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Bridging AI and Cyber Defense:A Stacked Ensemble Deep Learning Model with Explainable Insights 认领 引用
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作者 Faisal Albalwy Muhannad Almohaimeed 《Computers, Materials & Continua》 SCIE EI 2026年第5期559-578,共20页
Intrusion detection in Internet of Things(IoT)environments presents challenges due to heterogeneous devices,diverse attack vectors,and highly imbalanced datasets.Existing research on the ToN-IoT dataset has largely em... Intrusion detection in Internet of Things(IoT)environments presents challenges due to heterogeneous devices,diverse attack vectors,and highly imbalanced datasets.Existing research on the ToN-IoT dataset has largely emphasized binary classification and single-model pipelines,which often showstrong performance but limited generalizability,probabilistic reliability,and operational interpretability.This study proposes a stacked ensemble deep learning framework that integrates random forest,extreme gradient boosting,and a deep neural network as base learners,with CatBoost as the meta-learner.On the ToN-IoT Linux process dataset,the model achieved near-perfect discrimination(macro area under the curve=0.998),robust calibration,and superior F1-scores compared with standalone classifiers.Interpretability was achieved through SHapley Additive exPlanations–based feature attribution,which highlights actionable drivers ofmalicious behavior,such as command-line patterns,process scheduling anomalies,and CPU usage spikes,and aligns these indicators with MITRE ATT&CK tactics and techniques.Complementary analyses,including cumulative lift and sensitivity-specificity trade-offs,revealed the framework’s suitability for deployment in security operations centers,where calibrated risk scores,transparent explanations,and resource-aware triage are essential.These contributions bridge methodological rigor in artificial intelligence/machine learning with operational priorities in cybersecurity,delivering a scalable and explainable intrusion detection system suitable for real-world deployment in IoT environments. 展开更多
关键词 Cybersecurity IoT intrusion detection stacked ensemble learning deep learning explainable AI(XAI) probability calibration SHAP interpretability ToN-IoT dataset MITRE ATT&CK
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MalDetect-IoT:Enhanced IoT Malware Variant Detection with a Deep Stacked Ensemble Approach 认领 引用
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作者 Muhammad Shaheer Feng Zeng +3 位作者 Aqsa Yasmeen Mudasir Ahmad Wani Kashish Ara Shakil Muhammad Asim 《Computers, Materials & Continua》 SCIE EI 2026年第7期1824-1847,共24页
Malware remains a persistent and evolving threat to digital security,highlighting the need for advanced and resilient detection frameworks capable of mitigating increasingly sophisticated and evasive cyberattacks.Alth... Malware remains a persistent and evolving threat to digital security,highlighting the need for advanced and resilient detection frameworks capable of mitigating increasingly sophisticated and evasive cyberattacks.Although deep learning ensembles have been explored,many existing approaches fail to balance computational efficiency with the diverse feature extraction capabilities needed for complex variants.To address this gap,this study proposes a novel stacking ensemble framework,MalDetect-IoT,which specifically eliminates the requirement for manual feature engineering and domain specific preprocessing traditionally required in malware classification.By fine-tuning two pre-trained models MobileNetV3 for its lightweight efficiency and Xception for its depthwise distinct convolutions within a stacked architecture,the ensemble achieves superior reliability and predictive accuracy while remaining suitable for resource limited Internet of Things(IoT)environments.The proposed approach leverages complementary features to identify nuanced structural characteristics in malware binaries transformed into images,achieving domain knowledge independence.The proposed approach was evaluated on two benchmark datasets,achieving accuracies of 98.75%on the Malimg dataset(9335 images)and 98.55%on the MaleVis dataset(14,226 images).Statistical validation via McNemar’s test and a Cohen’s kappa coefficient of 0.983 confirm that the framework consistently surpasses state of the art methodologies in effectively identifying malware instances. 展开更多
关键词 Malware detection Internet of Things(IoT) deep learning stacking ensemble transfer learning image-based malware classification
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An Auto Encoder-Enhanced Stacked Ensemble for Intrusion Detection in Healthcare Networks 认领 引用
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作者 Fatma S.Alrayes Mohammed Zakariah +2 位作者 Mohammed K.Alzaylaee Syed Umar Amin Zafar Iqbal Khan 《Computers, Materials & Continua》 SCIE EI 2025年第11期3457-3484,共28页
Healthcare networks prove to be an urgent issue in terms of intrusion detection due to the critical consequences of cyber threats and the extreme sensitivity of medical information.The proposed Auto-Stack ID in the st... Healthcare networks prove to be an urgent issue in terms of intrusion detection due to the critical consequences of cyber threats and the extreme sensitivity of medical information.The proposed Auto-Stack ID in the study is a stacked ensemble of encoder-enhanced auctions that can be used to improve intrusion detection in healthcare networks.TheWUSTL-EHMS 2020 dataset trains and evaluates themodel,constituting an imbalanced class distribution(87.46% normal traffic and 12.53% intrusion attacks).To address this imbalance,the study balances the effect of training Bias through Stratified K-fold cross-validation(K=5),so that each class is represented similarly on training and validation splits.Second,the Auto-Stack ID method combines many base classifiers such as TabNet,LightGBM,Gaussian Naive Bayes,Histogram-Based Gradient Boosting(HGB),and Logistic Regression.We apply a two-stage training process based on the first stage,where we have base classifiers that predict out-of-fold(OOF)predictions,which we use as inputs for the second-stage meta-learner XGBoost.The meta-learner learns to refine predictions to capture complicated interactions between base models,thus improving detection accuracy without introducing bias,overfitting,or requiring domain knowledge of the meta-data.In addition,the auto-stack ID model got 98.41% accuracy and 93.45%F1 score,better than individual classifiers.It can identify intrusions due to its 90.55% recall and 96.53% precision with minimal false positives.These findings identify its suitability in ensuring healthcare networks’security through ensemble learning.Ongoing efforts will be deployed in real time to improve response to evolving threats. 展开更多
关键词 Intrusion detection auto encoder stacked ensemble WUSTL-EHMS 2020 dataset class imbalance XGBoost
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Deep Stacked Ensemble Learning Model for COVID-19 Classification 认领 引用
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作者 G.Madhu B.Lalith Bharadwaj +5 位作者 Rohit Boddeda Sai Vardhan K.Sandeep Kautish Khalid Alnowibet Adel F.Alrasheedi Ali Wagdy Mohamed 《Computers, Materials & Continua》 SCIE EI 2022年第3期5467-5486,共20页
COVID-19 is a growing problem worldwide with a high mortality rate.As a result,the World Health Organization(WHO)declared it a pandemic.In order to limit the spread of the disease,a fast and accurate diagnosis is requ... COVID-19 is a growing problem worldwide with a high mortality rate.As a result,the World Health Organization(WHO)declared it a pandemic.In order to limit the spread of the disease,a fast and accurate diagnosis is required.A reverse transcript polymerase chain reaction(RT-PCR)test is often used to detect the disease.However,since this test is time-consuming,a chest computed tomography(CT)or plain chest X-ray(CXR)is sometimes indicated.The value of automated diagnosis is that it saves time and money by minimizing human effort.Three significant contributions are made by our research.Its initial purpose is to use the essential finetuning methodology to test the action and efficiency of a variety of vision models,ranging from Inception to Neural Architecture Search(NAS)networks.Second,by plotting class activationmaps(CAMs)for individual networks and assessing classification efficiency with AUC-ROC curves,the behavior of these models is visually analyzed.Finally,stacked ensembles techniques were used to provide greater generalization by combining finetuned models with six ensemble neural networks.Using stacked ensembles,the generalization of the models improved.Furthermore,the ensemble model created by combining all of the finetuned networks obtained a state-of-the-art COVID-19 accuracy detection score of 99.17%.The precision and recall rates were 99.99%and 89.79%,respectively,highlighting the robustness of stacked ensembles.The proposed ensemble approach performed well in the classification of the COVID-19 lesions on CXR according to the experimental results. 展开更多
关键词 COVID-19 classification class activation maps(CAMs)visualization finetuning stacked ensembles automated diagnosis deep learning
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Malicious Traffic Detection in IoT and Local Networks Using Stacked Ensemble Classifier 认领 引用 被引量：2
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作者 R.D.Pubudu L.Indrasiri Ernesto Lee +2 位作者 Vaibhav Rupapara Furqan Rustam Imran Ashraf 《Computers, Materials & Continua》 SCIE EI 2022年第4期489-515,共27页
Malicious traffic detection over the internet is one of the challenging areas for researchers to protect network infrastructures from any malicious activity.Several shortcomings of a network system can be leveraged by... Malicious traffic detection over the internet is one of the challenging areas for researchers to protect network infrastructures from any malicious activity.Several shortcomings of a network system can be leveraged by an attacker to get unauthorized access through malicious traffic.Safeguard from such attacks requires an efficient automatic system that can detect malicious traffic timely and avoid system damage.Currently,many automated systems can detect malicious activity,however,the efficacy and accuracy need further improvement to detect malicious traffic from multi-domain systems.The present study focuses on the detection of malicious traffic with high accuracy using machine learning techniques.The proposed approach used two datasets UNSW-NB15 and IoTID20 which contain the data for IoT-based traffic and local network traffic,respectively.Both datasets were combined to increase the capability of the proposed approach in detecting malicious traffic from local and IoT networks,with high accuracy.Horizontally merging both datasets requires an equal number of features which was achieved by reducing feature count to 30 for each dataset by leveraging principal component analysis(PCA).The proposed model incorporates stacked ensemble model extra boosting forest(EBF)which is a combination of tree-based models such as extra tree classifier,gradient boosting classifier,and random forest using a stacked ensemble approach.Empirical results show that EBF performed significantly better and achieved the highest accuracy score of 0.985 and 0.984 on the multi-domain dataset for two and four classes,respectively. 展开更多
关键词 Stacked ensemble PCA malicious traffic detection classification machine learning
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A Stacked Ensemble Deep Learning Approach for Imbalanced Multi-Class Water Quality Index Prediction 认领 引用 被引量：1
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作者 Wen Yee Wong Khairunnisa Hasikin +4 位作者 Anis Salwa Mohd Khairuddin Sarah Abdul Razak Hanee Farzana Hizaddin Mohd Istajib Mokhtar Muhammad Mokhzaini Azizan 《Computers, Materials & Continua》 SCIE EI 2023年第8期1361-1384,共24页
A common difficulty in building prediction models with real-world environmental datasets is the skewed distribution of classes.There are significantly more samples for day-to-day classes,while rare events such as poll... A common difficulty in building prediction models with real-world environmental datasets is the skewed distribution of classes.There are significantly more samples for day-to-day classes,while rare events such as polluted classes are uncommon.Consequently,the limited availability of minority outcomes lowers the classifier’s overall reliability.This study assesses the capability of machine learning(ML)algorithms in tackling imbalanced water quality data based on the metrics of precision,recall,and F1 score.It intends to balance the misled accuracy towards the majority of data.Hence,10 ML algorithms of its performance are compared.The classifiers included are AdaBoost,SupportVector Machine,Linear Discriminant Analysis,k-Nearest Neighbors,Naive Bayes,Decision Trees,Random Forest,Extra Trees,Bagging,and the Multilayer Perceptron.This study also uses the Easy Ensemble Classifier,Balanced Bagging,andRUSBoost algorithm to evaluatemulti-class imbalanced learning methods.The comparison results revealed that a highaccuracy machine learning model is not always good in recall and sensitivity.This paper’s stacked ensemble deep learning(SE-DL)generalization model effectively classifies the water quality index(WQI)based on 23 input variables.The proposed algorithm achieved a remarkable average of 95.69%,94.96%,92.92%,and 93.88%for accuracy,precision,recall,and F1 score,respectively.In addition,the proposed model is compared against two state-of-the-art classifiers,the XGBoost(eXtreme Gradient Boosting)and Light Gradient Boosting Machine,where performance metrics of balanced accuracy and g-mean are included.The experimental setup concluded XGBoost with a higher balanced accuracy and G-mean.However,the SE-DL model has a better and more balanced performance in the F1 score.The SE-DL model aligns with the goal of this study to ensure the balance between accuracy and completeness for each water quality class.The proposed algorithm is also capable of higher efficiency at a lower computational time against using the standard SyntheticMinority Oversampling Technique(SMOTE)approach to imbalanced datasets. 展开更多
关键词 Water quality classification imbalanced data SMOTE stacked ensemble deep learning sensitivity analysis
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Early detection of wheat powdery mildew:A multi-source in situ remote sensing approach enabled by stacked ensemble learning 认领 引用
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作者 Li Song Jiliang Zhao +6 位作者 Yahui Li Linru Liu Jianzhao Duan Li He Yonghua Wang Tiancai Guo Wei Feng 《Artificial Intelligence in Agriculture》 SCIE EI CSCD 2026年第1期124-138,共15页
Powdery mildew seriously hinders photosynthesis and nutrient accumulation in wheat,and its early detection holds the key to enhancing control efficacy.In this research,solar-induced chlorophyll fluorescence(SIF)parame... Powdery mildew seriously hinders photosynthesis and nutrient accumulation in wheat,and its early detection holds the key to enhancing control efficacy.In this research,solar-induced chlorophyll fluorescence(SIF)parameters were derived from radiance and reflectance data,while vegetation indices(VI)were computed using reflectance.A suite of feature selection methods,including shadow feature(Boruta),feature selection(ReliefF),minimum redundancy maximum correlation(mRMR),and random forest(RF).Models were developed on the back propagation(BP)neural network,support vector regression(SVR),and partial least squares regression(PLSR).Furthermore,a stacking ensemble strategy was adopted,utilizing RF and decision tree(DT)algorithms as meta-models to integrate the predictions from base models.The findings revealed that the Boruta method selected a well-balanced number of feature parameters with normalized weights.The multi-source model(SIF+VI)is superior to the single-source model(SIF or VI).The BP model exhibited high accuracy in wheat disease monitoring,particularly during the initial infection phases.The multi-regressor stacked with RF ensemble model(MRSRF)generally surpassed the multi-regressor stacked with DT ensemble model(MRSDT),especially in the initial infection stage,where the MRSRF model's average R2 was 13.03%higher than that of the BP model.To validate these conclusions,reflectance data simulated by the PROSAIL model(PROSPECT and SAIL)were utilized.The Boruta-MRSRF model demonstrated exceptional advantages in early detection,achieving an R2 greater than 0.90 at all infection stages.This study provides effective ideas and methods for the active prevention and control of crop diseases,which are of great significance for ensuring agricultural production. 展开更多
关键词 Multi-source in-situ remote sensing Machine learning algorithms(MLA) Wheat powdery mildew PROSAIL Stacked ensemble learning
Fault Identification Method for In-Core Self-Powered Neutron Detectors Combining Graph Convolutional Network and Stacking Ensemble Learning 认领 引用 被引量：2
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作者 LIN Weiqing LU Yanzhen +1 位作者 MIAO Xiren QIU Xinghua 《Journal of Shanghai Jiaotong university(Science)》 EI 2025年第5期1018-1027,共10页
Self-powered neutron detectors(SPNDs)play a critical role in monitoring the safety margins and overall health of reactors,directly affecting safe operation within the reactor.In this work,a novel fault identification ... Self-powered neutron detectors(SPNDs)play a critical role in monitoring the safety margins and overall health of reactors,directly affecting safe operation within the reactor.In this work,a novel fault identification method based on graph convolutional networks(GCN)and Stacking ensemble learning is proposed for SPNDs.The GCN is employed to extract the spatial neighborhood information of SPNDs at different positions,and residuals are obtained by nonlinear fitting of SPND signals.In order to completely extract the time-varying features from residual sequences,the Stacking fusion model,integrated with various algorithms,is developed and enables the identification of five conditions for SPNDs:normal,drift,bias,precision degradation,and complete failure.The results demonstrate that the integration of diverse base-learners in the GCN-Stacking model exhibits advantages over a single model as well as enhances the stability and reliability in fault identification.Additionally,the GCN-Stacking model maintains higher accuracy in identifying faults at different reactor power levels. 展开更多
关键词 self-powered neutron detector(SPND) graph convolutional network(GCN) Stacking ensemble learning fault identification
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A stacking ensemble approach for pore pressure prediction in real-time during drilling based on mud log data 认领 引用
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作者 Dong-Yang Zhang Tian-Shou Ma +1 位作者 Yang Liu De-Cheng Zhang 《Petroleum Science》 SCIE EI CAS CSCD 2025年第12期5047-5067,共21页
The precise and real-time prediction of pore pressure is critical for optimizing drilling efficiency and mitigating the potential risks associated with drilling operations.In order to surmount the limitations of empir... The precise and real-time prediction of pore pressure is critical for optimizing drilling efficiency and mitigating the potential risks associated with drilling operations.In order to surmount the limitations of empirical methods and to reduce reliance on logging-while-drilling data,this study proposed a stacking ensemble approach that utilized only conventional mud log data.The workflow involves the preliminary processing of data using isolation forest and wavelet thresholding techniques to effectively eliminate outliers and noise.The Eaton index was estimated using a Bayesian inversion algorithm,and pore pressure was estimated by integrating the dc exponent and Eaton method.A feature selection strategy combining data distribution characteristics and regression-based importance ranking was used to optimize the input parameters.Subsequently,a stacking approach was developed for predicting pore pressure,and the corresponding base learner,meta learner,and hyperparameters were optimized.Finally,the validity of the optimized model was substantiated through field data from three test wells(X1,X5,X3)under different drilling scenarios.The results indicated that the global optimal Eaton index of the block was 0.2449,the maximum percentage error of pore pressure estimation was 3.66%,and the corresponding average error was 1.59%compared to the wireline formation test data.The optimal combination of input features was determined to be R-ROP,WOB,TG,PT,MW,PFI,TVD,H,BR,BT,and SPP.The optimal basic learners were identified as BPNN,CNN,LSTM,and LightGBM,while the optimal meta learners were XGBoost.Prediction accuracy is improved when offset wells are densely distributed,spatially balanced,and proximal to the target well;conversely,sparse or distant offset wells result in reduced prediction performance.The mean absolute percentage errors for test offset well X1,X5,and X3 were 0.4353%,0.4646%,and 0.6856%,respectively,with the corresponding R2 values of 0.9362,0.9078,and 0.8950,respectively.Consequently,this approach has the capacity to accurately and in real-time predict pore pressure using solely conventional mud log data.This capability enables timely adjustments to drilling parameters,thereby enhancing operational efficiency and mitigating drilling risks. 展开更多
关键词 Pore pressure Stacking ensemble approach Machine learning Real-time Mud log
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A Filter-Based Feature Selection Framework to Detect Phishing URLs Using Stacking Ensemble Machine Learning 认领 引用 被引量：1
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作者 Nimra Bari Tahir Saleem +3 位作者 Munam Shah Abdulmohsen Algarni Asma Patel Insaf Ullah 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第10期1167-1187,共21页
Today,phishing is an online attack designed to obtain sensitive information such as credit card and bank account numbers,passwords,and usernames.We can find several anti-phishing solutions,such as heuristic detection,... Today,phishing is an online attack designed to obtain sensitive information such as credit card and bank account numbers,passwords,and usernames.We can find several anti-phishing solutions,such as heuristic detection,virtual similarity detection,black and white lists,and machine learning(ML).However,phishing attempts remain a problem,and establishing an effective anti-phishing strategy is a work in progress.Furthermore,while most antiphishing solutions achieve the highest levels of accuracy on a given dataset,their methods suffer from an increased number of false positives.These methods are ineffective against zero-hour attacks.Phishing sites with a high False Positive Rate(FPR)are considered genuine because they can cause people to lose a lot ofmoney by visiting them.Feature selection is critical when developing phishing detection strategies.Good feature selection helps improve accuracy;however,duplicate features can also increase noise in the dataset and reduce the accuracy of the algorithm.Therefore,a combination of filter-based feature selection methods is proposed to detect phishing attacks,including constant feature removal,duplicate feature removal,quasi-feature removal,correlated feature removal,mutual information extraction,and Analysis of Variance(ANOVA)testing.The technique has been tested with differentMachine Learning classifiers:Random Forest,Artificial Neural Network(ANN),Ada-Boost,Extreme Gradient Boosting(XGBoost),Logistic Regression,Decision Trees,Gradient Boosting Classifiers,Support Vector Machine(SVM),and two types of ensemble models,stacking and majority voting to gain A low false positive rate is achieved.Stacked ensemble classifiers(gradient boosting,randomforest,support vector machine)achieve 1.31%FPR and 98.17%accuracy on Dataset 1,2.81%FPR and Dataset 3 shows 2.81%FPR and 97.61%accuracy,while Dataset 2 shows 3.47%FPR and 96.47%accuracy. 展开更多
关键词 Phishing detection feature selection phishing detection stacking ensemble machine learning phishing URL
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A Two-Layer Network Intrusion Detection Method Incorporating LSTM and Stacking Ensemble Learning 认领 引用 被引量：1
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作者 Jun Wang Chaoren Ge +4 位作者 Yihong Li Huimin Zhao Qiang Fu Kerang Cao Hoekyung Jung 《Computers, Materials & Continua》 SCIE EI 2025年第6期5129-5153,共25页
Network Intrusion Detection System(NIDS)detection of minority class attacks is always a difficult task when dealing with attacks in complex network environments.To improve the detection capability of minority-class at... Network Intrusion Detection System(NIDS)detection of minority class attacks is always a difficult task when dealing with attacks in complex network environments.To improve the detection capability of minority-class attacks,this study proposes an intrusion detection method based on a two-layer structure.The first layer employs a CNN-BiLSTM model incorporating an attention mechanism to classify network traffic into normal traffic,majority class attacks,and merged minority class attacks.The second layer further segments the minority class attacks through Stacking ensemble learning.The datasets are selected from the generic network dataset CIC-IDS2017,NSL-KDD,and the industrial network dataset Mississippi Gas Pipeline dataset to enhance the generalization and practical applicability of the model.Experimental results show that the proposed model achieves an overall detection accuracy of 99%,99%,and 95%on the CIC-IDS2017,NSL-KDD,and industrial network datasets,respectively.It also significantly outperforms traditional methods in terms of detection accuracy and recall rate for minority class attacks.Compared with the single-layer deep learning model,the two-layer structure effectively reduces the false alarm rate while improving the minority-class attack detection performance.The research in this paper not only improves the adaptability of NIDS to complex network environments but also provides a new solution for minority-class attack detection in industrial network security. 展开更多
关键词 Two-layer architecture minority class attack stacking ensemble learning network intrusion detection
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Improved prediction of slope stability using a hybrid stacking ensemble method based on finite element analysis and field data 认领 引用 被引量：43
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作者 Navid Kardani Annan Zhou +1 位作者 Majidreza Nazem Shui-Long Shen 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2021年第1期188-201,共14页
Slope failures lead to catastrophic consequences in numerous countries and thus the stability assessment for slopes is of high interest in geotechnical and geological engineering researches.A hybrid stacking ensemble ... Slope failures lead to catastrophic consequences in numerous countries and thus the stability assessment for slopes is of high interest in geotechnical and geological engineering researches.A hybrid stacking ensemble approach is proposed in this study for enhancing the prediction of slope stability.In the hybrid stacking ensemble approach,we used an artificial bee colony(ABC)algorithm to find out the best combination of base classifiers(level 0)and determined a suitable meta-classifier(level 1)from a pool of 11 individual optimized machine learning(OML)algorithms.Finite element analysis(FEA)was conducted in order to form the synthetic database for the training stage(150 cases)of the proposed model while 107 real field slope cases were used for the testing stage.The results by the hybrid stacking ensemble approach were then compared with that obtained by the 11 individual OML methods using confusion matrix,F1-score,and area under the curve,i.e.AUC-score.The comparisons showed that a significant improvement in the prediction ability of slope stability has been achieved by the hybrid stacking ensemble(AUC?90.4%),which is 7%higher than the best of the 11 individual OML methods(AUC?82.9%).Then,a further comparison was undertaken between the hybrid stacking ensemble method and basic ensemble classifier on slope stability prediction.The results showed a prominent performance of the hybrid stacking ensemble method over the basic ensemble method.Finally,the importance of the variables for slope stability was studied using linear vector quantization(LVQ)method. 展开更多
关键词 Slope stability Machine learning(ML) Stacking ensemble Variable importance Artificial bee colony(ABC)
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Real-time prediction of rock mass classification based on TBM operation big data and stacking technique of ensemble learning 认领 引用 被引量：61
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作者 Shaokang Hou Yaoru Liu Qiang Yang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2022年第1期123-143,共21页
Real-time prediction of the rock mass class in front of the tunnel face is essential for the adaptive adjustment of tunnel boring machines(TBMs).During the TBM tunnelling process,a large number of operation data are g... Real-time prediction of the rock mass class in front of the tunnel face is essential for the adaptive adjustment of tunnel boring machines(TBMs).During the TBM tunnelling process,a large number of operation data are generated,reflecting the interaction between the TBM system and surrounding rock,and these data can be used to evaluate the rock mass quality.This study proposed a stacking ensemble classifier for the real-time prediction of the rock mass classification using TBM operation data.Based on the Songhua River water conveyance project,a total of 7538 TBM tunnelling cycles and the corresponding rock mass classes are obtained after data preprocessing.Then,through the tree-based feature selection method,10 key TBM operation parameters are selected,and the mean values of the 10 selected features in the stable phase after removing outliers are calculated as the inputs of classifiers.The preprocessed data are randomly divided into the training set(90%)and test set(10%)using simple random sampling.Besides stacking ensemble classifier,seven individual classifiers are established as the comparison.These classifiers include support vector machine(SVM),k-nearest neighbors(KNN),random forest(RF),gradient boosting decision tree(GBDT),decision tree(DT),logistic regression(LR)and multilayer perceptron(MLP),where the hyper-parameters of each classifier are optimised using the grid search method.The prediction results show that the stacking ensemble classifier has a better performance than individual classifiers,and it shows a more powerful learning and generalisation ability for small and imbalanced samples.Additionally,a relative balance training set is obtained by the synthetic minority oversampling technique(SMOTE),and the influence of sample imbalance on the prediction performance is discussed. 展开更多
关键词 Tunnel boring machine(TBM)operation data Rock mass classification Stacking ensemble learning Sample imbalance Synthetic minority oversampling technique(SMOTE)
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Advancing flood susceptibility modeling using stacking ensemble machine learning: A multi-model approach 认领 引用 被引量：4
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作者 YANG Huilin YAO Rui +5 位作者 DONG Linyao SUN Peng ZHANG Qiang WEI Yongqiang SUN Shao AGHAKOUCHAK Amir 《Journal of Geographical Sciences》 SCIE CSCD 2024年第8期1513-1536,共24页
Flood susceptibility modeling is crucial for rapid flood forecasting, disaster reduction strategies, evacuation planning, and decision-making. Machine learning(ML) models have proven to be effective tools for assessin... Flood susceptibility modeling is crucial for rapid flood forecasting, disaster reduction strategies, evacuation planning, and decision-making. Machine learning(ML) models have proven to be effective tools for assessing flood susceptibility. However, most previous studies have focused on individual models or comparative performance, underscoring the unique strengths and weaknesses of each model. In this study, we propose a stacking ensemble learning algorithm that harnesses the strengths of a diverse range of machine learning models. The findings reveal the following:(1) The stacking ensemble learning, using RF-XGBCB-LR model, significantly enhances flood susceptibility simulation.(2) In addition to rainfall,key flood drivers in the study area include NDVI, and impervious surfaces. Over 40% of the study area, primarily in the northeast and southeast, exhibits high flood susceptibility, with higher risks for populations compared to cropland.(3) In the northeast of the study area,heavy precipitation, low terrain, and NDVI values are key indicators contributing to high flood susceptibility, while long-duration precipitation, mountainous topography, and upper reach vegetation are the main drivers in the southeast. This study underscores the effectiveness of ML, particularly ensemble learning, in flood modeling. It identifies vulnerable areas and contributes to improved flood risk management. 展开更多
关键词 flood susceptibility assessment machine learning stacking ensemble learning flood drivers XiangjiangRiverBasin
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Rockfill material uncertainty inversion analysis of concrete-faced rockfill dams using stacking ensemble strategy and Jaya optimizer 认领 引用 被引量：3
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作者 Qin Ke Ming-chao Li +1 位作者 Qiu-bing Ren Wen-chao Zhao 《Water Science and Engineering》 EI CAS CSCD 2023年第4期419-428,共10页
Numerical simulation of concrete-faced rockfill dams(CFRDs)considering the spatial variability of rockfill has become a popular research topic in recent years.In order to determine uncertain rockfill properties effici... Numerical simulation of concrete-faced rockfill dams(CFRDs)considering the spatial variability of rockfill has become a popular research topic in recent years.In order to determine uncertain rockfill properties efficiently and reliably,this study developed an uncertainty inversion analysis method for rockfill material parameters using the stacking ensemble strategy and Jaya optimizer.The comprehensive implementation process of the proposed model was described with an illustrative CFRD example.First,the surrogate model method using the stacking ensemble algorithm was used to conduct the Monte Carlo stochastic finite element calculations with reduced computational cost and improved accuracy.Afterwards,the Jaya algorithm was used to inversely calculate the combination of the coefficient of variation of rockfill material parameters.This optimizer obtained higher accuracy and more significant uncertainty reduction than traditional optimizers.Overall,the developed model effectively identified the random parameters of rockfill materials.This study provided scientific references for uncertainty analysis of CFRDs.In addition,the proposed method can be applied to other similar engineering structures. 展开更多
关键词 CFRD Uncertainty inversion analysis Stochastic finite element Surrogate model Stacking ensemble Jaya algorithm
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Detection and defending the XSS attack using novel hybrid stacking ensemble learning-based DNN approach 认领 引用 被引量：2
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作者 Muralitharan Krishnan Yongdo Lim +1 位作者 Seethalakshmi Perumal Gayathri Palanisamy 《Digital Communications and Networks》 SCIE CSCD 2024年第3期716-727,共12页
Existing web-based security applications have failed in many situations due to the great intelligence of attackers.Among web applications,Cross-Site Scripting(XSS)is one of the dangerous assaults experienced while mod... Existing web-based security applications have failed in many situations due to the great intelligence of attackers.Among web applications,Cross-Site Scripting(XSS)is one of the dangerous assaults experienced while modifying an organization's or user's information.To avoid these security challenges,this article proposes a novel,all-encompassing combination of machine learning(NB,SVM,k-NN)and deep learning(RNN,CNN,LSTM)frameworks for detecting and defending against XSS attacks with high accuracy and efficiency.Based on the representation,a novel idea for merging stacking ensemble with web applications,termed“hybrid stacking”,is proposed.In order to implement the aforementioned methods,four distinct datasets,each of which contains both safe and unsafe content,are considered.The hybrid detection method can adaptively identify the attacks from the URL,and the defense mechanism inherits the advantages of URL encoding with dictionary-based mapping to improve prediction accuracy,accelerate the training process,and effectively remove the unsafe JScript/JavaScript keywords from the URL.The simulation results show that the proposed hybrid model is more efficient than the existing detection methods.It produces more than 99.5%accurate XSS attack classification results(accuracy,precision,recall,f1_score,and Receiver Operating Characteristic(ROC))and is highly resistant to XSS attacks.In order to ensure the security of the server's information,the proposed hybrid approach is demonstrated in a real-time environment. 展开更多
关键词 Machine learning Deep neural networks Classification Stacking ensemble XSS attack URL encoding JScript/JavaScript Web security
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GA-Stacking:A New Stacking-Based Ensemble Learning Method to Forecast the COVID-19 Outbreak 认领 引用
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作者 Walaa N.Ismail Hessah A.Alsalamah Ebtesam Mohamed 《Computers, Materials & Continua》 SCIE EI 2023年第2期3945-3976,共32页
As a result of the increased number of COVID-19 cases,Ensemble Machine Learning(EML)would be an effective tool for combatting this pandemic outbreak.An ensemble of classifiers can improve the performance of single mac... As a result of the increased number of COVID-19 cases,Ensemble Machine Learning(EML)would be an effective tool for combatting this pandemic outbreak.An ensemble of classifiers can improve the performance of single machine learning(ML)classifiers,especially stacking-based ensemble learning.Stacking utilizes heterogeneous-base learners trained in parallel and combines their predictions using a meta-model to determine the final prediction results.However,building an ensemble often causes the model performance to decrease due to the increasing number of learners that are not being properly selected.Therefore,the goal of this paper is to develop and evaluate a generic,data-independent predictive method using stacked-based ensemble learning(GA-Stacking)optimized by aGenetic Algorithm(GA)for outbreak prediction and health decision aided processes.GA-Stacking utilizes five well-known classifiers,including Decision Tree(DT),Random Forest(RF),RIGID regression,Least Absolute Shrinkage and Selection Operator(LASSO),and eXtreme Gradient Boosting(XGBoost),at its first level.It also introduces GA to identify comparisons to forecast the number,combination,and trust of these base classifiers based on theMean Squared Error(MSE)as a fitness function.At the second level of the stacked ensemblemodel,a Linear Regression(LR)classifier is used to produce the final prediction.The performance of the model was evaluated using a publicly available dataset from the Center for Systems Science and Engineering,Johns Hopkins University,which consisted of 10,722 data samples.The experimental results indicated that the GA-Stacking model achieved outstanding performance with an overall accuracy of 99.99%for the three selected countries.Furthermore,the proposed model achieved good performance when compared with existing baggingbased approaches.The proposed model can be used to predict the pandemic outbreak correctly and may be applied as a generic data-independent model 3946 CMC,2023,vol.74,no.2 to predict the epidemic trend for other countries when comparing preventive and control measures. 展开更多
关键词 COVID-19 ensemble machine learning genetic algorithm machine learning stacking ensemble unbalanced dataset vaccine
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Near-infrared Spectroscopy Detection of Rice Protein Content Based on Stacking Multi-model Fusion 认领 引用
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作者 Shengye WANG Siting WU +2 位作者 Jinming LIU Chunqi WANG Zhijiang LI 《Agricultural Biotechnology》 2026年第1期42-46,共5页
[Objectives]This study was conducted to achieve rapid and accurate detection of protein content in rice with a particle size of 1.0 mm.[Methods]A multi-model fusion strategy was proposed on the basis of Stacking ensem... [Objectives]This study was conducted to achieve rapid and accurate detection of protein content in rice with a particle size of 1.0 mm.[Methods]A multi-model fusion strategy was proposed on the basis of Stacking ensemble learning.A base learner pool was constructed,containing Partial Least Squares(PLS),Support Vector Machine(SVM),Deep Extreme Learning Machine(DELM),Random Forest(RF),Gradient Boosting Decision Tree(GBDT),and Multilayer Perceptron(MLP).PLS,DELM,and Linear Regression(LR)were used as meta-learner candidates.Employing integer coding technology,systematic dynamic combinations of base learners and meta-learners were generated,resulting in a total of 40 non-repetitive fusion models.The optimal combination was selected through a comprehensive evaluation based on multiple assessment indicators.[Results]The combination"PLS-DELM-MLP-LR"(code 1367)achieved coefficients of determination of 0.9732 and 0.9780 on the validation set and independent test set,respectively,with relative root mean square errors of 2.35%and 2.36%,and residual predictive deviations of 6.1075 and 6.7479,respectively.[Conclusions]The Stacking fusion model significantly enhances the predictive accuracy and robustness of spectral quantitative analysis,providing an efficient and feasible solution for modeling complex agricultural product spectral data. 展开更多
关键词 Rice protein Near-infrared spectroscopy Stacking ensemble learning Multi-model fusion Integer encoding
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Ensemble-Based Approach for Efficient Intrusion Detection in Network Traffic 认领 引用 被引量：2
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作者 Ammar Almomani Iman Akour +5 位作者 Ahmed M.Manasrah Omar Almomani Mohammad Alauthman Esra’a Abdullah Amaal Al Shwait Razan Al Sharaa 《Intelligent Automation & Soft Computing》 SCIE 2023年第8期2499-2517,共19页
The exponential growth of Internet and network usage has neces-sitated heightened security measures to protect against data and network breaches.Intrusions,executed through network packets,pose a significant challenge... The exponential growth of Internet and network usage has neces-sitated heightened security measures to protect against data and network breaches.Intrusions,executed through network packets,pose a significant challenge for firewalls to detect and prevent due to the similarity between legit-imate and intrusion traffic.The vast network traffic volume also complicates most network monitoring systems and algorithms.Several intrusion detection methods have been proposed,with machine learning techniques regarded as promising for dealing with these incidents.This study presents an Intrusion Detection System Based on Stacking Ensemble Learning base(Random For-est,Decision Tree,and k-Nearest-Neighbors).The proposed system employs pre-processing techniques to enhance classification efficiency and integrates seven machine learning algorithms.The stacking ensemble technique increases performance by incorporating three base models(Random Forest,Decision Tree,and k-Nearest-Neighbors)and a meta-model represented by the Logistic Regression algorithm.Evaluated using the UNSW-NB15 dataset,the pro-posed IDS gained an accuracy of 96.16%in the training phase and 97.95%in the testing phase,with precision of 97.78%,and 98.40%for taring and testing,respectively.The obtained results demonstrate improvements in other measurement criteria. 展开更多
关键词 Intrusion detection system(IDS) machine learning techniques stacking ensemble random forest decision tree k-nearest-neighbor
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Boosted Stacking Ensemble Machine Learning Method for Wafer Map Pattern Classification 认领 引用 被引量：1
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作者 Jeonghoon Choi Dongjun Suh Marc-Oliver Otto 《Computers, Materials & Continua》 SCIE EI 2023年第2期2945-2966,共22页
Recently,machine learning-based technologies have been developed to automate the classification of wafer map defect patterns during semiconductormanufacturing.The existing approaches used in the wafer map pattern clas... Recently,machine learning-based technologies have been developed to automate the classification of wafer map defect patterns during semiconductormanufacturing.The existing approaches used in the wafer map pattern classification include directly learning the image through a convolution neural network and applying the ensemble method after extracting image features.This study aims to classify wafer map defects more effectively and derive robust algorithms even for datasets with insufficient defect patterns.First,the number of defects during the actual process may be limited.Therefore,insufficient data are generated using convolutional auto-encoder(CAE),and the expanded data are verified using the evaluation technique of structural similarity index measure(SSIM).After extracting handcrafted features,a boosted stacking ensemble model that integrates the four base-level classifiers with the extreme gradient boosting classifier as a meta-level classifier is designed and built for training the model based on the expanded data for final prediction.Since the proposed algorithm shows better performance than those of existing ensemble classifiers even for insufficient defect patterns,the results of this study will contribute to improving the product quality and yield of the actual semiconductor manufacturing process. 展开更多
关键词 Wafer map pattern classification machine learning boosted stacking ensemble semiconductor manufacturing processing
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