Improved Support Vector Machine Research Articles

Characteristics Analysis of Applied Mathematics in Colleges and Universities Based on Big Data Mining Algorithm Model

To analyze the mathematical nature of applied mathematics in colleges and universities, a method based on a model of big data mining algorithms is proposed. Firstly, the modeling is carried out through the deployment of nodes, which can accurately collect the characteristics of data information in the case of massive big data; secondly, the acquisition algorithm of multi-feature fusion is systematically optimized, which can avoid data interference and collect features quickly and accurately; thirdly, by transforming the multidimensional application-oriented university applied mathematics discipline model into an unlimited experience loss minimization problem with penalty factors, the improved support vector machine algorithm is used to construct and solve the objective kernel function. It is proved that the intelligent collection method based on big data mining algorithm model for the characteristics of applied mathematics in colleges and universities is effective. The sympathetic set corresponding to the flowing data is [115∼135]; the data similarity in big data environment is 1; in order to ensure that the intelligent collection method of applied mathematics discipline characteristics in colleges and universities based on big data analysis can collect data characteristics more accurately, which are 110/76.65/78/110, respectively.

Quality Evaluation for Physical Education Teaching in Colleges with Joint Neural Network

A new teaching curriculum for physical education in colleges has been proposed as part of the teaching reform. New developments in content, goals, and teaching methods have also occurred, and these have gradually become the focus of current physical education researchers. By developing a model for objectively, truly, comprehensively, and accurately evaluating college physical education teaching, it will be possible not only to improve the quality of physical education instruction and the effectiveness of faculty in achieving their goals but also to improve the quality of programs. This research work proposes a Joint Neural Network (JNN) composed of an Improved Support Vector Machine (ISVM) and improved Back Propagation (BP) network for evaluating physical education quality in colleges. When using a standard SVM to classify feature data, the parameters used have an impact on the SVM’s classification. This study introduces an improved SVM with hybrid optimization of PSO and GA algorithms, combining the properties of genetic algorithms and particle swarm optimization. As the existing BP network is trapped with a local optimum, this work proposes an optimized BP network algorithm using the bat algorithm. The improved SVM and improved BP are then combined to form a joint neural network for evaluating the quality of physical education in colleges. Comprehensive and systematic experiments validate the correctness and effectiveness of the algorithm proposed in this paper.

Motivation of Students’ Persistency for Online Learning under Multiple Mediation Effect

This paper probed deep into the motivation of students’ persistency for online learning from the perspective of user experience of online learning platforms, in the purpose of increasing user stickiness and formulating effective operation strategies in a targeted manner. Existing studies on the motivation of students’ persistency for online learning mostly focus on theories, while few of them have talked about the problem with the multiple mediation effect taken into consideration, for this reason, this paper aims to fill in this research gap and explore the mechanism behind the motivation of students to carry out online learning persistently under the multiple mediation effect. At first, this paper built an improved support vector machine (SVM) classifier and used it to predict the duration of students' online learning; then, it adopted a structural equation model to analyze the data of students’ willingness to continue online learning; after that, this paper gave a theoretical analysis on the motivation of students’ persistency for online learning under multiple mediation effect, and constructed a basic regression model for the said matter; at last, this paper employed experimental results to verify the prediction accuracy of the constructed model, and gave the corresponding estimation results.

Modeling Multistep Ahead Dissolved Oxygen Concentration Using Improved Support Vector Machines by a Hybrid Metaheuristic Algorithm

Dissolved oxygen (DO) concentration is an important water-quality parameter, and its estimation is very important for aquatic ecosystems, drinking water resources, and agro-industrial activities. In the presented study, a new support vector machine (SVM) method, which is improved by hybrid firefly algorithm–particle swarm optimization (FFAPSO), is proposed for the accurate estimation of the DO. Daily pH, temperature (T), electrical conductivity (EC), river discharge (Q) and DO data from Fountain Creek near Fountain, the United States, were used for the model development. Various combinations of pH, T, EC, and Q were used as inputs to the models to estimate the DO. The outcomes of the proposed SVM–FFAPSO model were compared with the SVM–PSO, SVM–FFA, and standalone SVM with respect to the root mean square errors (RMSE), the mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and determination coefficient (R2), and graphical methods, such as scatterplots, and Taylor and violin charts. The SVM–FFAPSO showed a superior performance to the other methods in the estimation of the DO. The best model of each method was also assessed in multistep-ahead (from 1- to 7-day ahead) DO, and the superiority of the proposed method was observed from the comparison. The general outcomes recommend the use of SVM–FFAPSO in DO modeling, and this method can be useful for decision-makers in urban water planning and management.

Prediction Method of Soft Fault and Service Life of DC-DC-Converter Circuit Based on Improved Support Vector Machine.

A data-driven prediction method is proposed to predict the soft fault and estimate the service life of a DC–DC-converter circuit. First, based on adaptive online non-bias least-square support-vector machine (AONBLSSVM) and the double-population particle-swarm optimization (DP-PSO), the prediction model of the soft fault is established. After analyzing the degradation-failure mechanisms of multiple key components and considering the influence of the co-degradation of these components over time on the performance of the circuit, the output ripple voltage is chosen as the fault-characteristic parameter. Finally, relying on historical output ripple voltages, the prediction model is utilized to gradually deduce the predicted values of the fault-characteristic parameter; further, in conjunction with the circuit-failure threshold, the soft fault and the service life of the circuit can be predicted. In the simulation experiment, (1) a time-series prediction is made for the output ripple voltage using the model proposed herein and the online least-square support-vector machine (OLS-SVM). Comparative analyses of fitting-assessment indicators of the predicted and experimental curves confirm that our model is superior to OLS-SVM in both modeling efficiency and prediction accuracy. (2) The effectiveness of the service life prediction method of the circuit is verified.

Big Data Recommendation Research Based on Travel Consumer Sentiment Analysis.

More and more tourists are sharing their travel feelings and posting their real experiences on the Internet, generating tourism big data. Online travel reviews can fully reflect tourists’ emotions, and mining and analyzing them can provide insight into the value of them. In order to analyze the potential value of online travel reviews by using big data technology and machine learning technology, this paper proposes an improved support vector machine (SVM) algorithm based on travel consumer sentiment analysis and builds an Hadoop Distributed File System (HDFS) system based on Map-Reduce model. Firstly, Internet travel reviews are pre-processed for sentiment analysis of the review text. Secondly, an improved SVM algorithm is proposed based on the main features of linear classification and kernel functions, so as to improve the accuracy of sentiment word classification. Then, HDFS data nodes are deployed on the basis of Hadoop platform with the actual tourism application context. And based on the Map-Reduce programming model, the map function and reduce function are designed and implemented, which greatly improves the possibility of parallel processing and reduces the time consumption at the same time. Finally, an improved SVM algorithm is implemented under the built Hadoop platform. The test results show that online travel reviews can be an important data source for travel big data recommendation, and the proposed method can quickly and accurately achieve travel sentiment classification.

Research on anomaly detection of wireless data acquisition in power system based on spark

In the era of big data, the network data of power system is more and more complex. Due to the limitation of data storage and processing capacity, the abnormal data detection of power grid terminal information system has the problems of low accuracy and high false alarm rate. The original machine learning algorithm with good detection effect is limited by the processing capacity and storage space of the traditional platform, and the detection effect and efficiency are significantly reduced. This paper takes improving the detection accuracy of abnormal data as the main research target, and designs an abnormal data behavior analysis program based on the Internet of Things under the Spark framework combined with improved Support Vector Machine (SVM) and random forest algorithm. The parallel SA_SVM_RF anomaly data behavior detection model based on Spark is mainly studied and applied to real-time detection. Combined with the respective advantages of Internet of Things technology and machine learning in anomaly data detection, the detection capability and rate of power grid anomaly data detection model are further improved. Experimental tests show that the proposed program is superior to traditional methods in data anomaly detection efficiency and quality, and has certain research significance in the field of power grid security.

Partial discharge fault diagnosis based on zernike moment and improved bacterial foraging optimization algorithm

Feature extraction and classifier optimization are important parts in the diagnosis of partial discharge (PD). Traditional feature extraction methods generally target not the images but structured data. Traditional classifier optimization methods have many disadvantages such as the many parameters that need to be adjusted, difficulty in determining the best parameters, and the ease in falling into local optimal solutions. To eliminate these defects, a PD diagnosis method of gas-insulated switchgear (GIS) based on the Zernike moment and improved support vector machine (SVM) is proposed. Firstly, five typical PD models of corona discharge, metal particle discharge, floating electrode discharge, void discharge, and surface discharge were designed. Then, the UHF sensor was used to collect discharge signals of different defects and different types of interference signals to build a PD phase-resolved pulse sequence (PRPS) patterns library. After that, the Zernike moment of the PRPS images were extracted as the PD features. Finally, the SVM model based on the differential evolution adaptive bacterial foraging optimization (DEABFO) algorithm was used to fulfill the GIS PD diagnosis. The results show that the Zernike moment feature extraction method is suitable for PD PRPS images, and DEABFO improves the convergence speed of the SVM parameter optimization process and can effectively avoid the probability of falling into the local optimum. Compared with other image feature extraction methods and other SVM optimization algorithms, this method has high accuracy in GIS PD fault diagnosis, with the recognition rate reaching 91.23%.

Multi-category intuitionistic fuzzy twin support vector machines with an application to plant leaf recognition

The intuitionistic fuzzy twin support vector machine for multi-categorization is developed in this study, which incorporates both structural and empirical risk concepts. In this method, each training pattern is first aggregated with the appropriate membership and non-membership degrees, which describe the position of a pattern in relation to its class centre and surrounding circumstances in input or feature space, and then the separating hyperplane is constructed using the kernel function and convex quadratic programming. Empirical findings on an artificial and thirteen UCI standard datasets show that it outperforms well-known existing methods including improved support vector machines, K-nearest neighbour, logistic regression, decision trees, random forests, and multilayer perceptrons. Furthermore, the suggested classifier with linear, polynomial, and Gaussian kernels has been used to identify the leaves of various plants, where the shape, texture, and margin data are extracted from the leaf in order to categorize the plant species. The method’s generalization capacity is demonstrated by the classification results on two leaf datasets of thirty and one hundred species, respectively. To compare the suggested method’s prediction capacity with others, statistical analysis is performed using two non-parametric tests, Friedman and Wilcoxon, with a 5% threshold of significance. The results show that the proposed method yields better performance for both linear and non-linear kernels.

Identification of coumarin-based food additives using terahertz spectroscopy combined with manifold learning and improved support vector machine.

The purpose of this paper is to use terahertz (THz) spectroscopy combined with manifold learning and improved support vector machine (SVM) model to identify the coumarin-based food additives. The 216 THz absorbance spectra (144 for calibration set and 72 for prediction set) of six coumarin-based food additives are measured by using THz time-domain spectroscopy (THz-TDS) in the range of 0.5-2.0 THz. The method (P-t-SNE) combined principal component analysis (PCA) with manifold learning t-distributed stochastic neighbor embedding (t-SNE) is used for feature extraction of the THz spectra. Then, an improved SVM using differential evolution (DE) to improve gray wolf optimization (GWO) to optimize parameters is proposed. Finally, the result shows that the prediction set accuracy of PCA-DEGWO-SVM, P-t-SNE-DEGWO-SVM, and P-t-SNE-GWO-SVM models are 97.22%, 98.61%, and 95.83%, respectively, indicating that the accuracy by P-t-SNE is increased by about 1.39% compared with that processed by PCA, and the accuracy by DEGWO is also increased by about 2.78% compared with that processed by GWO. In conclusion, the improved model (P-t-SNE-DEGWO-SVM) has the best identification effect, and it is proved to be an effective method to identify coumarin-based food additives. PRACTICAL APPLICATION: The method used in this paper can be applied in the field of food safety detection. When detecting coumarin-based food additives, the method proposed in this paper is more time-saving and efficient than traditional detection methods. Through some more tests and adjustments, it will be possible to achieve rapid and on-site identification of various food additives.

Evaluation and Optimization of College English Teaching Effect Based on Improved Support Vector Machine Algorithm

Improved SVM algorithm improves the efficiency of College English teaching effect evaluation and meets the requirements of College English teaching evaluation. Based on the relevant theories, this paper constructs the evaluation index system with teachers and students as the main body and takes the questionnaire survey results as the input samples of the LSSVM algorithm. Compared with the evaluation accuracy of an optimized BP neural network and the category weighted gray target decision-making method, the results show that the evaluation accuracy of optimized LSSVM algorithm is 96.26%. Taking SIT as an example, this paper uses the optimized LSSVM algorithm to evaluate its teaching effect and obtains that teachers’ literature and teaching contents are important factors to improve the effect of English teaching. Therefore, this paper introduces the intelligent voice system to optimize the English teaching design of SIT. The teaching design is optimized from the dimensions of teaching objectives, learning situation, teaching content, teaching media and curriculum materials, and teaching procedures.

A Whole Process Cost Prediction System for Construction Projects Based on Improved Support Vector Machines

The current project cost system requires high data scale, small amount of data and large prediction deviation. In order to improve the prediction accuracy of the whole process cost of construction project, this paper designs a whole process project cost prediction system based on improved support vector machine. In the hardware part of the system, the control core adopts arm controller S3C6410 and introduces 4G communication module to analyze the actual engineering data with the support of hardware. In the software part, the whole process cost prediction index system of the construction project is established, the index is reduced by the principal component method, and the support vector machine is improved by particle swarm optimization algorithm to realize the whole process cost prediction of the project. The system function test results show that the average prediction deviation of the designed system is 4.11%, the average prediction deviation of the cost prediction system is 3.05%, and the average prediction deviation of the system is 1.57%.

Nations performance evaluation during SARS-CoV-2 outbreak handling via data envelopment analysis and machine learning methods

Nowadays, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with COVID-19 public name (short for coronavirus disease 2019) encompassed the world and nations are trying to manage crisis with maximum medicine capacity, but how successful are they? Data envelopment analysis (DEA) is a powerful tool that answers the question with measuring the efficiency of nations, but the challenge is here that the COVID-19 data grow and change rapidly. So, efficiency measurement is difficult at any time of the epidemic because needs to re-implementing DEA models. At this point, Machine Learning (ML) comes to help, in way that efficiency scores prediction is feasible with supervised learning on DEA results, but accurate prediction in small-scale data is the next challenge. This paper investigates integrated DEA and ML (DEAML) to fix challenges. First, a relational two-stage model with desirable-undesirable variables is proposed to measure the efficiency of 50 nations by 5 December 2020. Then, a multi-layer perceptron (MLP) network with a Limited memory BFGS (L-BFGS) optimisation algorithm is proposed to predict the efficiency of nations at any time of the epidemic. The results are analysed and discussed. ABBREVIATIONS: CCR: Charnes–Cooper-Rhodes; BPNN: Back-Propagation Neural Network; GANN: Genetic Algorithm integrated with Neural Network; SVM: Support Vector Machines; ISVM: Improved Support Vector Machines; OECD: Organization for Economic Co-operation and Development; BCC: Banker-Charnes–Cooper; CART: Classification And Regression Trees; BT: Boosted Tree; PCR: Polymerase Chain Reaction; ICU: Intensive Care Unit; VRS: Variable Return to Scale; MSE: Mean Squared Error; GDP: Gross Domestic Product

Action Quality Recognition Based on Improved Support Vector Machine and Channel State Information

Action Quality Recognition Based on Improved Support Vector Machine and Channel State Information

Power Transformer Fault Diagnosis Based on Improved Support Vector Machine

Power Transformer Fault Diagnosis Based on Improved Support Vector Machine

Imaginary Control of a Mobile Vehicle Using Deep Learning Algorithm: A Brain Computer Interface Study

Controlling a remote mobile vehicle using electroencephalograph (EEG) signals is still a challenge specially achieving a high degree of accuracy and precision. In the present study, the focus is on implementing an efficient feature space in a deep-based learning (DL) algorithm for a single trial application. More specifically, a boosting feature algorithm by means of long short-term memory (LSTM) networks are implemented in a deep auto-encoder (DAE) algorithm for producing an effective feature space tp identify event related desynchronization/event related synchronization (ERD/ERS) patters in EEG signals. For this purpose, three different DL-based algorithms are implemented that the models are based on a convolutional neural network (CNN), DAE, and LSTM networks to extract and boost the main features. In addition, our previous improved support vector machine (SVM)-based algorithm is employed to consider the potential of SVM and implemented DL-based algorithms for a two classes identification. To consider the efficiency of our implemented methods, algorithms are employed for control of a remote mobile vehicle in an imaginary right-hand opening and making a right-hand fist task. In our experiment, eleven subjects participated in an imaginary movement task. In the experiment, the displayed movement pictures were colored in yellow and red colors for stimulating brain to generate stronger ERD/ERS patterns. Results showed that the proposed algorithm by using the boosting technique significantly increased the accuracy with a higher precision of 73.31% ± 0.03. The proposed method enabled the DL algorithm to be used in single trial experiments.

Application of Improved Support Vector Machine in Geochemical Lithology Identification

Application of Improved Support Vector Machine in Geochemical Lithology Identification

Traffic Sign Detection and Recognition Based on Color Features and Improved Support Vector Machine Algorithm

Traffic Sign Detection and Recognition Based on Color Features and Improved Support Vector Machine Algorithm

A pervasive health care device computing application for brain tumors with machine and deep learning techniques

Purpose Pervasive health-care computing applications in medical field provide better diagnosis of various organs such as brain, spinal card, heart, lungs and so on. The purpose of this study is to find brain tumor diagnosis using Machine learning (ML) and Deep Learning(DL) techniques. The brain diagnosis process is an important task to medical research which is the most prominent step for providing the treatment to patient. Therefore, it is important to have high accuracy of diagnosis rate so that patients easily get treatment from medical consult. There are many earlier investigations on this research work to diagnose brain diseases. Moreover, it is necessary to improve the performance measures using deep and ML approaches. Design/methodology/approach In this paper, various brain disorders diagnosis applications are differentiated through following implemented techniques. These techniques are computed through segment and classify the brain magnetic resonance imaging or computerized tomography images clearly. The adaptive median, convolution neural network, gradient boosting machine learning (GBML) and improved support vector machine health-care applications are the advance methods used to extract the hidden features and providing the medical information for diagnosis. The proposed design is implemented on Python 3.7.8 software for simulation analysis. Findings This research is getting more help for investigators, diagnosis centers and doctors. In each and every model, performance measures are to be taken for estimating the application performance. The measures such as accuracy, sensitivity, recall, F1 score, peak-to-signal noise ratio and correlation coefficient have been estimated using proposed methodology. moreover these metrics are providing high improvement compared to earlier models. Originality/value The implemented deep and ML designs get outperformance the methodologies and proving good application successive score.

A novel image‐orientation feature extraction method for partial discharges

Partial discharge identification is of great significance in monitoring power equipment. Although substantial work has been done in partial discharge pattern recognition, most studies have been performed in noise-free environments. To improve the accuracy of partial discharge pattern recognition, this paper introduces a partial discharge image feature extraction method based on upright speeded-up robust features. Each group of phase-resolved pulse sequence data is converted into grayscale images to construct a sample library of partial discharge defects. Upright speeded-up robust features is used to extract features in grayscale images. The improved support vector machine is used to achieve the best classification of partial discharge sources. The sensitivity of local features to different phase resolutions and different image resolutions of partial discharge phase-resolved pulse sequence patterns was investigated, and the optimal phase resolution and image resolution were used to construct phase-resolved pulse sequence patterns. In addition, the recognition effect of this feature extraction method under different noise conditions was also analysed. The results prove that upright speeded-up robust features can effectively deal with noisy data. The results of this research can provide references for the storage setting and recognition of three-dimensional images of on-site partial discharge data.