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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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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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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
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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Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimization 认领 引用 被引量：80
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作者 Jia Wu Xiu-Yun Chen +3 位作者 Hao Zhang Li-Dong Xiong Hang Lei Si-Hao Deng 《Journal of Electronic Science and Technology》 EI CAS CSCD 2019年第1期26-40,共15页
Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models. Several techn... Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models. Several techniques have been developed and successfully applied for certain application domains. However, this work demands professional knowledge and expert experience. And sometimes it has to resort to the brute-force search.Therefore, if an efficient hyperparameter optimization algorithm can be developed to optimize any given machine learning method, it will greatly improve the efficiency of machine learning. In this paper, we consider building the relationship between the performance of the machine learning models and their hyperparameters by Gaussian processes. In this way, the hyperparameter tuning problem can be abstracted as an optimization problem and Bayesian optimization is used to solve the problem. Bayesian optimization is based on the Bayesian theorem. It sets a prior over the optimization function and gathers the information from the previous sample to update the posterior of the optimization function. A utility function selects the next sample point to maximize the optimization function.Several experiments were conducted on standard test datasets. Experiment results show that the proposed method can find the best hyperparameters for the widely used machine learning models, such as the random forest algorithm and the neural networks, even multi-grained cascade forest under the consideration of time cost. 展开更多
关键词 Bayesian optimization Gaussian process hyperparameter optimization machine learning
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Scale adaptive fitness evaluation‐based particle swarm optimisation for hyperparameter and architecture optimisation in neural networks and deep learning 认领 引用 被引量：8
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作者 Ye‐Qun Wang Jian‐Yu Li +2 位作者 Chun‐Hua Chen Jun Zhang Zhi‐Hui Zhan 《CAAI Transactions on Intelligence Technology》 SCIE EI 2023年第3期849-862,共14页
Research into automatically searching for an optimal neural network(NN)by optimi-sation algorithms is a significant research topic in deep learning and artificial intelligence.However,this is still challenging due to ... Research into automatically searching for an optimal neural network(NN)by optimi-sation algorithms is a significant research topic in deep learning and artificial intelligence.However,this is still challenging due to two issues:Both the hyperparameter and ar-chitecture should be optimised and the optimisation process is computationally expen-sive.To tackle these two issues,this paper focusses on solving the hyperparameter and architecture optimization problem for the NN and proposes a novel light‐weight scale‐adaptive fitness evaluation‐based particle swarm optimisation(SAFE‐PSO)approach.Firstly,the SAFE‐PSO algorithm considers the hyperparameters and architectures together in the optimisation problem and therefore can find their optimal combination for the globally best NN.Secondly,the computational cost can be reduced by using multi‐scale accuracy evaluation methods to evaluate candidates.Thirdly,a stagnation‐based switch strategy is proposed to adaptively switch different evaluation methods to better balance the search performance and computational cost.The SAFE‐PSO algorithm is tested on two widely used datasets:The 10‐category(i.e.,CIFAR10)and the 100−cate-gory(i.e.,CIFAR100).The experimental results show that SAFE‐PSO is very effective and efficient,which can not only find a promising NN automatically but also find a better NN than compared algorithms at the same computational cost. 展开更多
关键词 deep learning evolutionary computation hyperparameter and architecture optimisation neural networks particle swarm optimisation scale‐adaptive fitness evaluation
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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：7
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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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Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy 认领 引用 被引量：6
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作者 Surjeet Dalal Edeh Michael Onyema Amit Malik 《World Journal of Gastroenterology》 SCIE CAS 2022年第46期6551-6563,共13页
BACKGROUND Liver disease indicates any pathology that can harm or destroy the liver or prevent it from normal functioning.The global community has recently witnessed an increase in the mortality rate due to liver dise... BACKGROUND Liver disease indicates any pathology that can harm or destroy the liver or prevent it from normal functioning.The global community has recently witnessed an increase in the mortality rate due to liver disease.This could be attributed to many factors,among which are human habits,awareness issues,poor healthcare,and late detection.To curb the growing threats from liver disease,early detection is critical to help reduce the risks and improve treatment outcome.Emerging technologies such as machine learning,as shown in this study,could be deployed to assist in enhancing its prediction and treatment.AIM To present a more efficient system for timely prediction of liver disease using a hybrid eXtreme Gradient Boosting model with hyperparameter tuning with a view to assist in early detection,diagnosis,and reduction of risks and mortality associated with the disease.METHODS The dataset used in this study consisted of 416 people with liver problems and 167 with no such history.The data were collected from the state of Andhra Pradesh,India,through http://gffzz32f81fe4765d4826scu56pcnw5wx066cf.ffgz.tsg.suse.edu.cn/datasets/uciml/indian-liver-patientrecords.The population was divided into two sets depending on the disease state of the patient.This binary information was recorded in the attribute"is_patient".RESULTS The results indicated that the chi-square automated interaction detection and classification and regression trees models achieved an accuracy level of 71.36%and 73.24%,respectively,which was much better than the conventional method.The proposed solution would assist patients and physicians in tackling the problem of liver disease and ensuring that cases are detected early to prevent it from developing into cirrhosis(scarring)and to enhance the survival of patients.The study showed the potential of machine learning in health care,especially as it concerns disease prediction and monitoring.CONCLUSION This study contributed to the knowledge of machine learning application to health and to the efforts toward combating the problem of liver disease.However,relevant authorities have to invest more into machine learning research and other health technologies to maximize their potential. 展开更多
关键词 Liver infection Machine learning Chi-square automated interaction detection Classification and regression trees Decision tree XGBoost Hyperparameter tuning
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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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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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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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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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Abstractive Arabic Text Summarization Using Hyperparameter Tuned Denoising Deep Neural Network 认领 引用
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作者 Ibrahim M.Alwayle Hala J.Alshahrani +5 位作者 Saud S.Alotaibi Khaled M.Alalayah Amira Sayed A.Aziz Khadija M.Alaidarous Ibrahim Abdulrab Ahmed Manar Ahmed Hamza 《Intelligent Automation & Soft Computing》 SCIE 2023年第11期153-168,共16页
ive Arabic Text Summarization using Hyperparameter Tuned Denoising Deep Neural Network(AATS-HTDDNN)technique.The presented AATS-HTDDNN technique aims to generate summaries of Arabic text.In the presented AATS-HTDDNN t... ive Arabic Text Summarization using Hyperparameter Tuned Denoising Deep Neural Network(AATS-HTDDNN)technique.The presented AATS-HTDDNN technique aims to generate summaries of Arabic text.In the presented AATS-HTDDNN technique,the DDNN model is utilized to generate the summary.This study exploits the Chameleon Swarm Optimization(CSO)algorithm to fine-tune the hyperparameters relevant to the DDNN model since it considerably affects the summarization efficiency.This phase shows the novelty of the current study.To validate the enhanced summarization performance of the proposed AATS-HTDDNN model,a comprehensive experimental analysis was conducted.The comparison study outcomes confirmed the better performance of the AATS-HTDDNN model over other approaches. 展开更多
关键词 Text summarization deep learning denoising deep neural networks hyperparameter tuning Arabic language
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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
Hyperparameter on-line learning of stochastic resonance based threshold networks 认领 引用
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作者 Weijin Li Yuhao Ren Fabing Duan 《Chinese Physics B》 SCIE EI CAS CSCD 2022年第8期289-295,共7页
Aiming at training the feed-forward threshold neural network consisting of nondifferentiable activation functions, the approach of noise injection forms a stochastic resonance based threshold network that can be optim... Aiming at training the feed-forward threshold neural network consisting of nondifferentiable activation functions, the approach of noise injection forms a stochastic resonance based threshold network that can be optimized by various gradientbased optimizers. The introduction of injected noise extends the noise level into the parameter space of the designed threshold network, but leads to a highly non-convex optimization landscape of the loss function. Thus, the hyperparameter on-line learning procedure with respective to network weights and noise levels becomes of challenge. It is shown that the Adam optimizer, as an adaptive variant of stochastic gradient descent, manifests its superior learning ability in training the stochastic resonance based threshold network effectively. Experimental results demonstrate the significant improvement of performance of the designed threshold network trained by the Adam optimizer for function approximation and image classification. 展开更多
关键词 noise injection adaptive stochastic resonance threshold neural network hyperparameter learning
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Energy Efficient Hyperparameter Tuned Deep Neural Network to Improve Accuracy of Near-Threshold Processor 认领 引用
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作者 K.Chanthirasekaran Raghu Gundaala 《Intelligent Automation & Soft Computing》 SCIE 2023年第7期471-489,共19页
When it comes to decreasing margins and increasing energy effi-ciency in near-threshold and sub-threshold processors,timing error resilience may be viewed as a potentially lucrative alternative to examine.On the other... When it comes to decreasing margins and increasing energy effi-ciency in near-threshold and sub-threshold processors,timing error resilience may be viewed as a potentially lucrative alternative to examine.On the other hand,the currently employed approaches have certain restrictions,including high levels of design complexity,severe time constraints on error consolidation and propagation,and uncontaminated architectural registers(ARs).The design of near-threshold circuits,often known as NT circuits,is becoming the approach of choice for the construction of energy-efficient digital circuits.As a result of the exponentially decreased driving current,there was a reduction in performance,which was one of the downsides.Numerous studies have advised the use of NT techniques to chip multiprocessors as a means to preserve outstanding energy efficiency while minimising performance loss.Over the past several years,there has been a clear growth in interest in the development of artificial intelligence hardware with low energy consumption(AI).This has resulted in both large corporations and start-ups producing items that compete on the basis of varying degrees of performance and energy use.This technology’s ultimate goal was to provide levels of efficiency and performance that could not be achieved with graphics processing units or general-purpose CPUs.To achieve this objective,the technology was created to integrate several processing units into a single chip.To accomplish this purpose,the hardware was designed with a number of unique properties.In this study,an Energy Effi-cient Hyperparameter Tuned Deep Neural Network(EEHPT-DNN)model for Variation-Tolerant Near-Threshold Processor was developed.In order to improve the energy efficiency of artificial intelligence(AI),the EEHPT-DNN model employs several AI techniques.The notion focuses mostly on the repercussions of embedded technologies positioned at the network’s edge.The presented model employs a deep stacked sparse autoencoder(DSSAE)model with the objective of creating a variation-tolerant NT processor.The time-consuming method of modifying hyperparameters through trial and error is substituted with the marine predators optimization algorithm(MPO).This method is utilised to modify the hyperparameters associated with the DSSAE model.To validate that the proposed EEHPT-DNN model has a higher degree of functionality,a full simulation study is conducted,and the results are analysed from a variety of perspectives.This was completed so that the enhanced performance could be evaluated and analysed.According to the results of the study that compared numerous DL models,the EEHPT-DNN model performed significantly better than the other models. 展开更多
关键词 Deep learning hyperparameter tuning artificial intelligence near-threshold processor embedded system
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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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