In this research work,we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma(SS)is the cell structure for cancer.Within this framework the histopathology images are d...In this research work,we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma(SS)is the cell structure for cancer.Within this framework the histopathology images are decomposed into a third-level sub-band using a two-dimensional Discrete Wavelet Transform.Subsequently,the structure features(SFs)such as PrincipalComponentsAnalysis(PCA),Independent ComponentsAnalysis(ICA)and Linear Discriminant Analysis(LDA)were extracted from this subband image representation with the distribution of wavelet coefficients.These SFs are used as inputs of the Support Vector Machine(SVM)classifier.Also,classification of PCA+SVM,ICA+SVM,and LDA+SVM with Radial Basis Function(RBF)kernel the efficiency of the process is differentiated and compared with the best classification results.Furthermore,data collected on the internet from various histopathological centres via the Internet of Things(IoT)are stored and shared on blockchain technology across a wide range of image distribution across secure data IoT devices.Due to this,the minimum and maximum values of the kernel parameter are adjusted and updated periodically for the purpose of industrial application in device calibration.Consequently,these resolutions are presented with an excellent example of a technique for training and testing the cancer cell structure prognosis methods in spindle shaped cell(SSC)histopathological imaging databases.The performance characteristics of cross-validation are evaluated with the help of the receiver operating characteristics(ROC)curve,and significant differences in classification performance between the techniques are analyzed.The combination of LDA+SVM technique has been proven to be essential for intelligent SS cancer detection in the future,and it offers excellent classification accuracy,sensitivity,specificity.展开更多
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
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
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
The support vector machine,a widely used binary classification method,may expose sensitive information during training.To address this,the authors propose a personalized differential privacy method that extends differ...The support vector machine,a widely used binary classification method,may expose sensitive information during training.To address this,the authors propose a personalized differential privacy method that extends differential privacy.Specifically,the authors introduce personalized differentially private support vector machines to meet different individuals'privacy requirements,using a reweighting strategy and the Laplace mechanism.Theoretical analysis demonstrates that the proposed methods simultaneously satisfy the requirements of personalized differential privacy and ensure model prediction accuracy at these privacy levels.Extensive experiments demonstrate that the proposed methods outperform the existing methods.展开更多
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 MoTe_(2)/MoS_(2)type-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
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
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
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(R^(2)=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
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
Objective To develop and evaluate an automated system for digitizing audiograms,classifying hearing loss levels,and comparing their performance with traditional methods and otolaryngologists'interpretations.Design...Objective To develop and evaluate an automated system for digitizing audiograms,classifying hearing loss levels,and comparing their performance with traditional methods and otolaryngologists'interpretations.Designed and Methods We conducted a retrospective diagnostic study using 1,959 audiogram images from patients aged 7 years and older at the Faculty of Medicine,Vajira Hospital,Navamindradhiraj University.We employed an object detection approach to digitize audiograms and developed multiple machine learning models to classify six hearing loss levels.The dataset was split into 70%training(1,407 images)and 30%testing(352 images)sets.We compared our model's performance with classifications based on manually extracted audiogram values and otolaryngologists'interpretations.Result Our object detection-based model achieved an F1-score of 94.72%in classifying hearing loss levels,comparable to the 96.43%F1-score obtained using manually extracted values.The Light Gradient Boosting Machine(LGBM)model is used as the classifier for the manually extracted data,which achieved top performance with 94.72%accuracy,94.72%f1-score,94.72 recall,and 94.72 precision.In object detection based model,The Random Forest Classifier(RFC)model showed the highest 96.43%accuracy in predicting hearing loss level,with a F1-score of 96.43%,recall of 96.43%,and precision of 96.45%.Conclusion Our proposed automated approach for audiogram digitization and hearing loss classification performs comparably to traditional methods and otolaryngologists'interpretations.This system can potentially assist otolaryngologists in providing more timely and effective treatment by quickly and accurately classifying hearing loss.展开更多
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 PM_(2.5),PM_(10),CO,NO_(2),SO_(2),and O_(3).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
In wireless communication,the problem of authenticating the transmitter’s identity is challeng-ing,especially for those terminal devices in which the security schemes based on cryptography are approxi-mately unfeasib...In wireless communication,the problem of authenticating the transmitter’s identity is challeng-ing,especially for those terminal devices in which the security schemes based on cryptography are approxi-mately unfeasible owing to limited resources.In this paper,a physical layer authentication scheme is pro-posed to detect whether there is anomalous access by the attackers disguised as legitimate users.Explicitly,channel state information(CSI)is used as a form of fingerprint to exploit spatial discrimination among de-vices in the wireless network and machine learning(ML)technology is employed to promote the improve-ment of authentication accuracy.Considering that the falsified messages are not accessible for authenticator during the training phase,deep support vector data de-scription(Deep SVDD)is selected to solve the one-class classification(OCC)problem.Simulation results show that Deep SVDD based scheme can tackle the challenges of physical layer authentication in wireless communication environments.展开更多
Aiming at the problems of the traditional method of assessing distribution of particle size in bench blasting, a support vector machines （SVMs） regression methodology was used to predict the mean particle size （X50...Aiming at the problems of the traditional method of assessing distribution of particle size in bench blasting, a support vector machines （SVMs） regression methodology was used to predict the mean particle size （X50） resulting from rock blast fragmentation in various mines based on the statistical learning theory. The data base consisted of blast design parameters, explosive parameters, modulus of elasticity and in-situ block size. The seven input independent variables used for the SVMs model for the prediction of X50 of rock blast fragmentation were the ratio of bench height to drilled burden （H/B）, ratio of spacing to burden （S/B）, ratio of burden to hole diameter （B/D）, ratio of stemming to burden （T/B）, powder factor （Pf）, modulus of elasticity （E） and in-situ block size （XB）. After using the 90 sets of the measured data in various mines and rock formations in the world for training and testing, the model was applied to 12 another blast data for validation of the trained support vector regression （SVR） model. The prediction results of SVR were compared with those of artificial neural network （ANN）, multivariate regression analysis （MVRA） models, conventional Kuznetsov method and the measured X50 values. The proposed method shows promising results and the prediction accuracy of SVMs model is acceptable.展开更多
In order to deal with the issue of huge computational cost very well in direct numerical simulation, the traditional response surface method (RSM) as a classical regression algorithm is used to approximate a functiona...In order to deal with the issue of huge computational cost very well in direct numerical simulation, the traditional response surface method (RSM) as a classical regression algorithm is used to approximate a functional relationship between the state variable and basic variables in reliability design. The algorithm has treated successfully some problems of implicit performance function in reliability analysis. However, its theoretical basis of empirical risk minimization narrows its range of applications for...展开更多
Support vector machines (SVMs) are utilized for emotion recognition in Chinese speech in this paper. Both binary class discrimination and the multi class discrimination are discussed. It proves that the emotional fe...Support vector machines (SVMs) are utilized for emotion recognition in Chinese speech in this paper. Both binary class discrimination and the multi class discrimination are discussed. It proves that the emotional features construct a nonlinear problem in the input space, and SVMs based on nonlinear mapping can solve it more effectively than other linear methods. Multi class classification based on SVMs with a soft decision function is constructed to classify the four emotion situations. Compared with principal component analysis (PCA) method and modified PCA method, SVMs perform the best result in multi class discrimination by using nonlinear kernel mapping.展开更多
The hard support vector regression attracts little attention owing to the overfitting phenomenon. Recently, a fast offiine method has been proposed to approximately train the hard support vector regression with the ge...The hard support vector regression attracts little attention owing to the overfitting phenomenon. Recently, a fast offiine method has been proposed to approximately train the hard support vector regression with the generation performance comparable to the soft support vector regression. Based on this achievement, this article advances a fast online approximation called the hard sup- port vector regression （FOAHSVR for short）. By adopting the greedy stagewise and iterative strategies, it is capable of online estimating parameters of complicated systems. In order to verify the effectiveness of the FOAHSVR, an FOAHSVR-based analytical redundancy for aeroengines is developed. Experiments on the sensor failure and drift evidence the viability and feasibility of the analytical redundancy for aeroengines together with its base--FOAHSVR. In addition, the FOAHSVR is anticipated to find applications in other scientific-technical fields.展开更多
In order to solve the fatigue damage identification problem of helicopter moving components, a new approach for acoustic emission （AE） source type identification based on the harmonic wavelet packet （HWPT） feature...In order to solve the fatigue damage identification problem of helicopter moving components, a new approach for acoustic emission （AE） source type identification based on the harmonic wavelet packet （HWPT） feature extraction and the hierarchy support vector machine （H-SVM） classifier is proposed. After a four-level decomposition of the HWPT, the energy feature of AE signals in different frequency bands is extracted, which overcomes the shortcomings of the traditional wavelet packet including energy leakage, and inflexible frequency band selection and different frequency resolutions on different levels. The H-SVM classifier is trained with a subset of the experimental data for known AE source types and tested using the remaining set of data. The results of pressure-off experiments on the specimens of carbon fiber materials indicate that the proposed approach can effectively implement the AE source type identification, and has a better performance in terms of computational efficiency and identification accuracy than the wavelet packet （WPT） feature extraction.展开更多
In order to assist the design of short interfering ribonucleic acids （siRNA）, 573 non-redundant siRNAs were collected from published literatures and the relationship between siRNAs sequences and RNA interference （R...In order to assist the design of short interfering ribonucleic acids （siRNA）, 573 non-redundant siRNAs were collected from published literatures and the relationship between siRNAs sequences and RNA interference （RNAi） effect is analyzed by a support vector machine （SVM） based algorithm relied on a basebase correlation （BBC） feature. The results show that the proposed algorithm has the highest area under curve （AUC） value （0. 73） of the receive operating characteristic （ROC） curve and the greatest r value （0. 43） of the Pearson＇s correlation coefficient. This indicates that the proposed algorithm is better than the published algorithms on the collected datasets and that more attention should be paid to the base-base correlation information in future siRNA design.展开更多
A simple and effective mechanism is proposed to realize the parsimoniousness of the online least squares support vector regression （LS-SVR）, and the approach is called the OPLS-SVR for short. Hence, the response tim...A simple and effective mechanism is proposed to realize the parsimoniousness of the online least squares support vector regression （LS-SVR）, and the approach is called the OPLS-SVR for short. Hence, the response time is curtailed. Besides, an OPLS-SVR based analytical redundancy technique is presented to cope with the sensor failure and drift problems to guarantee that the provided signals for the aeroengine controller are correct and acceptable. Experiments on the sensor failure and drift show the effectiveness and the validity of the proposed analytical redundancy.展开更多
基金This work was partly supported by the Technology development Program of MSS[No.S3033853]by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2020R1I1A3069700).
文摘In this research work,we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma(SS)is the cell structure for cancer.Within this framework the histopathology images are decomposed into a third-level sub-band using a two-dimensional Discrete Wavelet Transform.Subsequently,the structure features(SFs)such as PrincipalComponentsAnalysis(PCA),Independent ComponentsAnalysis(ICA)and Linear Discriminant Analysis(LDA)were extracted from this subband image representation with the distribution of wavelet coefficients.These SFs are used as inputs of the Support Vector Machine(SVM)classifier.Also,classification of PCA+SVM,ICA+SVM,and LDA+SVM with Radial Basis Function(RBF)kernel the efficiency of the process is differentiated and compared with the best classification results.Furthermore,data collected on the internet from various histopathological centres via the Internet of Things(IoT)are stored and shared on blockchain technology across a wide range of image distribution across secure data IoT devices.Due to this,the minimum and maximum values of the kernel parameter are adjusted and updated periodically for the purpose of industrial application in device calibration.Consequently,these resolutions are presented with an excellent example of a technique for training and testing the cancer cell structure prognosis methods in spindle shaped cell(SSC)histopathological imaging databases.The performance characteristics of cross-validation are evaluated with the help of the receiver operating characteristics(ROC)curve,and significant differences in classification performance between the techniques are analyzed.The combination of LDA+SVM technique has been proven to be essential for intelligent SS cancer detection in the future,and it offers excellent classification accuracy,sensitivity,specificity.
基金The work described in this paper was fully supported by a grant from Hong Kong Metropolitan University(RIF/2021/05).
文摘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.
基金supported by the China Agriculture Research System of MOF and MARAthe National Natural Science Foundation of China (31872337 and 31501919)the Agricultural Science and Technology Innovation Project,China (ASTIP-IAS02)。
文摘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.
基金supported by the Gansu Provincial Natural Science Foundation(grant number 25JRRA074)the Gansu Provincial Key R&D Science and Technology Program(grant number 24YFGA060)the National Natural Science Foundation of China(grant number 62161019).
文摘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.
基金supported by the National Key R&D Program of China under Grant No.2023YFA1008702the National Natural Science Foundation of China under Grant No.12571300。
文摘The support vector machine,a widely used binary classification method,may expose sensitive information during training.To address this,the authors propose a personalized differential privacy method that extends differential privacy.Specifically,the authors introduce personalized differentially private support vector machines to meet different individuals'privacy requirements,using a reweighting strategy and the Laplace mechanism.Theoretical analysis demonstrates that the proposed methods simultaneously satisfy the requirements of personalized differential privacy and ensure model prediction accuracy at these privacy levels.Extensive experiments demonstrate that the proposed methods outperform the existing methods.
基金co-supported by the National Natural Science Foundation of China(Grant Nos.62222404,T2450054,62304084,62504087,62361136587 and 92248304)the National Key Research and Development Plan of China(Grant No.2021YFB3601200)+3 种基金the Major Program of Hubei Province(Grant No.2023BAA009)the Research Grants Council of Hong Kong Postdoctoral Fellowship Scheme(Grant No.PDFS2223-4S06)the China Postdoctoral Science Foundation funded project(Grant No.2025M770530)the Postdoctoral Fellowship Program of CPSF(Grant No.GZB20250136).
文摘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 MoTe_(2)/MoS_(2)type-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.
基金funded by the Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture under Grant GJZJ20220802。
文摘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.
基金supported by the Deanship of Graduate Studies and Scientific Research at University of Bisha for funding this research through the promising program under grant number(UB-Promising-33-1445).
文摘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.
基金supported by the National Natural Science Foundation of China(Nos.32171627 and 62105252)the Science and Technology Research Program of Chongqing Municipal Education Commission(No.KJZD-M202200602)the Hangzhou Science and Technology Development Project(No.202204T04).
文摘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(R^(2)=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.
基金Newcastle University and EPSRC(Grant No.2020/21 DTP:ref.EP/T517914/1).
文摘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.
文摘Objective To develop and evaluate an automated system for digitizing audiograms,classifying hearing loss levels,and comparing their performance with traditional methods and otolaryngologists'interpretations.Designed and Methods We conducted a retrospective diagnostic study using 1,959 audiogram images from patients aged 7 years and older at the Faculty of Medicine,Vajira Hospital,Navamindradhiraj University.We employed an object detection approach to digitize audiograms and developed multiple machine learning models to classify six hearing loss levels.The dataset was split into 70%training(1,407 images)and 30%testing(352 images)sets.We compared our model's performance with classifications based on manually extracted audiogram values and otolaryngologists'interpretations.Result Our object detection-based model achieved an F1-score of 94.72%in classifying hearing loss levels,comparable to the 96.43%F1-score obtained using manually extracted values.The Light Gradient Boosting Machine(LGBM)model is used as the classifier for the manually extracted data,which achieved top performance with 94.72%accuracy,94.72%f1-score,94.72 recall,and 94.72 precision.In object detection based model,The Random Forest Classifier(RFC)model showed the highest 96.43%accuracy in predicting hearing loss level,with a F1-score of 96.43%,recall of 96.43%,and precision of 96.45%.Conclusion Our proposed automated approach for audiogram digitization and hearing loss classification performs comparably to traditional methods and otolaryngologists'interpretations.This system can potentially assist otolaryngologists in providing more timely and effective treatment by quickly and accurately classifying hearing loss.
文摘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 PM_(2.5),PM_(10),CO,NO_(2),SO_(2),and O_(3).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.
基金partially supported by the National Key Research and Development Project under Grant2020YFB1806805Social Development Projects of Jiangsu Science and Technology Department under Grant No.BE2018704
文摘In wireless communication,the problem of authenticating the transmitter’s identity is challeng-ing,especially for those terminal devices in which the security schemes based on cryptography are approxi-mately unfeasible owing to limited resources.In this paper,a physical layer authentication scheme is pro-posed to detect whether there is anomalous access by the attackers disguised as legitimate users.Explicitly,channel state information(CSI)is used as a form of fingerprint to exploit spatial discrimination among de-vices in the wireless network and machine learning(ML)technology is employed to promote the improve-ment of authentication accuracy.Considering that the falsified messages are not accessible for authenticator during the training phase,deep support vector data de-scription(Deep SVDD)is selected to solve the one-class classification(OCC)problem.Simulation results show that Deep SVDD based scheme can tackle the challenges of physical layer authentication in wireless communication environments.
基金Foundation item:Project (2006BAB02A02) supported by the National Key Technology R&D Program during the 11th Five-year Plan Period of ChinaProject (CX2011B119) supported by the Graduated Students' Research and Innovation Fund of Hunan Province, ChinaProject (2009ssxt230) supported by the Central South University Innovation Fund,China
文摘Aiming at the problems of the traditional method of assessing distribution of particle size in bench blasting, a support vector machines （SVMs） regression methodology was used to predict the mean particle size （X50） resulting from rock blast fragmentation in various mines based on the statistical learning theory. The data base consisted of blast design parameters, explosive parameters, modulus of elasticity and in-situ block size. The seven input independent variables used for the SVMs model for the prediction of X50 of rock blast fragmentation were the ratio of bench height to drilled burden （H/B）, ratio of spacing to burden （S/B）, ratio of burden to hole diameter （B/D）, ratio of stemming to burden （T/B）, powder factor （Pf）, modulus of elasticity （E） and in-situ block size （XB）. After using the 90 sets of the measured data in various mines and rock formations in the world for training and testing, the model was applied to 12 another blast data for validation of the trained support vector regression （SVR） model. The prediction results of SVR were compared with those of artificial neural network （ANN）, multivariate regression analysis （MVRA） models, conventional Kuznetsov method and the measured X50 values. The proposed method shows promising results and the prediction accuracy of SVMs model is acceptable.
基金National High-tech Research and Development Pro-gram (2006AA04Z405)
文摘In order to deal with the issue of huge computational cost very well in direct numerical simulation, the traditional response surface method (RSM) as a classical regression algorithm is used to approximate a functional relationship between the state variable and basic variables in reliability design. The algorithm has treated successfully some problems of implicit performance function in reliability analysis. However, its theoretical basis of empirical risk minimization narrows its range of applications for...
文摘Support vector machines (SVMs) are utilized for emotion recognition in Chinese speech in this paper. Both binary class discrimination and the multi class discrimination are discussed. It proves that the emotional features construct a nonlinear problem in the input space, and SVMs based on nonlinear mapping can solve it more effectively than other linear methods. Multi class classification based on SVMs with a soft decision function is constructed to classify the four emotion situations. Compared with principal component analysis (PCA) method and modified PCA method, SVMs perform the best result in multi class discrimination by using nonlinear kernel mapping.
基金National Natural Science Foundation of China (50576033)Aeronautical Science Foundation of China (04C52019)
文摘The hard support vector regression attracts little attention owing to the overfitting phenomenon. Recently, a fast offiine method has been proposed to approximately train the hard support vector regression with the generation performance comparable to the soft support vector regression. Based on this achievement, this article advances a fast online approximation called the hard sup- port vector regression （FOAHSVR for short）. By adopting the greedy stagewise and iterative strategies, it is capable of online estimating parameters of complicated systems. In order to verify the effectiveness of the FOAHSVR, an FOAHSVR-based analytical redundancy for aeroengines is developed. Experiments on the sensor failure and drift evidence the viability and feasibility of the analytical redundancy for aeroengines together with its base--FOAHSVR. In addition, the FOAHSVR is anticipated to find applications in other scientific-technical fields.
基金The Natural Science Foundation of Heilongjiang Province ( No. F201018)the National Natural Science Foundation of China( No. 60901042)
文摘In order to solve the fatigue damage identification problem of helicopter moving components, a new approach for acoustic emission （AE） source type identification based on the harmonic wavelet packet （HWPT） feature extraction and the hierarchy support vector machine （H-SVM） classifier is proposed. After a four-level decomposition of the HWPT, the energy feature of AE signals in different frequency bands is extracted, which overcomes the shortcomings of the traditional wavelet packet including energy leakage, and inflexible frequency band selection and different frequency resolutions on different levels. The H-SVM classifier is trained with a subset of the experimental data for known AE source types and tested using the remaining set of data. The results of pressure-off experiments on the specimens of carbon fiber materials indicate that the proposed approach can effectively implement the AE source type identification, and has a better performance in terms of computational efficiency and identification accuracy than the wavelet packet （WPT） feature extraction.
基金The National Natural Science Foundation of China(No60671018,60121101)
文摘In order to assist the design of short interfering ribonucleic acids （siRNA）, 573 non-redundant siRNAs were collected from published literatures and the relationship between siRNAs sequences and RNA interference （RNAi） effect is analyzed by a support vector machine （SVM） based algorithm relied on a basebase correlation （BBC） feature. The results show that the proposed algorithm has the highest area under curve （AUC） value （0. 73） of the receive operating characteristic （ROC） curve and the greatest r value （0. 43） of the Pearson＇s correlation coefficient. This indicates that the proposed algorithm is better than the published algorithms on the collected datasets and that more attention should be paid to the base-base correlation information in future siRNA design.
基金Supported by the National Natural Science Foundation of China(50576033)the Aeronautical ScienceFoundation of China(04C52019)~~
文摘A simple and effective mechanism is proposed to realize the parsimoniousness of the online least squares support vector regression （LS-SVR）, and the approach is called the OPLS-SVR for short. Hence, the response time is curtailed. Besides, an OPLS-SVR based analytical redundancy technique is presented to cope with the sensor failure and drift problems to guarantee that the provided signals for the aeroengine controller are correct and acceptable. Experiments on the sensor failure and drift show the effectiveness and the validity of the proposed analytical redundancy.
