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Landslide susceptibility mapping(LSM)based on different boosting and hyperparameter optimization algorithms:A case of Wanzhou District,China 认领 引用 被引量：6
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作者 Deliang Sun Jing Wang +2 位作者 Haijia Wen YueKai Ding Changlin Mi 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2024年第8期3221-3232,共12页
Boosting algorithms have been widely utilized in the development of landslide susceptibility mapping(LSM)studies.However,these algorithms possess distinct computational strategies and hyperparameters,making it challen... Boosting algorithms have been widely utilized in the development of landslide susceptibility mapping(LSM)studies.However,these algorithms possess distinct computational strategies and hyperparameters,making it challenging to propose an ideal LSM model.To investigate the impact of different boosting algorithms and hyperparameter optimization algorithms on LSM,this study constructed a geospatial database comprising 12 conditioning factors,such as elevation,stratum,and annual average rainfall.The XGBoost(XGB),LightGBM(LGBM),and CatBoost(CB)algorithms were employed to construct the LSM model.Furthermore,the Bayesian optimization(BO),particle swarm optimization(PSO),and Hyperband optimization(HO)algorithms were applied to optimizing the LSM model.The boosting algorithms exhibited varying performances,with CB demonstrating the highest precision,followed by LGBM,and XGB showing poorer precision.Additionally,the hyperparameter optimization algorithms displayed different performances,with HO outperforming PSO and BO showing poorer performance.The HO-CB model achieved the highest precision,boasting an accuracy of 0.764,an F1-score of 0.777,an area under the curve(AUC)value of 0.837 for the training set,and an AUC value of 0.863 for the test set.The model was interpreted using SHapley Additive exPlanations(SHAP),revealing that slope,curvature,topographic wetness index(TWI),degree of relief,and elevation significantly influenced landslides in the study area.This study offers a scientific reference for LSM and disaster prevention research.This study examines the utilization of various boosting algorithms and hyperparameter optimization algorithms in Wanzhou District.It proposes the HO-CB-SHAP framework as an effective approach to accurately forecast landslide disasters and interpret LSM models.However,limitations exist concerning the generalizability of the model and the data processing,which require further exploration in subsequent studies. 展开更多
关键词 Landslide susceptibility Hyperparameter optimization Boosting algorithms SHapley additive exPlanations(SHAP)
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Hyperparameter Optimization for Capsule Network Based Modified Hybrid Rice Optimization Algorithm 认领 引用
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作者 Zhiwei Ye Ziqian Fang +4 位作者 Zhina Song Haigang Sui Chunyan Yan Wen Zhou Mingwei Wang 《Intelligent Automation & Soft Computing》 SCIE 2023年第8期2019-2035,共17页
Hyperparameters play a vital impact in the performance of most machine learning algorithms.It is a challenge for traditional methods to con-figure hyperparameters of the capsule network to obtain high-performance manu... Hyperparameters play a vital impact in the performance of most machine learning algorithms.It is a challenge for traditional methods to con-figure hyperparameters of the capsule network to obtain high-performance manually.Some swarm intelligence or evolutionary computation algorithms have been effectively employed to seek optimal hyperparameters as a com-binatorial optimization problem.However,these algorithms are prone to get trapped in the local optimal solution as random search strategies are adopted.The inspiration for the hybrid rice optimization(HRO)algorithm is from the breeding technology of three-line hybrid rice in China,which has the advantages of easy implementation,less parameters and fast convergence.In the paper,genetic search is combined with the hybrid rice optimization algorithm(GHRO)and employed to obtain the optimal hyperparameter of the capsule network automatically,that is,a probability search technique and a hybridization strategy belong with the primary HRO.Thirteen benchmark functions are used to evaluate the performance of GHRO.Furthermore,the MNIST,Chest X-Ray(pneumonia),and Chest X-Ray(COVID-19&pneumonia)datasets are also utilized to evaluate the capsule network learnt by GHRO.The experimental results show that GHRO is an effective method for optimizing the hyperparameters of the capsule network,which is able to boost the performance of the capsule network on image classification. 展开更多
关键词 Hyperparameter optimization hybrid rice optimization algorithm genetic algorithm capsule network image classification
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Neural network hyperparameter optimization based on improved particle swarm optimization 认领 引用
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作者 谢晓燕 HE Wanqi +1 位作者 ZHU Yun YU Jinhao 《High Technology Letters》 EI CAS 2023年第4期427-433,共7页
Hyperparameter optimization is considered as one of the most challenges in deep learning and dominates the precision of model in a certain.Recent proposals tried to solve this issue through the particle swarm optimiza... Hyperparameter optimization is considered as one of the most challenges in deep learning and dominates the precision of model in a certain.Recent proposals tried to solve this issue through the particle swarm optimization(PSO),but its native defect may result in the local optima trapped and convergence difficulty.In this paper,the genetic operations are introduced to the PSO,which makes the best hyperparameter combination scheme for specific network architecture be located easier.Spe-cifically,to prevent the troubles caused by the different data types and value scopes,a mixed coding method is used to ensure the effectiveness of particles.Moreover,the crossover and mutation opera-tions are added to the process of particles updating,to increase the diversity of particles and avoid local optima in searching.Verified with three benchmark datasets,MNIST,Fashion-MNIST,and CIFAR10,it is demonstrated that the proposed scheme can achieve accuracies of 99.58%,93.39%,and 78.96%,respectively,improving the accuracy by about 0.1%,0.5%,and 2%,respectively,compared with that of the PSO. 展开更多
关键词 hyperparameter optimization particle swarm optimization(PSO)algorithm neu-ral network
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Improving Prediction Efficiency of Machine Learning Models for Cardiovascular Disease in IoST-Based Systems through Hyperparameter Optimization 认领 引用 被引量：1
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作者 Tajim Md.Niamat Ullah Akhund Waleed M.Al-Nuwaiser 《Computers, Materials & Continua》 SCIE EI 2024年第9期3485-3506,共22页
This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)device.Ten distinct machine learning ap... This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)device.Ten distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter tuning.Significant improvements were observed across various models,with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score,recall,and precision.The study underscores the critical role of tailored hyperparameter tuning in optimizing these models,revealing diverse outcomes among algorithms.Decision Trees and Random Forests exhibited stable performance throughout the evaluation.While enhancing accuracy,hyperparameter optimization also led to increased execution time.Visual representations and comprehensive results support the findings,confirming the hypothesis that optimizing parameters can effectively enhance predictive capabilities in cardiovascular disease.This research contributes to advancing the understanding and application of machine learning in healthcare,particularly in improving predictive accuracy for cardiovascular disease management and intervention strategies. 展开更多
关键词 Internet of sensing things(IoST) machine learning hyperparameter optimization cardiovascular disease prediction execution time analysis performance analysis wilcoxon signed-rank test
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Hyperparameter optimization for cardiovascular disease data-driven prognostic system 认领 引用
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作者 Jayson Saputra Cindy Lawrencya +1 位作者 Jecky Mitra Saini Suharjito Suharjito 《Visual Computing for Industry,Biomedicine,and Art》 EI 2023年第1期218-244,共27页
Prediction and diagnosis of cardiovascular diseases(CVDs)based,among other things,on medical examinations and patient symptoms are the biggest challenges in medicine.About 17.9 million people die from CVDs annually,ac... Prediction and diagnosis of cardiovascular diseases(CVDs)based,among other things,on medical examinations and patient symptoms are the biggest challenges in medicine.About 17.9 million people die from CVDs annually,accounting for 31%of all deaths worldwide.With a timely prognosis and thorough consideration of the patient’s medical history and lifestyle,it is possible to predict CVDs and take preventive measures to eliminate or control this life-threatening disease.In this study,we used various patient datasets from a major hospital in the United States as prognostic factors for CVD.The data was obtained by monitoring a total of 918 patients whose criteria for adults were 28-77 years old.In this study,we present a data mining modeling approach to analyze the performance,classification accuracy and number of clusters on Cardiovascular Disease Prognostic datasets in unsupervised machine learning(ML)using the Orange data mining software.Various techniques are then used to classify the model parameters,such as k-nearest neighbors,support vector machine,random forest,artificial neural network(ANN),naïve bayes,logistic regression,stochastic gradient descent(SGD),and AdaBoost.To determine the number of clusters,various unsupervised ML clustering methods were used,such as k-means,hierarchical,and density-based spatial clustering of applications with noise clustering.The results showed that the best model performance analysis and classification accuracy were SGD and ANN,both of which had a high score of 0.900 on Cardiovascular Disease Prognostic datasets.Based on the results of most clustering methods,such as k-means and hierarchical clustering,Cardiovascular Disease Prognostic datasets can be divided into two clusters.The prognostic accuracy of CVD depends on the accuracy of the proposed model in determining the diagnostic model.The more accurate the model,the better it can predict which patients are at risk for CVD. 展开更多
关键词 Cardiovascular disease Data-driven analytics Data mining Hyperparameter optimization Orange data mining software Prognostic system Unsupervised machine learning
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Real-Time Optimization of Vertical Roller Mills Using XGBoost Prediction and Q-Learning Control 认领 引用
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作者 Anping Wan Yingchang Gao +2 位作者 Weikang Liu Rui Yin Khalil Al-Bukhaiti 《Computers, Materials & Continua》 SCIE EI 2026年第8期1515-1534,共20页
Vertical roller mills are essential for energy-intensive grinding in cement,minerals,and metallurgy industries,consuming up to 50%of plant electricity and frequently experiencing operational instabilities(including ex... Vertical roller mills are essential for energy-intensive grinding in cement,minerals,and metallurgy industries,consuming up to 50%of plant electricity and frequently experiencing operational instabilities(including excessive vibration and main motor current fluctuations)that drive unplanned downtime,increased wear,and reduced throughput.Despite their importance,real-time autonomous optimization remains challenging due to the nonlinear interactions among grinding pressure,feed rate,separator speed,and aerodynamic factors,which limit traditional control strategies under varying loads.This paper presents a real-time operational optimization system for large-scale vertical roller mills using big industrial data and artificial intelligence(AI).From a 5400 kW Loesche LM56.4 mill,2,764,800 samples were collected at 1 Hz over 32 days of continuous production.A systematic pipeline was developed:quartile-based outlier-robust cleaning;domain-informed feature engineering including Total Current;Random Forest(RF)permutation importance selection of the top 15 parameters;and Extreme Gradient Boosting(XGBoost)regression models with hyperparameters tuned by Tree-structured Parzen Estimator(TPE)Bayesian optimization.The resulting models achieved strong predictive performance,Mean Absolute Percentage Error(MAPE)of 1.3%(95%CI:1.1%–1.5%)for main motor current(R2=0.9997)and 5.8%(95%CI:5.3%–6.3%)for shell vibration(R2=0.9717),representing reductions of 89%and 59%,respectively,relative to the Long Short-Term Memory(LSTM)baseline.These surrogates were embedded into a tabular Q-learning Reinforcement Learning(RL)agent that autonomously adjusts feed rate,grinding pressure,separator speed,and exhaust damper position via a discrete action space and multi-objective reward function,communicating with the Distributed Control System(DCS)via Open Platform Communications Unified Architecture(OPC-UA).Closed-loop evaluation yielded simultaneous reductions of 6.0%in peak current(181.92→170.04 A)and 9.4%in peak vibration(5.51→4.99 mm/s)while maintaining throughput.A PyQt5-based graphical interface enabling real-time monitoring,predictive alerts,and automatic DCS write-back was deployed and operated stably for two weeks. 展开更多
关键词 Vertical roller mill operational optimization XGBoost Bayesian hyperparameter optimization Q-learning energy efficiency vibration reduction real-time control Industry 4.0
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Automated reinforcement learning for sequential ordering problem using hyperparameter optimization and metalearning 认领 引用
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作者 Andre Luiz Carvalho Ottoni 《Autonomous Intelligent Systems》 EI 2025年第1期177-190,共14页
AutoML systems seek to assist Artificial Intelligence users in finding the best configurations for machine learning models.Following this line,recently the area of Automated Reinforcement Learning(AutoRL)has become in... AutoML systems seek to assist Artificial Intelligence users in finding the best configurations for machine learning models.Following this line,recently the area of Automated Reinforcement Learning(AutoRL)has become increasingly relevant,given the growing increase in applications for reinforcement learning algorithms.However,the literature still lacks specific AutoRL systems for combinatorial optimization,especially for the Sequential Ordering Problem(SOP).Therefore,this paper aims to present a new AutoRL approach for SOP.For this,two new methods are proposed using hyperparameter optimization and metalearning:AutoRL-SOP and AutoRL-SOP-MtL.The proposed AutoRL techniques enable the combined tuning of three SARSA hyperparameters,beingϵ-greedy policy,learning rate,and discount factor.Furthermore,the new metalearning approach enables the transfer of hyperparameters between two combinatorial optimization domains:TSP(source)and SOP(target).The results show that the application of metalearning generates a reduction in computational cost in hyperparameter optimization.Furthermore,the proposed AutoRL methods achieved the best solutions in 23 out of 28 simulated TSPLIB instances compared to recent literature studies. 展开更多
关键词 Reinforcement Learning AutoML Sequential Ordering Problem Hyperparameter Optimization Metalearning
Conformal Prediction for Reliable Hyperparameter Selection in Sparse FIR Filter Design 认领 引用
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作者 Mohammed Hassan Alnemari Abdelrahman Osman Elfaki +1 位作者 Anas Bushnag Mohamed Hussien Mohamed Nerma 《Computers, Materials & Continua》 SCIE EI 2026年第9期1358-1382,共25页
Sparse finite impulse response(FIR)filters reduce computational cost on resource-constrained devices,but selecting the sparsification thresholdλis typically left to grid search or hand tuning.We propose a two-stage m... Sparse finite impulse response(FIR)filters reduce computational cost on resource-constrained devices,but selecting the sparsification thresholdλis typically left to grid search or hand tuning.We propose a two-stage method:a 67,331-parameter surrogate network predicts(Ap,As,S)(passband ripple in dB,stopband attenuation in dB,sparsity in%)from a filter specification and a candidate λ,and split conformal prediction(CP)calibrates±intervals around each prediction.We then select λ by minimizing a worst-case penalty computed on the conservative ends of the intervals(the upper bound on Ap and the lower bound on As).On 10,000 test specifications the method reaches 76.5%specification satisfaction,near-parity with grid search(78.4%)with a 1.9× speedup,while point-prediction surrogates reach only 39.4%.On feasible specifications(where any grid λ satisfies both constraints),the method reaches 97.6%.Stratified(Mondrian)conformal prediction lifts standard CP coverage from 67%-75%to 95.5%,and adaptive recalibration brings passband coverage to 91.3%.The procedure transfers without modification to iteratively reweighted least squares(IRLS)sparsification(76.6%)and to highpass(79.2%)and bandpass(52.4%)filters.The implementation runs on a central processing unit(CPU)and is suitable for edge deployment;code and data are public. 展开更多
关键词 Conformal prediction sparse FIR filter edge IoT surrogate modeling uncertainty quantification hyperparameter optimization
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A benchmark-based method for evaluating hyperparameter optimization techniques of neural networks for surface water quality prediction 认领 引用 被引量：1
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作者 Xuan Wang Yan Dong +2 位作者 Jing Yang Zhipeng Liu Jinsuo Lu 《Frontiers of Environmental Science & Engineering》 SCIE EI CSCD 2024年第5期13-27,共15页
Neural networks(NNs)have been used extensively in surface water prediction tasks due to computing algorithm improvements and data accumulation.An essential step in developing an NN is the hyperparameter selection.In p... Neural networks(NNs)have been used extensively in surface water prediction tasks due to computing algorithm improvements and data accumulation.An essential step in developing an NN is the hyperparameter selection.In practice,it is common to manually determine hyperparameters in the studies of NNs in water resources tasks.This may result in considerable randomness and require significant computation time;therefore,hyperparameter optimization(HPO)is essential.This study adopted five representatives of the HPO techniques in the surface water quality prediction tasks,including the grid sampling(GS),random search(RS),genetic algorithm(GA),Bayesian optimization(BO)based on the Gaussian process(GP),and the tree Parzen estimator(TPE).For the evaluation of these techniques,this study proposed a method:first,the optimal hyperparameter value sets achieved by GS were regarded as the benchmark;then,the other HPO techniques were evaluated and compared with the benchmark in convergence,optimization orientation,and consistency of the optimized values.The results indicated that the TPE-based BO algorithm was recommended because it yielded stable convergence,reasonable optimization orientation,and the highest consistency rates with the benchmark values.The optimization consistency rates via TPE for the hyperparameters hidden layers,hidden dimension,learning rate,and batch size were 86.7%,73.3%,73.3%,and 80.0%,respectively.Unlike the evaluation of HPO techniques directly based on the prediction performance of the optimized NN in a single HPO test,the proposed benchmark-based HPO evaluation approach is feasible and robust. 展开更多
关键词 Neural networks Hyperparameter optimization Surface water quality prediction Bayes optimization Genetic algorithm
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Enhancing Fire Detection with YOLO Models:A Bayesian Hyperparameter Tuning Approach 认领 引用
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作者 Van-Ha Hoang Jong Weon Lee Chun-Su Park 《Computers, Materials & Continua》 SCIE EI 2025年第6期4097-4116,共20页
Fire can cause significant damage to the environment,economy,and human lives.If fire can be detected early,the damage can be minimized.Advances in technology,particularly in computer vision powered by deep learning,ha... Fire can cause significant damage to the environment,economy,and human lives.If fire can be detected early,the damage can be minimized.Advances in technology,particularly in computer vision powered by deep learning,have enabled automated fire detection in images and videos.Several deep learning models have been developed for object detection,including applications in fire and smoke detection.This study focuses on optimizing the training hyperparameters of YOLOv8 andYOLOv10models usingBayesianTuning(BT).Experimental results on the large-scale D-Fire dataset demonstrate that this approach enhances detection performance.Specifically,the proposed approach improves the mean average precision at an Intersection over Union(IoU)threshold of 0.5(mAP50)of the YOLOv8s,YOLOv10s,YOLOv8l,and YOLOv10lmodels by 0.26,0.21,0.84,and 0.63,respectively,compared tomodels trainedwith the default hyperparameters.The performance gains are more pronounced in larger models,YOLOv8l and YOLOv10l,than in their smaller counterparts,YOLOv8s and YOLOv10s.Furthermore,YOLOv8 models consistently outperform YOLOv10,with mAP50 improvements of 0.26 for YOLOv8s over YOLOv10s and 0.65 for YOLOv8l over YOLOv10l when trained with BT.These results establish YOLOv8 as the preferred model for fire detection applications where detection performance is prioritized. 展开更多
关键词 Fire detection smoke detection deep learning YOLO Bayesian hyperparameter tuning hyperparameter optimization Optuna
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Particle Swarm Optimization-Based Hyperparameters Tuning of Machine Learning Models for Big COVID-19 Data Analysis 认领 引用
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作者 Hend S. Salem Mohamed A. Mead Ghada S. El-Taweel 《Journal of Computer and Communications》 2024年第3期160-183,共24页
Analyzing big data, especially medical data, helps to provide good health care to patients and face the risks of death. The COVID-19 pandemic has had a significant impact on public health worldwide, emphasizing the ne... Analyzing big data, especially medical data, helps to provide good health care to patients and face the risks of death. The COVID-19 pandemic has had a significant impact on public health worldwide, emphasizing the need for effective risk prediction models. Machine learning (ML) techniques have shown promise in analyzing complex data patterns and predicting disease outcomes. The accuracy of these techniques is greatly affected by changing their parameters. Hyperparameter optimization plays a crucial role in improving model performance. In this work, the Particle Swarm Optimization (PSO) algorithm was used to effectively search the hyperparameter space and improve the predictive power of the machine learning models by identifying the optimal hyperparameters that can provide the highest accuracy. A dataset with a variety of clinical and epidemiological characteristics linked to COVID-19 cases was used in this study. Various machine learning models, including Random Forests, Decision Trees, Support Vector Machines, and Neural Networks, were utilized to capture the complex relationships present in the data. To evaluate the predictive performance of the models, the accuracy metric was employed. The experimental findings showed that the suggested method of estimating COVID-19 risk is effective. When compared to baseline models, the optimized machine learning models performed better and produced better results. 展开更多
关键词 Big COVID-19 Data Machine Learning Hyperparameter Optimization Particle Swarm Optimization Computational Intelligence
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Developing river water quality prediction model incorporating reliable indexing approach 认领 引用
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作者 Agnieszka I.Olbert Mir Talas Mahammad Diganta +6 位作者 Apoorva Bamal William Burke Abdul Majed Sajib Mohamed Abioui S.M.Ashekuzzaman Azizur Rahman Md Galal Uddin 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2026年第6期581-598,共18页
Hyperparameter optimization techniques can influence the prediction model(s)capabilities in terms of delivering reliable results.As a part of the development of the data-driven water quality(WQ)model(s),this research ... Hyperparameter optimization techniques can influence the prediction model(s)capabilities in terms of delivering reliable results.As a part of the development of the data-driven water quality(WQ)model(s),this research evaluated five hyperparameter optimization techniques and their impacts on WQ prediction model(s)using machine learning(ML)/artificial intelligence(AI)techniques to predict the WQI scores through the root mean squared(RMS)-WQI approach.For developing the ML/AI models the research utilized the ten ML algorithms by comparing fifty models.To evaluate the ML-AI model(s),the study used five widely used metrics including Root Mean Squared Error(RMSE),Mean Squared Error(MSE),Mean Absolute Error(MAE),Percentage of Absolute Bias Error(PABE),Nash Sutcliffe Efficiency(NSE)while the coefficient of determination(R2)utilized for sensitivity assessment.The results indicated that the gradient boosting(GBR)model with the OPTUNA(OPT)optimization technique outperformed others in predicting WQI score during both training(RMSE=0.84,MSE=0.71,MAE=0.74,PABE=1.00)and testing(RMSE=0.45,MSE=0.20,MAE=0.30,PABE=0.41)phase.Additionally,the study also revealed that the GBR-OPT demonstrated higher sensitivity(R2for the year 2021=0.99 and testing R2for the year 2022=0.98)and the highest efficiency(an average NSE for the year 2021=0.71 and an average NSE for the year 2022=0.60)compared to other models.Overall,the findings of the research reveal that the study outcomes could be effective in developing more efficient and accurate WQ prediction model(s)that would be helpful for sustainable WQ management. 展开更多
关键词 Artificial intelligence Machine learning Water quality index model Artificial intelligence in environmental science Hyperparameter optimization technique
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Rockburst prediction based on data preprocessing and hyperband-RNN-DNN 认领 引用
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作者 Yong Fan Chenxi Yin +2 位作者 Guangdong Yang Shengyong Ding Bin Tian 《Deep Underground Science and Engineering》 EI CAS CSCD 2026年第2期446-464,共19页
A data preprocessing workflow is proposed to address key challenges in rockburst data analysis,including dimensionality differences among various sample features,variations in data values within the same feature,missi... A data preprocessing workflow is proposed to address key challenges in rockburst data analysis,including dimensionality differences among various sample features,variations in data values within the same feature,missing data,poor data consistency,and sample class imbalance.The workflow is divided into four steps.Each step introduces multiple algorithms,which are sequentially combined according to the order of the four steps.Then,these coupled algorithms are utilized to preprocess the rockburst data set.The rockburst data set contains 459 samples,and the maximum tangential stress(MTS),the uniaxial compressive strength(UCS),the uniaxial tensile strength(UTS),the elastic strain energy index(WET),the rock stress factor(SCF),and the rock brittleness coefficient(B)are selected as the feature parameters.Subsequently,three architectures,Deep Neural Network(DNN),Convolutional Neural Network(CNN),and Recurrent Neural Network(RNN),are used to evaluate the data sets processed by different coupled algorithms.The hyperband algorithm is introduced to optimize the hyperparameters of the RNN model,and the prediction accuracy of different architectures is compared between the RNN model with dense layers and without dense layers.Finally,a rockburst prediction model based on data preprocessing and the Hyperband-DNN model is developed.The prediction results show that data preprocessing can significantly improve the model prediction accuracy;the model architecture with the highest prediction accuracy can be found quickly using the hyperband algorithm;and adding the dense layer can improve the stability and prediction accuracy of the model. 展开更多
关键词 data preprocessing hyperband algorithm hyperparameter optimization RNN-DNN rockburst prediction
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Hyperparameter Tuned Deep Learning Enabled Cyberbullying Classification in Social Media 认领 引用 被引量：1
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作者 Mesfer Al Duhayyim Heba G.Mohamed +5 位作者 Saud S.Alotaibi Hany Mahgoub Abdullah Mohamed Abdelwahed Motwakel Abu Sarwar Zamani Mohamed I.Eldesouki 《Computers, Materials & Continua》 SCIE EI 2022年第12期5011-5024,共14页
Cyberbullying(CB)is a challenging issue in social media and it becomes important to effectively identify the occurrence of CB.The recently developed deep learning(DL)models pave the way to design CB classifier models ... Cyberbullying(CB)is a challenging issue in social media and it becomes important to effectively identify the occurrence of CB.The recently developed deep learning(DL)models pave the way to design CB classifier models with maximum performance.At the same time,optimal hyperparameter tuning process plays a vital role to enhance overall results.This study introduces a Teacher Learning Genetic Optimization with Deep Learning Enabled Cyberbullying Classification(TLGODL-CBC)model in Social Media.The proposed TLGODL-CBC model intends to identify the existence and non-existence of CB in social media context.Initially,the input data is cleaned and pre-processed to make it compatible for further processing.Followed by,independent recurrent autoencoder(IRAE)model is utilized for the recognition and classification of CBs.Finally,the TLGO algorithm is used to optimally adjust the parameters related to the IRAE model and shows the novelty of the work.To assuring the improved outcomes of the TLGODLCBC approach,a wide range of simulations are executed and the outcomes are investigated under several aspects.The simulation outcomes make sure the improvements of the TLGODL-CBC model over recent approaches. 展开更多
关键词 Social media deep learning cyberbullying cybersecurity hyperparameter optimization
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Hunger Search Optimization with Hybrid Deep Learning Enabled Phishing Detection and Classification Model 认领 引用 被引量：1
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作者 Hadil Shaiba Jaber S.Alzahrani +3 位作者 Majdy M.Eltahir Radwa Marzouk Heba Mohsen Manar Ahmed Hamza 《Computers, Materials & Continua》 SCIE EI 2022年第12期6425-6441,共17页
Phishing is one of the simplest ways in cybercrime to hack the reliable data of users such as passwords,account identifiers,bank details,etc.In general,these kinds of cyberattacks are made at users through phone calls... Phishing is one of the simplest ways in cybercrime to hack the reliable data of users such as passwords,account identifiers,bank details,etc.In general,these kinds of cyberattacks are made at users through phone calls,emails,or instant messages.The anti-phishing techniques,currently under use,aremainly based on source code features that need to scrape the webpage content.In third party services,these techniques check the classification procedure of phishing Uniform Resource Locators(URLs).Even thoughMachine Learning(ML)techniques have been lately utilized in the identification of phishing,they still need to undergo feature engineering since the techniques are not well-versed in identifying phishing offenses.The tremendous growth and evolution of Deep Learning(DL)techniques paved the way for increasing the accuracy of classification process.In this background,the current research article presents a Hunger Search Optimization with Hybrid Deep Learning enabled Phishing Detection and Classification(HSOHDL-PDC)model.The presented HSOHDL-PDC model focuses on effective recognition and classification of phishing based on website URLs.In addition,SOHDL-PDC model uses character-level embedding instead of word-level embedding since the URLs generally utilize words with no importance.Moreover,a hybrid Convolutional Neural Network-Long Short Term Memory(HCNN-LSTM)technique is also applied for identification and classification of phishing.The hyperparameters involved in HCNN-LSTM model are optimized with the help of HSO algorithm which in turn produced improved outcomes.The performance of the proposed HSOHDL-PDC model was validated using different datasets and the outcomes confirmed the supremacy of the proposed model over other recent approaches. 展开更多
关键词 Uniform resource locators phishing cyberattacks machine learning deep learning hyperparameter optimization
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Jellyfish Search Optimization with Deep Learning Driven Autism Spectrum Disorder Classification 认领 引用
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作者 S.Rama Sree Inderjeet Kaur +5 位作者 Alexey Tikhonov E.Laxmi Lydia Ahmed A.Thabit Zahraa H.Kareem Yousif Kerrar Yousif Ahmed Alkhayyat 《Computers, Materials & Continua》 SCIE EI 2023年第1期2195-2209,共15页
Autism spectrum disorder(ASD)is regarded as a neurological disorder well-defined by a specific set of problems associated with social skills,recurrent conduct,and communication.Identifying ASD as soon as possible is f... Autism spectrum disorder(ASD)is regarded as a neurological disorder well-defined by a specific set of problems associated with social skills,recurrent conduct,and communication.Identifying ASD as soon as possible is favourable due to prior identification of ASD permits prompt interferences in children with ASD.Recognition of ASD related to objective pathogenicmutation screening is the initial step against prior intervention and efficient treatment of children who were affected.Nowadays,healthcare and machine learning(ML)industries are combined for determining the existence of various diseases.This article devises a Jellyfish Search Optimization with Deep Learning Driven ASD Detection and Classification(JSODL-ASDDC)model.The goal of the JSODL-ASDDC algorithm is to identify the different stages of ASD with the help of biomedical data.The proposed JSODLASDDC model initially performs min-max data normalization approach to scale the data into uniform range.In addition,the JSODL-ASDDC model involves JSO based feature selection(JFSO-FS)process to choose optimal feature subsets.Moreover,Gated Recurrent Unit(GRU)based classification model is utilized for the recognition and classification of ASD.Furthermore,the Bacterial Foraging Optimization(BFO)assisted parameter tuning process gets executed to enhance the efficacy of the GRU system.The experimental assessment of the JSODL-ASDDC model is investigated against distinct datasets.The experimental outcomes highlighted the enhanced performances of the JSODL-ASDDC algorithm over recent approaches. 展开更多
关键词 Autism spectral disorder biomedical data deep learning feature selection hyperparameter optimization data classification machine learning
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Earthworm Optimization with Improved SqueezeNet Enabled Facial Expression Recognition Model 认领 引用
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作者 N.Sharmili Saud Yonbawi +5 位作者 Sultan Alahmari E.Laxmi Lydia Mohamad Khairi Ishak Hend Khalid Alkahtani Ayman Aljarbouh Samih M.Mostafa 《Computer Systems Science & Engineering》 SCIE EI 2023年第8期2247-2262,共16页
Facial expression recognition(FER)remains a hot research area among computer vision researchers and still becomes a challenge because of high intraclass variations.Conventional techniques for this problem depend on ha... Facial expression recognition(FER)remains a hot research area among computer vision researchers and still becomes a challenge because of high intraclass variations.Conventional techniques for this problem depend on hand-crafted features,namely,LBP,SIFT,and HOG,along with that a classifier trained on a database of videos or images.Many execute perform well on image datasets captured in a controlled condition;however not perform well in the more challenging dataset,which has partial faces and image variation.Recently,many studies presented an endwise structure for facial expression recognition by utilizing DL methods.Therefore,this study develops an earthworm optimization with an improved SqueezeNet-based FER(EWOISN-FER)model.The presented EWOISN-FER model primarily applies the contrast-limited adaptive histogram equalization(CLAHE)technique as a pre-processing step.In addition,the improved SqueezeNet model is exploited to derive an optimal set of feature vectors,and the hyperparameter tuning process is performed by the stochastic gradient boosting(SGB)model.Finally,EWO with sparse autoencoder(SAE)is employed for the FER process,and the EWO algorithm appropriately chooses the SAE parameters.Awide-ranging experimental analysis is carried out to examine the performance of the proposed model.The experimental outcomes indicate the supremacy of the presented EWOISN-FER technique. 展开更多
关键词 Facial expression recognition deep learning computer vision earthworm optimization hyperparameter optimization
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Stall prediction model based on deep learning network in axial flow compressor 认领 引用 被引量：2
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作者 Yuyang DENG Jichao LI +3 位作者 Jingyuan LIU Feng PENG Hongwu ZHANG Marco P.SCHOEN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2025年第4期44-54,共11页
To predict stall and surge in advance that make the aero-engine compressor operatesafely,a stall prediction model based on deep learning theory is established in the current study.The Long Short-Term Memory(LSTM)origi... To predict stall and surge in advance that make the aero-engine compressor operatesafely,a stall prediction model based on deep learning theory is established in the current study.The Long Short-Term Memory(LSTM)originating from the recurrent neural network is used,and a set of measured dynamic pressure datasets including the stall process is used to learn whatdetermines the weight of neural network nodes.Subsequently,the structure and function hyperpa-rameters in the model are deeply optimized,and a set of measured pressure data is used to verify theprediction effects of the model.On this basis of the above good predictive capability,stall in low-and high-speed compressor are predicted by using the established model.When a period of non-stallpressure data is used as input in the model,the model can quickly complete the prediction of sub-sequent time series data through the self-learning and prediction mechanism.Comparison with thereal-time measured pressure data demonstrates that the starting point of the predicted stall is basi-cally the same as that of the measured stall,and the stall can be predicted more than 1 s in advanceso that the occurrence of stall can be avoided.The model of stall prediction in the current study canmake up for the uncertainty of threshold selection of the existing stall warning methods based onmeasured data signal processing.It has a great application potential to predict the stall occurrenceof aero-engine compressor in advance and avoid the accidents. 展开更多
关键词 Compressor Stall Deep learning LSTM Hyperparameters optimization
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IMLMA:An Intelligent Algorithm for Model Lifecycle Management with Automated Retraining,Versioning,and Monitoring 认领 引用
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作者 Yu Cao Yiyun He Chi Zhang 《Journal of Electronic Research and Application》 2025年第5期233-248,共16页
With the rapid adoption of artificial intelligence(AI)in domains such as power,transportation,and finance,the number of machine learning and deep learning models has grown exponentially.However,challenges such as dela... With the rapid adoption of artificial intelligence(AI)in domains such as power,transportation,and finance,the number of machine learning and deep learning models has grown exponentially.However,challenges such as delayed retraining,inconsistent version management,insufficient drift monitoring,and limited data security still hinder efficient and reliable model operations.To address these issues,this paper proposes the Intelligent Model Lifecycle Management Algorithm(IMLMA).The algorithm employs a dual-trigger mechanism based on both data volume thresholds and time intervals to automate retraining,and applies Bayesian optimization for adaptive hyperparameter tuning to improve performance.A multi-metric replacement strategy,incorporating MSE,MAE,and R2,ensures that new models replace existing ones only when performance improvements are guaranteed.A versioning and traceability database supports comparison and visualization,while real-time monitoring with stability analysis enables early warnings of latency and drift.Finally,hash-based integrity checks secure both model files and datasets.Experimental validation in a power metering operation scenario demonstrates that IMLMA reduces model update delays,enhances predictive accuracy and stability,and maintains low latency under high concurrency.This work provides a practical,reusable,and scalable solution for intelligent model lifecycle management,with broad applicability to complex systems such as smart grids. 展开更多
关键词 Model lifecycle management Intelligent algorithms Hyperparameter optimization Versioning and traceability Power metering
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A hybrid-model optimization algorithm based on the Gaussian process and particle swarm optimization for mixed-variable CNN hyperparameter automatic search 认领 引用 被引量：6
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作者 Han YAN Chongquan ZHONG +2 位作者 Yuhu WU Liyong ZHANG Wei LU 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2023年第11期1557-1573,共17页
Convolutional neural networks(CNNs)have been developed quickly in many real-world fields.However,CNN’s performance depends heavily on its hyperparameters,while finding suitable hyperparameters for CNNs working in app... Convolutional neural networks(CNNs)have been developed quickly in many real-world fields.However,CNN’s performance depends heavily on its hyperparameters,while finding suitable hyperparameters for CNNs working in application fields is challenging for three reasons:(1)the problem of mixed-variable encoding for different types of hyperparameters in CNNs,(2)expensive computational costs in evaluating candidate hyperparameter configuration,and(3)the problem of ensuring convergence rates and model performance during hyperparameter search.To overcome these problems and challenges,a hybrid-model optimization algorithm is proposed in this paper to search suitable hyperparameter configurations automatically based on the Gaussian process and particle swarm optimization(GPPSO)algorithm.First,a new encoding method is designed to efficiently deal with the CNN hyperparameter mixed-variable problem.Second,a hybrid-surrogate-assisted model is proposed to reduce the high cost of evaluating candidate hyperparameter configurations.Third,a novel activation function is suggested to improve the model performance and ensure the convergence rate.Intensive experiments are performed on image-classification benchmark datasets to demonstrate the superior performance of GPPSO over state-of-the-art methods.Moreover,a case study on metal fracture diagnosis is carried out to evaluate the GPPSO algorithm performance in practical applications.Experimental results demonstrate the effectiveness and efficiency of GPPSO,achieving accuracy of 95.26%and 76.36%only through 0.04 and 1.70 GPU days on the CIFAR-10 and CIFAR-100 datasets,respectively. 展开更多
关键词 Convolutional neural network Gaussian process Hybrid model Hyperparameter optimization Mixed-variable Particle swarm optimization
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