The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduce...The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.展开更多
With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy...With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.展开更多
High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of ...High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.展开更多
Populus species,important economic species combining rapid growth with broad ecological adaptability,play a critical role in sustainable forestry and bioenergy production.In this study,we performed whole-genome resequ...Populus species,important economic species combining rapid growth with broad ecological adaptability,play a critical role in sustainable forestry and bioenergy production.In this study,we performed whole-genome resequencing of 707 individuals from a full-sib family to develop comprehensive single nucleotide polymorphism(SNP)markers and constructed a high-density genetic linkage map of 19 linkage groups.The total genetic length of the map reached 3623.65 cM with an average marker interval of 0.34 cM.By integrating multidimensional phenotypic data,89 quantitative trait loci(QTL)associated with growth,wood physical and chemical properties,disease resistance,and leaf morphology traits were identified,with logarithm of odds(LOD)scores ranging from 3.13 to 21.72 Notably,pleiotropic analysis revealed significant colocaliza and phenotypic variance explained between 1.7% and 11.6%.-tion hotspots on chromosomes LG1,LG5,LG6,LG8,and LG14,with epistatic interaction network analysis confirming genetic basis of coordinated regulation across multiple traits.Functional annotation of 207 candidate genes showed that R2R3-MYB and bHLH transcription factors and pyruvate kinase-encoding genes were significantly enriched,suggesting crucial roles in lignin biosynthesis and carbon metabolic pathways.Allelic effect analysis indicated that the frequency of favorable alleles associated with target traits ranged from 0.20 to 0.55.Incorporation of QTL-derived favorable alleles as random effects into Bayesian-based genomic selection models led to an increase in prediction accuracy ranging from 1% to 21%,with Bayesian ridge regression as the best predictive model.This study provides valuable genomic resources and genetic insights for deciphering complex trait architecture and advancing molecular breeding in poplar.展开更多
Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is q...Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is quite challenging to make statistical inference on distributed high-dimensional QR with missing data due to the distributed nature,sparsity and missingness of data and nondifferentiable quantile loss function.To overcome the challenge,this paper develops a communicationefficient method to select variables and estimate parameters by utilizing a smooth function to approximate the non-differentiable quantile loss function and incorporating the idea of the inverse probability weighting and the penalty function.The proposed approach has three merits.First,it is both computationally and communicationally efficient because only the first-and second-order information of the approximate objective function are communicated at each iteration.Second,the proposed estimators possess the oracle property after a limited number of iterations without constraint on the number of machines.Third,the proposed method simultaneously selects variables and estimates parameters within a distributed framework,ensuring robustness to the specified response probability or propensity score function of the missing data mechanism.Simulation studies and a real example are used to illustrate the effectiveness of the proposed methodologies.展开更多
The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects acc...The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS.展开更多
As the core of cathode materials,sensitive metals play important roles in the optimization of acetate production from carbon dioxide(CO_(2))in microbial electrochemical system(MES).In this work,iron(Fe),copper(Cu),and...As the core of cathode materials,sensitive metals play important roles in the optimization of acetate production from carbon dioxide(CO_(2))in microbial electrochemical system(MES).In this work,iron(Fe),copper(Cu),and nickel(Ni)as sensitive metal cathode materials were evaluated for CO_(2) conversion in MES.The MES with Feelectrode as a promising electrode material demonstrated a superior CO_(2) reduction performance with a maximum acetate accumulation of 417.9±39.2 mg/L,which was 1.5 and 1.7 folds higher than that in the Ni-electrode and Cu-electrode groups,respectively.Furthermore,an outstanding electron recovery efficiency of 67.7%was shown in the Fe-electrode group.The electron transfer between electrode-suspended sludge was systematically cross-evaluated by the electrochemical behavior and extracellular polymeric substances.The Fe-electrode group had the highest electron transfer rate with 0.194 s-1(k_(app)),which was 17.6 and 21.5 times higher than that of the Cu-and Ni-electrode groups,respectively.Fe-electrode was beneficial for reducing electrochemical impedance between the electrode and suspended sludge.Additionally,redox substances in extracellular polymeric substances of the Fe-electrode group were increased,implying more favorable electron transport dynamics.Simultaneously,enrichments of functional bacteria Acetoanerobium and increased key enzymes involved in the carbonyl pathway of the Fe-electrode group were observed,which also promoted CO_(2) conversion in MES.This study provides a perspective on evaluating the promising sensitive metal electrode material for the process of CO_(2) valorization in MES and offers a reference for the subsequent electrode modification.展开更多
Human-modified landscapes serve as ecological filters,determining species distributions and persistence.Energy-efficient technologies,while crucial for climate change mitigation,represent novel filters whose impacts o...Human-modified landscapes serve as ecological filters,determining species distributions and persistence.Energy-efficient technologies,while crucial for climate change mitigation,represent novel filters whose impacts on synanthropic biodiversity are poorly understood.We investigated how attached sunspaces,a widely adopted energy-saving technology in rural China,filter the distribution of two ecologically important aerial insectivores,the Barn Swallow(Hirundo rustica)and Red-rumped Swallow(Cecropis daurica).We surveyed 106 villages during the 2024 and 2025 breeding seasons and recorded a total of 2323 nests(612 Barn Swallow,1711 Red-rumped Swallow).Using Generalized Linear Models,we assessed their responses to building characteristics,landscape composition and the prevalence of sunspaces.Barn Swallow nests preferred perches at the base and single attachment faces,while Red-rumped Swallow nests favored multiple attachment faces and avoided long shelters.The proportion of buildings with sunspaces acted as a strong positive filter for Barn Swallow nest abundance(+24%)but as a significant negative filter for Red-rumped Swallow(-51%).Other landscape variables(e.g.,human population density,NDVI,Human Footprint Index)were not significant.This study demonstrates that specific architectural innovations can act as powerful ecological filters,leading to divergent distributional outcomes for sympatric species reliant on anthropogenic structures.Our findings reveal a critical trade-off in sustainable development:energy efficiency gains may inadvertently reduce habitat suitability for certain species.To reconcile climate and biodiversity goals in rural landscapes,we advocate integrating species-specific habitat requirements into building design.We propose actionable modifications to sunspaces to support swallows without compromising energy savings.These principles provide a template for mitigating the distributional impacts of green infrastructure globally.展开更多
In Wireless Sensor Networks(WSNs),survivability is a crucial issue that is greatly impacted by energy efficiency.Solutions that satisfy application objectives while extending network life are needed to address severe ...In Wireless Sensor Networks(WSNs),survivability is a crucial issue that is greatly impacted by energy efficiency.Solutions that satisfy application objectives while extending network life are needed to address severe energy constraints inWSNs.This paper presents an Adaptive Enhanced GreyWolf Optimizer(AEGWO)for energy-efficient cluster head(CH)selection that mitigates the exploration–exploitation imbalance,preserves population diversity,and avoids premature convergence inherent in baseline GWO.The AEGWO combines adaptive control of the parameter of the search pressure to accelerate convergence without stagnation,a hybrid velocity-momentum update based on the dynamics of PSO,and an intelligent mutation operator to maintain the diversity of the population.The search is guided by a multi-objective fitness,which aims at maximizing the residual energy,equal distribution of CH,minimizing the intra-cluster distance,desirable proximity to sinks,and enhancing the coverage.Simulations on 100 nodes homogeneousWSN Tested the proposed AEGWO under the same conditions with LEACH,GWO,IGWO,PSO,WOA,and GA,AEGWO significantly increases stability and lifetime compared to LEACHand other tested algorithms;it has the best first,half,and last node dead,and higher residual energy and smaller communication overhead.The findings prove that AEGWO provides sustainable energy management and better lifetime extension,which makes it a robust,flexible clustering protocol of large-scaleWSNs.展开更多
Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that ...Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail.It uses the NASA CMAPSS dataset,which has over 200,000 engine cycles from260 engines.The process begins with systematic preprocessing,which includes imputation,outlier removal,scaling,and labelling of the remaining useful life.Dimensionality is reduced using a hybrid selection method that combines variance filtering,recursive elimination,and gradient-boosted importance scores,yielding a stable set of 10 informative sensors.To mitigate class imbalance,minority cases are oversampled,and class-weighted losses are applied during training.Benchmarking is carried out with logistic regression,gradient boosting,and a recurrent design that integrates gated recurrent units with long short-term memory networks.The Long Short-Term Memory–Gated Recurrent Unit(LSTM–GRU)hybrid achieved the strongest performance with an F1 score of 0.92,precision of 0.93,recall of 0.91,ReceiverOperating Characteristic–AreaUnder the Curve(ROC-AUC)of 0.97,andminority recall of 0.75.Interpretability testing using permutation importance and Shapley values indicates that sensors 13,15,and 11 are the most important indicators of engine wear.The proposed system combines imbalance handling,feature reduction,and Interpretability into a practical design suitable for real industrial settings.展开更多
Feature selection serves as a critical preprocessing step inmachine learning,focusing on identifying and preserving the most relevant features to improve the efficiency and performance of classification algorithms.Par...Feature selection serves as a critical preprocessing step inmachine learning,focusing on identifying and preserving the most relevant features to improve the efficiency and performance of classification algorithms.Particle Swarm Optimization has demonstrated significant potential in addressing feature selection challenges.However,there are inherent limitations in Particle Swarm Optimization,such as the delicate balance between exploration and exploitation,susceptibility to local optima,and suboptimal convergence rates,hinder its performance.To tackle these issues,this study introduces a novel Leveraged Opposition-Based Learning method within Fitness Landscape Particle Swarm Optimization,tailored for wrapper-based feature selection.The proposed approach integrates:(1)a fitness-landscape adaptive strategy to dynamically balance exploration and exploitation,(2)the lever principle within Opposition-Based Learning to improve search efficiency,and(3)a Local Selection and Re-optimization mechanism combined with random perturbation to expedite convergence and enhance the quality of the optimal feature subset.The effectiveness of is rigorously evaluated on 24 benchmark datasets and compared against 13 advancedmetaheuristic algorithms.Experimental results demonstrate that the proposed method outperforms the compared algorithms in classification accuracy on over half of the datasets,whilst also significantly reducing the number of selected features.These findings demonstrate its effectiveness and robustness in feature selection tasks.展开更多
Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from...Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.展开更多
Unconfined Compressive Strength(UCS)is a key parameter for the assessment of the stability and performance of stabilized soils,yet traditional laboratory testing is both time and resource intensive.In this study,an in...Unconfined Compressive Strength(UCS)is a key parameter for the assessment of the stability and performance of stabilized soils,yet traditional laboratory testing is both time and resource intensive.In this study,an interpretable machine learning approach to UCS prediction is presented,pairing five models(Random Forest(RF),Gradient Boosting(GB),Extreme Gradient Boosting(XGB),CatBoost,and K-Nearest Neighbors(KNN))with SHapley Additive exPlanations(SHAP)for enhanced interpretability and to guide feature removal.A complete dataset of 12 geotechnical and chemical parameters,i.e.,Atterberg limits,compaction properties,stabilizer chemistry,dosage,curing time,was used to train and test the models.R2,RMSE,MSE,and MAE were used to assess performance.Initial results with all 12 features indicated that boosting-based models(GB,XGB,CatBoost)exhibited the highest predictive accuracy(R^(2)=0.93)with satisfactory generalization on test data,followed by RF and KNN.SHAP analysis consistently picked CaO content,curing time,stabilizer dosage,and compaction parameters as the most important features,aligning with established soil stabilization mechanisms.Models were then re-trained on the top 8 and top 5 SHAP-ranked features.Interestingly,GB,XGB,and CatBoost maintained comparable accuracy with reduced input sets,while RF was moderately sensitive and KNN was somewhat better owing to reduced dimensionality.The findings confirm that feature reduction through SHAP enables cost-effective UCS prediction through the reduction of laboratory test requirements without significant accuracy loss.The suggested hybrid approach offers an explainable,interpretable,and cost-effective tool for geotechnical engineering practice.展开更多
Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic...Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.展开更多
Advanced intensity measures(IMs)based on an inelastic deformation spectrum improved the evaluation of the median engineering demand parameters(EDPs)and reduced dispersion.In this regard,an optimized two-degreefreedom(...Advanced intensity measures(IMs)based on an inelastic deformation spectrum improved the evaluation of the median engineering demand parameters(EDPs)and reduced dispersion.In this regard,an optimized two-degreefreedom(2DOF)modal pushover-based scaling procedure(2DMPS)has been developed for a nonlinear dynamic analysis of asymmetric in-plan buildings.The 2DMPS procedure scales ground motions to approach close enough to a target value of the inelastic displacement of the first-mode inelastic 2DOF modal stick,extended for structures with significant contributions of higher modes.Further,4-,6-and 13-story RC SMRF buildings were selected for analyses using ground motion records scaled by the 2DMPS procedure,the modal pushover-based scaling method(MPS),and ASCE/SEI 7-16 scaling procedures.The median values of EDPs on scaled records closely matched the benchmark results.The bias in the EDP values due to the scaled records in every group regarding their median value was lower than the dispersion of the 21 unscaled records.These results generally demonstrate the accuracy and efficiency of the 2DMPS method.Additionally,the 2DOF modal stick’s inelastic response spectra are better suited for calculating seismic demands for one-way asymmetric-plan structures than the SDOF inelastic response spectra.展开更多
The selection of a suitable navigation area is pivotal in aircraft scene matching guidance technology.This study addresses the challenge of identifying suitable reference image ranges for precise scene matching,which ...The selection of a suitable navigation area is pivotal in aircraft scene matching guidance technology.This study addresses the challenge of identifying suitable reference image ranges for precise scene matching,which is crucial for enhancing aircraft positioning accuracy.Traditional methods for image matchability analysis are often limited by their reliance on manual feature parameter design and threshold-based filtering,resulting in suboptimal accuracy and efficiency.This paper proposes a novel network architecture for selecting suitable navigation areas using image Matching Level Segmentation(MLSNet).The approach involves two key innovations:a method for generating segmentation labels that quantify matchability levels and an end-to-end network architecture for rapid and precise prediction of reference image matchability segmentation maps.The network includes two core modules:the saliency analysis module uses multi-layer convolutional networks to accurately detect image saliency features across various levels and scales;the multidimensional attention module utilizes attention mechanisms to focus on feature channels and spatial neighborhood scenes to assess the image’s matchability.Our method was rigorously tested on an extensive collection of remote sensing images,where it was benchmarked against a range of both traditional and cutting-edge deep learning methods.The findings indicate that MLSNet is significantly superior to traditional methods in accuracy and efficiency of matchability analysis,and is also relatively ahead of state-of-the-art deep learning models.展开更多
Reconfigurable intelligent surface(RIS)have been cast as a promising alternative to alleviate blockage vulnerability and enhance coverage capability for terahertz(THz)communications.Owing to large-scale array elements...Reconfigurable intelligent surface(RIS)have been cast as a promising alternative to alleviate blockage vulnerability and enhance coverage capability for terahertz(THz)communications.Owing to large-scale array elements at transceivers and RIS,the codebook based beamforming can be utilized in a computationally efficient manner.However,the codeword selection for analog beamforming is an intractable combinatorial optimization(CO)problem.To this end,by taking the CO problem as a classification problem,a multi-task learning based analog beam selection(MTL-ABS)framework is developed to implement cooperative beam selection concurrently at transceivers and RIS.In addition,residual network and self-attention mechanism are used to combat the network degradation and mine intrinsic THz channel features.Finally,the network convergence is analyzed from a blockwise perspective,and numerical results demonstrate that the MTL-ABS framework greatly decreases the beam selection overhead and achieves near optimal sum-rate compared with heuristic search based counterparts.展开更多
Federated Learning(FL)provides an effective framework for efficient processing in vehicular edge computing.However,the dynamic and uncertain communication environment,along with the performance variations of vehicular...Federated Learning(FL)provides an effective framework for efficient processing in vehicular edge computing.However,the dynamic and uncertain communication environment,along with the performance variations of vehicular devices,affect the distribution and uploading processes of model parameters.In FL-assisted Internet of Vehicles(IoV)scenarios,challenges such as data heterogeneity,limited device resources,and unstable communication environments become increasingly prominent.These issues necessitate intelligent vehicle selection schemes to enhance training efficiency.Given this context,we propose a new scenario involving FL-assisted IoV systems under dynamic and uncertain communication conditions,and develop a dynamic interval multi-objective optimization algorithm to jointly optimize various factors including training experiments,system energy consumption,and bandwidth utilization to meet multi-criteria resource optimization requirements.For the problem at hand,we design a dynamic interval multi-objective optimization algorithm based on interval overlap detection.Simulation results demonstrate that our method outperforms other solutions in terms of accuracy,training cost,and server utilization.It effectively enhances training efficiency under wireless channel environments while rationally utilizing bandwidth resources,thus possessing significant scientific value and application potential in the field of IoV.展开更多
Data serves as the foundation for training and testing machine learning and artificial intelligencemodels.The most fundamental part of data is its attributes or features.The feature set size changes from one dataset t...Data serves as the foundation for training and testing machine learning and artificial intelligencemodels.The most fundamental part of data is its attributes or features.The feature set size changes from one dataset to another.Only the relevant features contributemeaningfully to classificationaccuracy.The presence of irrelevant features reduces the system’s effectiveness.Classification performance often deteriorates on high-dimensional datasets due to the large search space.Thus,one of the significant obstacles affecting the performance of the learning process in the majority of machine learning and data mining techniques is the dimensionality of the datasets.Feature selection(FS)is an effective preprocessing step in classification tasks.The aim of applying FS is to exclude redundant and unrelated features while retaining the most informative ones to optimize classification capability and compress computational complexity.In this paper,a novel hybrid binary metaheuristic algorithm,termed hSC-FPA,is proposed by hybridizing the Flower Pollination Algorithm(FPA)and the Sine Cosine Algorithm(SCA).Hybridization controls the exploration capacity of SCA and the exploitation behavior of FPA to maintain a balanced search process.SCA guides the global search in the early iterations,while FPA’s local pollination refines promising solutions in later stages.A binary conversion mechanism using a threshold function is implemented to handle the discrete nature of the feature selection problem.The functionality of the proposed hSC-FPA is authenticated on fourteen standard datasets from the UCI repository using the K-Nearest Neighbors(K-NN)classifier.Experimental results are benchmarked against the standalone SCA and FPA algorithms.The hSC-FPA consistently achieves higher classification accuracy,selects a more compact feature subset,and demonstrates superior convergence behavior.These findings support the stability and outperformance of the hybrid feature selection method presented.展开更多
Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant chal...Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.展开更多
基金supported by Ho Chi Minh City Open University,Vietnam under grant number E2024.02.1CD and Suan Sunandha Rajabhat University,Thailand.
文摘The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.
文摘With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2020-NR049579).
文摘High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.
基金supported by the National Key Research and Development Plan of China(2021YFD2200202)the Key Research and Development Project of Jiangsu Province,China(BE2021366).
文摘Populus species,important economic species combining rapid growth with broad ecological adaptability,play a critical role in sustainable forestry and bioenergy production.In this study,we performed whole-genome resequencing of 707 individuals from a full-sib family to develop comprehensive single nucleotide polymorphism(SNP)markers and constructed a high-density genetic linkage map of 19 linkage groups.The total genetic length of the map reached 3623.65 cM with an average marker interval of 0.34 cM.By integrating multidimensional phenotypic data,89 quantitative trait loci(QTL)associated with growth,wood physical and chemical properties,disease resistance,and leaf morphology traits were identified,with logarithm of odds(LOD)scores ranging from 3.13 to 21.72 Notably,pleiotropic analysis revealed significant colocaliza and phenotypic variance explained between 1.7% and 11.6%.-tion hotspots on chromosomes LG1,LG5,LG6,LG8,and LG14,with epistatic interaction network analysis confirming genetic basis of coordinated regulation across multiple traits.Functional annotation of 207 candidate genes showed that R2R3-MYB and bHLH transcription factors and pyruvate kinase-encoding genes were significantly enriched,suggesting crucial roles in lignin biosynthesis and carbon metabolic pathways.Allelic effect analysis indicated that the frequency of favorable alleles associated with target traits ranged from 0.20 to 0.55.Incorporation of QTL-derived favorable alleles as random effects into Bayesian-based genomic selection models led to an increase in prediction accuracy ranging from 1% to 21%,with Bayesian ridge regression as the best predictive model.This study provides valuable genomic resources and genetic insights for deciphering complex trait architecture and advancing molecular breeding in poplar.
基金supported by the National Key R&D Program of China under Grant No.2022YFA1003701the Open Research Fund of Yunnan Key Laboratory of Statistical Modeling and Data Analysis,Yunnan University under Grant No.SMDAYB2023004。
文摘Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is quite challenging to make statistical inference on distributed high-dimensional QR with missing data due to the distributed nature,sparsity and missingness of data and nondifferentiable quantile loss function.To overcome the challenge,this paper develops a communicationefficient method to select variables and estimate parameters by utilizing a smooth function to approximate the non-differentiable quantile loss function and incorporating the idea of the inverse probability weighting and the penalty function.The proposed approach has three merits.First,it is both computationally and communicationally efficient because only the first-and second-order information of the approximate objective function are communicated at each iteration.Second,the proposed estimators possess the oracle property after a limited number of iterations without constraint on the number of machines.Third,the proposed method simultaneously selects variables and estimates parameters within a distributed framework,ensuring robustness to the specified response probability or propensity score function of the missing data mechanism.Simulation studies and a real example are used to illustrate the effectiveness of the proposed methodologies.
基金supported by the China Agriculture Research System of MOF and MARAthe National Natural Science Foundation of China (31872337 and 31501919)the Agricultural Science and Technology Innovation Project,China (ASTIP-IAS02)。
文摘The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS.
基金supported by the Science and Technology Commission of Shanghai Municipality Foundation(No.22230710500)the Interdisciplinary joint research project of Tongji University(No.2023-3-YB-07).
文摘As the core of cathode materials,sensitive metals play important roles in the optimization of acetate production from carbon dioxide(CO_(2))in microbial electrochemical system(MES).In this work,iron(Fe),copper(Cu),and nickel(Ni)as sensitive metal cathode materials were evaluated for CO_(2) conversion in MES.The MES with Feelectrode as a promising electrode material demonstrated a superior CO_(2) reduction performance with a maximum acetate accumulation of 417.9±39.2 mg/L,which was 1.5 and 1.7 folds higher than that in the Ni-electrode and Cu-electrode groups,respectively.Furthermore,an outstanding electron recovery efficiency of 67.7%was shown in the Fe-electrode group.The electron transfer between electrode-suspended sludge was systematically cross-evaluated by the electrochemical behavior and extracellular polymeric substances.The Fe-electrode group had the highest electron transfer rate with 0.194 s-1(k_(app)),which was 17.6 and 21.5 times higher than that of the Cu-and Ni-electrode groups,respectively.Fe-electrode was beneficial for reducing electrochemical impedance between the electrode and suspended sludge.Additionally,redox substances in extracellular polymeric substances of the Fe-electrode group were increased,implying more favorable electron transport dynamics.Simultaneously,enrichments of functional bacteria Acetoanerobium and increased key enzymes involved in the carbonyl pathway of the Fe-electrode group were observed,which also promoted CO_(2) conversion in MES.This study provides a perspective on evaluating the promising sensitive metal electrode material for the process of CO_(2) valorization in MES and offers a reference for the subsequent electrode modification.
基金funded by the National Natural Science Foundation of China(No.32201304)the Fundamental Research Funds for the Central Universities(2412022QD026)。
文摘Human-modified landscapes serve as ecological filters,determining species distributions and persistence.Energy-efficient technologies,while crucial for climate change mitigation,represent novel filters whose impacts on synanthropic biodiversity are poorly understood.We investigated how attached sunspaces,a widely adopted energy-saving technology in rural China,filter the distribution of two ecologically important aerial insectivores,the Barn Swallow(Hirundo rustica)and Red-rumped Swallow(Cecropis daurica).We surveyed 106 villages during the 2024 and 2025 breeding seasons and recorded a total of 2323 nests(612 Barn Swallow,1711 Red-rumped Swallow).Using Generalized Linear Models,we assessed their responses to building characteristics,landscape composition and the prevalence of sunspaces.Barn Swallow nests preferred perches at the base and single attachment faces,while Red-rumped Swallow nests favored multiple attachment faces and avoided long shelters.The proportion of buildings with sunspaces acted as a strong positive filter for Barn Swallow nest abundance(+24%)but as a significant negative filter for Red-rumped Swallow(-51%).Other landscape variables(e.g.,human population density,NDVI,Human Footprint Index)were not significant.This study demonstrates that specific architectural innovations can act as powerful ecological filters,leading to divergent distributional outcomes for sympatric species reliant on anthropogenic structures.Our findings reveal a critical trade-off in sustainable development:energy efficiency gains may inadvertently reduce habitat suitability for certain species.To reconcile climate and biodiversity goals in rural landscapes,we advocate integrating species-specific habitat requirements into building design.We propose actionable modifications to sunspaces to support swallows without compromising energy savings.These principles provide a template for mitigating the distributional impacts of green infrastructure globally.
基金The Open Access publication fee for this article was fully covered by Abu Dhabi University.
文摘In Wireless Sensor Networks(WSNs),survivability is a crucial issue that is greatly impacted by energy efficiency.Solutions that satisfy application objectives while extending network life are needed to address severe energy constraints inWSNs.This paper presents an Adaptive Enhanced GreyWolf Optimizer(AEGWO)for energy-efficient cluster head(CH)selection that mitigates the exploration–exploitation imbalance,preserves population diversity,and avoids premature convergence inherent in baseline GWO.The AEGWO combines adaptive control of the parameter of the search pressure to accelerate convergence without stagnation,a hybrid velocity-momentum update based on the dynamics of PSO,and an intelligent mutation operator to maintain the diversity of the population.The search is guided by a multi-objective fitness,which aims at maximizing the residual energy,equal distribution of CH,minimizing the intra-cluster distance,desirable proximity to sinks,and enhancing the coverage.Simulations on 100 nodes homogeneousWSN Tested the proposed AEGWO under the same conditions with LEACH,GWO,IGWO,PSO,WOA,and GA,AEGWO significantly increases stability and lifetime compared to LEACHand other tested algorithms;it has the best first,half,and last node dead,and higher residual energy and smaller communication overhead.The findings prove that AEGWO provides sustainable energy management and better lifetime extension,which makes it a robust,flexible clustering protocol of large-scaleWSNs.
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia Grant No.KFU253765.
文摘Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail.It uses the NASA CMAPSS dataset,which has over 200,000 engine cycles from260 engines.The process begins with systematic preprocessing,which includes imputation,outlier removal,scaling,and labelling of the remaining useful life.Dimensionality is reduced using a hybrid selection method that combines variance filtering,recursive elimination,and gradient-boosted importance scores,yielding a stable set of 10 informative sensors.To mitigate class imbalance,minority cases are oversampled,and class-weighted losses are applied during training.Benchmarking is carried out with logistic regression,gradient boosting,and a recurrent design that integrates gated recurrent units with long short-term memory networks.The Long Short-Term Memory–Gated Recurrent Unit(LSTM–GRU)hybrid achieved the strongest performance with an F1 score of 0.92,precision of 0.93,recall of 0.91,ReceiverOperating Characteristic–AreaUnder the Curve(ROC-AUC)of 0.97,andminority recall of 0.75.Interpretability testing using permutation importance and Shapley values indicates that sensors 13,15,and 11 are the most important indicators of engine wear.The proposed system combines imbalance handling,feature reduction,and Interpretability into a practical design suitable for real industrial settings.
基金supported by National Natural Science Foundation of China(62106092)Natural Science Foundation of Fujian Province(2024J01822,2024J01820,2022J01916)Natural Science Foundation of Zhangzhou City(ZZ2024J28).
文摘Feature selection serves as a critical preprocessing step inmachine learning,focusing on identifying and preserving the most relevant features to improve the efficiency and performance of classification algorithms.Particle Swarm Optimization has demonstrated significant potential in addressing feature selection challenges.However,there are inherent limitations in Particle Swarm Optimization,such as the delicate balance between exploration and exploitation,susceptibility to local optima,and suboptimal convergence rates,hinder its performance.To tackle these issues,this study introduces a novel Leveraged Opposition-Based Learning method within Fitness Landscape Particle Swarm Optimization,tailored for wrapper-based feature selection.The proposed approach integrates:(1)a fitness-landscape adaptive strategy to dynamically balance exploration and exploitation,(2)the lever principle within Opposition-Based Learning to improve search efficiency,and(3)a Local Selection and Re-optimization mechanism combined with random perturbation to expedite convergence and enhance the quality of the optimal feature subset.The effectiveness of is rigorously evaluated on 24 benchmark datasets and compared against 13 advancedmetaheuristic algorithms.Experimental results demonstrate that the proposed method outperforms the compared algorithms in classification accuracy on over half of the datasets,whilst also significantly reducing the number of selected features.These findings demonstrate its effectiveness and robustness in feature selection tasks.
基金supported by the Major Science and Technology Programs in Henan Province(No.241100210100)Henan Provincial Science and Technology Research Project(No.252102211085,No.252102211105)+3 种基金Endogenous Security Cloud Network Convergence R&D Center(No.602431011PQ1)The Special Project for Research and Development in Key Areas of Guangdong Province(No.2021ZDZX1098)The Stabilization Support Program of Science,Technology and Innovation Commission of Shenzhen Municipality(No.20231128083944001)The Key scientific research projects of Henan higher education institutions(No.24A520042).
文摘Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.
文摘Unconfined Compressive Strength(UCS)is a key parameter for the assessment of the stability and performance of stabilized soils,yet traditional laboratory testing is both time and resource intensive.In this study,an interpretable machine learning approach to UCS prediction is presented,pairing five models(Random Forest(RF),Gradient Boosting(GB),Extreme Gradient Boosting(XGB),CatBoost,and K-Nearest Neighbors(KNN))with SHapley Additive exPlanations(SHAP)for enhanced interpretability and to guide feature removal.A complete dataset of 12 geotechnical and chemical parameters,i.e.,Atterberg limits,compaction properties,stabilizer chemistry,dosage,curing time,was used to train and test the models.R2,RMSE,MSE,and MAE were used to assess performance.Initial results with all 12 features indicated that boosting-based models(GB,XGB,CatBoost)exhibited the highest predictive accuracy(R^(2)=0.93)with satisfactory generalization on test data,followed by RF and KNN.SHAP analysis consistently picked CaO content,curing time,stabilizer dosage,and compaction parameters as the most important features,aligning with established soil stabilization mechanisms.Models were then re-trained on the top 8 and top 5 SHAP-ranked features.Interestingly,GB,XGB,and CatBoost maintained comparable accuracy with reduced input sets,while RF was moderately sensitive and KNN was somewhat better owing to reduced dimensionality.The findings confirm that feature reduction through SHAP enables cost-effective UCS prediction through the reduction of laboratory test requirements without significant accuracy loss.The suggested hybrid approach offers an explainable,interpretable,and cost-effective tool for geotechnical engineering practice.
基金funded by Deanship of Graduate studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-01264).
文摘Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.
文摘Advanced intensity measures(IMs)based on an inelastic deformation spectrum improved the evaluation of the median engineering demand parameters(EDPs)and reduced dispersion.In this regard,an optimized two-degreefreedom(2DOF)modal pushover-based scaling procedure(2DMPS)has been developed for a nonlinear dynamic analysis of asymmetric in-plan buildings.The 2DMPS procedure scales ground motions to approach close enough to a target value of the inelastic displacement of the first-mode inelastic 2DOF modal stick,extended for structures with significant contributions of higher modes.Further,4-,6-and 13-story RC SMRF buildings were selected for analyses using ground motion records scaled by the 2DMPS procedure,the modal pushover-based scaling method(MPS),and ASCE/SEI 7-16 scaling procedures.The median values of EDPs on scaled records closely matched the benchmark results.The bias in the EDP values due to the scaled records in every group regarding their median value was lower than the dispersion of the 21 unscaled records.These results generally demonstrate the accuracy and efficiency of the 2DMPS method.Additionally,the 2DOF modal stick’s inelastic response spectra are better suited for calculating seismic demands for one-way asymmetric-plan structures than the SDOF inelastic response spectra.
基金supported in part by the National Natural Science Foundation of China(No.42271446)in part by the Tianjin Key Laboratory of Rail Transit Navigation Positioning and Spatio-Temporary Big Data Technology,China(No.TKL2024B13)in part by the Science and Technology Program of Tianjin,China(No.24YFYSHZ00080)。
文摘The selection of a suitable navigation area is pivotal in aircraft scene matching guidance technology.This study addresses the challenge of identifying suitable reference image ranges for precise scene matching,which is crucial for enhancing aircraft positioning accuracy.Traditional methods for image matchability analysis are often limited by their reliance on manual feature parameter design and threshold-based filtering,resulting in suboptimal accuracy and efficiency.This paper proposes a novel network architecture for selecting suitable navigation areas using image Matching Level Segmentation(MLSNet).The approach involves two key innovations:a method for generating segmentation labels that quantify matchability levels and an end-to-end network architecture for rapid and precise prediction of reference image matchability segmentation maps.The network includes two core modules:the saliency analysis module uses multi-layer convolutional networks to accurately detect image saliency features across various levels and scales;the multidimensional attention module utilizes attention mechanisms to focus on feature channels and spatial neighborhood scenes to assess the image’s matchability.Our method was rigorously tested on an extensive collection of remote sensing images,where it was benchmarked against a range of both traditional and cutting-edge deep learning methods.The findings indicate that MLSNet is significantly superior to traditional methods in accuracy and efficiency of matchability analysis,and is also relatively ahead of state-of-the-art deep learning models.
文摘Reconfigurable intelligent surface(RIS)have been cast as a promising alternative to alleviate blockage vulnerability and enhance coverage capability for terahertz(THz)communications.Owing to large-scale array elements at transceivers and RIS,the codebook based beamforming can be utilized in a computationally efficient manner.However,the codeword selection for analog beamforming is an intractable combinatorial optimization(CO)problem.To this end,by taking the CO problem as a classification problem,a multi-task learning based analog beam selection(MTL-ABS)framework is developed to implement cooperative beam selection concurrently at transceivers and RIS.In addition,residual network and self-attention mechanism are used to combat the network degradation and mine intrinsic THz channel features.Finally,the network convergence is analyzed from a blockwise perspective,and numerical results demonstrate that the MTL-ABS framework greatly decreases the beam selection overhead and achieves near optimal sum-rate compared with heuristic search based counterparts.
基金supported in part by the Central Guidance for Local Science and Technology Development Funds under Grant No.YDZJSX2025D049Shanxi Provincial Graduate Innovation Research Program under Grant No.2024KY652.
文摘Federated Learning(FL)provides an effective framework for efficient processing in vehicular edge computing.However,the dynamic and uncertain communication environment,along with the performance variations of vehicular devices,affect the distribution and uploading processes of model parameters.In FL-assisted Internet of Vehicles(IoV)scenarios,challenges such as data heterogeneity,limited device resources,and unstable communication environments become increasingly prominent.These issues necessitate intelligent vehicle selection schemes to enhance training efficiency.Given this context,we propose a new scenario involving FL-assisted IoV systems under dynamic and uncertain communication conditions,and develop a dynamic interval multi-objective optimization algorithm to jointly optimize various factors including training experiments,system energy consumption,and bandwidth utilization to meet multi-criteria resource optimization requirements.For the problem at hand,we design a dynamic interval multi-objective optimization algorithm based on interval overlap detection.Simulation results demonstrate that our method outperforms other solutions in terms of accuracy,training cost,and server utilization.It effectively enhances training efficiency under wireless channel environments while rationally utilizing bandwidth resources,thus possessing significant scientific value and application potential in the field of IoV.
基金supported by a research grant from Lahore College for Women University(LCWU),Lahore,Pakistan.
文摘Data serves as the foundation for training and testing machine learning and artificial intelligencemodels.The most fundamental part of data is its attributes or features.The feature set size changes from one dataset to another.Only the relevant features contributemeaningfully to classificationaccuracy.The presence of irrelevant features reduces the system’s effectiveness.Classification performance often deteriorates on high-dimensional datasets due to the large search space.Thus,one of the significant obstacles affecting the performance of the learning process in the majority of machine learning and data mining techniques is the dimensionality of the datasets.Feature selection(FS)is an effective preprocessing step in classification tasks.The aim of applying FS is to exclude redundant and unrelated features while retaining the most informative ones to optimize classification capability and compress computational complexity.In this paper,a novel hybrid binary metaheuristic algorithm,termed hSC-FPA,is proposed by hybridizing the Flower Pollination Algorithm(FPA)and the Sine Cosine Algorithm(SCA).Hybridization controls the exploration capacity of SCA and the exploitation behavior of FPA to maintain a balanced search process.SCA guides the global search in the early iterations,while FPA’s local pollination refines promising solutions in later stages.A binary conversion mechanism using a threshold function is implemented to handle the discrete nature of the feature selection problem.The functionality of the proposed hSC-FPA is authenticated on fourteen standard datasets from the UCI repository using the K-Nearest Neighbors(K-NN)classifier.Experimental results are benchmarked against the standalone SCA and FPA algorithms.The hSC-FPA consistently achieves higher classification accuracy,selects a more compact feature subset,and demonstrates superior convergence behavior.These findings support the stability and outperformance of the hybrid feature selection method presented.
文摘Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.
