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Deep Learning-Based Stacked Auto-Encoder with Dynamic Differential Annealed Optimization for Skin Lesion Diagnosis 认领 引用
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作者 Ahmad Alassaf 《Computer Systems Science & Engineering》 SCIE EI 2023年第12期2773-2789,共17页
Intelligent diagnosis approaches with shallow architectural models play an essential role in healthcare.Deep Learning(DL)models with unsupervised learning concepts have been proposed because high-quality feature extra... Intelligent diagnosis approaches with shallow architectural models play an essential role in healthcare.Deep Learning(DL)models with unsupervised learning concepts have been proposed because high-quality feature extraction and adequate labelled details significantly influence shallow models.On the other hand,skin lesionbased segregation and disintegration procedures play an essential role in earlier skin cancer detection.However,artefacts,an unclear boundary,poor contrast,and different lesion sizes make detection difficult.To address the issues in skin lesion diagnosis,this study creates the UDLS-DDOA model,an intelligent Unsupervised Deep Learning-based Stacked Auto-encoder(UDLS)optimized by Dynamic Differential Annealed Optimization(DDOA).Pre-processing,segregation,feature removal or separation,and disintegration are part of the proposed skin lesion diagnosis model.Pre-processing of skin lesion images occurs at the initial level for noise removal in the image using the Top hat filter and painting methodology.Following that,a Fuzzy C-Means(FCM)segregation procedure is performed using a Quasi-Oppositional Elephant Herd Optimization(QOEHO)algorithm.Besides,a novel feature extraction technique using the UDLS technique is applied where the parameter tuning takes place using DDOA.In the end,the disintegration procedure would be accomplished using a SoftMax(SM)classifier.The UDLS-DDOA model is tested against the International Skin Imaging Collaboration(ISIC)dataset,and the experimental results are examined using various computational attributes.The simulation results demonstrated that the UDLS-DDOA model outperformed the compared methods significantly. 展开更多
关键词 Intelligent diagnosis stacked auto-encoder skin lesion unsupervised learning parameter selection
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Predicting the Antigenic Variant of Human Influenza A(H3N2) Virus with a Stacked Auto-Encoder Model 认领 引用
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作者 Zhiying Tan Kenli Li +1 位作者 Taijiao Jiang Yousong Peng 《国际计算机前沿大会会议论文集》 EI 2017年第2期71-73,共3页
The influenza virus changes its antigenicity frequently due to rapid mutations, leading to immune escape and failure of vaccination. Rapid determination of the influenza antigenicity could help identify the antigenic ... The influenza virus changes its antigenicity frequently due to rapid mutations, leading to immune escape and failure of vaccination. Rapid determination of the influenza antigenicity could help identify the antigenic variants in time. Here, we built a stacked auto-encoder (SAE) model for predicting the antigenic variant of human influenza A(H3N2) viruses based on the hemagglutinin (HA) protein sequences. The model achieved an accuracy of 0.95 in five-fold cross-validations, better than the logistic regression model did. Further analysis of the model shows that most of the active nodes in the hidden layer reflected the combined contribution of multiple residues to antigenic variation. Besides, some features (residues on HA protein) in the input layer were observed to take part in multiple active nodes, such as residue 189, 145 and 156, which were also reported to mostly determine the antigenic variation of influenza A(H3N2) viruses. Overall,this work is not only useful for rapidly identifying antigenic variants in influenza prevention, but also an interesting attempt in inferring the mechanisms of biological process through analysis of SAE model, which may give some insights into interpretation of the deep learning 展开更多
关键词 Stacked auto-encoder Antigenic variation nfluenza Machine learning
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RKKY-Dipolar Interactions and 3D Spin Supersolid on Stacked Triangular Lattice 认领 引用
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作者 Ning Xi Xitong Xu +9 位作者 Guoliang Wu Mingfang Shu Hao Chen Yuan Gao Zhentao Wang Gang Su Jie Ma Zhe Qu Xi Chen Wei Li 《Chinese Physics Letters》 SCIE EI CAS CSCD 2026年第5期305-313,共9页
Inspired by the recent discovery of metallic spin supersolidity and its giant magnetocaloric effect in the rareearth alloy EuCo2Al9,we perform a combined study through electronic structure analysis,effective spi... Inspired by the recent discovery of metallic spin supersolidity and its giant magnetocaloric effect in the rareearth alloy EuCo2Al9,we perform a combined study through electronic structure analysis,effective spin model construction,and Monte Carlo simulations on a stacked triangular lattice(STL),and reveal a novel mechanism for the emergence of 3D spin supersolid in a metallic antiferromagnet.From first-principles inputs,we derive a minimal spin model on a STL,which arises from the interplay between Ruderman-Kittel-Kasuya-Yosida and dipolar interactions and accurately reproduces the experimental thermodynamics.Based on the STL model,we identify a ground state that simultaneously breaks discrete lattice translational symmetry and continuous spinrotational symmetry--the hallmark of a spin supersolid.Furthermore,we present the field-temperature phase diagram of the 3D STL model and discuss the various magnetic phases and associated phase transitions.Under zero field,the spin supersolid Y order establishes in two steps:an upper transition at TN1,where an emergent U(1)symmetry appears and the system enters a fluctuating collinear regime,followed by a lower transition at TN2into the spin supersolid Y phase.In contrast,the supersolid V phase undergoes a single phase transition at T.Our results not only provide a comprehensive theoretical understanding of the metallic spin supersolid reported for EuCo2Al9but also pave the way for further experimental investigations into its supersolid transitions and universality class. 展开更多
关键词 monte carlo simulations giant magnetocaloric effect electronic structure analysiseffective spin model constructionand d spin supersolid rareearth alloy metallic spin supersolidity minimal spin model stacked triangular lattice stl
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Fault Diagnosis of Motor in Frequency Domain Signal by Stacked De-noising Auto-encoder 认领 引用 被引量：10
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作者 Xiaoping Zhao Jiaxin Wu +2 位作者 Yonghong Zhang Yunqing Shi Lihua Wang 《Computers, Materials & Continua》 SCIE EI 2018年第11期223-242,共20页
With the rapid development of mechanical equipment,mechanical health monitoring field has entered the era of big data.Deep learning has made a great achievement in the processing of large data of image and speech due ... With the rapid development of mechanical equipment,mechanical health monitoring field has entered the era of big data.Deep learning has made a great achievement in the processing of large data of image and speech due to the powerful modeling capabilities,this also brings influence to the mechanical fault diagnosis field.Therefore,according to the characteristics of motor vibration signals(nonstationary and difficult to deal with)and mechanical‘big data’,combined with deep learning,a motor fault diagnosis method based on stacked de-noising auto-encoder is proposed.The frequency domain signals obtained by the Fourier transform are used as input to the network.This method can extract features adaptively and unsupervised,and get rid of the dependence of traditional machine learning methods on human extraction features.A supervised fine tuning of the model is then carried out by backpropagation.The Asynchronous motor in Drivetrain Dynamics Simulator system was taken as the research object,the effectiveness of the proposed method was verified by a large number of data,and research on visualization of network output,the results shown that the SDAE method is more efficient and more intelligent. 展开更多
关键词 Big data deep learning stacked de-noising auto-encoder fourier transform
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Prediction of soil-water retention curves in unsaturated soils based on stacked generalization 认领 引用
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作者 Kaibin Sun You Gao +2 位作者 Wei He Long Wang Xi Sun 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第3期2421-2436,共16页
The soil-water retention curve(SWRC)plays a pivotal role in understanding water movement across numerous geological engineering applications.Despite significant advancements in theoretical modeling approaches,accurate... The soil-water retention curve(SWRC)plays a pivotal role in understanding water movement across numerous geological engineering applications.Despite significant advancements in theoretical modeling approaches,accurate prediction of SWRCs remains challenging due to the inherently sparse and incomplete nature of site-specific data.This study compiled a comprehensive dataset of SWRCs spanning a wide suction range from various published literature sources.Based on this dataset,multiple machine learning(ML)algorithms were employed to predict SWRCs.The performance of each algorithm was evaluated and ranked using four statistical indicators that quantify simulation accuracy.Feature importance analysis was subsequently conducted to reduce dimensionality by eliminating weakly correlated variables,thereby enhancing both model adaptability and computational efficiency.Following dimensionality reduction,a base learner pool was constructed and integrated through stacked generalization to create a multi-algorithm ensemble model.The proposed stacked model demonstrated robust performance in simulating SWRCs across diverse soil types,using only basic physical properties as inputs,achieving accuracy comparable to or marginally superior to the LightGBM model.The principal advantage of the stacked approach lies in its substantially improved accuracy within high suction ranges,effectively overcoming the limitations observed in LightGBM and enhancing the estimation under these conditions.This study provides valuable insights for researchers evaluating SWRCs through ML algorithms and demonstrates the potential of ensemble techniques in geotechnical prediction tasks. 展开更多
关键词 Unsaturated soil Soil-water retention curve Stacked generalization Prediction
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Sandwich-structured long-wave infrared transparent electromagnetic shielding film using transmittance-enhanced wetting layer/Ag stacked conductive layers 认领 引用
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作者 Zhirui Zhang Yuyang Zhang +6 位作者 Le Zhao Zhi Wang Chi Zhang Yao Wu Ruifan Li Yonghao Han Chaoquan Hu 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第4期175-181,共7页
Designing infrared transparent electromagnetic shielding films(ITESFs)is challenging because carrier absorption and carrier transport occur simultaneously.Sandwich structures with Ag inserted into semiconductors can a... Designing infrared transparent electromagnetic shielding films(ITESFs)is challenging because carrier absorption and carrier transport occur simultaneously.Sandwich structures with Ag inserted into semiconductors can achieve synergy between transparency and electromagnetic shielding effectiveness,but Ag films alone suffer from island growth and optical loss.This work presents a sandwich structure using a transmittance-enhancing conductive layer composed of a wetting layer and Ag(WL/Ag).As a proof of concept,a Bi2Se3/Ti WL/Ag/Bi2Se3film was prepared,achieving a longwavelength infrared transmittance of 77% and a conductivity of 5988 S/cm.The electromagnetic shielding effectiveness reached~22 dB in the X band(8.2-12.4 GHz),meeting the requirement of protecting infrared optoelectronic devices from electromagnetic interference.High-resolution transmission electron microscopy and theoretical calculations showed that the Ti wetting layer enhances performance through high surface energy,low nk product values,and admittance matching.We proposed design criteria for wetting layers and identified candidate materials such as Cr.This study provides an optimization strategy for sandwich structures and introduces high-performance ITESFs for infrared optoelectronic devices. 展开更多
关键词 transmittance-enhanced wetting layer/Ag stacked films sandwich structure infrared transparent electromagnetic shielding film
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Design of catalysts for electrochemical nitric oxide reduction to ammonia based on stacked ensemble learning 认领 引用
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作者 DUAN Wenhao ZHAO Yan +2 位作者 WANG Huanran ZHU Yaming LI Xianchun 《燃料化学学报（中英文）》 EI CAS CSCD 北大核心 2026年第4期128-139,共12页
The electrocatalytic reduction of nitric oxide for ammonia synthesis(NORR)is a key green energy conversion technology.Its efficiency relies on high-performance electrocatalysts to enhance both ammonia yield(YNH3)an... The electrocatalytic reduction of nitric oxide for ammonia synthesis(NORR)is a key green energy conversion technology.Its efficiency relies on high-performance electrocatalysts to enhance both ammonia yield(YNH3)and Faradaic efficiency(FNH3).However,conventional experimental methods for screening high-activity NORR catalysts often entail high resource consumption and time costs.Machine learning combined with SHAP feature analysis was employed to establish a stacked ensemble model that integrates multiple algorithms,to allow for a systematic investigation of the key descriptors governing NORR performance based on an experimental dataset.Evaluation of eight model algorithms revealed that the Stacked-SVR model achieved an R2of 0.9223 and an RMSE of 0.0608 for predicting on the test set,whereas the Stacked-RF model achieved an R2of 0.9042 and an RMSE of 0.0900 for predicting.The stacked ensemble model integrates the strengths of individual algorithms and demonstrates strong NORR prediction performance while avoiding overfitting.SHAP feature analysis results revealed that the Cu content in the catalyst composition has the most significant impact on catalytic performance.Moreover,the combination of the wet chemical reduction synthesis,a carbon fiber(CF)conductive substrate,and HCl electrolyte is more favorable for enhancing catalytic activity.Additionally,moderately lowering the working potential,controlling the electrolyte volume at low to medium levels,reducing catalyst loading,and increasing electrolyte concentration were found to synergistically enhance both and. 展开更多
关键词 NORR machine learning stacked model ammonia yield ammonia Faraday efficiency
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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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Hierarchical Mixed-Effects and Stacked Machine Learning Ensembles with Data Augmentation for Leakage-Safe E-Waste Forecasting 认领 引用
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作者 Hatim Madkhali Abdullah Sheneamer +3 位作者 Linh Nguyen Gnana Bharathy Ritu Chauhan Mukesh Prasad 《Computers, Materials & Continua》 SCIE EI 2026年第6期2531-2557,共27页
Consumer electronics,with 62 million tons of electronic waste(e-waste)generated in 2022 and e-waste expected to grow to 82 million tons annually by 2030,pose critical challenges when it comes to national infrastructur... Consumer electronics,with 62 million tons of electronic waste(e-waste)generated in 2022 and e-waste expected to grow to 82 million tons annually by 2030,pose critical challenges when it comes to national infrastructure and circular economy policies.This paper compares forecasting approaches using sparse panel data for 32 European countries(2005-2018,Eurostat/Waste Electrical and Electronic Equipment(WEEE)Directive),focusing on leakage-safe prospective validation to guarantee true predictive performance.We make one-step-ahead predictions with conservative features(primarily lagged values)to account for temporal autocorrelation but with reduced multicollinearity(Variance Inflation Factor(VIF)≈1.0).Cross-paradigm comparisons such as time-series baselines Autoregressive Integrated Moving Average(ARIMA),Seasonal ARIMA(SARIMA),Long Short-Term Memory(LSTM),hierarchical mixed-effects models,pooled machine learning(9 methods),and block-bootstrap-augmented stacking ensembles demonstrate stacking’s effectiveness,with a weighted validation R2 of 0.992 for held-out 2017-2018 data.Time-series approaches demonstrate negligible predictive power(mean R2=−9683)given non-stationarity and limited samples,while the hierarchical approach provides virtually no benefit(Intraclass Correlation Coefficient(ICC)0.011)amidst computational instability.Bootstrapping improves high-variance tonnage forecasts(Root Mean Squared Error(RMSE)reductions of 18.6%)while being detrimental to stable units,thus reinforcing parsimony.Feature ablation validates that only a few lags are necessary,preventing leakage from rolling means or calendar trends.Our method enables conservative year-ahead forecasts with quantified uncertainty,conservative estimates of e-waste management,allowing for buffer planning and policies even when data is scarce.By using strictly out-of-sample tests rather than biased ones,this work characterizes achievable year-ahead performance under sparse annual panels. 展开更多
关键词 E-waste forecasting ensemble learning temporal leakage panel data bootstrap augmentation stacking models prospective validation circular economy sustainable waste management
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Efficient Dataset Generation for Stacked Meat Products Instance Segmentation in Food Automation 认领 引用
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作者 Hoang Minh Pham Anh Dong Le +2 位作者 Pablo Malvido-Fresnillo Saigopal Vasudevan JoséL.Martínez Lastra 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第1期224-226,共3页
Dear Editor,This letter presents techniques to simplify dataset generation for instance segmentation of raw meat products,a critical step toward automating food production lines.Accurate segmentation is essential for ... Dear Editor,This letter presents techniques to simplify dataset generation for instance segmentation of raw meat products,a critical step toward automating food production lines.Accurate segmentation is essential for addressing challenges such as occlusions,indistinct edges,and stacked configurations,which demand large,diverse datasets.To meet these demands,we propose two complementary approaches:a semi-automatic annotation interface using tools like the segment anything model(SAM)and GrabCut and a synthetic data generation pipeline leveraging 3D-scanned models.These methods reduce reliance on real meat,mitigate food waste,and improve scalability.Experimental results demonstrate that incorporating synthetic data enhances segmentation model performance and,when combined with real data,further boosts accuracy,paving the way for more efficient automation in the food industry. 展开更多
关键词 dataset generation segment anything model sam food automation raw meat productsa automating food production linesaccurate instance segmentation stacked meat products semi automatic annotation
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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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Fault Diagnosis for Rolling Bearings with Stacked Denoising Auto-encoder of Information Aggregation 认领 引用
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作者 Li Zhang Xin Gao Xiao Xu 《Journal of Harbin Institute of Technology(New Series)》 CAS 2019年第4期69-77,共9页
Rolling bearings are important central components in rotating machines, whose fault diagnosis is crucial in condition-based maintenance to reduce the complexity of different kinds of faults. To classify various rollin... Rolling bearings are important central components in rotating machines, whose fault diagnosis is crucial in condition-based maintenance to reduce the complexity of different kinds of faults. To classify various rolling bearing faults, a prognostic algorithm consisting of four phases was proposed. Since stacked denoising auto-encoder can be filtered, noise of large numbers of mechanical vibration signals was used for deep learning structure to extract the characteristics of the noise. Unsupervised pre-training method, which can greatly simplify the traditional manual extraction approach, was utilized to process the depth of the data automatically. Furthermore, the aggregation layer of stacked denoising auto-encoder(SDA) was proposed to get rid of gradient disappearance in deeper layers of network, mix superficial nodes’ expression with deeper layers, and avoid the insufficient express ability in deeper layers. Principal component analysis(PCA) was adopted to extract different features for classification. According to the experimental data of this method and from the comparison results, the proposed method of rolling bearing fault classification reached 97.02% of correct rate, suggesting a better performance than other algorithms. 展开更多
关键词 deep learning stacked denoising auto-encoder fault diagnosis PCA classification
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A comparative evaluation of Stacked Auto-Encoder neural network and Multi-Layer Extreme Learning Machine for detection and classification of faults in transmission lines using WAMS data 认领 引用 被引量：2
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作者 Ani Harish Prince Asok Jayan M.V. 《Energy and AI》 EI 2023年第4期598-611,共14页
Smart grid is envisaged as a power grid that is extremely reliable and flexible.The electrical grid has wide-area measuring devices like Phasor measurement units(PMUs)deployed to provide real-time grid information and... Smart grid is envisaged as a power grid that is extremely reliable and flexible.The electrical grid has wide-area measuring devices like Phasor measurement units(PMUs)deployed to provide real-time grid information and resolve issues effectively and speedily without compromising system availability.The development and application of machine learning approaches for power system protection and state estimation have been facilitated by the availability of measurement data.This research proposes a transmission line fault detection and classification(FD&C)system based on an auto-encoder neural network.A comparison between a Multi-Layer Extreme Learning Machine(ML-ELM)network model and a Stacked Auto-Encoder neural network(SAE)is made.Additionally,the performance of the models developed is compared to that of state-of-the-art classifier models employing feature datasets acquired by wavelet transform based feature extraction as well as other deep learning models.With substantially shorter testing time,the suggested auto-encoder models detect faults with 100% accuracy and classify faults with 99.92% and 99.79%accuracy.The computational efficiency of the ML-ELM model is demonstrated with high accuracy of classification with training time and testing time less than 50 ms.To emulate real system scenarios the models are developed with datasets with noise with signal-to-noise-ratio(SNR)ranging from 10 dB to 40 dB.The efficacy of the models is demonstrated with data from the IEEE 39 bus test system. 展开更多
关键词 Machine learning Fault detection Fault classification Auto-Encoder Transmission line Smart grid Neural network Extreme Learning Machine
Multi-scale feature fused stacked autoencoder and its application for soft sensor modeling 认领 引用 被引量：3
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作者 Zhi Li Yuchong Xia +2 位作者 Jian Long Chensheng Liu Longfei Zhang 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2025年第5期241-254,共14页
Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE... Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE)has been widely used to improve the model accuracy of soft sensors.However,with the increase of network layers,SAE may encounter serious information loss issues,which affect the modeling performance of soft sensors.Besides,there are typically very few labeled samples in the data set,which brings challenges to traditional neural networks to solve.In this paper,a multi-scale feature fused stacked autoencoder(MFF-SAE)is suggested for feature representation related to hierarchical output,where stacked autoencoder,mutual information(MI)and multi-scale feature fusion(MFF)strategies are integrated.Based on correlation analysis between output and input variables,critical hidden variables are extracted from the original variables in each autoencoder's input layer,which are correspondingly given varying weights.Besides,an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers.Then,the MFF-SAE method is designed and stacked to form deep networks.Two practical industrial processes are utilized to evaluate the performance of MFF-SAE.Results from simulations indicate that in comparison to other cutting-edge techniques,the proposed method may considerably enhance the accuracy of soft sensor modeling,where the suggested method reduces the root mean square error(RMSE)by 71.8%,17.1%and 64.7%,15.1%,respectively. 展开更多
关键词 Multi-scale feature fusion Soft sensors Stacked autoencoders Computational chemistry Chemical processes Parameter estimation
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Electrical performance optimization and low-frequency noise evaluation of In2O3 TFT with CeAlOx/Al2O3 stacked gate dielectrics 认领 引用 被引量：1
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作者 Lei-Ni Wang Gang He +5 位作者 Jian-Guo Lv Hai Yu Wen-Hao Wang Yang Hu Peng-Yu Hu Chen Qiu 《Rare Metals》 SCIE EI CAS CSCD 2025年第12期10567-10579,共13页
This article reports In2O3 thin-film transistors(TFTs) that utilize a CeAlOx/Al2O3 stacked gate dielectric architecture.The CeAlOx gate dielectric films were optimized by doping Al into CeO2,where... This article reports In2O3 thin-film transistors(TFTs) that utilize a CeAlOx/Al2O3 stacked gate dielectric architecture.The CeAlOx gate dielectric films were optimized by doping Al into CeO2,where the adjustment of the Al/Ce atomic ratio effectively suppressed oxygen-vacancy-related defects and optimized gate dielectric leakage current.Due to the large bandgap and high density of Al2O3 prepared via atomic layer deposition(ALD),CeAlOx/Al2O3 stacked gate dielectric exhibits lower leakage current compared to single-layer CeAlOx.We systematically investigated the Al/Ce ratio's dependence on In2O3 TFT performance,identifying an optimal stoichiometry of 3:7(Al:Ce).The CeAlOx/Al2O3-based In2O3 TFT fabricated at this ratio achieved exceptional characteristics:higher saturation mobility(26.86 cm2 V-1 s-1) and on/off current ratio(3.58×107),lower sub threshold swing(0.08 V decade-1) and interface state density(1.39×1012 cm-2),coupled with excellent bias stress stability.By combining low-frequency noise analysis and X-ray photoelectron spectroscopy(XPS),we confirmed that Al doping reduces the trap density in CeO2 while simultaneously enhancing its dielectric properties.Furthermore,a resistive-load inverter based on In2O3 TFT presents a voltage gain up to 15.1 at an applied voltage of 5 V with a typical reverse behavior,demonstrating that In2O3 TFT based on CeAlOx/Al2O3 stacked gate dielectric exhibits potential for application in advanced digital circuits. 展开更多
关键词 CeAlOx Stack gate dielectrics Interfacial state density Inverter Low-frequency noise
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基于Stacking算法与钻进参数的岩石单轴抗压强度预测 认领 引用 被引量：4
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作者 岳中文 龙思晨 +5 位作者 闫逸飞 张梦佳 胡昊 薛克军 马文彪 李杨 《采矿与安全工程学报》 EI CAS CSCD 北大核心 2026年第1期198-207,共10页
针对传统岩石强度参数测试方法周期长、成本高的问题,本文提出一种基于Stacking集成算法的新型岩石单轴抗压强度预测方法。通过自主研发的岩石数字钻探测试系统,对不同强度材料的组合试件开展数字钻探试验;选择4种不同的机器学习算法(... 针对传统岩石强度参数测试方法周期长、成本高的问题,本文提出一种基于Stacking集成算法的新型岩石单轴抗压强度预测方法。通过自主研发的岩石数字钻探测试系统,对不同强度材料的组合试件开展数字钻探试验;选择4种不同的机器学习算法(包括支持向量机、随机森林、LightGBM和BP-神经网络),利用钻进数据训练相应的算法模型,探究钻进速度、扭矩和推进力与岩石单轴抗压强度之间的关系;采用双层Stacking框架融合4种抗压强度预测模型,构建集成算法模型,以解决单一算法模型预测精度不足、泛化能力差的问题。研究结果表明,Stacking算法模型在不同转速下对岩石单轴抗压强度的预测性能优异,300 r/min转速与400 r/min转速下对不同试件的单轴抗压强度预测结果决定系数R2基本高于0.9,优于其他4种基学习器,且平均绝对误差占实际强度值的比例小于5%。现场应用表明,Stacking算法模型能有效预测巷道岩层的岩石单轴抗压强度,可为岩体随钻探测研究提供新的思路和方法。 展开更多
关键词 钻进参数 Stacking算法 强度预测 集成学习 模型融合
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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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Electrochemical-driven activation by stacked layered sulfur-carbon anode for fast and stable sodium storage 认领 引用
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作者 Huijuan Zhu Qiming Liu +1 位作者 Jie Wang Han Su 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2025年第8期819-831,共13页
Carbonaceous material has attracted much attention in the application of sodium-ion batteries(SIBs)anode.However,sluggish reaction kinetics and structure stability impede the application.Therefore,a stacked layered su... Carbonaceous material has attracted much attention in the application of sodium-ion batteries(SIBs)anode.However,sluggish reaction kinetics and structure stability impede the application.Therefore,a stacked layered sulfur-carbon complex with long-chain C–Sx–C bond(M-SC-S)is prepared.The layered structure ensures structural stability,and long-chain C–Sx–C bond expanding interlayer spacing boosts facile Na+diffusion.When assembled into cells,a high-quality solid-electrolyte interphase film would be formed due to a good match between the M-SC-S electrode and ether electrolyte.Moreover,an electrochemical activation process would happen between the Cu current collector and proper S-doped electrode material to in-situ form Cu2S.The formation of Cu2S in active material can not only provide more active sites for sodium storage and enhance pseudo-capacitance,but also reinforce the electrode/current collector interface and decrease the interfacial transfer resistance for rapid Na+kinetics.The synergistic effect of structure design and interface engineering optimizes the sodium storage system.Thus,the M-SC-S electrode delivers an excellent cyclic performance(321.6 mAh g−1after 1000 cycles at 2 A g−1with a capacity retention rate of 97.4%)and good rate capability(282.8 mAh g−1after 4000 cycles even at a high current density of 10 A g−1).The full cell also has an impressive cyclic performance(151.4 mAh g−1after 500 cycles at 0.5 A g−1). 展开更多
关键词 Heteroatom-doping Stacked layered structure Cu current collector Electrochemical activation Sodium-ion batteries
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基于细菌觅食优化Stacking集成学习的钻孔效率预测模型研究 认领 引用
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作者 关涛 缴春祺 +3 位作者 俞澎 余佳 郭振邦 程正飞 《水利学报》 EI CSCD 北大核心 2026年第2期232-244,共13页
钻孔效率是堆石坝料场开挖施工进度仿真的关键参数,其预测准确性直接关系到仿真模型的可靠性。针对现有数学方法预测效率较低、单一学习器难以满足仿真精度要求、集成学习研究中超参数调整方法局部搜索精细度不足的问题,本文提出了一种... 钻孔效率是堆石坝料场开挖施工进度仿真的关键参数,其预测准确性直接关系到仿真模型的可靠性。针对现有数学方法预测效率较低、单一学习器难以满足仿真精度要求、集成学习研究中超参数调整方法局部搜索精细度不足的问题,本文提出了一种基于细菌觅食优化Stacking集成学习的钻孔效率预测模型。首先,以某堆石坝现场采集的钻孔效率数据为目标变量,以其影响因素(如钻孔深度、岩石性质、高程等)为特征变量构建数据集;其次,采用XGBoost、LightGBM和MLP三种异质基学习器并行训练,并引入细菌觅食优化算法模拟趋化和繁殖行为,通过R2曲线实时追踪,迭代优化各基学习器的超参数,确保输出稳定的“元特征”;最后,将各基学习器的预测结果输入支持向量回归(SVR)元学习器,通过整合多模型的互补信息,在抑制偏差与方差的同时获得集成预测结果。实验结果表明,经细菌觅食优化后,各基学习器的R2均可达到0.93以上,PCC值均超过0.97,集成模型在整个样本数据集上的学习曲线也平滑稳定,残差分析显示预测值与真实值的残差序列在零均值线附近均匀分布,最终结果的PCC值接近0.98,可以满足施工过程仿真需求。 展开更多
关键词 钻孔效率 施工仿真 Stacking集成学习 XGBoost LightGBM MLP 支持向量机
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基于改进ER规则的隧道围岩智能分级Stacking集成学习模型 认领 引用
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作者 赵思光 王明年 +4 位作者 童建军 夏覃永 李泽星 霍建勋 易文豪 《隧道建设（中英文）》 EI CSCD 北大核心 2026年第4期777-789,共13页
为进一步提高钻进参数用于围岩分级方法的适用性,基于改进ER(evidence reasoning)规则的Stacking集成学习方法,建立以钻进参数为驱动的围岩智能分级模型。首先,基于现场采集的大量钻进参数围岩分级样本,用支持向量机、随机森林等6种常... 为进一步提高钻进参数用于围岩分级方法的适用性,基于改进ER(evidence reasoning)规则的Stacking集成学习方法,建立以钻进参数为驱动的围岩智能分级模型。首先,基于现场采集的大量钻进参数围岩分级样本,用支持向量机、随机森林等6种常规机器学习方法构建围岩智能分级基模型;其次,通过考虑各基模型在验证集上的准确率和错误样本类型建立基模型可靠性计算方法,通过考虑各基模型在验证集上的输出分类概率向量的多样性建立基模型权重计算方法,实现ER规则的改进;最后,基于ER规则的元分类器推理过程,以各基模型在测试集上的分级概率向量为输入,构建综合考虑基模型可靠性和权重的围岩智能分级模型。结果显示:1)与各基模型相比,集成模型在预测集上的总体准确率由90.0%~92.5%提高到96.0%,可靠性由86.0%~89.0%增加到94.7%;2)对全部基模型均判对的样本,集成模型全部判断正确,对多数基模型均判错的样本,集成模型也能进行部分改正;3)集成模型较各基模型计算时间延长4~10倍,但整体效率仍为毫秒级,可以满足现场工程应用需要。 展开更多
关键词 隧道 围岩分级 ER规则 Stacking集成学习 钻进参数 多样性度量
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