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Optimizing the neural network hyperparameters utilizing genetic algorithm 认领 引用 被引量：23
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作者 Saeid NIKBAKHT Cosmin ANITESCU Timon RABCZUK 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2021年第6期407-426,共20页
Neural networks(NNs),as one of the most robust and efficient machine learning methods,have been commonly used in solving several problems.However,choosing proper hyperparameters(e.g.the numbers of layers and neurons i... Neural networks(NNs),as one of the most robust and efficient machine learning methods,have been commonly used in solving several problems.However,choosing proper hyperparameters(e.g.the numbers of layers and neurons in each layer)has a significant influence on the accuracy of these methods.Therefore,a considerable number of studies have been carried out to optimize the NN hyperpaxameters.In this study,the genetic algorithm is applied to NN to find the optimal hyperpaxameters.Thus,the deep energy method,which contains a deep neural network,is applied first on a Timoshenko beam and a plate with a hole.Subsequently,the numbers of hidden layers,integration points,and neurons in each layer are optimized to reach the highest accuracy to predict the stress distribution through these structures.Thus,applying the proper optimization method on NN leads to significant increase in the NN prediction accuracy after conducting the optimization in various examples. 展开更多
关键词 Machine learning Neural network(NN) Hyperparameters Genetic algorithm
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The posterior selection method for hyperparameters in regularized least squares method 认领 引用
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作者 Yanxin Zhang Jing Chen +1 位作者 Yawen Mao Quanmin Zhu 《Control Theory and Technology》 EI CSCD 2024年第2期184-194,共11页
The selection of hyperparameters in regularized least squares plays an important role in large-scale system identification. The traditional methods for selecting hyperparameters are based on experience or marginal lik... The selection of hyperparameters in regularized least squares plays an important role in large-scale system identification. The traditional methods for selecting hyperparameters are based on experience or marginal likelihood maximization method, which are inaccurate or computationally expensive. In this paper, two posterior methods are proposed to select hyperparameters based on different prior knowledge (constraints), which can obtain the optimal hyperparameters using the optimization theory. Moreover, we also give the theoretical optimal constraints, and verify its effectiveness. Numerical simulation shows that the hyperparameters and parameter vector estimate obtained by the proposed methods are the optimal ones. 展开更多
关键词 Regularization method Hyperparameter System identification Least squares algorithm
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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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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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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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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Hyperparameter Optimisation and Comparative Analysis of Machine Learning Models for Travel Mode Choice Prediction 认领 引用
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作者 Mujahid Ali 《Computers, Materials & Continua》 SCIE EI 2026年第9期1946-1966,共21页
Understanding the determinants of travel mode choice(TMC)in urban contexts is essential for effective transport planning and policy development.Past studies predominantly employed traditional discrete choice models be... Understanding the determinants of travel mode choice(TMC)in urban contexts is essential for effective transport planning and policy development.Past studies predominantly employed traditional discrete choice models because of their simplicity,diversity,and high interpretability;however,they rely on restrictive assumptions.Although machine learning(ML)techniques have shown promising predictive capabilities,comparative assessments of traditional and ML approaches,particularly considering hyperparameter optimisation,remain limited.This study addresses this gap by comparing a traditional model with four ML algorithms:decision tree(DT),random forest(RF),support vector machine(SVM),and k-nearest neighbour(KNN).In addition,systematic hyperparameter optimisation is performed to evaluate its impact on predictive performance relative to default model settings.Feature importance analysis is also conducted to identify the key determinants of TMC.The analysis is based on a multi-dimensional,three-week household time-use and activity diary dataset comprising 508 individuals from 191 households in the Bandung Metropolitan Area,Indonesia.The results demonstrate that ML models outperform traditional methods,while hyperparameter optimisation substantially improves model performance across all considered algorithms against default models.Notably,the KNN model exhibits a 16.67%increase in accuracy,followed by the SVM model with an 11.15%improvement.Among the optimised evaluated models,SVM achieves the best overall performance,with a macro-averaged accuracy of 0.588 and a precision of 0.591.Feature importance analysis reveals that total travel time is the most influential determinant of TMC.These findings highlight the importance of model tuning and hyperparameter optimisation in ML-based TMC prediction and provide insights into the factors shaping travel behaviour.The outcomes can support more informed decision-making in urban transport planning and policy formulation. 展开更多
关键词 Travel mode choice hyperparameter optimisation random forest travel behaviour sustainable transportation
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Advancing Breast Cancer Molecular Subtyping:A Comparative Study of Convolutional Neural Networks and Vision Transformers on Mammograms 认领 引用
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作者 Chee Chin Lim Hui Wen Tiu +2 位作者 Qi Wei Oung Chiew Chea Lau Xiao Jian Tan 《Computers, Materials & Continua》 SCIE EI 2026年第3期1287-1308,共22页
critical for guiding treatment and improving patient outcomes.Traditional molecular subtyping via immuno-histochemistry(IHC)test is invasive,time-consuming,and may not fully represent tumor heterogeneity.This study pr... critical for guiding treatment and improving patient outcomes.Traditional molecular subtyping via immuno-histochemistry(IHC)test is invasive,time-consuming,and may not fully represent tumor heterogeneity.This study proposes a non-invasive approach using digital mammography images and deep learning algorithm for classifying breast cancer molecular subtypes.Four pretrained models,including two Convolutional Neural Networks(MobileNet_V3_Large and VGG-16)and two Vision Transformers(ViT_B_16 and ViT_Base_Patch16_Clip_224)were fine-tuned to classify images into HER2-enriched,Luminal,Normal-like,and Triple Negative subtypes.Hyperparameter tuning,including learning rate adjustment and layer freezing strategies,was applied to optimize performance.Among the evaluated models,ViT_Base_Patch16_Clip_224 achieved the highest test accuracy(94.44%),with equally high precision,recall,and F1-score of 0.94,demonstrating excellent generalization.MobileNet_V3_Large achieved the same accuracy but showed less training stability.In contrast,VGG-16 recorded the lowest performance,indicating a limitation in its generalizability for this classification task.The study also highlighted the superior performance of the Vision Transformer models over CNNs,particularly due to their ability to capture global contextual features and the benefit of CLIP-based pretraining in ViT_Base_Patch16_Clip_224.To enhance clinical applicability,a graphical user interface(GUI)named“BCMS Dx”was developed for streamlined subtype prediction.Deep learning applied to mammography has proven effective for accurate and non-invasive molecular subtyping.The proposed Vision Transformer-based model and supporting GUI offer a promising direction for augmenting diagnostic workflows,minimizing the need for invasive procedures,and advancing personalized breast cancer management. 展开更多
关键词 Artificial intelligence breast cancer classification convolutional neural network deep learning hyperparameter tuning mammography medical imaging molecular subtypes vision transformer
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Computer Modeling and Characterization of Plastic Strain Hardening in Ti-6Al-4V under Tension and Compression 认领 引用
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作者 Teng Long Leyu Wang +1 位作者 James D.Lee Cing-Dao Kan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第7期287-302,共16页
Titanium alloy Ti-6Al-4V has been widely applied in many industries,for example,aerospace,marine,automotive,and biomedical engineering systems,where accurate characterization of plastic deformation is important for ev... Titanium alloy Ti-6Al-4V has been widely applied in many industries,for example,aerospace,marine,automotive,and biomedical engineering systems,where accurate characterization of plastic deformation is important for evaluating material performance and potential failure under severe loading conditions.This material shows nonlinear plasticity and tension–compression asymmetry,which makes the strain hardening characterization important for computational failure analysis and crashworthiness-related simulations.However,conventional strain hardening models and parameter identificationmethods often rely on linear or extrapolation-based assumptions and are sensitive to initial guesses due to the non-convex nature of the optimization problem.In this study,a flexible rational-polynomialbased strain-hardening model was employed to characterize the stress–strain responses of Ti-6Al-4V under both tensile and compressive loading.To identify the polynomial parameters,an online hyperparameter tuning Bayesian optimization framework was adopted.The finite element predictions closely reproduce the experimental force–displacement responses under both tensile and compressive loading.This consistency demonstrates the capability of the proposed data-driven computational framework to identify strain-hardening parameters and characterize the plastic deformation behavior of Ti-6Al-4V alloy. 展开更多
关键词 Titanium alloy strain hardening plasticity Bayesian optimization hyperparameter tuning
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Using extreme gradient boosting with Optuna hyperparameter tuning for efficient lost circulation prediction 认领 引用
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作者 Osama GAl-Salih Dong Guangjian +1 位作者 Watheq J.Al-Mudhafar David A.Wood 《Energy Geoscience》 EI CAS CSCD 2026年第2期133-152,共20页
Lost circulation(LC)remains a significant challenge in drilling operations,leading to increased costs,non-productive time,and potential well integrity issues.This study focuses on predicting lost circulation during dr... Lost circulation(LC)remains a significant challenge in drilling operations,leading to increased costs,non-productive time,and potential well integrity issues.This study focuses on predicting lost circulation during drilling operations using the XGBoost machine learning algorithm,coupled with hyperparameter tuning RPM via the Optuna framework.The dataset includes drilling parameters,mud properties,and geological features,incorporating noisy and outlier-prone real-world data.Three Optuna samplers—TPE(Tree-structured Parzen Estimator),CmaEs(Covariance Matrix Adaptation Evolution Strategy),and NSGAIII(Non-dominated Sorting Genetic Algorithm)—were evaluated for their effectiveness in optimizing the model.The TPE sampler achieved the highest lost circulation prediction performance,yielding a coefficient of determination R2 of 82.27%,an adjusted R2 of 81.40%,and a root mean squared error(RMSE)of 4.134.Feature importance analysis highlighted measured depth(MD),weight on bit(WOB),rotations per minute(RPM),and rate of penetration(ROP)as the primary predictors of lost circulation,underscoring the critical influence of geological and operational parameters.Each of these input features plays a significant role in predicting lost circulation:MD is crucial as it correlates with geological formations prone to mud loss;WOB influences the stress exerted on the formation,which can lead to fractures;RPM impacts the mechanical action of the drill string,contributing to wellbore instability;ROP reflects the drilling efficiency and the interaction between the bit and subsurface layers,which are critical for identifying lost-circulation zones.These features collectively enable the model to capture complex relationships and enhance its predictive performance.The findings provide actionable insights for improving drilling techniques,mitigating lost circulation risks,and enhancing operational efficiency.By retaining noisy and outlier data,the study aligns the predictive model with real-world complexities,demonstrating its robustness and practical relevance. 展开更多
关键词 Lost circulation prediction XGBoost hyperparameter tuning Optuna framework Drilling efficiency Optimized machine learning
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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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Noise-Aware Metaheuristic Optimization of Non-Local Means Denoising via a Ratel Optimization Algorithm 认领 引用
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作者 Botambu Collins Jin-Taek Seong 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期909-931,共23页
Classical image denoising methods remain relevant in practical scenarios where training data or noise models are unavailable,yet their performance is highly sensitive to parameter selection.Non-Local Means(NLM)is a re... Classical image denoising methods remain relevant in practical scenarios where training data or noise models are unavailable,yet their performance is highly sensitive to parameter selection.Non-Local Means(NLM)is a representative example whose effectiveness depends critically on smoothing strength,patch size,and search window configuration.This paper formulates NLM parameter selection as a black-box optimization problem under unknown noise conditions and employs adaptive metaheuristic optimization strategies for this task.We propose an adaptive optimization framework that integrates rank-based perturbation,opposition-based learning,Lévy-flight exploration,and noise-aware parameter constraints to improve robustness and convergence.The proposed method is evaluated against fixed-parameter NLM and NLM optimized using standard evolutionary algorithms under identical protocols.Experiments on three sets of datatset demonstrate consistent improvements in PSNR and SSIM,highlighting the continued relevance of adaptive optimization for classical denoising. 展开更多
关键词 Image denoising hyperparameter selection image restoration non-local means
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Feature Selection and Hyperparameter Optimization for Machine Learned Classification of 3D Single-Particle Tracking 认领 引用
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作者 Jagriti Chatterjee Subhojyoti Chatterjee +3 位作者 Emil Gillett Nikita Kovalenko Dongyu Fan Christy F.Landes 《Chemical & Biomedical Imaging》 CAS CSCD 2026年第1期79-91,共13页
Understanding diffusion in charged and crowded media is crucial for solving a wide range of biological and materials challenges.Classifying diffusion by traditional methods such as mean square displacement in three-di... Understanding diffusion in charged and crowded media is crucial for solving a wide range of biological and materials challenges.Classifying diffusion by traditional methods such as mean square displacement in three-dimensional single-particle tracking(3D SPT)is difficult,especially when there are mixed motion types.To address this,we employed machine learning(ML),specifically decision tree algorithms with feature selection,to identify the six most relevant features for accurate characterization of trajectories.This work demonstrates the value of ML in advancing our understanding of heterogeneous transport that occurs in charged and crowded environments,with a broad range of applications. 展开更多
关键词 single particle dynamics diffusion machine learning feature selection hyperparameter optimization
An ROI-Guided Optimized Machine Learning Framework for Orange Disease Recognition with Feature Selection and Explainability 认领 引用
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作者 Israt Jahan Munny Anup Majumder +6 位作者 Bibhas Roy Chowdhury Piyas Fahmid Al Farid MdRafsan Jani Fatama Jannat Tisha Israt Jahan Abu Saleh Musa Miah Hezerul Abdul Karim 《Computers, Materials & Continua》 SCIE EI 2026年第10期1660-1683,共24页
Orange is one of the most economically significant citrus crops worldwide,which is essential for the global food distribution network and supports rural livelihoods.However,its high susceptibility to destructive disea... Orange is one of the most economically significant citrus crops worldwide,which is essential for the global food distribution network and supports rural livelihoods.However,its high susceptibility to destructive diseases results in substantial yield losses and long-term economic damage.Despite recent advances in smart agriculture,early and precise disease diagnosis remains challenging due to visual resemblance among disease symptoms,high computational cost,and limited model interpretability.To overcome these difficulties,we introduce a novel lightweight and Region of Interest(ROI)-guided explainable machine learning framework to identify orange disease that integrates a strategic feature selection method with Adaptive Step-Controlled Gorilla Troops Optimizer(ASC-GTO).The proposed method starts with Contrast Limited Adaptive Histogram Equalization(CLAHE)-based image enhancement,followed by K-means clustering to accurately segment and separate the diseased part,which is labelled as ROI.To extract discriminative features from the ROI,Gray-Level Co-occurrence Matrix(GLCM)-based texture and color features are first extracted.Least Absolute Shrinkage and Selection Operator(LASSO)is then used for ranking the features and finding the most discriminative features for each class.Finally,the proposed feature selection method integrates the union and intersection of top features identified in the class-wise scenario using LASSO with globally dominant features found by feature ranking to get a compact and discriminative feature subset for better multi-class classification.Model hyperparameter optimization was performed using the proposed ASC-GTO.Experimental findings indicate that the proposed method outperforms existing techniques with an accuracy of 99.57%on the widely adopted orange disease dataset from Kaggle.Furthermore,it significantly reduces computational complexity,with reductions of 22.2%,34.76%,and 7.5%in training time,model size,and inference time,respectively,compared to models trained on unprocessed raw input images.Model explainability is further analyzed using SHAP and LIME to identify the most influential features contributing to the prediction outcomes.Overall,the proposed method supports early disease intervention,precision agriculture,and sustainable farming. 展开更多
关键词 Orange disease detection explainable machine learning ROI extraction citrus disease feature selection plant disease detection LASSO SHAP LIME hyperparameter optimization Gorilla Troops Optimizer
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OptimumMachine Learning on Gas Extraction and Production for Adaptive Negative Control 认领 引用 被引量：1
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作者 Cheng Cheng Xuan-Ping Gong +2 位作者 Xiao-Yu Cheng Lu Xiao Xing-Ying Ma 《Frontiers in Heat and Mass Transfer》 EI CAS 2025年第3期1037-1051,共15页
To overcome the challenges associated with predicting gas extraction performance and mitigating the gradual decline in extraction volume,which adversely impacts gas utilization efficiency in mines,a gas extraction pur... To overcome the challenges associated with predicting gas extraction performance and mitigating the gradual decline in extraction volume,which adversely impacts gas utilization efficiency in mines,a gas extraction pure volume prediction model was developed using Support Vector Regression(SVR)and Random Forest(RF),with hyperparameters fine-tuned via the Genetic Algorithm(GA).Building upon this,an adaptive control model for gas extraction negative pressure was formulated to maximize the extracted gas volume within the pipeline network,followed by field validation experiments.Experimental results indicate that the GA-SVR model surpasses comparable models in terms of mean absolute error,root mean square error,and mean absolute percentage error.In the extraction process of bedding boreholes,the influence of negative pressure on gas extraction concentration diminishes over time,yet it remains a critical factor in determining the extracted pure volume.In contrast,throughout the entire extraction period of cross-layer boreholes,both extracted pure volume and concentration exhibit pronounced sensitivity to fluctuations in extraction negative pressure.Field experiments demonstrated that the adaptive controlmodel enhanced the average extracted gas volume by 5.08% in the experimental borehole group compared to the control group during the later extraction stage,with a more pronounced increase of 7.15% in the first 15 days.The research findings offer essential technical support for the efficient utilization and long-term sustainable development of mine gas resources.The research findings offer essential technical support for gas disaster mitigation and the sustained,efficient utilization of mine gas. 展开更多
关键词 Gas extraction support vector regression(SVR) genetic algorithm hyperparameters fine-tuned negative pressure adaptive control
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Tuning hyperparameters of doublet-detection methods for single-cell RNA sequencing data 认领 引用
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作者 Nan Miles Xi Angelos Vasilopoulos 《Quantitative Biology》 CAS CSCD 2023年第3期297-305,共9页
Background:The existence of doublets in single-cell RNA sequencing(scRNA-seq)data poses a great challenge in downstream data analysis.Computational doublet-detection methods have been developed to remove doublets from... Background:The existence of doublets in single-cell RNA sequencing(scRNA-seq)data poses a great challenge in downstream data analysis.Computational doublet-detection methods have been developed to remove doublets from scRNA-seq data.Yet,the default hyperparameter settings of those methods may not provide optimal performance.Methods:We propose a strategy to tune hyperparameters for a cutting-edge doublet-detection method.We utilize a full factorial design to explore the relationship between hyperparameters and detection accuracy on 16 real scRNA-seq datasets.The optimal hyperparameters are obtained by a response surface model and convex optimization.Results:We show that the optimal hyperparameters provide top performance across scRNA-seq datasets under various biological conditions.Our tuning strategy can be applied to other computational doublet-detection methods.It also offers insights into hyperparameter tuning for broader computational methods in scRNA-seq data analysis.Conclusions:The hyperparameter configuration significantly impacts the performance of computational doublet-detection methods.Our study is the first attempt to systematically explore the optimal hyperparameters under various biological conditions and optimization objectives.Our study provides much-needed guidance for hyperparameter tuning in computational doublet-detection methods. 展开更多
关键词 scRNA-seq doublet detection hyperparameter tuning experimental design response surface model
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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Adaptive backward stepwise selection of fast sparse identification of nonlinear dynamics 认领 引用 被引量：1
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作者 Feng JIANG Lin DU +2 位作者 Qing XUE Zichen DENG C.GREBOGI 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2025年第12期2361-2384,共24页
Sparse identification of nonlinear dynamics(SINDy)has made significant progress in data-driven dynamics modeling.However,determining appropriate hyperparameters and addressing the time-consuming symbolic regression pr... Sparse identification of nonlinear dynamics(SINDy)has made significant progress in data-driven dynamics modeling.However,determining appropriate hyperparameters and addressing the time-consuming symbolic regression process remain substantial challenges.This study proposes the adaptive backward stepwise selection of fast SINDy(ABSS-FSINDy),which integrates statistical learning-based estimation and technical advancements to significantly reduce simulation time.This approach not only provides insights into the conditions under which SINDy performs optimally but also highlights potential failure points,particularly in the context of backward stepwise selection(BSS).By decoding predefined features into textual expressions,ABSS-FSINDy significantly reduces the simulation time compared with conventional symbolic regression methods.We validate the proposed method through a series of numerical experiments involving both planar/spatial dynamics and high-dimensional chaotic systems,including Lotka-Volterra,hyperchaotic Rossler,coupled Lorenz,and Lorenz 96 benchmark systems.The experimental results demonstrate that ABSS-FSINDy autonomously determines optimal hyperparameters within the SINDy framework,overcoming the curse of dimensionality in high-dimensional simulations.This improvement is substantial across both lowand high-dimensional systems,yielding efficiency gains of one to three orders of magnitude.For instance,in a 20D dynamical system,the simulation time is reduced from 107.63 s to just 0.093 s,resulting in a 3-order-of-magnitude improvement in simulation efficiency.This advancement broadens the applicability of SINDy for the identification and reconstruction of high-dimensional dynamical systems. 展开更多
关键词 data-driven dynamics modeling backward stepwise selection(BSS) sparse identification of nonlinear dynamics(SINDy) sparse regression hyperparameter determination curse of dimensionality
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A Comparative Study of Optimized-LSTM Models Using Tree-Structured Parzen Estimator for Traffic Flow Forecasting in Intelligent Transportation 认领 引用 被引量：1
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作者 Hamza Murad Khan Anwar Khan +3 位作者 Santos Gracia Villar Luis Alonso DzulLopez Abdulaziz Almaleh Abdullah M.Al-Qahtani 《Computers, Materials & Continua》 SCIE EI 2025年第5期3369-3388,共20页
Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models... Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes. 展开更多
关键词 Short-term traffic prediction sequential time series prediction TPE tree-structured parzen estimator LSTM hyperparameter tuning hybrid prediction model
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Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate 认领 引用
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作者 Suganya Athisayamani A.Robert Singh +1 位作者 Gyanendra Prasad Joshi Woong Cho 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第1期155-183,共29页
In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue... In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue anomalies.Traditionally,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of data.To address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI scans.This manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning methods.There are three stages for learning;in the first stage,the whole dataset is used to learn the features.In the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented dataset.This method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for analysis.Various hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning process.HWBA-dataset registers maximum classification performance.We evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%. 展开更多
关键词 MRI tumors classification AlexNet50 transfer learning hyperparameter tuning optimizer
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