Traditional Support Vector Machine Research Articles

ResNet Model Automatically Extracts and Identifies FT-NIR Features for Geographical Traceability of Polygonatum kingianum.

Medicinal plants have incredibly high economic value, and a practical evaluation of their quality is the key to promoting industry development. The deep learning model based on residual convolutional neural network (ResNet) has the advantage of automatic extraction and the recognition of Fourier transform near-infrared spectroscopy (FT-NIR) features. Models are difficult to understand and interpret because of unknown working mechanisms and decision-making processes. Therefore, in this study, artificial feature extraction methods combine traditional partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) models to understand and compare deep learning models. The results show that the ResNet model has significant advantages over traditional models in feature extraction and recognition. Secondly, preprocessing has a great impact on the feature extraction and feature extraction, and is beneficial for improving model performance. Competitive adaptive reweighted sampling (CARS) and variable importance in projection (VIP) methods screen out more feature variables after preprocessing, but the number of potential variables (LVs) and successive projections algorithm (SPA) methods obtained is fewer. The SPA method only extracts two variables after preprocessing, causing vital information to be lost. The VIP feature of traditional modelling yields the best results among the four methods. After spectral preprocessing, the recognition rates of the PLS-DA and SVM models are up to 90.16% and 88.52%. For the ResNet model, preprocessing is beneficial for extracting and identifying spectral image features. The ResNet model based on synchronous two-dimensional correlation spectra has a recognition accuracy of 100%. This research is beneficial to the application development of the ResNet model in foods, spices, and medicinal plants.

Correntropy long short term memory soft sensor for quality prediction in industrial polyethylene process

A typical challenge for construction of accurate soft sensors in the process industries is that industrial process data often contains various noise and outliers. Inspired by correntropy in tackling non-Gaussian noise effectively, a maximum correntropy criterion-based long short term memory (MCC-LSTM) neural network is proposed to develop a reliable soft sensor model for quality prediction. Without tedious data preprocessing approaches, the MCC-LSTM adopts the objective function using the maximum correntropy criterion (MCC) centered on a Gaussian kernel, which assigns relatively smaller weights to outliers automatically. Once the MCC-LSTM model is constructed, the outliers can be identified and their negative effects on the prediction can be reduced to some extent. Consequently, the prediction performance can be enhanced for modeling of process data with uncertainties. Additionally, an index is introduced to assess the performance of prediction models when data contain outliers and noise. A numerical example and an industrial polyethylene process demonstrate that the MCC-LSTM soft sensor can achieve more reliable predictive performance compared to traditional LSTM and support vector machine candidates.

An uncertain support vector machine based on soft margin method.

Traditional support vector machines (SVMs) play an important role in the classification of precise data. However, due to various reasons, available data are sometimes imprecise. In this paper, uncertain variables are adopted to describe the imprecise data, and an uncertain support vector machine (USVM) is built for linearly alpha-nonseparable sets based on soft margin method, where a penalty coefficient is utilized as the trade-off between the maximum margin and the sum of slack variables. Then the equivalent crisp model is derived based on the inverse uncertainty distributions. Numerical experiments are designed to illustrate the application of the soft margin USVM. Finally, metrics, such as accuracy, precision, and recall are used to evaluate the robustness of the proposed model.

New PSO-SVM Short-Term Wind Power Forecasting Algorithm Based on the CEEMDAN Model

Accurate wind power forecasting can help reduce disturbance to the grid in wind power integration. In this paper, a short-term power forecasting model is established by using complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) and nonlinear fitting characteristics of support vector machines (SVM) to accurately predict wind power. First, the wind power data are preprocessed and decomposed to 6 stable power components using CEEMDAN, thus reducing the impact of excessive forecasting errors of oscillatory points at peaks and valleys. Then, particle swarm optimization (PSO) based on improved empirical mode decomposition is designed to optimize the kernel function and penalty factor of the SVM. It establishes a new short-term power forecasting CEEMDAN-combined model. Finally, each stable component data is processed using the power forecasting model, and then, the results are combined to get the final power forecasting value. Analysis of test results and comparative studies show that the RMSE and MAPE of the new model are only one-third of that of the traditional SVM algorithm. The forecasting accuracy and speed meet the requirements for safe operation of wind farms.

EEG-based Emotion Recognition Using Multiple Kernel Learning

Emotion recognition based on electroencephalography (EEG) has a wide range of applications and has great potential value, so it has received increasing attention from academia and industry in recent years. Meanwhile, multiple kernel learning (MKL) has also been favored by researchers for its data-driven convenience and high accuracy. However, there is little research on MKL in EEG-based emotion recognition. Therefore, this paper is dedicated to exploring the application of MKL methods in the field of EEG emotion recognition and promoting the application of MKL methods in EEG emotion recognition. Thus, we proposed a support vector machine (SVM) classifier based on the MKL algorithm EasyMKL to investigate the feasibility of MKL algorithms in EEG-based emotion recognition problems. We designed two data partition methods, random division to verify the validity of the MKL method and sequential division to simulate practical applications. Then, tri-categorization experiments were performed for neutral, negative and positive emotions based on a commonly used dataset, the Shanghai Jiao Tong University emotional EEG dataset (SEED). The average classification accuracies for random division and sequential division were 92.25% and 74.37%, respectively, which shows better classification performance than the traditional single kernel SVM. The final results show that the MKL method is obviously effective, and the application of MKL in EEG emotion recognition is worthy of further study. Through the analysis of the experimental results, we discovered that the simple mathematical operations of the features on the symmetrical electrodes could not effectively integrate the spatial information of the EEG signals to obtain better performance. It is also confirmed that higher frequency band information is more correlated with emotional state and contributes more to emotion recognition. In summary, this paper explores research on MKL methods in the field of EEG emotion recognition and provides a new way of thinking for EEG-based emotion recognition research.

Comparisons of deep learning and machine learning while using text mining methods to identify suicide attempts of patients with mood disorders

BackgroundSuicide attempt is one of the most severe consequences for patients with mood disorders. This study aimed to perform deep learning and machine learning while using text mining to identify patients with suicide attempts and to compare their effectiveness. MethodsA total of 13,100 patients with mood disorders were selected. Two traditional text mining methods, logistic regression and Support vector machine (SVM), and one deep learning model (Convolutional neural network, CNN) were adopted to perform overall analysis and gender-specific subgroup analysis of patients to identify suicide attempts. The classification effectiveness of these models was evaluated by accuracy, F1-value, precision, recall, and the area under Receiver operator characteristic curve (ROC). ResultsCNN's results were greater than the other two for all indicators except recall which was slightly smaller than SVM in male subgroup analysis. The accuracy values of the CNN were 98.4 %, 98.2 %, and 98.5 % in the overall analysis and the subgroup analysis for males and females, respectively. The results of McNemar's test showed that CNN and SVM models' predictions were statistically different from the logistic regression model's predictions in the overall analysis and the subgroup analysis for females (P < 0.050). LimitationsA fixed number of features were selected based on document frequency to train models; this was a single-site study. ConclusionsCNN model was a better way to detect suicide attempts in patients with mood disorders prior to hospital admission, saving time and resources in recognizing high-risk patients and preventing suicide.

Application of Graph Neural Network in Driving Fatigue Detection Based on EEG Signals.

The objective of this article is to solve the current social phenomenon of a large number of fatigue driving, so that social safety becomes more stable in the future, and the detection and application of driving fatigue are more meaningful. This article aims to study the application of graph neural network (GNN) in driving fatigue detection (this article is abbreviated as DFD) based on EEG signals. This article uses a pattern classification method based on a multilayer perceptual overlimit learning machine to find the hidden information of the signal through an unsupervised learning self-encoding structure, which achieves the optimization purpose and has a better classification effect than traditional classifiers. An improved soft threshold (the soft threshold can be used to solve the optimization problem, and the optimization problem solved is similar to the base pursuit noise reduction problem, but it is not the same, and it should be noted that the soft threshold cannot solve the base pursuit noise reduction problem) denoising algorithm is selected, and the collected EEG (a technique for capturing brain activity using electrophysiological markers is the electroencephalogram). The sum of the postsynaptic potentials produced simultaneously by a large number of neurons occurs when the brain is active. It records the process of brain activity in the cerebral cortex or scalp surface) signals are preprocessed, so that the feature extraction efficiency of extracting EEG signals is improved. The final experimental data show that the traditional support vector machine, SVM algorithm, and the KNN convolutional neural (the K-nearest neighbor method, often known as KNN, was first put forth by Cover and Hart in 1968. It is one of the most straightforward machine learning algorithms and a theoretically sound approach) algorithms has a recognition rate of 79% and 81% for fatigue. The improved algorithm in this article has an average recognition rate of 87.5% for driver fatigue, which is greatly improved.

New Results on Classification Modeling of Noisy Tensor Datasets: A Fuzzy Support Tensor Machine Dual Model

In this article, classification problems for a class of tensor datasets with a noisy environment are investigated. To address such issues, a novel fuzzy support tensor machine (FSTM) dual model with robustness is established. First, for each input sample in the noisy tensor dataset, we define three kinds of fuzzy membership functions, such as linear, cosine, and exponential forms. In particular, the reconstruction process from one-dimensional (1-D) vector data to third-order tensor data is also derived in the Appendix. Second, the original optimization model of an FSTM on fuzzy membership is designed by constructing the vector pattern of the traditional support vector machine (SVM) models into a tensor pattern. Next, by introducing the Lagrangian multiplier method and tensor-Tucker decomposition method to the original FSTM model, an FSTM dual model without tensor inner product operation is obtained for the first time. Such a dual model with tensor-Tucker decomposition form can avoid conservativeness caused by the vectorization of tensor data in the traditional SVM model. Furthermore, an FSTM classifier is derived by the designed numerical algorithm, and the classification generalization error bound of the FSTM model with a general form is developed. It is worth noting that a linear least-squares FSTM (LLS-FSTM) equation with tensor-Tucker decomposition is also designed in the Appendix to further reduce the slightly time-consuming problem of the solving the dual optimization model FSTM. Finally, two numerical examples are presented to verify the feasibility and validity of the derived FSTM classifier.

A novel SVM-based decision framework considering feature distribution for Power Transformer Fault Diagnosis

International Electrotechnical Commission (IEC) proposed the IEC three-ratio method based on Dissolved Gas Analysis (DGA), which is one of the most effective tools for Power Transformer Fault Diagnosis (PTFD). However, the PTFD accuracy is generally limited because the classification boundary could be too stiff to classify samples located near the boundary. The Support Vector Machine (SVM) was applied to PTFD to improve diagnosis accuracy, while traditional SVM multi-classification methods and parameter optimization algorithms are subject to poor training efficiency. As a result, the SVM-based PTFD model is difficult to update frequently with the accumulation of fault data. A new SVM-based PTFD decision framework is proposed in this paper which can significantly boost the training efficiency and ensure the accuracy. In the proposed framework, a multi-step feature extraction process consisting of characteristic gas concentration and its ratios is applied. Based on the feature distribution of various samples, a proper SVM multi-classification method is presented using a hierarchical decision tree structure. In addition, according to the principles of SVM and radial basis kernel function, a Support Vector feature-based parameter optimization algorithm (SVFB) is proposed. IEC TC 10 data and the historical data of online transformer monitoring provided by the State Grid Corporation of China are adopted as sample sets. The simulation results demonstrate that the proposed decision framework can reach high diagnosis accuracy while shortening the training time.

Hydrological time series forecasting via signal decomposition and twin support vector machine using cooperation search algorithm for parameter identification

Accurate hydrologic forecasting is of vital importance for water resource management and reservoir operation under the changing environments. However, it is difficult for a standalone method to track the complex hydrological time series with highly nonlinear and nonstationary features. To improve the forecasting accuracy, this study proposes a hybrid model using compete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and twin support vector machine (TSVM) optimized by metaheuristic algorithm. Firstly, the CEEMDAN tool is chosen to divide the original runoff data into several relatively stable subcomponents with various resolutions and frequencies. Then, the TSVM forecasting model is established to extract the deep features of each subseries, while the emerging cooperation search algorithm is used to search for satisfying parameter combination. The final forecasting value for the original hydrological time series is formulated by aggregating the outputs of all TSVM models. Long-term streamflow data from several hydrological stations in China’s Yangtze River is used for simulation. Experimental results reveal that the hybrid model significantly outperforms several conventional forecasting models in prediction accuracy. For instance, the proposed method improves the root mean squared error and mean absolute error value with about 36.5% and 41.6% reductions compared with the traditional support vector machine at station B. Thus, a novel and effective evolutionary artificial intelligence model is developed for hydrological forecasting.

PV Power Forecasting Based on Relevance Vector Machine with Sparrow Search Algorithm Considering Seasonal Distribution and Weather Type

Accurate photovoltaic (PV) power forecasting is indispensable to enhancing the stability of the power grid and expanding the absorptive photoelectric capacity of the power grid. As an excellent nonlinear regression model, the relevance vector machine (RVM) can be employed to forecast PV power. However, the optimization of the free parameters is still a key problem for improving the performance of the RVM. Taking advantage of the strong global search capability, good stability, and fast convergence rate of the sparrow search algorithm (SSA), this paper optimizes the parameters of the RVM by using the SSA to develop an excellent RVM (called SSA-RVM). Consequently, a novel hybrid PV power forecasting model via the SSA-RVM is proposed to perform ultra-short-term (4 h ahead) prediction. In addition, the effects of seasonal distribution and weather type on PV power are fully considered, and different seasonal prediction models are established separately to improve the prediction capability. The benchmark is used to verify the accuracy of the SSA-RVM-based forecasting model under various conditions, and the experiment results demonstrate that the proposed SSA-RMV method outperforms the traditional RVM and support vector machine models, and it even shows a better prediction effect than the RVM models with other optimization approaches.

A hierarchical prediction method based on hybrid-kernel GWO-SVM for metal tube bending wrinkling detection

Metal bending tube is widely used in industry while its forming defects extremely affect the bending quality. Among all defects, the bending-inside wrinkling caused by the non-uniform compressive stress is a zero-tolerated defect, particularly when the tube is for transportation. However, the current wrinkling detection approach, suffering from the lack of insight into wrinkling mechanism, is normally posteriori. To obtain the priori wrinkling condition for a certain go-to-bend tube, we put forward a metal tube bending wrinkling hierarchical prediction method based on hybrid-kernel gray wolf optimizer (GWO) support vector machine (SVM). Three typical kernel combinations are utilized for the GWO-SVM prediction model. To verify the proposed wrinkling prediction method, aluminum alloy series tubes are tested. By constructing the 12 typical designations of aluminum alloy tubes’ finite element bending simulation case base, the prediction model is trained through three hybrid-kernel GWO-SVMs, respectively. The results are compared with the traditional SVM and GWO-SVM, which show that the proposed hybrid-kernel GWO-SVM model has the best performance for hierarchically predicting bending wrinkling. Analysis of the predicted results shows that when the relative wall thickness is less than 0.015, wrinkling is very likely to occur with any relative bending radius within the range. On the contrary, there is less tendency to wrinkle. At the same time, the smaller the R/D, the higher the hierarchy of wrinkling. This proposed prediction method lays the foundation for metal tube bending wrinkling detection and prevention.

Research on Risk Features and Prediction of China’s Crude Oil Futures Market Based on Machine Learning

Facing the rapidly changing domestic and foreign futures markets, how to accurately and immediately predict the price trend of crude oil futures in order to avoid the risks caused by price fluctuations is very important for all participants in the crude oil futures market. Based on the 5-min high-frequency trading data of China’s crude oil futures market in recent 3 years, this paper uses the EMD-MFDFA model combined with multifractal detrended fluctuation analysis (MF-DFA) and empirical mode decomposition unsupervised K-means clustering and Gaussian mixture model (GMM) to identify the risk status of each trading day. Further, Support vector machine (SVM), extreme gradient lifting (XGBoost) and their improved algorithms are used to predict the risk state of China’s crude oil futures market. The empirical results are as follows: first, There are obvious multifractal features in the return rate series of China’s crude oil futures market and its single trading day; Second, compared with the traditional SVM model, the improved Twin Support Vector Machine (TWSVM) based on solving the sample imbalance issue has better prediction ability for China’s crude oil futures risk.; Third, The XGBoost has a great impact on the prediction of China’s crude oil risk, and the Focal-XGBoost with focal loss function performs the best in predicting the risk of China’s crude oil futures market.

Identifying molecular functional groups of organic compounds by deep learning of NMR data.

We preprocess the raw nuclear magnetic resonance (NMR) spectrum and extract key features by using two different methodologies, called equidistant sampling and peak sampling for subsequent substructure pattern recognition. We also provide a strategy to address the imbalance issue frequently encountered in statistical modeling of NMR data set and establish two conventional support vector machine (SVM) and K-nearest neighbor (KNN) models to assess the capability of two feature selections, respectively. Our results in this study show that the models using the selected features of peak sampling outperform those using equidistant sampling. Then we build the recurrent neural network (RNN) model trained by data collected from peak sampling. Furthermore, we illustrate the easier optimization of hyperparameters and the better generalization ability of the RNN deep learning model by detailed comparison with traditional machine learning SVM and KNN models.

Privacy-Preserving Outsourcing Scheme for SVM on Vertically Partitioned Data

Support vector machine (SVM) is an important technique for data classification. Traditional SVM assumes free access to data. If the data are split and held by different users, for privacy reasons, users are likely unwilling to submit their data to a third party for classification. In this paper, by using additive homomorphic encryption and random transformations (matrix transformation and vector decomposition), we design a privacy-preserving outsourcing scheme for conducting Least Squares SVM (LS-SVM) classification on vertically partitioned data. In our system, multiple data owners (users) submit their encrypted data to two non-colluding service providers, which conduct SVM algorithm on it. During the execution of our algorithm, neither service provider learns anything about the input data, the intermediate results, or the predicted result. In other words, our algorithm is encrypted in the whole process. Extensive theoretical analysis and experimental evaluation demonstrate the correctness, security, and efficiency of the method.

Application of a New Loss Function-Based Support Vector Machine Algorithm in Quality Control of Measurement Observation Data

The loss function of the traditional support vector machine (SVM) method consists of hinge function and regularization, which is difficult to achieve the quality control of observation data. It requires a new loss function to measure the quality of the observed data. At this stage, researchers will use data cleaning or data preprocessing to process observational data. The data preprocessing method will normalize the data features, which will make the data process the same interval and a distribution. However, this method is also difficult to guarantee the accuracy of the data. This study uses the SVM method to study the integrity, unity, and accuracy of the observation data. This study also proposes a new loss function, which can obtain the uncertainty distribution of the observed data. The loss function of traditional SVM methods is that the uncertainty distribution of the data cannot be obtained. The results show that the SVM algorithm with the new loss function has better accuracy in processing the observed data. For the maximum prediction error of the observed data, the error with the new loss function is only 2.58%, which is reduced by 0.21% compared to the SVM method with the old loss function. However, the prediction error of the SVM algorithm with the old loss function is 2.78%, and the SVM algorithm with the new loss function has obvious prediction accuracy.

Comparative Study of Ensemble Method vs Deep Learning on Human Activity Recognition for Elderly Care

A drastic increase in healthcare demand has come from the explosive growth of the older population. The elderly are, on average, more vulnerable to health problems than other age groups. Unpredictable events, such as sudden falls, can be avoided with proper monitoring. Activity recognition can help people avoid potentially dangerous behaviours by aiding in the detection of unexpected events. Most of the existing approaches require complex sensors and environment setup, involve data filtering and noise removal steps, and most often the chosen learning models need to be tuned and carefully designed for optimal performance. This study emphasizes light implementation, fast training time, easy experimental setup, and minimal parameter tuning. Human activities are captured using smartphone sensors in this study. Students and senior residents from a local home care facility are among the volunteers for this study. The necessary data sets are obtained from the accelerometer sensor on the smartphone. To provide baseline performance, the traditional instant-based learning architectures k-Nearest Neighbors (kNN) and Support Vector Machine (SVM) are used. To represent the ensemble learning model, the Random Forest (RF) and XGBoost (XGB) are investigated. The Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) are the more advanced deep learning models used in this study (CNN). The results reveal that ensemble methods and deep learning models provide improved accuracy, with ensemble learning models outperforming deep learning models.

Design and Application of Smart Vision Sensor Using Embedded Technology in Cost Management of Power Transmission and Transformation Project in Ningxia Companies

The purpose is to solve the problems of cumbersome calculation, low accuracy, poor timeliness, rigid data acquisition, high cost, large volume, and insufficient signal processing capacity of traditional vision sensor (VS) in Ningxia companies. Firstly, this paper designs an embedded smart VS based on advanced RISC machines (ARM) processor. Secondly, it proposes a cost estimation algorithm for power transmission and transformation project (PTTP) based on particle swarm optimization-least squares support vector regression (PSO–LSSVR). Afterward, a cost estimation model of PTTP based on building information modeling (BIM) is proposed. Thirdly, historical cost data of PTTP of a Ningxia company within five years are selected as data samples to verify the accuracy of the PSO-LSSVR estimation algorithm and BIM model. The results show the following: (I) The measurement error of the designed smart VS is less than 4%, with high accuracy, which is suitable for large-scale measurement in the construction site. (II) The error of the PSO-LSSVR algorithm in engineering cost prediction is less than 20%, and the accuracy is higher than that of traditional support vector machine (SVM) and LSSVR algorithms. The optimization effect is remarkable and can be used for the feasibility analysis of PTTPs. (III) The proposed BIM-based PTTP cost estimation model error in the project cost estimation is controlled within 10%. With high accuracy, it can be applied to the PTTP management of Ningxia company. The purpose is to provide important technical support for the upgradation of traditional VS technology and the realization of visual management and rapid cost estimation of PTTP of Ningxia companies.

Data Fusion Approach for Managing Clinical Data in an Industrial Environment using IoT

As health issues continue to become more prevalent as the population grows, building a public health network is critical for enhancing the overall health quality of the community. This study offers an Internet of Things (IoT) based health care system that can be employed in the context of community medical care industrial areas. The main focus of this research is to develop a disease prediction strategy that could be applied to community health services using theoretical modelling. Using principal component analysis (PCA) and cluster analysis, an artificial bee colony (ABC) creates a nonlinear support vector machine (SVM) classifier pair. Feature-level fusion analysis was performed to detect probable abnormalities. The results of the experiments reveal that the SVM model offers significant benefits in disease prediction. In the SVM illness prediction model, the ABC algorithm has the best parameter optimization effect in terms of accuracy, time, and other factors. The suggested method outperformed the traditional SVM and BP neural network methods by 17.24 percent and 72.41 percent, respectively. It can lower the RMSE and improve assessment indicators like the precision recall rate and the F-measure, demonstrating the method’s validity and accuracy. As a result, it is frequently used in community health management, geriatric community monitoring, and clinical medical therapy in an industrial environment.

Hyperspectral Image Classification Based on Non-Parallel Support Vector Machine

Support vector machine (SVM) has a good effect in the supervised classification of hyperspectral images. In view of the shortcomings of the existing parallel structure SVM, this article proposes a non-parallel SVM model. Based on the traditional parallel boundary structure vector machine, this model adds an additional empirical risk minimization term to the original optimization problem by adding the least square term of the sample and obtains two non-parallel hyperplanes, respectively, forming a new non-parallel SVM algorithm to minimize the additional empirical risk of non-parallel SVM (Additional Empirical Risk Minimization Non-parallel Support Vector Machine, AERM-NPSVM). On the basis of AERM-NPSVM, the bias constraint is added to it, and AERM-NPSVM (BC-AERM-NPSVM) is further obtained. The experimental results show that, compared with the traditional parallel SVM model and the classical non-parallel SVM model, Twin Support Vector Machine (TWSVM), the new model, has a better effect in hyperspectral image classification and better generalization performance.