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A Metaheuristic Football Optimization Algorithm Integrated with Large Language Models for Automated Seismic Time-Series Modeling 认领 引用
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作者 Amal H.Alharbi Marwa M.Eid +2 位作者 Nima Khodadadi Ebrahim A.Mattar Sayed Elkenawy 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期947-987,共41页
Seismic time series forecasting remains challenging due to the nonlinearity,non-stationarity,and noise of earthquake data,and because deep learning models are sensitive to preprocessing and hyperparameter settings.Alt... Seismic time series forecasting remains challenging due to the nonlinearity,non-stationarity,and noise of earthquake data,and because deep learning models are sensitive to preprocessing and hyperparameter settings.Although recent studies have improved neural architectures and optimization techniques,preprocessing is often treated as a fixed or manually designed stage,with limited integration into model optimization.To address this,this paper proposes an integrated,data-driven modelling framework that combines guided preprocessing with systematic hyperparameter optimization for seismic prediction,specifically forecasting earthquake magnitude from seismic catalog time-series data,with experiments conducted on Canadian seismic records.The method uses a Large Language Model to guide data preparation and feature engineering,rather than fully automate them,and applies deep learning-based forecasting with the N-HITS architecture,optimized via metaheuristic-assisted feature selection and hyperparameter tuning.The Football Optimization Algorithm(FbOA),employed as a metaheuristic optimization strategy in this study,is evaluated and compared with several well-known optimizers under identical conditions.The results show significant performance gains,with FbOA achieving superior accuracy,robustness,and convergence compared to baseline and competing methods.Notably,error metrics are reduced(MSE 3.10×10-7,RMSE 5.57×103),with high performance indicators(r=0.982,R2=0.979,NSE=0.981,WI=0.985).These results highlight the value of integrating guided preprocessing with optimization and demonstrate a scalable framework for high-precision time-series prediction in geophysical and related domains. 展开更多
关键词 Seismic time-series forecasting large language models metaheuristic algorithms football optimization algorithm earthquake modeling
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Flood predictions from metrics to classes by multiple machine learning algorithms coupling with clustering-deduced membership degree 认领 引用
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作者 ZHAI Xiaoyan ZHANG Yongyong +5 位作者 XIA Jun ZHANG Yongqiang TANG Qiuhong SHAO Quanxi CHEN Junxu ZHANG Fan 《Journal of Geographical Sciences》 SCIE CSCD 2026年第1期149-176,共28页
Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting... Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach. 展开更多
关键词 flood regime metrics class prediction machine learning algorithms hydrological model
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A Firefly Algorithm-Optimized CNN-BiLSTM Model for Automated Detection of Bone Cancer and Marrow Cell Abnormalities 认领 引用
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作者 Ishaani Priyadarshini 《Computers, Materials & Continua》 SCIE EI 2026年第3期1510-1535,共26页
Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a ... Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a Convolutional Neural Network(CNN)with a Bidirectional Long Short-Term Memory(BiLSTM)architecture,optimized using the Firefly Optimization algorithm(FO).The proposed CNN-BiLSTM-FO model is tailored for structured biomedical data,capturing both local patterns and sequential dependencies in diagnostic features,while the Firefly Algorithm fine-tunes key hyperparameters to maximize predictive performance.The approach is evaluated on two benchmark biomedical datasets:one comprising diagnostic data for bone cancer detection and another for identifying marrow cell abnormalities.Experimental results demonstrate that the proposed method outperforms standard deep learning models,including CNN,LSTM,BiLSTM,and CNN-LSTM hybrids,significantly.The CNNBiLSTM-FO model achieves an accuracy of 98.55%for bone cancer detection and 96.04%for marrow abnormality classification.The paper also presents a detailed complexity analysis of the proposed algorithm and compares its performance across multiple evaluation metrics such as precision,recall,F1-score,and AUC.The results confirm the effectiveness of the firefly-based optimization strategy in improving classification accuracy and model robustness.This work introduces a scalable and accurate diagnostic solution that holds strong potential for integration into intelligent clinical decision-support systems. 展开更多
关键词 Firefly optimization algorithm(FO) marrow cell abnormalities bidirectional long short term memory(Bi-LSTM) temporal dependency modeling
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Information Diffusion Models and Fuzzing Algorithms for a Privacy-Aware Data Transmission Scheduling in 6G Heterogeneous ad hoc Networks 认领 引用 被引量：1
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作者 Borja Bordel Sánchez Ramón Alcarria Tomás Robles 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期1214-1234,共21页
In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic h... In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services. 展开更多
关键词 6G networks ad hoc networks privacy scheduling algorithms diffusion models fuzzing algorithms
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Mimicry of whole-body blood circulation through genetic algorithm in reduced-order hemodynamic model 认领 引用
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作者 Minghao Liao Taoping Bai +2 位作者 Ming Zhang Zhongyou Li Wentao Jiang 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第3期1-14,共14页
Physics-based reduced-order hemodynamic models have garnered significant interest because of their ability to capture whole-body cardiovascular fluctuations.However,coordinating the numerous interdependent parameters ... Physics-based reduced-order hemodynamic models have garnered significant interest because of their ability to capture whole-body cardiovascular fluctuations.However,coordinating the numerous interdependent parameters within these models remains a long-standing challenge,and the demand for the personalization of these models persists.We constructed a complex whole-body model of blood circulation(containing the heart,arterial trunk,and branches)and utilized genetic algorithms to automatically and efficiently coordinate the model parameters.Additionally,we introduced a“pseudo-distance”metric by updating the derivative dynamic time-warping algorithm to evaluate the similarity between the simulated waveforms and the target waveforms.After 40 rapid iterations,a complete match was achieved with the target in terms of the blood pressure and flow waveforms amplitude as well as the time domain,resulting in highly realistic waveform mimicry(i.e.,the pseudo-distance approached zero).This model takes about 40 min,far less than the manual modeling that usually takes several months.These results indicate that GAs significantly improve the modeling efficiency of reduced-order models,thus lowering the user threshold. 展开更多
关键词 Reduced-order model Genetic algorithm Hemodynamics Cardiovascular Pseudo-distance
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Effect of crosslinking on the mechanical properties of multiple network elastomers:experiments and modeling 认领 引用
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作者 Ning Han Peng Sun +2 位作者 Danming Zhong Rui Xiao Shaoxing Qu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第5期399-411,共13页
Multiple network elastomers(MNEs)consist of a highly crosslinked sacrificial network and one or more loosely crosslinked matrix networks.Their outstanding mechanical properties are highly sensitive to the design of cr... Multiple network elastomers(MNEs)consist of a highly crosslinked sacrificial network and one or more loosely crosslinked matrix networks.Their outstanding mechanical properties are highly sensitive to the design of crosslinker density.This study explores how the crosslinking degree of the sacrificial network affects the mechanical behavior of doubleriple network elastomers(DNE/TNE)through a combination of experiments and modeling.Cyclic loading tests show negligible hysteresis in DNEs,regardless of the crosslinking degree.In contrast,TNE exhibits prominent hysteresis that increases with crosslinker density.A progressively damaged model is further developed to describe the stress response.The results show that as the crosslinking degree increases,the peak of the probability density-chain length distribution curve becomes higher and shifts leftward,indicating a decrease in average chain length.The fracture tests reveal that the fracture toughness of MNEs first increases and then decreases with increasing crosslinker density.Notably,in TNE,although energy dissipation continues to rise with crosslinking degree,fracture toughness does not increase accordingly,suggesting that the fracture toughness of MNEs is not solely dominated by energy dissipation.These findings offer valuable insights for tailoring the modulus,energy dissipation,and fracture toughness of MNEs. 展开更多
关键词 Multiple network elastomers Crosslinking Constitutive model Damage Fracture toughness
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Predictive modeling for mechanical properties of cold-rolled strip steel based on random forest regression and whale optimization algorithm 认领 引用
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作者 Hong-Lei Cai Yi-Ming Fang +3 位作者 Le Liu Li-Hui Ren Zhen-Dong Liu Xiao-Dong Zhao 《Journal of Iron and Steel Research International》 SCIE EI CSCD 2026年第3期73-87,共15页
In response to the challenges of inadequate predictive accuracy and limited generalization capability in data-driven modeling for the mechanical properties of the cold-rolled strip steel,a predictive modeling method n... In response to the challenges of inadequate predictive accuracy and limited generalization capability in data-driven modeling for the mechanical properties of the cold-rolled strip steel,a predictive modeling method named RFR-WOA is developed based on random forest regression(RFR)and whale optimization algorithm(WOA).Firstly,using Pearson and Spearman correlation analysis and Gini coefficient importance ranking on an actual production dataset containing 37,878 samples,22 key variables are selected as model inputs from 112 variables that affect mechanical properties.Subsequently,an RFR-based predictive model for the mechanical properties of cold-rolled strip steel is constructed.Then,with the combination of the coefficient of determination(R2)and root mean square error as the optimization objective,the hyperparameters of RFR model are iteratively optimized using WOA,and better predictive effectiveness is obtained.Finally,the mechanical properties prediction model based on RFR-WOA is compared with models established using deep neural networks,convolutional neural networks,and other methods.The test results on 9469 samples of actual production data show that the model developed present has better predictive accuracy and generalization capability. 展开更多
关键词 Cold-rolled strip steel Mechanical property Predictive modeling Random forest regression Whale optimization algorithm
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Integrating Multiple Time Step Method and Implicit Solvent Model for Molecular Dynamics Simulations of Proteins 认领 引用
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作者 Zihan He Bo-Wen Yin +2 位作者 Bin-Bin Xie Lin Shen Wei-Hai Fang 《Chinese Journal of Chemical Physics》 SCIE EI CAS CSCD 2026年第3期433-447,共15页
In this work,we integrated the multiple time step(MTS)approach with the generalized Born(GB)implicit solvent model for molecular dynamics(MD)simulations of protein systems.The total forces for the protein systems can ... In this work,we integrated the multiple time step(MTS)approach with the generalized Born(GB)implicit solvent model for molecular dynamics(MD)simulations of protein systems.The total forces for the protein systems can be decomposed into two terms:slow forces corresponding to the solvation energy term in GB,and fast forces relevant to potential energy in vacuum.An integrator with the reversible Trotter decomposition scheme,in which the former is determined each N steps and used for the next N1 steps,was performed on MD simulations of four protein systems with different secondary structures.It was observed that the results using MTS until N=4 are in good agreement with the reference simulations in which each force term is updated at each step.We further compared the performances of four widelyused GB models:STILL,HCT,OBC and GRYCUK,in the framework of GB-MTS.The influence of different Born radius calculation methods is usually more significant than that of the MTS approach. 展开更多
关键词 Multiple time step approach Generalized Born implicit solvent model Molecu-lar dynamics simulations
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Prediction of Asphalt Pavement Rutting Depth Based on Multi-Model Fusion of Stacking Algorithm 认领 引用
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作者 Chenhui Peng Jinbiao Tang Derun Zhang 《Structural Durability & Health Monitoring》 EI 2026年第3期156-171,共16页
Rutting is a serious issue in asphalt pavement,which may reduce the pavement driving quality and safety.Accurately predicting rutting depth is a crucial task in pavement engineering,providing crucial decision support ... Rutting is a serious issue in asphalt pavement,which may reduce the pavement driving quality and safety.Accurately predicting rutting depth is a crucial task in pavement engineering,providing crucial decision support for asphalt pavement design and maintenance.However,accurate prediction of pavement rutting still remains a significant challenge for pavement engineers.This research first selects the loading number,temperature,dynamic modulus,asphalt layer thickness,and base layer type and thickness as candidate features.Data preprocessing,including outlier handling and feature selection,is then performed.Finally,based on the stacking algorithm,a multi-model fusion approach for predicting rutting depth in asphalt pavements is proposed,using ridge regression(RidgeR),K-nearest neighbor(KNN),multilayer perceptron(MLP),and random forest(RF)models as base models,and support vector machine(SVM)as a meta-model.The model is optimized using a Bayesian optimization model.Results demonstrate the feasibility of using correlation analysis for feature selection.Seven features,including axle weight,upper layer temperature,and middle layer modulus,were selected as predictive features.While all the basic models achieved good prediction accuracy,the stacking ensemble model exhibited lower variance and bias,demonstrating superior generalization capability.The asphalt pavement rutting depth prediction method based on the stacking algorithm multi-model fusion proposed in this research can accurately predict the rutting depth. 展开更多
关键词 Rutting prediction asphalt pavement stacking algorithm Bayesian optimization model
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Empirical tropospheric zenith wet delay models with strong generalization capability based on a robust machine learning fusion algorithm 认领 引用
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作者 Jiahao Zhang Qin Liang Yunqing Huang 《Geodesy and Geodynamics》 EI CSCD 2026年第2期211-224,共14页
Tropospheric zenith wet delay(ZWD)plays a vital role in the analysis of space geodetic observations.In recent years,machine learning methods have been increasingly applied to improve the accuracy of ZWD calculations.H... Tropospheric zenith wet delay(ZWD)plays a vital role in the analysis of space geodetic observations.In recent years,machine learning methods have been increasingly applied to improve the accuracy of ZWD calculations.However,a single machine learning model has limited generalization capabilities.To address these limitations,this study introduces a novel machine learning fusion(MLF)algorithm with stronger generalization capabilities to enhance ZWD modeling and prediction accuracy.The MLF algorithm utilizes a two-layer structure integrating extra trees(ET),backpropagation neural network(BPNN),and linear regression models.By comparing the root mean square error(RMSE)of these models,we found that both ET-based and MLF-based models outperform RF-based and BPNN-based models in terms of internal and external accuracy,across both surface meteorological data-based and blind models.The improvement in exte rnal accuracy is particularly significant in the blind models.Our re sults show that the MLF(with an RMSE of 3.93 cm)and ET(3.99 cm)models outperform the traditional GPT3model(4.07 cm),while the RF(4.21 cm)and BPNN(4.14 cm)have worse external accuracies than the GPT3 model.It is worth noting that the BPNN suffered from overfitting during external accuracy tests,which was avoided by the MLF.In summary,regardless of the availability of surface meteorological data,the MLF-based empirical models demonstrate superior internal and external accuracy compared to the other tested models in this study. 展开更多
关键词 Tropospheric zenith wet delay Machine learning Extra trees Machine learning fusion algorithm Empirical models
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DYNAMICS OF A REACTION-CONVECTION-DIFFUSION WATER-BORNE PATHOGEN MODEL WITH IMMUNITY WANING AND MULTIPLE ROUTES OF TRANSMISSION 认领 引用
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作者 Jiao LI Linfei NIE 《Acta Mathematica Scientia》 SCIE CSCD 2026年第3期1426-1462,共37页
In order to investigate the influences of vaccines,water currents,diffusion rates and spatial heterogeneity on the transmission of water-borne infectious diseases,a SVIRS-B reaction-convection-diffusion model is const... In order to investigate the influences of vaccines,water currents,diffusion rates and spatial heterogeneity on the transmission of water-borne infectious diseases,a SVIRS-B reaction-convection-diffusion model is constructed.First,the fitness of the model and the existence of the global attractor are investigated.Second,the basic reproduction number R0,which describes the extinction and persistence of the disease is obtained,this ensures that the disease-free steady state is globally asymptotically stable when R01,the disease is uniformly persistent and the model allows for at least an endemic steady state.Furthermore,the asymptotic behavior of the disease-free steady state in the absence of the convection term is discussed for R0=1.In addition,in the case of neglecting water convection,the global asymptotic stability of various steady states for the model are obtained in the homogeneous environment.Finally,some numerical simulations illustrate the main theoretical results and reveal some interesting problems,such as the effects of parameters on the spatial and temporal distribution of these diseases. 展开更多
关键词 reaction-convection-diffusion model immunity waning and multiple routes of transmission basic reproduction number stability and persistence
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Multiple model tracking algorithms based on neural network and multiple process noise soft switching 认领 引用 被引量：2
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作者 NieXiaohua 《Journal of Systems Engineering and Electronics》 SCIE EI 2009年第6期1227-1232,共6页
A multiple model tracking algorithm based on neural network and multiple-process noise soft-switching for maneuvering targets is presented.In this algorithm, the＂current＂statistical model and neural network are runn... A multiple model tracking algorithm based on neural network and multiple-process noise soft-switching for maneuvering targets is presented.In this algorithm, the＂current＂statistical model and neural network are running in parallel.The neural network algorithm is used to modify the adaptive noise filtering algorithm based on the mean value and variance of the＂current＂statistical model for maneuvering targets, and then the multiple model tracking algorithm of the multiple processing switch is used to improve the precision of tracking maneuvering targets.The modified algorithm is proved to be effective by simulation. 展开更多
关键词 maneuvering target current statistical model neural network multiple model algorithm.
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Spatial heterogeneity of groundwater depths in coastal cities and their responses to multiple factors interactions by interpretable machine learning models 认领 引用 被引量：2
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作者 Yuming Mo Jing Xu +5 位作者 Senlin Zhu Beibei Xu Jinran Wu Guangqiu Jin You-Gan Wang Ling Li 《Geoscience Frontiers》 SCIE CAS CSCD 2025年第3期223-241,共19页
Understanding spatial heterogeneity in groundwater responses to multiple factors is critical for water resource management in coastal cities.Daily groundwater depth(GWD)data from 43 wells(2018-2022)were collected in t... Understanding spatial heterogeneity in groundwater responses to multiple factors is critical for water resource management in coastal cities.Daily groundwater depth(GWD)data from 43 wells(2018-2022)were collected in three coastal cities in Jiangsu Province,China.Seasonal and Trend decomposition using Loess(STL)together with wavelet analysis and empirical mode decomposition were applied to identify tide-influenced wells while remaining wells were grouped by hierarchical clustering analysis(HCA).Machine learning models were developed to predict GWD,then their response to natural conditions and human activities was assessed by the Shapley Additive exPlanations(SHAP)method.Results showed that eXtreme Gradient Boosting(XGB)was superior to other models in terms of prediction performance and computational efficiency(R2>0.95).GWD in Yancheng and southern Lianyungang were greater than those in Nantong,exhibiting larger fluctuations.Groundwater within 5 km of the coastline was affected by tides,with more pronounced effects in agricultural areas compared to urban areas.Shallow groundwater(3-7 m depth)responded immediately(0-1 day)to rainfall,primarily influenced by farmland and topography(slope and distance from rivers).Rainfall recharge to groundwater peaked at 50%farmland coverage,but this effect was suppressed by high temperatures(>30℃)which intensified as distance from rivers increased,especially in forest and grassland.Deep groundwater(>10 m)showed delayed responses to rainfall(1-4 days)and temperature(10-15 days),with GDP as the primary influence,followed by agricultural irrigation and population density.Farmland helped to maintain stable GWD in low population density regions,while excessive farmland coverage(>90%)led to overexploitation.In the early stages of GDP development,increased industrial and agricultural water demand led to GWD decline,but as GDP levels significantly improved,groundwater consumption pressure gradually eased.This methodological framework is applicable not only to coastal cities in China but also could be extended to coastal regions worldwide. 展开更多
关键词 Groundwater depth Spatial heterogeneity Multiple influence factorsCoastal cities Machine Learning models SHAP values
A novel heuristic pathfinding algorithm for 3D security modeling and vulnerability assessment 认领 引用 被引量：1
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作者 Jun Yang Yue-Ming Hong +2 位作者 Yu-Ming Lv Hao-Ming Ma Wen-Lin Wang 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2025年第5期152-166,共15页
Vulnerability assessment is a systematic process to identify security gaps in the design and evaluation of physical protection systems.Adversarial path planning is a widely used method for identifying potential vulner... Vulnerability assessment is a systematic process to identify security gaps in the design and evaluation of physical protection systems.Adversarial path planning is a widely used method for identifying potential vulnerabilities and threats to the security and resilience of critical infrastructures.However,achieving efficient path optimization in complex large-scale three-dimensional(3D)scenes remains a significant challenge for vulnerability assessment.This paper introduces a novel A*-algorithmic framework for 3D security modeling and vulnerability assessment.Within this framework,the 3D facility models were first developed in 3ds Max and then incorporated into Unity for A*heuristic pathfinding.The A*-heuristic pathfinding algorithm was implemented with a geometric probability model to refine the detection and distance fields and achieve a rational approximation of the cost to reach the goal.An admissible heuristic is ensured by incorporating the minimum probability of detection(PDmin)and diagonal distance to estimate the heuristic function.The 3D A*heuristic search was demonstrated using a hypothetical laboratory facility,where a comparison was also carried out between the A*and Dijkstra algorithms for optimal path identification.Comparative results indicate that the proposed A*-heuristic algorithm effectively identifies the most vulnerable adversarial pathfinding with high efficiency.Finally,the paper discusses hidden phenomena and open issues in efficient 3D pathfinding for security applications. 展开更多
关键词 Physical protection system 3D modeling and simulation Vulnerability assessment A*Heuristic Pathfinding Dijkstra algorithm
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Large Language Models for Effective Detection of Algorithmically Generated Domains:A Comprehensive Review 认领 引用 被引量：1
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作者 Hamed Alqahtani Gulshan Kumar 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第8期1439-1479,共41页
Domain Generation Algorithms(DGAs)continue to pose a significant threat inmodernmalware infrastructures by enabling resilient and evasive communication with Command and Control(C&C)servers.Traditional detection me... Domain Generation Algorithms(DGAs)continue to pose a significant threat inmodernmalware infrastructures by enabling resilient and evasive communication with Command and Control(C&C)servers.Traditional detection methods-rooted in statistical heuristics,feature engineering,and shallow machine learning-struggle to adapt to the increasing sophistication,linguistic mimicry,and adversarial variability of DGA variants.The emergence of Large Language Models(LLMs)marks a transformative shift in this landscape.Leveraging deep contextual understanding,semantic generalization,and few-shot learning capabilities,LLMs such as BERT,GPT,and T5 have shown promising results in detecting both character-based and dictionary-based DGAs,including previously unseen(zeroday)variants.This paper provides a comprehensive and critical review of LLM-driven DGA detection,introducing a structured taxonomy of LLM architectures,evaluating the linguistic and behavioral properties of benchmark datasets,and comparing recent detection frameworks across accuracy,latency,robustness,and multilingual performance.We also highlight key limitations,including challenges in adversarial resilience,model interpretability,deployment scalability,and privacy risks.To address these gaps,we present a forward-looking research roadmap encompassing adversarial training,model compression,cross-lingual benchmarking,and real-time integration with SIEM/SOAR platforms.This survey aims to serve as a foundational resource for advancing the development of scalable,explainable,and operationally viable LLM-based DGA detection systems. 展开更多
关键词 Adversarial domains cyber threat detection domain generation algorithms large language models machine learning security
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Interacting Multiple Model Algorithm with the Unscented Particle Filter (UPF) 认领 引用 被引量：9
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作者 邓小龙 谢剑英 倪宏伟 《Chinese Journal of Aeronautics》 EI CAS 2005年第4期366-371,共6页
Combining interacting multiple model （IMM） and unscented particle filter （UPF）, a new multiple model filtering algorithm is presented. Multiple models can be adapted to targets＇ high maneu- vering. Particle filte... Combining interacting multiple model （IMM） and unscented particle filter （UPF）, a new multiple model filtering algorithm is presented. Multiple models can be adapted to targets＇ high maneu- vering. Particle filter can be used to deal with the nonlinear or non-Gaussian problems and the unscented Kalman filter （UKF） can improve the approximate accuracy. Compared with other interacting multiple model algorithms in the simulations, the results demonstrate the validity of the new filtering method. 展开更多
关键词 interacting multiple model UPF UKF nonlinearon-Gaussian
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Hierarchical interacting multiple model algorithm based on improved current model 认领 引用 被引量：6
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作者 Xianghua Wang Xinyu Yang +1 位作者 Zheng Qin Huijie Yang 《Journal of Systems Engineering and Electronics》 SCIE EI 2010年第6期961-967,共7页
Interacting multiple models is the hotspot in the research of maneuvering target models at present. A hierarchical idea is introduced into IMM algorithm. The method is that the whole models are organized as two levels... Interacting multiple models is the hotspot in the research of maneuvering target models at present. A hierarchical idea is introduced into IMM algorithm. The method is that the whole models are organized as two levels to co-work, and each cell model is an improved ＂current＂ statistical model. In the improved model, a kind of nonlinear fuzzy membership function is presented to get over the limitation of original model, which can not track weak maneuvering target precisely. At last, simulation experiments prove the efficient of the novel algorithm compared to interacting multiple model and hierarchical interacting multiple model based original ＂current＂ statistical model in tracking precision. 展开更多
关键词 target tracking ＂current＂ statistical model multiple model hierarchical.
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Adaptive Multi-Learning Cooperation Search Algorithm for Photovoltaic Model Parameter Identification 认领 引用
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作者 Xu Chen Shuai Wang Kaixun He 《Computers, Materials & Continua》 SCIE EI 2025年第10期1779-1806,共28页
Accurate and reliable photovoltaic(PV)modeling is crucial for the performance evaluation,control,and optimization of PV systems.However,existing methods for PV parameter identification often suffer from limitations in... Accurate and reliable photovoltaic(PV)modeling is crucial for the performance evaluation,control,and optimization of PV systems.However,existing methods for PV parameter identification often suffer from limitations in accuracy and efficiency.To address these challenges,we propose an adaptive multi-learning cooperation search algorithm(AMLCSA)for efficient identification of unknown parameters in PV models.AMLCSA is a novel algorithm inspired by teamwork behaviors in modern enterprises.It enhances the original cooperation search algorithm in two key aspects:(i)an adaptive multi-learning strategy that dynamically adjusts search ranges using adaptive weights,allowing better individuals to focus on local exploitation while guiding poorer individuals toward global exploration;and(ii)a chaotic grouping reflection strategy that introduces chaotic sequences to enhance population diversity and improve search performance.The effectiveness of AMLCSA is demonstrated on single-diode,double-diode,and three PV-module models.Simulation results show that AMLCSA offers significant advantages in convergence,accuracy,and stability compared to existing state-of-the-art algorithms. 展开更多
关键词 Photovoltaic model parameter identification cooperation search algorithm adaptive multiple learning chaotic grouping reflection
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Evolutionary Algorithm Based on Surrogate and Inverse Surrogate Models for Expensive Multiobjective Optimization 认领 引用
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作者 Qi Deng Qi Kang +4 位作者 MengChu Zhou Xiaoling Wang Shibing Zhao Siqi Wu Mohammadhossein Ghahramani 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第5期961-973,共13页
When dealing with expensive multiobjective optimization problems,majority of existing surrogate-assisted evolutionary algorithms(SAEAs)generate solutions in decision space and screen candidate solutions mostly by usin... When dealing with expensive multiobjective optimization problems,majority of existing surrogate-assisted evolutionary algorithms(SAEAs)generate solutions in decision space and screen candidate solutions mostly by using designed surrogate models.The generated solutions exhibit excessive randomness,which tends to reduce the likelihood of generating good-quality solutions and cause a long evolution to the optima.To improve SAEAs greatly,this work proposes an evolutionary algorithm based on surrogate and inverse surrogate models by 1)Employing a surrogate model in lieu of expensive(true)function evaluations;and 2)Proposing and using an inverse surrogate model to generate new solutions.By using the same training data but with its inputs and outputs being reversed,the latter is simple to train.It is then used to generate new vectors in objective space,which are mapped into decision space to obtain their corresponding solutions.Using a particular example,this work shows its advantages over existing SAEAs.The results of comparing it with state-of-the-art algorithms on expensive optimization problems show that it is highly competitive in both solution performance and efficiency. 展开更多
关键词 Expensives multi-objective optimization reverse model surrogate-assisted evolutionary algorithms(SAEAs)
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Geophysics-informed stratigraphic modeling using spatial sequential Bayesian updating algorithm 认领 引用
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作者 Wei Yan Shouyong Yi +3 位作者 Taosheng Huang Jie Zou Wan-Huan Zhou Ping Shen 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第7期4400-4412,共13页
Challenges in stratigraphic modeling arise from underground uncertainty.While borehole exploration is reliable,it remains sparse due to economic and site constraints.Electrical resistivity tomography(ERT)as a cost-eff... Challenges in stratigraphic modeling arise from underground uncertainty.While borehole exploration is reliable,it remains sparse due to economic and site constraints.Electrical resistivity tomography(ERT)as a cost-effective geophysical technique can acquire high-density data;however,uncertainty and nonuniqueness inherent in ERT impede its usage for stratigraphy identification.This paper integrates ERT and onsite observations for the first time to propose a novel method for characterizing stratigraphic profiles.The method consists of two steps:(1)ERT for prior knowledge:ERT data are processed by soft clustering using the Gaussian mixture model,followed by probability smoothing to quantify its depthdependent uncertainty;and(2)Observations for calibration:a spatial sequential Bayesian updating(SSBU)algorithm is developed to update the prior knowledge based on likelihoods derived from onsite observations,namely topsoil and boreholes.The effectiveness of the proposed method is validated through its application to a real slope site in Foshan,China.Comparative analysis with advanced borehole-driven methods highlights the superiority of incorporating ERT data in stratigraphic modeling,in terms of prediction accuracy at borehole locations and sensitivity to borehole data.Informed by ERT,reduced sensitivity to boreholes provides a fundamental solution to the longstanding challenge of sparse measurements.The paper further discusses the impact of ERT uncertainty on the proposed model using time-lapse measurements,the impact of model resolution,and applicability in engineering projects.This study,as a breakthrough in stratigraphic modeling,bridges gaps in combining geophysical and geotechnical data to address measurement sparsity and paves the way for more economical geotechnical exploration. 展开更多
关键词 Stratigraphic modeling Electrical resistivity tomography(ERT) Site characterization Spatial sequential Bayesian updating(SSBU)algorithm Sparse measurements
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