RMSE.

• Advances in Computational Design
• /
• 제8권1호
• /
• pp.37-59
• /
• 2023

Squat reinforced concrete (SRC) shear walls are a critical part of the structure for both office/residential buildings and nuclear structures due to their significant role in withstanding seismic loads. Despite this, empirical formulae in current design standards and published studies demonstrate a considerable disparity in predicting SRC wall shear strength. The goal of this research is to develop and evaluate hybrid and ensemble artificial neural network (ANN) models. State-of-the-art population-based algorithms are used in this research for hybrid intelligence algorithms. Six models are developed, including Honey Badger Algorithm (HBA) with ANN (HBA-ANN), Hunger Games Search with ANN (HGS-ANN), fitness-distance balance coyote optimization algorithm (FDB-COA) with ANN (FDB-COA-ANN), Averaging Ensemble (AE) neural network, Snapshot Ensemble (SE) neural network, and Stacked Generalization (SG) ensemble neural network. A total of 434 test results of SRC walls is utilized to train and assess the models. The results reveal that the SG model not only minimizes prediction variance but also produces predictions (with R²= 0.99) that are superior to other models.

• 대한전자공학회논문지TC
• /
• 제41권3호
• /
• pp.173-173
• /
• 2004

본 논문에서는 무선 근거리 지역 통신망(WLAN: Wireless Local Area Networks)용 광대역 2층 H-형 마이크로스트립 패치 안테나를 설계한다. 마이크로스트립 패치 안테나의 대역폭을 개선하기 위해서 기생패치를 부가하여 다층배열한다. 그리고 안테나의 크기를 줄이기 위해, 기본 방사소자와 기생패치는 10개의 단락봉으로 단락된 H-모양의 패치로 설계한다. 마이크로스트립 안테나는 모멘트법으로 작성된 ENSEMBLE ver 5.0의 소프트웨어를 사용하여 설계하고 실험치와 비교한다. 제작된 안테나의 대역폭은 5.46㎓에서 740㎒(13.5%)이며, 이것은 계산치 770㎒(13%)와 거의 근사하다. 또한 동일 주파수에서 동일 기판에 설계된 안테나 크기는 반파장 구형 마이크로스트립 패치 안테나에 비해 71.5%로 축소되었다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제8권1호
• /
• pp.196-212
• /
• 2014

Facial expression recognition is an important part in emotional interaction between human and machine. This paper proposes a facial expression recognition approach based on multi-classifier fusion with stacking algorithm. The kappa-error diagram is employed in base-level classifiers selection, which gains insights about which individual classifier has the better recognition performance and how diverse among them to help improve the recognition accuracy rate by fusing the complementary functions. In order to avoid the influence of the chance factor caused by guessing in algorithm evaluation and get more reliable awareness of algorithm performance, kappa and informedness besides accuracy are utilized as measure criteria in the comparison experiments. To verify the effectiveness of our approach, two public databases are used in the experiments. The experiment results show that compared with individual classifier and two other typical ensemble methods, our proposed stacked ensemble system does recognize facial expression more accurately with less standard deviation. It overcomes the individual classifier's bias and achieves more reliable recognition results.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2003년도 하계종합학술대회 논문집 I
• /
• pp.625-628
• /
• 2003

In this paper, a wideband two-layer H-shaped microstrip antenna for WLAN is designed and studied experimentally. To increase the bandwidth of microstrip patch antenna, a configuration of stacked type using parastic element is used, Furthermore, to reduce the size of microstrip patch antenna, the first technique is H-shaped patch type. the second technique is that the main radiator and parastic patch are shorted to the ground plane using ten shorting posts. The antenna bandwidth and radiation characteristics are calculated by ENSEMBLE ver. 5.0 simulation software, and compared with the experimental results, Experimental results show that the return loss is less than -10dB over the band of 5.086GHz to 5.832GHz, which is quite good agreement with the calculations.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2001년도 하계종합학술대회 논문집(1)
• /
• pp.397-400
• /
• 2001

In this paper, to improve bandwidth of microstrip antenna, we discussed the patch structure using Aperture Stacked Patch. To provid PCS service and IMT-2000 service simultaneous, a microstrip patch antenna needs impedance bandwidth of 22%. But typical microstrip patch antennas have impedance bandwidth of 3∼6%. To analyze characteristics of microstrip pach antenna, we used Ensemble of commercial software. The microsrtip patch antenna was designed and fabricated, tuned. We get following results; 650MHz(33%) of impedance bandwidth for VSWR 1.5. The measured gain of ASP microstrip antenna is 6.94dBi.

• 대한전자공학회논문지TC
• /
• 제41권3호
• /
• pp.15-20
• /
• 2004

본 논문에서는 무선 근거리 지역 통신망(WLAN: Wireless Local Area Networks)용 광대역 2층 H-형 마이크로스트립 패치 안테나를 설계한다. 마이크로스트립 패치 안테나의 대역폭을 개선하기 위해서 기생패치를 부가하여 다층배열한다. 그리고 안테나의 크기를 줄이기 위해, 기본 방사소자와 기생패치는 10개의 단락봉으로 단락된 H-모양의 패치로 설계한다. 마이크로스트립 안테나는 모멘트법으로 작성된 ENSEMBLE ver 5.0의 소프트웨어를 사용하여 설계하고 실험치와 비교한다. 제작된 안테나의 대역폭은 5.46㎓에서 740㎒(13.5%)이며, 이것은 계산치 770㎒(13%)와 거의 근사하다. 또한 동일 주파수에서 동일 기판에 설계된 안테나 크기는 반파장 구형 마이크로스트립 패치 안테나에 비해 71.5%로 축소되었다.