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Using mixed kernel support vector machine to improve the predictive accuracy of genome selection 认领 引用 被引量：2
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作者 Jinbu Wang Wencheng Zong +6 位作者 Liangyu Shi Mianyan Li Jia Li Deming Ren Fuping Zhao Lixian Wang Ligang Wang 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第2期775-787,共13页
The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects acc... The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS. 展开更多
关键词 genome selection machine learning support vector machine kernel function mixed kernel function
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Programmable mixed-kernel based on MoTe2/MoS2heterojunction for support vector machine learning 认领 引用
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作者 Xinyu Huang Jiapeng Du +3 位作者 Langlang Xu Lei Tong Xiangxiang Yu Lei Ye 《Journal of Semiconductors》 EI CAS CSCD 2026年第3期110-116,共7页
The von Neumann bottleneck in conventional computing architectures presents a significant challenge for data-inten-sive artificial intelligence applications.A promising approach involves designing specialized hardware... The von Neumann bottleneck in conventional computing architectures presents a significant challenge for data-inten-sive artificial intelligence applications.A promising approach involves designing specialized hardware with on-chip parameter tunability,which directly accelerates machine learning functions.This work demonstrates a continuously tunable mixed-kernel function physically realized within a van der Waals heterostructure.We designed and fabricated a MoTe2/MoS2type-Ⅱvertical heterojunction phototransistor,which exhibits a non-monotonic,Gaussian-like optoelectronic response owing to its unique inter-layer charge transfer mechanism.This intrinsic physical behavior directly maps to a mixed-kernel function combining Gaussian and Sigmoid characteristics.Furthermore,the hardware kernel can be continuously modulated by in-situ tuning of external opti-cal stimuli.The mixed-kernel exhibited exceptional performance,achieving precision,accuracy,and area under the curve(AUC)values of 95.8%,96%,and 0.9986,respectively,significantly outperforming conventional kernels.By successfully embedding a complex,adaptable mathematical function into the intrinsic physical properties of a single device,this work pioneers a novel pathway toward next-generation,energy-efficient intelligent systems with hardware-level adaptability. 展开更多
关键词 programmable mixed-kernel heterojunction support vector machine
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A Convolutional Neural Network-Based Deep Support Vector Machine for Parkinson’s Disease Detection with Small-Scale and Imbalanced Datasets 认领 引用
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作者 Kwok Tai Chui Varsha Arya +2 位作者 Brij B.Gupta Miguel Torres-Ruiz Razaz Waheeb Attar 《Computers, Materials & Continua》 SCIE EI 2026年第1期1410-1432,共23页
Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d... Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested. 展开更多
关键词 Convolutional neural network data generation deep support vector machine feature extraction generative artificial intelligence imbalanced dataset medical diagnosis Parkinson’s disease small-scale dataset
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An Optimized Multiclass Support Vector Machine for Recognizing EEG Signals based on a Lower-Limb Exoskeleton 认领 引用
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作者 Kamel K.Mohammed Heba M.Afify Aboul Ella Hassanien 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第3期1605-1620,共16页
The lower-limb prosthesis is used to assist patients with dysfunction of motor dysfunction or aging through Brain-Machine Interface(BMI)based on Electroencephalography(EEG)signals to control cognitive tasks.This paper... The lower-limb prosthesis is used to assist patients with dysfunction of motor dysfunction or aging through Brain-Machine Interface(BMI)based on Electroencephalography(EEG)signals to control cognitive tasks.This paper presents a remarkable model to improve the estimation of the EEG signal and further help improve the control performance for the lower-limb prosthesis,and then improve the rehabilitation.It is based on an optimized Multiclass Support Vector Machine(MSVM)using Snake Optimizer(SO)to get the best possible parameter tuning for classifying different cognitive tasks to control of lower-limb exoskeleton.A public EEG dataset for a lower-limb exoskeleton using Motor Imagery(MI)during the control of the prosthesis and attention to gait(Att)on two surfaces,including flat(Experience)and non-flat(Slopes),has been used as benchmark data sets for this work.The results of the proposed model revealed the superiority of this technique in accuracy,compared with two optimization methods,including Genetic Algorithm(GA)and Particle Swarm Optimization(PSO).By comparing the outcomes of SO-MSVM with state of the arts,it achieved an accuracy of more than 85%for MI and Att metric,demonstrating intriguing results for solving the rehabilitation challenge.The devised technique could help people with neurological conditions who have trouble using manual controls. 展开更多
关键词 Electroencephalography(EEG) Brain-Machine Interface(BMI) Multiclass Support Vector Machine(MSVM) Genetic Algorithm(GA) Particle Swarm Optimization(PSO) and Snake Optimization(SO)
An Improved Support Vector Machine Method for Fault Diagnosis of Inter-Turn Short Circuit in PMSM with Enhanced Fault Representation 认领 引用
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作者 Yue Su Shukuan Zhang +2 位作者 Jinghao Jiao Jiankang Zhong Qianxi Zhao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期613-629,共17页
This paper introduces a novel dual-layer optimization fault diagnosis framework for inter-turn shortcircuit(ITSC)faults in permanent magnet synchronous motors(PMSMs).The synergistic of a SABO-optimized VMD for enhance... This paper introduces a novel dual-layer optimization fault diagnosis framework for inter-turn shortcircuit(ITSC)faults in permanent magnet synchronous motors(PMSMs).The synergistic of a SABO-optimized VMD for enhanced feature extraction and an MFO-optimized SVM for intelligent classification is proposed.Firstly,mathematical and simulation models of ITSC faults in PMSMs are established to obtain fault phase currents and motor electromagnetic torques as characteristic fault signals.Then,the SABO algorithm is used to optimize the VMD parameters,followed by VMD decomposition of the characteristic fault signals to obtain Intrinsic Mode Functions(IMFs),and the time-domain parameters of the optimal IMF are calculated to obtain feature vectors.Finally,the fault type is predicted using an SVM optimized by the Moth-Flame Optimizer(MFO).Simulation results show that the accuracy of fault diagnosis can reach 93.6%,indicating that the proposed method can achieve accurate diagnosis of ITSC faults and effectively improve the accuracy of fault diagnosis. 展开更多
关键词 Permanent magnet synchronous motor inter-turn short circuit fault support vector machine fault diagnosis
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A Support Vector Machine(SVM)Model for Privacy Recommending Data Processing Model(PRDPM)in Internet of Vehicles 认领 引用
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作者 Ali Alqarni 《Computers, Materials & Continua》 SCIE EI 2025年第1期389-406,共18页
Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experie... Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experience in driving,navigation,and communication.These privacy needs are influenced by various factors,such as data collected at different intervals,trip durations,and user interactions.To address this,the paper proposes a Support Vector Machine(SVM)model designed to process large amounts of aggregated data and recommend privacy preserving measures.The model analyzes data based on user demands and interactions with service providers or neighboring infrastructure.It aims to minimize privacy risks while ensuring service continuity and sustainability.The SVMmodel helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy recommendations.The results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance. 展开更多
关键词 Support vector machine big data IoV privacy-preserving
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Support Vector Clustering Uncovered:Insights,Challenges,and Future Outlook 认领 引用
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作者 M.Tanveer Mohammad Tabish +2 位作者 Anuradha Kumari Ashwani Kumar Malik Weiping Ding 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第4期749-775,共27页
Support vector clustering(SVC)has emerged as a powerful unsupervised learning technique,derived from support vector machines(SVMs),offering a robust solution to a wide range of complex clustering challenges.Its unique... Support vector clustering(SVC)has emerged as a powerful unsupervised learning technique,derived from support vector machines(SVMs),offering a robust solution to a wide range of complex clustering challenges.Its unique ability to handle noise,outliers,and clusters of diverse,irregular shapes sets it apart from traditional clustering methods.SVC's distinct advantage lies in its capacity to autonomously determine the optimal number of clusters without prior topological knowledge of the data.SVC maps data to a higher-dimensional space,encloses it in a minimal sphere,and identifies clusters when mapped back,supporting complex shapes and ensuring optimality through kernel functions.This review paper provides a comprehensive analysis of the SVC algorithms,exploring their variants such as robust,sparse,and fuzzy-based models and adaptations for large-scale data.Moreover,we analyze the potential of twin support vector clustering(TWSVC),with an emphasis on the use of various loss functions.Finally,the paper explores emerging trends and outlines promising future research directions for both SVC and twin SVC.These include advancements in feature engineering,extension to semi-supervised and weakly supervised learning,and the integration of multi-view and multi-modal data.Our work aims to deepen the understanding of SVC,fostering advancements that address the evolving needs of clustering in real-world scenarios. 展开更多
关键词 Clustering kernel methods support vector machine(SVM) twin support vector clustering (TWSVC) unsupervised learning
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Support Vector-Guided Class-Incremental Learning:Discriminative Replay with Dual-Alignment Distillation 认领 引用
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作者 Moyi Zhang Yixin Wang Yu Cheng 《Computers, Materials & Continua》 SCIE EI 2026年第3期2040-2061,共22页
Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural netwo... Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural networks learn new classes sequentially,they suffer from catastrophic forgetting—the tendency to lose knowledge of earlier classes.This challenge,which lies at the core of class-incremental learning,severely limits the deployment of continual learning systems in real-world applications with streaming data.Existing approaches,including rehearsalbased methods and knowledge distillation techniques,have attempted to address this issue but often struggle to effectively preserve decision boundaries and discriminative features under limited memory constraints.To overcome these limitations,we propose a support vector-guided framework for class-incremental learning.The framework integrates an enhanced feature extractor with a Support Vector Machine classifier,which generates boundary-critical support vectors to guide both replay and distillation.Building on this architecture,we design a joint feature retention strategy that combines boundary proximity with feature diversity,and a Support Vector Distillation Loss that enforces dual alignment in decision and semantic spaces.In addition,triple attention modules are incorporated into the feature extractor to enhance representation power.Extensive experiments on CIFAR-100 and Tiny-ImageNet demonstrate effective improvements.On CIFAR-100 and Tiny-ImageNet with 5 tasks,our method achieves 71.68%and 58.61%average accuracy,outperforming strong baselines by 3.34%and 2.05%.These advantages are consistently observed across different task splits,highlighting the robustness and generalization of the proposed approach.Beyond benchmark evaluations,the framework also shows potential in few-shot and resource-constrained applications such as edge computing and mobile robotics. 展开更多
关键词 Class-incremental learning catastrophic forgetting support vector machine knowledge distillation
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Prediction of total nitrogen in water based on UV spectroscopy and Bayesian optimized least squares support vector machine 认领 引用 被引量：1
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作者 ZHENG Peichao YANG Qin +3 位作者 LI Chenglin YIN Xukun WANG Jinmei GUO Lianbo 《Optoelectronics Letters》 EI 2025年第11期698-704,共7页
The total nitrogen(TN)is a major factor contributing to eutrophication and is a crucial parameter in assessing surface water quality.Accurate and rapid methods are crucial for determining the TN content in water.Herei... The total nitrogen(TN)is a major factor contributing to eutrophication and is a crucial parameter in assessing surface water quality.Accurate and rapid methods are crucial for determining the TN content in water.Herein,a fast,highly sensitive,and pollution-free approach is proposed,which combines ultraviolet(UV)absorption spectroscopy with Bayesian optimized least squares support vector machine(LSSVM)for detecting TN content in water.Water samples collected from sampling points near the Yangtze River basin in Chongqing of China were analyzed using national standard methods to measure TN content as reference values.The prediction of TN content in water was achieved by integrating the UV absorption spectra of water samples with LSSVM.To make the model quickly and accurately select the optimal parameters to improve the accuracy of the prediction model,the Bayesian optimization(BO)algorithm was used to optimize the parameters of the LSSVM.Results show that the prediction model performs well in predicting TN concentration,with a high coefficient of prediction determination(R2=0.9413)and a low root mean square error of prediction(RMSE=0.0779 mg/L).Comparative analysis with previous studies indicates that the model used in this paper achieves lower prediction errors and superior predictive performance. 展开更多
关键词 Bayesian optimization eutrophication total nitrogen tn bayesian optimized least squares support vector machine lssvm least squares support vector machine assessing surface water water quality total nitrogen
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Joint Estimation of SOH and RUL for Lithium-Ion Batteries Based on Improved Twin Support Vector Machineh 认领 引用 被引量：2
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作者 Liyao Yang Hongyan Ma +1 位作者 Yingda Zhang Wei He 《Energy Engineering》 EI 2025年第1期243-264,共22页
Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex int... Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance. 展开更多
关键词 State of health remaining useful life variational modal decomposition random forest twin support vector machine convolutional optimization algorithm
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POSITIVE DEFINITE KERNEL IN SUPPORT VECTOR MACHINE(SVM) 认领 引用 被引量：3
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作者 谢志鹏 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2009年第2期114-121,共8页
The relationship among Mercer kernel, reproducing kernel and positive definite kernel in support vector machine （SVM） is proved and their roles in SVM are discussed. The quadratic form of the kernel matrix is used t... The relationship among Mercer kernel, reproducing kernel and positive definite kernel in support vector machine （SVM） is proved and their roles in SVM are discussed. The quadratic form of the kernel matrix is used to confirm the positive definiteness and their construction. Based on the Bochner theorem, some translation invariant kernels are checked in their Fourier domain. Some rotation invariant radial kernels are inspected according to the Schoenberg theorem. Finally, the construction of discrete scaling and wavelet kernels, the kernel selection and the kernel parameter learning are discussed. 展开更多
关键词 support vector machines（SVMs） mercer kernel reproducing kernel positive definite kernel scaling and wavelet kernel
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Predicting Marine Fuels with Unusual Wax Appearance Temperatures Using One-Class Support Vector Machines 认领 引用
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作者 Njideka Chima-Amaeshi Chris O’Malley Mark Willis 《哈尔滨工程大学学报（英文版）》 CSCD 2025年第6期1208-1217,共10页
Accurate and robust detection of wax appearance(a medium-to high-molecular-weight component of crude oil)is crucial for the efficient operation of hydrocarbon transportation.The wax appearance temperature(WAT)is the l... Accurate and robust detection of wax appearance(a medium-to high-molecular-weight component of crude oil)is crucial for the efficient operation of hydrocarbon transportation.The wax appearance temperature(WAT)is the lowest temperature at which the wax begins to form.When crude oil cools to its WAT,wax crystals precipitate,forming deposits on pipelines as the solubility limit is reached.Therefore,WAT is a crucial quality assurance parameter,especially when dealing with modern fuel oil blends.In this study,we use machine learning via MATLAB’s Bioinformatics Toolbox to predict the WAT of marine fuel samples by correlating near-infrared spectral data with laboratory-measured values.The dataset provided by Intertek PLC-a total quality assurance provider of inspection,testing,and certification services-includes industrial data that is imbalanced,with a higher proportion of high-WAT samples compared to low-WAT samples.The objective is to predict marine fuel oil blends with unusually high WAT values(>35℃)without relying on time-consuming and irregular laboratory-based measurements.The results demonstrate that the developed model,based on the one-class support vector machine(OCSVM)algorithm,achieved a Recall of 96,accurately predicting 96%of fuel samples with WAT>35℃.For standard binary classification,the Recall was 85.7.The trained OCSVM model is expected to facilitate rapid and well-informed decision-making for logistics and storage when choosing fuel oils. 展开更多
关键词 Marine fuel One-class support vector machines Wax appearance temperature Wax Machine learning
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Support Vector Machine(SVM)and Object Based Classification in Earth Linear Features Extraction:A Comparison 认领 引用
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作者 Siti Aekbal Salleh Nafisah Khalid +4 位作者 Natasha Danny Nurul Ain Mohd.Zaki Mustafa Ustuner Zulkiflee Abd Latif Vladimir Foronda 《Revue Internationale de Géomatique》 2024年第1期183-199,共17页
Due to the spectral and spatial properties of pervious and impervious surfaces,image classification and information extraction in detailed,small-scale mapping of urban surface materials is quite difficult and complex.... Due to the spectral and spatial properties of pervious and impervious surfaces,image classification and information extraction in detailed,small-scale mapping of urban surface materials is quite difficult and complex.Emerging methods and innovations in image classification have centred on object-based classification techniques and various segmentation techniques,which are fundamental to this approach.Consequently,the purpose of this study is to determine which classification method is most suitable for extracting linear features in terms of techniques and performance by comparing two classification methods,pixel-based approach and object-based approach,using WorldView-2 satellite imagery to specifically highlight linear features such as roads,building edges,and road dividers.Two applied algorithms,including support vector machines(SVM)and ruled-based,were evaluated using two distinct software.A comparison of the results reveals that the object-based classification has a higher overall resolution than the pixel-based classification.The output of rule-based classificationwas satisfactory,with an overall accuracy of 88.6%(ENVI)and 92.2%(e-Cognition).The SVM classification result contained misclassified impervious surfaces and other urban features,as well as mixed objects.This classification achieved an overall accuracy of 75.1%.Nonetheless,this study provides an excellent overview for understanding the differences in their performances on the same data,as well as a comparison of the software employed. 展开更多
关键词 Support vector machine(SVM) remote sensing image classification
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NEW HYBRID AI-SVM ALGORITHM: COMBINATION OF SUPPORT VECTOR MACHINES AND ARTIFICIAL IMMUNE NETWORKS 认领 引用
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作者 张焕萍 王惠南 宋晓峰 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2006年第4期272-277,共6页
Support vector machines （SVMs） are combined with the artificial immune network （aiNet）, thus forming a new hybrid ai-SVM algorithm. The algorithm is used to reduce the number of samples and the training time of SV... Support vector machines （SVMs） are combined with the artificial immune network （aiNet）, thus forming a new hybrid ai-SVM algorithm. The algorithm is used to reduce the number of samples and the training time of SVM on large datasets, aiNet is an artificial immune system （AIS） inspired method to perform the automatic data compression, extract the relevant information and retain the topology of the original sample distribution. The output of aiNet is a set of antibodies for representing the input dataset in a simplified way. Then the SVM model is built in the compressed antibody network instead of the original input data. Experimental results show that the ai-SVM algorithm is effective to reduce the computing time and simplify the SVM model, and the accuracy is not decreased. 展开更多
关键词 support vector machine artificial immune network sample reduction
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Using Audiometric Data to Weigh and Prioritize Factors that Affect Workers’ Hearing Loss through Support Vector Machine (SVM) Algorithm 认领 引用 被引量：3
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作者 Hossein ElahiShirvan MohammadReza Ghotbi-Ravandi +1 位作者 Sajad Zare Mostafa Ghazizadeh Ahsaee 《Sound & Vibration》 EI 2020年第2期99-112,共14页
Workers’exposure to excessive noise is a big universal work-related challenges.One of the major consequences of exposure to noise is permanent or transient hearing loss.The current study sought to utilize audiometric... Workers’exposure to excessive noise is a big universal work-related challenges.One of the major consequences of exposure to noise is permanent or transient hearing loss.The current study sought to utilize audiometric data to weigh and prioritize the factors affecting workers’hearing loss based using the Support Vector Machine(SVM)algorithm.This cross sectional-descriptive study was conducted in 2017 in a mining industry in southeast Iran.The participating workers(n=150)were divided into three groups of 50 based on the sound pressure level to which they were exposed(two experimental groups and one control group).Audiometric tests were carried out for all members of each group.The study generally entailed the following steps:(1)selecting predicting variables to weigh and prioritize factors affecting hearing loss;(2)conducting audiometric tests and assessing permanent hearing loss in each ear and then evaluating total hearing loss;(3)categorizing different types of hearing loss;(4)weighing and prioritizing factors that affect hearing loss based on the SVM algorithm;and(5)assessing the error rate and accuracy of the models.The collected data were fed into SPSS 18,followed by conducting linear regression and paired samples t-test.It was revealed that,in the first model(SPL85 dBA),the frequency of 4 KHz had the highest impact(with a weight of 22%),while the frequency of 250 Hz had the smallest influence(with a weight of 3%).The accuracy of this model was 100%too.In the fourth model,the frequency of 4 KHz had the greatest effect(with a weight of 24%),while the frequency of 500 Hz had the smallest effect(with a weight of 4%).The accuracy of this model was found to be 94%.According to the modeling conducted using the SVM algorithm,the frequency of 4 KHz has the most profound effect on predicting changes in hearing loss.Given the high accuracy of the obtained model,this algorithm is an appropriate and powerful tool to predict and model hearing loss. 展开更多
关键词 Noise modeling hearing loss data mining support vector machine algorithm
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Research on an Air Pollutant Data Correction Method Based on Bayesian Optimization Support Vector Machine 认领 引用
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作者 Xingfu Ou Miao Zhang Wenfeng Chen 《Journal of Electronic Research and Application》 2025年第4期190-203,共14页
Miniature air quality sensors are widely used in urban grid-based monitoring due to their flexibility in deployment and low cost.However,the raw data collected by these devices often suffer from low accuracy caused by... Miniature air quality sensors are widely used in urban grid-based monitoring due to their flexibility in deployment and low cost.However,the raw data collected by these devices often suffer from low accuracy caused by environmental interference and sensor drift,highlighting the need for effective calibration methods to improve data reliability.This study proposes a data correction method based on Bayesian Optimization Support Vector Regression(BO-SVR),which combines the nonlinear modeling capability of Support Vector Regression(SVR)with the efficient global hyperparameter search of Bayesian Optimization.By introducing cross-validation loss as the optimization objective and using Gaussian process modeling with an Expected Improvement acquisition strategy,the approach automatically determines optimal hyperparameters for accurate pollutant concentration prediction.Experiments on real-world micro-sensor datasets demonstrate that BO-SVR outperforms traditional SVR,grid search SVR,and random forest(RF)models across multiple pollutants,including PM2.5,PM10,CO,NO2,SO2,and O3.The proposed method achieves lower prediction residuals,higher fitting accuracy,and better generalization,offering an efficient and practical solution for enhancing the quality of micro-sensor air monitoring data. 展开更多
关键词 Air quality monitoring Data calibration Support vector regression Bayesian optimization Machine learning
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Support Vector Machines(SVM)-Markov Chain Prediction Model of Mining Water Inflow 认领 引用 被引量：2
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作者 Kai HUANG 《Agricultural Science & Technology》 CAS 2017年第8期1551-1554,1558,共4页
This study was conducted to establish a Support Vector Machines(SVM)-Markov Chain prediction model for prediction of mining water inflow. According to the raw data sequence, the Support Vector Machines(SVM) model was ... This study was conducted to establish a Support Vector Machines(SVM)-Markov Chain prediction model for prediction of mining water inflow. According to the raw data sequence, the Support Vector Machines(SVM) model was built, and then revised by means of a Markov state change probability matrix. Through dividing the state and analyzing absolute errors and relative errors and other indexes of the measured value and the fitted value of SVM, the prediction results were improved. Finally,the model was used to calculate relative errors. Through predicting and analyzing mining water inflow, the prediction results of the model were satisfactory. The results of this study enlarge the application scope of the Support Vector Machines(SVM) prediction model and provide a new method for scientific forecasting water inflow in coal mining. 展开更多
关键词 Mining water inflow Support Vector Machines （SVM） Markov Chain
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An Efficient and Robust Fall Detection System Using Wireless Gait Analysis Sensor with Artificial Neural Network (ANN) and Support Vector Machine (SVM) Algorithms 认领 引用 被引量：3
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作者 Bhargava Teja Nukala Naohiro Shibuya +5 位作者 Amanda Rodriguez Jerry Tsay Jerry Lopez Tam Nguyen Steven Zupancic Donald Yu-Chun Lie 《Open Journal of Applied Biosensor》 2014年第4期29-39,共11页
In this work, a total of 322 tests were taken on young volunteers by performing 10 different falls, 6 different Activities of Daily Living (ADL) and 7 Dynamic Gait Index (DGI) tests using a custom-designed Wireless Ga... In this work, a total of 322 tests were taken on young volunteers by performing 10 different falls, 6 different Activities of Daily Living (ADL) and 7 Dynamic Gait Index (DGI) tests using a custom-designed Wireless Gait Analysis Sensor (WGAS). In order to perform automatic fall detection, we used Back Propagation Artificial Neural Network (BP-ANN) and Support Vector Machine (SVM) based on the 6 features extracted from the raw data. The WGAS, which includes a tri-axial accelerometer, 2 gyroscopes, and a MSP430 microcontroller, is worn by the subjects at either T4 (at back) or as a belt-clip in front of the waist during the various tests. The raw data is wirelessly transmitted from the WGAS to a near-by PC for real-time fall classification. The BP ANN is optimized by varying the training, testing and validation data sets and training the network with different learning schemes. SVM is optimized by using three different kernels and selecting the kernel for best classification rate. The overall accuracy of BP ANN is obtained as 98.20% with LM and RPROP training from the T4 data, while from the data taken at the belt, we achieved 98.70% with LM and SCG learning. The overall accuracy using SVM was 98.80% and 98.71% with RBF kernel from the T4 and belt position data, respectively. 展开更多
关键词 Artificial Neural Network (ANN) Back Propagation Fall Detection Fall Prevention Gait Analysis Sensor Support Vector Machine (SVM) Wireless Sensor
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Analysis of Ammonia Nitrogen Content in Water Based on Weighted Least Squares Support Vector Machine (WLSSVM) Algorithm 认领 引用 被引量：2
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作者 Jinwu Ju Lanying Wang 《Journal of Software Engineering and Applications》 2016年第2期45-51,共7页
Determination of ammonia nitrogen content in water is the basic item of the environmental water pollution, and is the key index to evaluate the water quality. This article designs a water quality monitoring system bas... Determination of ammonia nitrogen content in water is the basic item of the environmental water pollution, and is the key index to evaluate the water quality. This article designs a water quality monitoring system based on the on-line automatic ammonia nitrogen monitoring system, and establishes a forecasting model based on the weighted least squares support vector machine algorithm. The weighted least squares support vector machine algorithm increases the weight parameter setting, improves the speed and accuracy of prediction learning, and improves the robustness. In this article, a comparison between neural network model and weighted least square support vector machine model is made, which shows that the weighted least squares support vector machine model has better prediction accuracy. 展开更多
关键词 Support Vector Machine Water Quality Ammonia Nitrogen Forecasting Model
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Application of Least Square Support Vector Machine (LSSVM) for Determination of Evaporation Losses in Reservoirs 认领 引用 被引量：5
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作者 Pijush Samui 《Engineering（科研）》 2011年第4期431-434,共4页
This article adopts Least Square Support Vector Machine (LSSVM) for prediction of Evaporation Losses (EL) in reservoirs. LSSVM is firmly based on the theory of statistical learning, uses regression technique. The inpu... This article adopts Least Square Support Vector Machine (LSSVM) for prediction of Evaporation Losses (EL) in reservoirs. LSSVM is firmly based on the theory of statistical learning, uses regression technique. The input of LSSVM model is Mean air temperature (T) (?C), Average wind speed (WS)(m/sec), Sunshine hours (SH)(hrs/day), and Mean relative humidity(RH)(%). LSSVM has been used to compute error barn of predicted data. An equation has been developed for the determination of EL. Sensitivity analysis has been also performed to investigate the importance of each of the input parameters. A comparative study has been presented between LSSVM and artificial neural network (ANN) models. This study shows that LSSVM is a powerful tool for determination EL in reservoirs. 展开更多
关键词 Evaporation Losses Least Square Support Vector Machine Prediction Artificial Neural Network
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