The support vector machine(SVM)is a classical machine learning method.Both the hinge loss and least absolute shrinkage and selection operator(LASSO)penalty are usually used in traditional SVMs.However,the hinge loss i...The support vector machine(SVM)is a classical machine learning method.Both the hinge loss and least absolute shrinkage and selection operator(LASSO)penalty are usually used in traditional SVMs.However,the hinge loss is not differentiable,and the LASSO penalty does not have the Oracle property.In this paper,the huberized loss is combined with non-convex penalties to obtain a model that has the advantages of both the computational simplicity and the Oracle property,contributing to higher accuracy than traditional SVMs.It is experimentally demonstrated that the two non-convex huberized-SVM methods,smoothly clipped absolute deviation huberized-SVM(SCAD-HSVM)and minimax concave penalty huberized-SVM(MCP-HSVM),outperform the traditional SVM method in terms of the prediction accuracy and classifier performance.They are also superior in terms of variable selection,especially when there is a high linear correlation between the variables.When they are applied to the prediction of listed companies,the variables that can affect and predict financial distress are accurately filtered out.Among all the indicators,the indicators per share have the greatest influence while those of solvency have the weakest influence.Listed companies can assess the financial situation with the indicators screened by our algorithm and make an early warning of their possible financial distress in advance with higher precision.展开更多
In computer vision,emotion recognition using facial expression images is considered an important research issue.Deep learning advances in recent years have aided in attaining improved results in this issue.According t...In computer vision,emotion recognition using facial expression images is considered an important research issue.Deep learning advances in recent years have aided in attaining improved results in this issue.According to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of emotion.It is feasible and useful to convert face photos into collections of visual words and carry out global expression recognition.The main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is used.AffectNet uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos online.The FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization phase.Linear discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously enhanced.Grid search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.展开更多
Geomechanical parameters are complex and uncertain.In order to take this complexity and uncertainty into account,a probabilistic back-analysis method combining the Bayesian probability with the least squares support v...Geomechanical parameters are complex and uncertain.In order to take this complexity and uncertainty into account,a probabilistic back-analysis method combining the Bayesian probability with the least squares support vector machine(LS-SVM) technique was proposed.The Bayesian probability was used to deal with the uncertainties in the geomechanical parameters,and an LS-SVM was utilized to establish the relationship between the displacement and the geomechanical parameters.The proposed approach was applied to the geomechanical parameter identification in a slope stability case study which was related to the permanent ship lock within the Three Gorges project in China.The results indicate that the proposed method presents the uncertainties in the geomechanical parameters reasonably well,and also improves the understanding that the monitored information is important in real projects.展开更多
Mental task classification is one of the most important problems in Brain-computer interface.This paper studies the classification of five-class mental tasks.The nonlinear parameter of mean period obtained from freque...Mental task classification is one of the most important problems in Brain-computer interface.This paper studies the classification of five-class mental tasks.The nonlinear parameter of mean period obtained from frequency domain information was used as features for classification implemented by using the method of SVM(support vector machines).The averaged classification accuracy of 85.6% over 7 subjects was achieved for 2-second EEG segments.And the results for EEG segments of 0.5s and 5.0s compared favorably to those of Garrett’s.The results indicate that the parameter of mean period represents mental tasks well for classification.Furthermore,the method of mean period is less computationally demanding,which indicates its potential use for online BCI systems.展开更多
An adaptive blind support vector machine equalizer(ABSVME) is presented in this paper.The method is based upon least square support vector machine(LSSVM),and stems from signal feature reconstruction idea.By oversampli...An adaptive blind support vector machine equalizer(ABSVME) is presented in this paper.The method is based upon least square support vector machine(LSSVM),and stems from signal feature reconstruction idea.By oversampling the output of a LSSVM equalizer and exploiting a reasonable decorrelation cost function design,the method achieves fine online channel tracing with Kumar express algorithm and static iterative learning algorithm incorporated.The method is verified through simulation and compared with other nonlinear equalizers.The results show that it provides excellent performance in nonlinear equalization and time-varying channel tracing.Although a constant module equalization algorithm requires that the signal has characteristic of constant module,this method has no such requirement.展开更多
In today’s digital era,the text may be in form of images.This research aims to deal with the problem by recognizing such text and utilizing the support vector machine(SVM).A lot of work has been done on the English l...In today’s digital era,the text may be in form of images.This research aims to deal with the problem by recognizing such text and utilizing the support vector machine(SVM).A lot of work has been done on the English language for handwritten character recognition but very less work on the under-resourced Hindi language.A method is developed for identifying Hindi language characters that use morphology,edge detection,histograms of oriented gradients(HOG),and SVM classes for summary creation.SVM rank employs the summary to extract essential phrases based on paragraph position,phrase position,numerical data,inverted comma,sentence length,and keywords features.The primary goal of the SVM optimization function is to reduce the number of features by eliminating unnecessary and redundant features.The second goal is to maintain or improve the classification system’s performance.The experiment included news articles from various genres,such as Bollywood,politics,and sports.The proposed method’s accuracy for Hindi character recognition is 96.97%,which is good compared with baseline approaches,and system-generated summaries are compared to human summaries.The evaluated results show a precision of 72%at a compression ratio of 50%and a precision of 60%at a compression ratio of 25%,in comparison to state-of-the-art methods,this is a decent result.展开更多
In order to improve the accuracy of using visual methods to detect the quality of fluff fabrics,based on the previous research,this paper proposes a method of rapid classification detection using support vector machin...In order to improve the accuracy of using visual methods to detect the quality of fluff fabrics,based on the previous research,this paper proposes a method of rapid classification detection using support vector machine(SVM).The fabric image is acquired by the principle of light-cut imaging,and the region of interest is extracted by the method of grayscale horizontal projection.The obtained coordinates of the upper edge of the fabric are decomposed into high frequency information and low frequency information by wavelet transform,and the high frequency information is used as a data set for training.After experimental comparison and analysis,the detection rate of the SVM method proposed in this paper is higher than the previously proposed back propagation(BP)neural network and particle swarm optimization BP(PSO-BP)neural network detection methods,and the accuracy rate can reach 99.41%,which can meet the needs of industrial testing.展开更多
It is a challenging topic to develop an efficient algorithm for large scale classification problems in many applications of machine learning. In this paper, a hierarchical clustering and fixed-layer local learning (HC...It is a challenging topic to develop an efficient algorithm for large scale classification problems in many applications of machine learning. In this paper, a hierarchical clustering and fixed-layer local learning (HCFLL) based support vector machine(SVM) algorithm is proposed to deal with this problem. Firstly, HCFLL hierarchically clusters a given dataset into a modified clustering feature tree based on the ideas of unsupervised clustering and supervised clustering. Then it locally trains SVM on each labeled subtree at a fixed-layer of the tree. The experimental results show that compared with the existing popular algorithms such as core vector machine and decision-tree support vector machine, HCFLL can significantly improve the training and testing speeds with comparable testing accuracy.展开更多
Soft failure of mechanical equipment makes its performance drop gradually,which occupies a large proportion and has certain regularity. The performance can be evaluated and predicted through early state monitoring and...Soft failure of mechanical equipment makes its performance drop gradually,which occupies a large proportion and has certain regularity. The performance can be evaluated and predicted through early state monitoring and data analysis. The vibration signal was modeled from the double row bearing,and wavelet transform and support vector machine model( WT-SVM model) was constructed and trained for bearing degradation process prediction. Besides Hazen plotting position relationships was applied to describing the degradation trend distribution and a 95%confidence level based on t-distribution was given. The single SVM model and neural network( NN) approach were also investigated as a comparison. Results indicate that the WT-SVM model outperforms the NN and single SVM models,and is feasible and effective in machinery condition prediction.展开更多
Aiming at the reliability analysis of small sample data or implicit structural function,a novel structural reliability analysis model based on support vector machine(SVM)and neural network direct integration method(DN...Aiming at the reliability analysis of small sample data or implicit structural function,a novel structural reliability analysis model based on support vector machine(SVM)and neural network direct integration method(DNN)is proposed.Firstly,SVM with good small sample learning ability is used to train small sample data,fit structural performance functions and establish regular integration regions.Secondly,DNN is approximated the integral function to achieve multiple integration in the integration region.Finally,structural reliability was obtained by DNN.Numerical examples are investigated to demonstrate the effectiveness of the present method,which provides a feasible way for the structural reliability analysis.展开更多
To reduce the influences of outliers on support vector machine(SVM) classification problem,a new tangent loss function was constructed.Since the tangent loss function was not smooth in some interval,a smoothing functi...To reduce the influences of outliers on support vector machine(SVM) classification problem,a new tangent loss function was constructed.Since the tangent loss function was not smooth in some interval,a smoothing function was used to approximate it in this interval.According to this loss function,the corresponding tangent SVM(TSVM) was got.The experimental results show that TSVM is less sensitive to outliers than SVM.So the proposed new loss function and TSVM are both effective.展开更多
Geospatial objects detection within complex environment is a challenging problem in remote sensing area. In this paper, we derive an extension of the Relevance Vector Machine (RVM) technique to multiple kernel version...Geospatial objects detection within complex environment is a challenging problem in remote sensing area. In this paper, we derive an extension of the Relevance Vector Machine (RVM) technique to multiple kernel version. The proposed method learns an optimal kernel combination and the associated classifier simultaneously. Two feature types are extracted from images, forming basis kernels. Then these basis kernels are weighted combined and resulted the composite kernel exploits interesting points and appearance information of objects simultaneously. Weights and the detection model are finally learnt by a new algorithm. Experimental results show that the proposed method improve detection accuracy to above 88%, yields good interpretation for the selected subset of features and appears sparser than traditional single-kernel RVMs.展开更多
Temperature prediction plays an important role in ring die granulator control,which can influence the quantity and quality of production. Temperature prediction modeling is a complicated problem with its MIMO, nonline...Temperature prediction plays an important role in ring die granulator control,which can influence the quantity and quality of production. Temperature prediction modeling is a complicated problem with its MIMO, nonlinear, and large time-delay characteristics. Support vector machine( SVM) has been successfully based on small data. But its accuracy is not high,in contrast,if the number of data and dimension of feature increase,the training time of model will increase dramatically. In this paper,a linear SVM was applied combing with cyclic coordinate descent( CCD) to solving big data regression. It was mathematically strictly proved and validated by simulation. Meanwhile,real data were conducted to prove the linear SVM model's effect. Compared with other methods for big data in simulation, this algorithm has apparent advantage not only in fast modeling but also in high fitness.展开更多
Emotion recognition from speech is an important field of research in human computer interaction. In this letter the framework of Support Vector Machines (SVM) with Gaussian Mixture Model (GMM) supervector is introduce...Emotion recognition from speech is an important field of research in human computer interaction. In this letter the framework of Support Vector Machines (SVM) with Gaussian Mixture Model (GMM) supervector is introduced for emotional speech recognition. Because of the importance of variance in reflecting the distribution of speech, the normalized mean vectors potential to exploit the information from the variance are adopted to form the GMM supervector. Comparative experiments from five aspects are conducted to study their corresponding effect to system performance. The experiment results, which indicate that the influence of number of mixtures is strong as well as influence of duration is weak, provide basis for the train set selection of Universal Background Model (UBM).展开更多
In cloud computing Resource allocation is a very complex task.Handling the customer demand makes the challenges of on-demand resource allocation.Many challenges are faced by conventional methods for resource allocatio...In cloud computing Resource allocation is a very complex task.Handling the customer demand makes the challenges of on-demand resource allocation.Many challenges are faced by conventional methods for resource allocation in order tomeet the Quality of Service(QoS)requirements of users.For solving the about said problems a new method was implemented with the utility of machine learning framework of resource allocation by utilizing the cloud computing technique was taken in to an account in this research work.The accuracy in the machine learning algorithm can be improved by introducing Bat Algorithm with feature selection(BFS)in the proposed work,this further reduces the inappropriate features from the data.The similarities that were hidden can be demoralized by the Support Vector Machine(SVM)classifier which is also determine the subspace vector and then a new feature vector can be predicted by using SVM.For an unexpected circumstance SVM model can make a resource allocation decision.The efficiency of proposed SVM classifier of resource allocation can be highlighted by using a singlecell multiuser massive Multiple-Input Multiple Output(MIMO)system,with beam allocation problem as an example.The proposed resource allocation based on SVM performs efficiently than the existing conventional methods;this has been proven by analysing its results.展开更多
In this paper,Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)based methods are to be applied on fault diagnosis in a robot manipulator.A comparative study between the two classifiers in terms of successfully det...In this paper,Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)based methods are to be applied on fault diagnosis in a robot manipulator.A comparative study between the two classifiers in terms of successfully detecting and isolating the seven classes of sensor faults is considered in this work.For both classifiers,the torque,the position and the speed of the manipulator have been employed as the input vector.However,it is to mention that a large database is needed and used for the training and testing phases.The SVM method used in this paper is based on the Gaussian kernel with the parametersγand the penalty margin parameter“C”,which were adjusted via the PSO algorithm to achieve a maximum accuracy diagnosis.Simulations were carried out on the model of a Selective Compliance Assembly Robot Arm(SCARA)robot manipulator,and the results showed that the Particle Swarm Optimization(PSO)increased the per-formance of the SVM algorithm with the 96.95%accuracy while the KNN algo-rithm achieved a correlation up to 94.62%.These results showed that the SVM algorithm with PSO was more precise than the KNN algorithm when was used in fault diagnosis on a robot manipulator.展开更多
Invasive breast carcinoma(BRCA)is associated with poor prognosis and high risk of mortality.Therefore,it is critical to identify novel biomarkers for the prognostic assessment of BRCA.Methods:The expression data of po...Invasive breast carcinoma(BRCA)is associated with poor prognosis and high risk of mortality.Therefore,it is critical to identify novel biomarkers for the prognostic assessment of BRCA.Methods:The expression data of polo-like kinase 1(PLK1)in BRCA and the corresponding clinical information were extracted from TCGA and GEO databases.PLK1 expression was validated in diverse breast cancer cell lines by quantitative real-time polymerase chain reaction(qRT-PCR)and western blotting.Single sample gene set enrichment analysis(ssGSEA)was performed to evaluate immune infiltration in the BRCA microenvironment,and the random forest(RF)and support vector machine(SVM)algorithms were used to screen for the hub infiltrating cells and calculate the immunophenoscore(IPS).The RF algorithm and COX regression model were applied to calculate survival risk scores based on the PLK1 expression and immune cell infiltration.Finally,a prognostic nomogram was constructed with the risk score and pathological stage,and its clinical potential was evaluated by plotting calibration charts and DCA curves.The application of the nomogram was further validated in an immunotherapy cohort.Results:PLK1 expression was significantly higher in the tumor samples in TCGA-BRCA cohort.Furthermore,PLK1 expression level,age and stage were identified as independent prognostic factors of BRCA.While the IPS was unaffected by PLK1 expression,the TMB and MATH scores were higher in the PLK1-high group,and the TIDE scores were higher for the PLK1-low patients.We also identified 6 immune cell types with high infiltration,along with 11 immune cell types with low infiltration in the PLK1-high tumors.A risk score was devised using PLK1 expression and hub immune cells,which predicted the prognosis of BRCA patients.In addition,a nomogram was constructed based on the risk score and pathological staging,and showed good predictive performance.Conclusions:PLK1 expression and immune cell infiltration can predict post-immunotherapy prognosis of BRCA patients.展开更多
Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR ...Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to errors.This paper proposes a novel VTDR detection and classification model that combines different models through majority voting.Our proposed methodology involves preprocessing,data augmentation,feature extraction,and classification stages.We use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for classification.We tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,respectively.Our proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.展开更多
This paper proposes a single-ended fault detection scheme for long transmission lines using support vector machine(SVM)for multi-terminal direct current systems based on modular multilevel converter(MMC-MTDC).The sche...This paper proposes a single-ended fault detection scheme for long transmission lines using support vector machine(SVM)for multi-terminal direct current systems based on modular multilevel converter(MMC-MTDC).The scheme overcomes existing detection difficulties in the protection of long transmission lines resulting from high grounding resistance and attenuation,and also avoids the sophisticated process of threshold value selection.The high-frequency components in the measured voltage extracted by a wavelet transform and the amplitude of the zero-mode set of the positive-sequence voltage are the inputs to a trained SVM.The output of the SVM determines the fault type.A model of a four-terminal DC power grid with overhead transmission lines is built in PSCAD/EMTDC.Simulation results of EMTDC confirm that the proposed scheme achieves 100%accuracy in detecting short-circuit faults with high resistance on long transmission lines.The proposed scheme eliminates mal-operation of DC circuit breakers when faced with power order changes or AC-side faults.Its robustness and time delay are also assessed and shown to have no perceptible effect on the speed and accuracy of the detection scheme,thus ensuring its reliability and stability.展开更多
Purpose-Automated crop prediction is needed for the following reasons:First,agricultural yields were decided by a farmer’s ability to work in a certain field and with a particular crop previously.They were not always...Purpose-Automated crop prediction is needed for the following reasons:First,agricultural yields were decided by a farmer’s ability to work in a certain field and with a particular crop previously.They were not always able to predict the crop and its yield solely on that idea alone.Second,seed firms frequently monitor how well new plant varieties would grow in certain settings.Third,predicting agricultural production is critical for solving emerging food security concerns,especially in the face of global climate change.Accurate production forecasts not only assist farmers inmaking informed economic andmanagement decisions but they also aid in the prevention of famine.This results in farming systems’efficiency and productivity gains,as well as reduced risk from environmental factors.Design/methodology/approach-This research paper proposes a machine learning technique for effective autonomous crop and yield prediction,which makes use of solution encoding to create solutions randomly,and then for every generated solution,fitness is evaluated to meet highest accuracy.Major focus of the proposed work is to optimize the weight parameter in the input data.The algorithm continues until the optimal agent or optimal weight is selected,which contributes to maximum accuracy in automated crop prediction.Findings-Performance of the proposed work is compared with different existing algorithms,such as Random Forest,support vector machine(SVM)and artificial neural network(ANN).The proposed method support vector neural network(SVNN)with gravitational search agent(GSA)is analysed based on different performance metrics,such as accuracy,sensitivity,specificity,CPU memory usage and training time,and maximum performance is determined.Research limitations/implications-Rather than real-time data collected by Internet of Things(IoT)devices,this research focuses solely on historical data;the proposed work does not impose IoT-based smart farming,which enhances the overall agriculture system by monitoring the field in real time.The present study only predicts the sort of crop to sow not crop production.Originality/value-The paper proposes a novel optimization algorithm,which is based on the law of gravity and mass interactions.The search agents in the proposed algorithm are a cluster of weights that interact with one another using Newtonian gravity and motion principles.A comparison was made between the suggested method and various existing strategies.The obtained results confirm the high-performance in solving diverse nonlinear functions.展开更多
文摘The support vector machine(SVM)is a classical machine learning method.Both the hinge loss and least absolute shrinkage and selection operator(LASSO)penalty are usually used in traditional SVMs.However,the hinge loss is not differentiable,and the LASSO penalty does not have the Oracle property.In this paper,the huberized loss is combined with non-convex penalties to obtain a model that has the advantages of both the computational simplicity and the Oracle property,contributing to higher accuracy than traditional SVMs.It is experimentally demonstrated that the two non-convex huberized-SVM methods,smoothly clipped absolute deviation huberized-SVM(SCAD-HSVM)and minimax concave penalty huberized-SVM(MCP-HSVM),outperform the traditional SVM method in terms of the prediction accuracy and classifier performance.They are also superior in terms of variable selection,especially when there is a high linear correlation between the variables.When they are applied to the prediction of listed companies,the variables that can affect and predict financial distress are accurately filtered out.Among all the indicators,the indicators per share have the greatest influence while those of solvency have the weakest influence.Listed companies can assess the financial situation with the indicators screened by our algorithm and make an early warning of their possible financial distress in advance with higher precision.
文摘In computer vision,emotion recognition using facial expression images is considered an important research issue.Deep learning advances in recent years have aided in attaining improved results in this issue.According to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of emotion.It is feasible and useful to convert face photos into collections of visual words and carry out global expression recognition.The main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is used.AffectNet uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos online.The FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization phase.Linear discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously enhanced.Grid search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.
基金Projects(2013BAB02B01,2013BAB02B03)supported by the National Key Technologies R&D Program of ChinaProjects(41072224,41272347)supported by the National Natural Science Foundation of China
文摘Geomechanical parameters are complex and uncertain.In order to take this complexity and uncertainty into account,a probabilistic back-analysis method combining the Bayesian probability with the least squares support vector machine(LS-SVM) technique was proposed.The Bayesian probability was used to deal with the uncertainties in the geomechanical parameters,and an LS-SVM was utilized to establish the relationship between the displacement and the geomechanical parameters.The proposed approach was applied to the geomechanical parameter identification in a slope stability case study which was related to the permanent ship lock within the Three Gorges project in China.The results indicate that the proposed method presents the uncertainties in the geomechanical parameters reasonably well,and also improves the understanding that the monitored information is important in real projects.
基金This work was supportedin part by the National Natural Science Foundation of China(No.60271025,No.30370395)in part by the Science and Technology Depart ment of Shaanxi Province(No.2003K10-G24).
文摘Mental task classification is one of the most important problems in Brain-computer interface.This paper studies the classification of five-class mental tasks.The nonlinear parameter of mean period obtained from frequency domain information was used as features for classification implemented by using the method of SVM(support vector machines).The averaged classification accuracy of 85.6% over 7 subjects was achieved for 2-second EEG segments.And the results for EEG segments of 0.5s and 5.0s compared favorably to those of Garrett’s.The results indicate that the parameter of mean period represents mental tasks well for classification.Furthermore,the method of mean period is less computationally demanding,which indicates its potential use for online BCI systems.
基金Supported by the National Natural Science Foundation of China(60772056)the Postdoctoral Science Foundation of China(20070421094)
文摘An adaptive blind support vector machine equalizer(ABSVME) is presented in this paper.The method is based upon least square support vector machine(LSSVM),and stems from signal feature reconstruction idea.By oversampling the output of a LSSVM equalizer and exploiting a reasonable decorrelation cost function design,the method achieves fine online channel tracing with Kumar express algorithm and static iterative learning algorithm incorporated.The method is verified through simulation and compared with other nonlinear equalizers.The results show that it provides excellent performance in nonlinear equalization and time-varying channel tracing.Although a constant module equalization algorithm requires that the signal has characteristic of constant module,this method has no such requirement.
文摘In today’s digital era,the text may be in form of images.This research aims to deal with the problem by recognizing such text and utilizing the support vector machine(SVM).A lot of work has been done on the English language for handwritten character recognition but very less work on the under-resourced Hindi language.A method is developed for identifying Hindi language characters that use morphology,edge detection,histograms of oriented gradients(HOG),and SVM classes for summary creation.SVM rank employs the summary to extract essential phrases based on paragraph position,phrase position,numerical data,inverted comma,sentence length,and keywords features.The primary goal of the SVM optimization function is to reduce the number of features by eliminating unnecessary and redundant features.The second goal is to maintain or improve the classification system’s performance.The experiment included news articles from various genres,such as Bollywood,politics,and sports.The proposed method’s accuracy for Hindi character recognition is 96.97%,which is good compared with baseline approaches,and system-generated summaries are compared to human summaries.The evaluated results show a precision of 72%at a compression ratio of 50%and a precision of 60%at a compression ratio of 25%,in comparison to state-of-the-art methods,this is a decent result.
基金National Natural Science Foundation of China(No.61701384)Natural Science Basic Research Plan in Shaanxi Province of China(No.2017JM5141)+5 种基金Shaanxi Provincial Education Department,China(No.17JK0334)Xi'an Polytechnic University Graduate Innovation Fund,China(No.chx2019083)Science Foundation for Doctorate Research of Xi'an Polytechnic University,China(No.BS1535)Key Research and Development Program of Shaanxi,China(No.2020GY-172)Technology Innovation Leading Program of Xi'an,China(No.201805030YD8CG14(5))Xi'an Key Laboratory of Modern Intelligent Textile Equipment,China(No.2019220614SYS021CG043)。
文摘In order to improve the accuracy of using visual methods to detect the quality of fluff fabrics,based on the previous research,this paper proposes a method of rapid classification detection using support vector machine(SVM).The fabric image is acquired by the principle of light-cut imaging,and the region of interest is extracted by the method of grayscale horizontal projection.The obtained coordinates of the upper edge of the fabric are decomposed into high frequency information and low frequency information by wavelet transform,and the high frequency information is used as a data set for training.After experimental comparison and analysis,the detection rate of the SVM method proposed in this paper is higher than the previously proposed back propagation(BP)neural network and particle swarm optimization BP(PSO-BP)neural network detection methods,and the accuracy rate can reach 99.41%,which can meet the needs of industrial testing.
基金National Natural Science Foundation of China ( No. 61070033 )Fundamental Research Funds for the Central Universities,China( No. 2012ZM0061)
文摘It is a challenging topic to develop an efficient algorithm for large scale classification problems in many applications of machine learning. In this paper, a hierarchical clustering and fixed-layer local learning (HCFLL) based support vector machine(SVM) algorithm is proposed to deal with this problem. Firstly, HCFLL hierarchically clusters a given dataset into a modified clustering feature tree based on the ideas of unsupervised clustering and supervised clustering. Then it locally trains SVM on each labeled subtree at a fixed-layer of the tree. The experimental results show that compared with the existing popular algorithms such as core vector machine and decision-tree support vector machine, HCFLL can significantly improve the training and testing speeds with comparable testing accuracy.
基金National Natural Science Foundation of China(No.51205043)the Special Fundamental Research Funds for Central Universities of China(No.DUT14QY21)
文摘Soft failure of mechanical equipment makes its performance drop gradually,which occupies a large proportion and has certain regularity. The performance can be evaluated and predicted through early state monitoring and data analysis. The vibration signal was modeled from the double row bearing,and wavelet transform and support vector machine model( WT-SVM model) was constructed and trained for bearing degradation process prediction. Besides Hazen plotting position relationships was applied to describing the degradation trend distribution and a 95%confidence level based on t-distribution was given. The single SVM model and neural network( NN) approach were also investigated as a comparison. Results indicate that the WT-SVM model outperforms the NN and single SVM models,and is feasible and effective in machinery condition prediction.
基金National Natural Science Foundation of China(Nos.11262014,11962021 and 51965051)Inner Mongolia Natural Science Foundation,China(No.2019MS05064)+1 种基金Inner Mongolia Earthquake Administration Director Fund Project,China(No.2019YB06)Inner Mongolia University of Technology Foundation,China(No.2020015)。
文摘Aiming at the reliability analysis of small sample data or implicit structural function,a novel structural reliability analysis model based on support vector machine(SVM)and neural network direct integration method(DNN)is proposed.Firstly,SVM with good small sample learning ability is used to train small sample data,fit structural performance functions and establish regular integration regions.Secondly,DNN is approximated the integral function to achieve multiple integration in the integration region.Finally,structural reliability was obtained by DNN.Numerical examples are investigated to demonstrate the effectiveness of the present method,which provides a feasible way for the structural reliability analysis.
基金National Natural Science Foundations of China(Nos.61272015,11201123)the Scientific Research Foundation for the Doctor of Henan University of Science&Technology,China(No.09001476)School Foundation of Henan University of Science&Technology,China(No.2012QN011)
文摘To reduce the influences of outliers on support vector machine(SVM) classification problem,a new tangent loss function was constructed.Since the tangent loss function was not smooth in some interval,a smoothing function was used to approximate it in this interval.According to this loss function,the corresponding tangent SVM(TSVM) was got.The experimental results show that TSVM is less sensitive to outliers than SVM.So the proposed new loss function and TSVM are both effective.
基金Supported by the National Natural Science Foundation of China (No.41001285)
文摘Geospatial objects detection within complex environment is a challenging problem in remote sensing area. In this paper, we derive an extension of the Relevance Vector Machine (RVM) technique to multiple kernel version. The proposed method learns an optimal kernel combination and the associated classifier simultaneously. Two feature types are extracted from images, forming basis kernels. Then these basis kernels are weighted combined and resulted the composite kernel exploits interesting points and appearance information of objects simultaneously. Weights and the detection model are finally learnt by a new algorithm. Experimental results show that the proposed method improve detection accuracy to above 88%, yields good interpretation for the selected subset of features and appears sparser than traditional single-kernel RVMs.
基金Nantong Research Program of Application Foundation,China(No.BK2012030)Key Project of Science and Technology Commission of Shanghai Municipality,China(No.10JC1405000)
文摘Temperature prediction plays an important role in ring die granulator control,which can influence the quantity and quality of production. Temperature prediction modeling is a complicated problem with its MIMO, nonlinear, and large time-delay characteristics. Support vector machine( SVM) has been successfully based on small data. But its accuracy is not high,in contrast,if the number of data and dimension of feature increase,the training time of model will increase dramatically. In this paper,a linear SVM was applied combing with cyclic coordinate descent( CCD) to solving big data regression. It was mathematically strictly proved and validated by simulation. Meanwhile,real data were conducted to prove the linear SVM model's effect. Compared with other methods for big data in simulation, this algorithm has apparent advantage not only in fast modeling but also in high fitness.
基金Supported by the National Natural Science Foundation of China (No. 61105076)Natural Science Foundation of Anhui Province of China (No. 11040606M127) as well as Key ScientificTechnological Project of Anhui Province (No. 11010202192)
文摘Emotion recognition from speech is an important field of research in human computer interaction. In this letter the framework of Support Vector Machines (SVM) with Gaussian Mixture Model (GMM) supervector is introduced for emotional speech recognition. Because of the importance of variance in reflecting the distribution of speech, the normalized mean vectors potential to exploit the information from the variance are adopted to form the GMM supervector. Comparative experiments from five aspects are conducted to study their corresponding effect to system performance. The experiment results, which indicate that the influence of number of mixtures is strong as well as influence of duration is weak, provide basis for the train set selection of Universal Background Model (UBM).
文摘In cloud computing Resource allocation is a very complex task.Handling the customer demand makes the challenges of on-demand resource allocation.Many challenges are faced by conventional methods for resource allocation in order tomeet the Quality of Service(QoS)requirements of users.For solving the about said problems a new method was implemented with the utility of machine learning framework of resource allocation by utilizing the cloud computing technique was taken in to an account in this research work.The accuracy in the machine learning algorithm can be improved by introducing Bat Algorithm with feature selection(BFS)in the proposed work,this further reduces the inappropriate features from the data.The similarities that were hidden can be demoralized by the Support Vector Machine(SVM)classifier which is also determine the subspace vector and then a new feature vector can be predicted by using SVM.For an unexpected circumstance SVM model can make a resource allocation decision.The efficiency of proposed SVM classifier of resource allocation can be highlighted by using a singlecell multiuser massive Multiple-Input Multiple Output(MIMO)system,with beam allocation problem as an example.The proposed resource allocation based on SVM performs efficiently than the existing conventional methods;this has been proven by analysing its results.
基金supported by Taif University Researchers Supporting Project(Number TURSP-2020/122),Taif University,Taif,Saudi Arabia.
文摘In this paper,Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)based methods are to be applied on fault diagnosis in a robot manipulator.A comparative study between the two classifiers in terms of successfully detecting and isolating the seven classes of sensor faults is considered in this work.For both classifiers,the torque,the position and the speed of the manipulator have been employed as the input vector.However,it is to mention that a large database is needed and used for the training and testing phases.The SVM method used in this paper is based on the Gaussian kernel with the parametersγand the penalty margin parameter“C”,which were adjusted via the PSO algorithm to achieve a maximum accuracy diagnosis.Simulations were carried out on the model of a Selective Compliance Assembly Robot Arm(SCARA)robot manipulator,and the results showed that the Particle Swarm Optimization(PSO)increased the per-formance of the SVM algorithm with the 96.95%accuracy while the KNN algo-rithm achieved a correlation up to 94.62%.These results showed that the SVM algorithm with PSO was more precise than the KNN algorithm when was used in fault diagnosis on a robot manipulator.
基金funded by the Natural Science Foundation of Higher Education Institutions of Auhui Province(Grant No.KJ2021A0352)the Research Fund Project of Anhui Medical University(Grant No.2020xkj236)Applied Medicine Research Project of Hefei Health Commission(Grant No.HWKJ2019-172-14).
文摘Invasive breast carcinoma(BRCA)is associated with poor prognosis and high risk of mortality.Therefore,it is critical to identify novel biomarkers for the prognostic assessment of BRCA.Methods:The expression data of polo-like kinase 1(PLK1)in BRCA and the corresponding clinical information were extracted from TCGA and GEO databases.PLK1 expression was validated in diverse breast cancer cell lines by quantitative real-time polymerase chain reaction(qRT-PCR)and western blotting.Single sample gene set enrichment analysis(ssGSEA)was performed to evaluate immune infiltration in the BRCA microenvironment,and the random forest(RF)and support vector machine(SVM)algorithms were used to screen for the hub infiltrating cells and calculate the immunophenoscore(IPS).The RF algorithm and COX regression model were applied to calculate survival risk scores based on the PLK1 expression and immune cell infiltration.Finally,a prognostic nomogram was constructed with the risk score and pathological stage,and its clinical potential was evaluated by plotting calibration charts and DCA curves.The application of the nomogram was further validated in an immunotherapy cohort.Results:PLK1 expression was significantly higher in the tumor samples in TCGA-BRCA cohort.Furthermore,PLK1 expression level,age and stage were identified as independent prognostic factors of BRCA.While the IPS was unaffected by PLK1 expression,the TMB and MATH scores were higher in the PLK1-high group,and the TIDE scores were higher for the PLK1-low patients.We also identified 6 immune cell types with high infiltration,along with 11 immune cell types with low infiltration in the PLK1-high tumors.A risk score was devised using PLK1 expression and hub immune cells,which predicted the prognosis of BRCA patients.In addition,a nomogram was constructed based on the risk score and pathological staging,and showed good predictive performance.Conclusions:PLK1 expression and immune cell infiltration can predict post-immunotherapy prognosis of BRCA patients.
基金This research was funded by the National Natural Science Foundation of China(Nos.71762010,62262019,62162025,61966013,12162012)the Hainan Provincial Natural Science Foundation of China(Nos.823RC488,623RC481,620RC603,621QN241,620RC602,121RC536)+1 种基金the Haikou Science and Technology Plan Project of China(No.2022-016)the Project supported by the Education Department of Hainan Province,No.Hnky2021-23.
文摘Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to errors.This paper proposes a novel VTDR detection and classification model that combines different models through majority voting.Our proposed methodology involves preprocessing,data augmentation,feature extraction,and classification stages.We use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for classification.We tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,respectively.Our proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.
文摘This paper proposes a single-ended fault detection scheme for long transmission lines using support vector machine(SVM)for multi-terminal direct current systems based on modular multilevel converter(MMC-MTDC).The scheme overcomes existing detection difficulties in the protection of long transmission lines resulting from high grounding resistance and attenuation,and also avoids the sophisticated process of threshold value selection.The high-frequency components in the measured voltage extracted by a wavelet transform and the amplitude of the zero-mode set of the positive-sequence voltage are the inputs to a trained SVM.The output of the SVM determines the fault type.A model of a four-terminal DC power grid with overhead transmission lines is built in PSCAD/EMTDC.Simulation results of EMTDC confirm that the proposed scheme achieves 100%accuracy in detecting short-circuit faults with high resistance on long transmission lines.The proposed scheme eliminates mal-operation of DC circuit breakers when faced with power order changes or AC-side faults.Its robustness and time delay are also assessed and shown to have no perceptible effect on the speed and accuracy of the detection scheme,thus ensuring its reliability and stability.
文摘Purpose-Automated crop prediction is needed for the following reasons:First,agricultural yields were decided by a farmer’s ability to work in a certain field and with a particular crop previously.They were not always able to predict the crop and its yield solely on that idea alone.Second,seed firms frequently monitor how well new plant varieties would grow in certain settings.Third,predicting agricultural production is critical for solving emerging food security concerns,especially in the face of global climate change.Accurate production forecasts not only assist farmers inmaking informed economic andmanagement decisions but they also aid in the prevention of famine.This results in farming systems’efficiency and productivity gains,as well as reduced risk from environmental factors.Design/methodology/approach-This research paper proposes a machine learning technique for effective autonomous crop and yield prediction,which makes use of solution encoding to create solutions randomly,and then for every generated solution,fitness is evaluated to meet highest accuracy.Major focus of the proposed work is to optimize the weight parameter in the input data.The algorithm continues until the optimal agent or optimal weight is selected,which contributes to maximum accuracy in automated crop prediction.Findings-Performance of the proposed work is compared with different existing algorithms,such as Random Forest,support vector machine(SVM)and artificial neural network(ANN).The proposed method support vector neural network(SVNN)with gravitational search agent(GSA)is analysed based on different performance metrics,such as accuracy,sensitivity,specificity,CPU memory usage and training time,and maximum performance is determined.Research limitations/implications-Rather than real-time data collected by Internet of Things(IoT)devices,this research focuses solely on historical data;the proposed work does not impose IoT-based smart farming,which enhances the overall agriculture system by monitoring the field in real time.The present study only predicts the sort of crop to sow not crop production.Originality/value-The paper proposes a novel optimization algorithm,which is based on the law of gravity and mass interactions.The search agents in the proposed algorithm are a cluster of weights that interact with one another using Newtonian gravity and motion principles.A comparison was made between the suggested method and various existing strategies.The obtained results confirm the high-performance in solving diverse nonlinear functions.
