Optimized Support Vector Machine Research Articles

Multivariate multi-scale cross-fuzzy entropy and SSA-SVM-based fault diagnosis method of gearbox

Abstract Fuzzy entropy (FuzzyEn) is widely recognized as a powerful tool for analyzing nonlinear dynamics and measuring the complexity of time series data. It has been utilized as an effective indicator to capture nonlinear fault features in gearbox vibration signals. However, FuzzyEn only measures complexity at a single scale, ignoring the valuable information contained in large-scale features of the time series. Furthermore, FuzzyEn does not account for coupling characteristics between related or synchronized time series. To address these limitations, a novel entropy-based approach called multivariate multi-scale cross-fuzzy entropy (MvMCFE) is proposed in this paper for measuring the complexity and mutual predictability of two multivariate time series. Relying on the advantages of MvMCFE in nonlinear feature extraction, a new fault diagnosis method for gearboxes is proposed based on MvMCFE and an optimized support vector machine (SVM) using the salp swarm algorithm (SSA-SVM). Ultimately, the proposed gearbox diagnostic method is employed to analyze the gearbox experimental data and a comparison with existing fault diagnosis approaches is conducted. The comparison results indicate that the proposed method can effectively extract nonlinear fault features of gearboxes and achieve the highest recognition rate compared to the other methods.

Support vector machine based fault detection in inverter‐fed electric vehicle

AbstractInverters play a prominent role in the power train system of electric vehicles (EVs). Devices in EV connected power system are threatened by faults due to the continuous working and varying speed range of motors in EVs. Hence, in the EV connected application, the detection of fault is essential since it secures the system from severe damage and dangerous operating conditions. This paper deals with fault detection in inverter‐fed EV using a dual‐tree complex wavelet transform (DTCWT) based squeeze net (SN) and optimized support vector machine (SVM). Due to the simple structure and high power density, most EV models on the market are equipped with induction motors. In the proposed work, the voltage, current, and speed signals are measured at different faulty conditions, and then the features are extracted through the DTCWT‐based SN. Extracted data are processed and classified through the sucker‐vulture optimization algorithm (SVOA) based SVM. In the proposed SVOA, the exploration phase of remora optimization algorithm is used for the exploitation phase of African vulture optimization algorithm (AVOA). Thus, the convergence speed of AVOA is improved. The proposed method is implemented in MATLAB/SIMULINK, and the results are used for different scenarios. The accuracy and F1‐score for the proposed methodology are attained as 99.92441% and 99.92441%. From the obtained results, it is clear that the proposed DTCWT‐based SN effectively detects the faults in the inverter.

Near-Infrared Off-Axis Cavity-Enhanced Optical Frequency Comb Spectroscopy for CO2/CO Dual-Gas Detection Assisted by Machine Learning.

Cavity-enhanced direct frequency comb spectroscopy (CE-DFCS) is widely used as a highly sensitive gas sensing technology in various gas detection fields. For the on-axis coupling incidence scheme, the detection accuracy and stability are seriously affected by the cavity-mode noise, and therefore, stable operation inevitably requires external electronic mode-locking and sweeping devices, substantially increasing system complexity. To address this issue, we propose off-axis cavity-enhanced optical frequency comb spectroscopy from both theoretical and experimental aspects, which is applied to the detection of single- and dual-gas of carbon monoxide (CO) and carbon dioxide (CO2) in the near-infrared. An erbium-doped fiber frequency comb with a repetition frequency of ∼41.709 MHz is coupled into a resonant cavity with a length of ∼360 mm in an off-axis manner, exciting numerous high-order modes to effectively suppress cavity-mode noise. The performance of multiple machine learning models is compared for the inversion of a single/dual gas concentration. A few absorbance spectra are collected to build a sample data set, which is then utilized for model training and learning. The results demonstrate that the Particle Swarm Optimization Support Vector Machine (PSO-SVM) model achieves the highest predictive accuracy for gas concentration and is ultimately applied to the detection system. Based on Allan deviation, the detection limit for CO in single-gas detection can reach 8.247 parts per million by volume (ppmv) by averaging 87 spectra. Meanwhile, for simultaneous CO2/CO measurement with highly overlapping absorbance spectra, the LoD can be reduced to 13.196 and 4.658 ppmv, respectively. The proposed optical gas sensing technique indicates the potential for the development of a field-deployable and intelligent sensor system capable of simultaneous detection of multiple gases.

An efficient method for MRI brain tumor tissue segmentation and classification using an optimized support vector machine

An efficient method for MRI brain tumor tissue segmentation and classification using an optimized support vector machine

Particle swarm optimization support vector machine-based coal and rock cutting tool load spectrum identification method

The goal of this research is to achieve safe and efficient excavation of coal and rock tunnels with complex geological structures, and to enhance the self-sensing ability of coal and rock cutting equipment and tools. Particle swarm optimization support vector machine is used to identify the cutting state of disc cutting tools. EDEM finite element analysis software is used to analyze cutting process characteristics of the disc cutting tool when used to cut through coal and rock with different compressive strengths. Empirical mode decomposition is used to decompose the load spectrum characteristics; for this purpose, the first-order and seventh-order intrinsic mode functions containing all the feature information of the original signal of the load spectrum are selected. The sample entropy is calculated as the feature input vector. The extracted feature vector is input into the trained support vector machine model and the particle swarm optimization support vector machine model. By extracting the sample entropy of the load spectrum of the disc cutter as the feature vector, the particle swarm optimization support vector model is used to identify the cutting state of the coal and rock. The recognition accuracy of the support vector machine model before and after the improvement is compared and analyzed. The results show that compared to the unoptimized support vector machine, the support vector machine optimized by particle swarm optimization can identify the load spectrum of the coal more quickly and accurately. The recognition accuracy is 96,82%, which verifies the effectiveness of the particle swarm optimization support vector machine model in identifying the load spectrum of the coal and rock disc cutter.

Recognition of partial discharge in GIS based on image feature fusion

<p>Partial discharge (PD) is a significant electrical fault in gas-insulated switchgear (GIS), with various types posing different risks to insulation. Accurate identification of PD types is essential for enhancing GIS management and ensuring the reliability of electrical grids. This study proposes a novel approach for PD identification in GIS integrating completed local binary pattern (CLBP) feature extraction, feature engineering, and an optimized support vector machine (SVM). PD faults were simulated in GIS and phase-resolved pulse sequence (PRPS) data for four different forms of PD were gathered. CLBP was used to extract image features, and then the support vector machine recursive feature elimination (SVM-RFE) algorithm was used to evaluate feature importance. Then, linear discriminant analysis (LDA) was used to fuse the selected features and reduce redundancy. The fused features were classified using a bald eagle search algorithm combined with differential evolution (IBES)-optimized SVM, achieving a recognition accuracy of 99.38%. The results indicate that the proposed method effectively distinguishes between different PD PRPS patterns in GIS.</p>

Rapid discrimination between wild and cultivated Ophiocordyceps sinensis through comparative analysis of label-free SERS technique and mass spectrometry

Ophiocordyceps sinensis is a genus of ascomycete fungi that has been widely used as a valuable tonic or medicine. However, due to over-exploitation and the destruction of natural ecosystems, the shortage of wild O. sinensis resources has led to an increase in artificially cultivated O. sinensis. To rapidly and accurately identify the molecular differences between cultivated and wild O. sinensis, this study employs surface-enhanced Raman spectroscopy (SERS) combined with machine learning algorithms to distinguish the two O. sinensis categories. Specifically, we collected SERS spectra for wild and cultivated O. sinensis and validated the metabolic profiles of SERS spectra using Ultra-Performance Liquid Chromatography coupled with Orbitrap High-Resolution Mass Spectrometry (UPLC-Orbitrap-HRMS). Subsequently, we constructed machine learning classifiers to mine potential information from the spectral data, and the spectral feature importance map is determined through an optimized algorithm. The results indicate that the representative characteristic peaks in the SERS spectra are consistent with the metabolites identified through metabolomics analysis, confirming the feasibility of the SERS method. The optimized support vector machine (SVM) model achieved the most accurate and efficient capacity in discriminating between wild and cultivated O. sinensis (accuracy = 98.95%, 5-fold cross-validation = 98.38%, time = 0.89s). The spectral feature importance map revealed subtle compositional differences between wild and cultivated O. sinensis. Taken together, these results are expected to enable the application of SERS in the quality control of O. sinensis raw materials, providing a foundation for the efficient and rapid identification of their quality and origin.

A rapid method for assessing seed drought resistance using integrated ID-BOA-SVM.

This study investigates the application of near-infrared spectroscopy (NIR) for assessing drought resistance in seeds, aiming to offer a rapid and efficient method suitable for large-scale primary screening. NIR spectroscopy is utilized to analyze four key factors (water, sugars, amino acids content, and genes) associated with maize seed drought responses. Signature NIR bands indicative of drought resistance-related molecules are identified using the Competitive Adaptive Reweighted Sampling (CARS) technique. Furthermore, an Improved Discrete Bayesian Optimization Support Vector Machine (ID-BOA-SVM) classification model is developed to address issues related to sparse features in traditional Bayesian Optimization Support Vector Machines (BOA-SVM). To enhance classification performance, a stacking model integrating Random Forest (RF), ID-BOA-SVM, Logistic Regression (LR), and Gradient Boosted Decision Trees (GBDT) classifiers is constructed, ensuring robustness and minimizing overfitting risks. The model achieves satisfactory recognition accuracy (94.28% accuracy, 94% precision, 94.61% recall, and 94.23% F1-score) even under conditions of substantial interference and dataset variability. This research demonstrates that NIR spectroscopy-derived data can support genetic and physiological studies of drought-resistant seed varieties, facilitating a deeper understanding of drought resistance mechanisms and optimizing breeding strategies.

Accuracy of IGBT Junction Temperature Prediction: An Improved Sailfish Algorithm to Optimize Support Vector Machine

Accuracy of IGBT Junction Temperature Prediction: An Improved Sailfish Algorithm to Optimize Support Vector Machine

Classification of Weaving Motifs Based on Their Area of Origin Using the Support Vector Machine Algorithm

Indonesia has many cultural riches in the form of traditional fabrics, one of which is woven fabrics. Woven fabrics from each region showcase distinctive motifs, manifesting the local community’s daily life, culture, natural conditions, and beliefs. The diverse weaving motifs pose a challenge in determining the origin of the woven fabrics. It highlights the necessity of a system to detect and identify woven fabrics. Texture analysis was performed using the Gray Level Co-occurrence Matrix (GLCM). A classification method based on a Support Vector Machine (SVM) consisting of four models: Linear SVM, Quadratic SVM, Cubic SVM, and Fine Gaussian SVM was developed in this research. The images of woven fabrics came from three regions in Indonesia: Sumatra, Kalimantan, and Nusa Tenggara. This research utilized 240 training images and 12 testing images. The testing results unveiled that the Cubic SVM model, which achieved a 100% accuracy rate in 1.0835s, was the optimum SVM model for the weaving classification.

An Improved Internet of Thing-based Optimized SVM Approach for ECG-founded Cardiac Arrhythmia Classification

Cardiovascular diseases (CVD) stand as the leading cause of global mortality, claiming millions of lives annually. An electrocardiogram (ECG) records the heart's electrical activity based on the Internet of Things (IoT), crucial in detecting cardiac arrhythmias (CA), characterized by irregular heart rates and rhythms. Signals from the MIT-BIH Arrhythmia Physio net database are analyzed. This chapter aims to propose a hybrid approach merging Genetic Algorithm-Support Vector Machine (GSVM) and Particle Swarm Optimization-Support Vector Machine (PSVM) for CA classification. The study introduces an algorithm for categorizing ECG beats into six groups using Independent Component Analysis (ICA)-derived features. Optimal SVM settings are determined using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) on ICA features computed via non-parametric power spectral estimation. The research delves into the origins and methodologies of GA and PSO. Simulation results comparing GSVM and PSVM are presented, emphasizing PSVM's superior performance in accuracy, sensitivity, specificity, and positive predictivity. Detailed performance metrics, including Sensitivity, Specificity, Positive Predictivity, and Accuracy percentages, are scrutinized and compared against the top classifier. The findings endorse PSVM's superiority over GSVM, indicating enhanced performance across multiple evaluation criteria.

An Efficient Spam Mail Classification Approach Using Improved Moth Flame Optimization and Multi-class Support Vector Machine

An Efficient Spam Mail Classification Approach Using Improved Moth Flame Optimization and Multi-class Support Vector Machine

Coal structure identification based on geophysical logging data: Insights from Wavelet Transform (WT) and Particle Swarm Optimization Support Vector Machine (PSO-SVM) algorithms

Coal structure is closely related to microscopic and macroscopic properties of coal, and its accurate identification is of great significance to coalbed methane (CBM) reservoir evaluation, hydraulic fracturing performance and production efficiency prediction. The identification of coal structure based on geophysical logging data has become a popular topic as well as a challenge. The emerging Machine Learning (ML) provides convenience for this issue. Under this background, in the case of the Shanxi Formation No. 3 coal seam and the Taiyuan Formation No. 15 coal seam in Mabidong Block, Qinshui Basin, China, this paper proposed a new coal structure identification method based on geophysical logging data using Wavelet Transform (WT) and Particle Swarm Optimization Support Vector Machine (PSO-SVM) algorithms. Our results showed that the vertical resolution of logging data can be effectively improved when sym5 wavelet basis and third level decomposition were selected in wavelet decomposition, and 4.5 was selected as the weighting coefficient in wavelet reconstruction. The coal structure prediction model based on the logging data processed by WT was established by PSO-SVM algorithm, where PSO was used for parameter optimization (optimal penalty factor C and width parameter σ) and SVM with Radial Basis Function (RBF) kernel was used for model establishment. The Hold-out Cross Validation (HO CV) method was used to test the generalization ability of the prediction model, and the accuracy (ACC) of coal structure prediction in the training set and testing set was 94.26% and 88.46%, respectively. The prediction model was applied to identify the coal structure of two coring wells and the predicted coal structure class was consistent with the true coal structure class, confirming the validity of the model. The coal structure prediction results in the whole study area showed that the tectonic conditions control the coal structure. This work provides new insights for coal structure identification.

Wildfire Burnt Area Severity Classification from UAV-Based RGB and Multispectral Imagery

Wildfires present a significant threat to ecosystems and human life, requiring effective prevention and response strategies. Equally important is the study of post-fire damages, specifically burnt areas, which can provide valuable insights. This research focuses on the detection and classification of burnt areas and their severity using RGB and multispectral aerial imagery captured by an unmanned aerial vehicle. Datasets containing features computed from multispectral and/or RGB imagery were generated and used to train and optimize support vector machine (SVM) and random forest (RF) models. Hyperparameter tuning was performed to identify the best parameters for a pixel-based classification. The findings demonstrate the superiority of multispectral data for burnt area and burn severity classification with both RF and SVM models. While the RF model achieved a 95.5% overall accuracy for the burnt area classification using RGB data, the RGB models encountered challenges in distinguishing between mildly and severely burnt classes in the burn severity classification. However, the RF model incorporating mixed data (RGB and multispectral) achieved the highest accuracy of 96.59%. The outcomes of this study contribute to the understanding and practical implementation of machine learning techniques for assessing and managing burnt areas.

A multi-sensor monitoring methodology for grinding wheel wear evaluation based on INFO-SVM

It’s a significant challenge to accurate and efficient evaluation of grinding wheel wear. The evaluating grinding wheel wear traditional evaluation model has several weaknesses, including low accuracy, poor efficiency, and the need for a large database. To address these issues, an evaluating grinding wheel wear optimize model method is proposed based on weIghted meaN oF vectOrs optimized Support Vector Machine (INFO-SVM), and an data processing method is proposed based on Whale Optimization Algorithm to optimize Variational Mode Decomposition (WOA-VMD). Firstly, the grinding wheel wear was analyzed by grinding wheel and workpiece topography images. Secondly, the WOA-VMD data processing method has distinguished frequency bands between the grinding process and environmental noise signal, the method thereby eliminating environmental noise to enhance the signal-to-noise ratio in evaluating grinding process signals. Based on ReliefF algorithm established dataset, finally, the INFO-SVM algorithm method to evaluate grinding wheel wear has verified the robustness, effectiveness, and computational efficiency. The experimental results demonstrate the method's effectiveness in noise reduction, high accuracy, fast recognition speed, and strong robustness. Therefore, multi-sensor monitoring holds promising potential for application in the field of grinding wheel wear evaluation.

Forecast earthquake precursor in the Flores Sea

<span>Artificial intelligence (AI) can use seismic training data to discover relationships between inputs and outcomes in real-world applications. Despite this, particularly when using geographical data, it has not been used to predict earthquakes in the Flores Sea. The algorithm will read the seismic data as a pattern of iterations throughout the operation. The output data is created by grouping based on clusters using the most effective WCSS analysis, while the input features are derived from the original international resource information system (IRIS) web service data. Given that earthquake prediction is an effort to reduce seismic disasters, this research is essential. By generating predictions, it can reduce the devastation caused by earthquakes. Using the support vector machine (SVM), hyperparameter support vector machine (HP-SVM), and particle swarm optimization support vector machine (PSO-SVM) algorithms, this study seeks to forecast the Flores Sea earthquake. According to the estimation results, the SVM algorithm’s evaluation value is less precise than that of the HP-SVM, especially the linear HP-SVM and HP-SVM Polynomial models. However, the HP-SVM RBF model’s accuracy rating is identical to that of the traditional SVM model. <br /> The improvement of the PSO-SVM model, which has the finest gamma position and a value of 9.</span>

Research on surface defect identification of steel balls based on improved K-CV parameter optimization support vector machine

Surface defects generated during the production process of steel balls can lead to bearing failures, which makes it crucial to promptly detect and classify these defects. Defects classify is helpful for analysis and improving the production process. An algorithm that incorporates K-fold cross-validation (K-CV) with improved grid search is proposed to optimize the parameters of SVM, in order to detect surface defects with steel balls. Principal Component Analysis (PCA) was employed to reduce the dimensionality of the effective features data. The K-CV algorithm was employed in conjunction with an improved grid search method to find the optimal parameters “c” and “g.” This approach not only reduced the search time but also diminished the influence of individual samples on the model, thereby enhancing its robustness and ultimately improving the classification accuracy. The model’s performance was evaluated using a confusion matrix, and a comparison was made with three other machine learning models. The experimental results demonstrated the effectiveness of the proposed algorithm in classifying defects on highly reflective metal surfaces such as steel balls. The model achieved a classification accuracy of 97.15%.

Penerapan Fitur Seleksi dan Particle Swarm Optimization pada Algoritma Support Vector Machine untuk Analisis Credit Scoring

After the Covid-19 pandemic, the banking sector faced significant challenges in contributing to achieving national goals in terms of increasing living standards and equalizing the regional economy. Hundreds of millions of low-income people have no credit or bank accounts because they do not have sufficient credit history to warrant the credit scores assigned to them. An estimated 1.7 billion people (31% of the adult population) do not have an account with a financial institution. People today are usually concentrated in developing countries, especially in China 204 million, India 357 million and Indonesia 102 million people. Because it is very difficult to make accurate predictions in determining credit worthiness for low-income people. Cooperatives are financial institutions that have a crucial role in channeling financing to members and the community to develop their businesses. An inappropriate credit distribution process can have a negative effect on KSP, resulting in frequent losses. This risk is known as problem loans, the cause is the KSP's failure to analyze the credit of prospective debtors. Therefore, calculations are needed to detect opportunities for credit risk default by prospective debtors objectively and precisely so that loan problems do not occur. Credit scoring is a method used to evaluate credit risk in terms of loan applications from consumers [4]. In this research we will provide a solution using classification techniques with feature selection methods in the Particle Swarm Optimization (PSO) Algorithm and Support Vector Machine (SVM) to predict the credit risk of prospective debtors failing to make loan payments. The application of the SVM algorithm in credit scoring research is because SVM is good at data classification. However, the standard SVM model still does not produce optimal results due to the difficulty of determining the best parameters, therefore researchers will optimize it with the Feature Selection and PSO algorithms to determine the best parameters. The results from the combination of several parameters using PSO-SVM obtained an accuracy of 87.23%, therefore the application of this method was proven to improve the performance of the SVM algorithm to increase its accuracy results in predicting the feasibility of granting credit.

Debris Flow Scale Prediction Based on Correlation Analysis and Improved Support Vector Machine

The occurrence of debris flows are a significant threat to human lives and property. Estimating the debris flow scale is a crucial parameter for assessing disaster losses in such events. Currently, the commonly used method for estimating debris flow runoff relies on fitting techniques, which often yield low prediction accuracy and limited data representation capabilities. Addressing these challenges, this study proposes an improved grey wolf algorithm optimized support vector machine prediction model. The model’s effectiveness is validated using data from 72 debris flow events in Beichuan County. The results demonstrate a prediction accuracy of 95.9% using this approach, indicating its strong predictive capabilities for debris flow scale. Additionally, it is observed that the basin area, the basin relative, and the main channel length are the key factors influencing debris flow scale in Beichuan County.

A rapid and non-destructive identification for paper cup evidence based on shifted-excitation Raman difference spectroscopy and SOM clustering.

In order to achieve rapid, non-destructive, efficient, and accurate classification of paper cup samples, we propose a classification model that integrates shifted-excitation Raman difference spectroscopy (SERDS) with self-organizing map (SOM) and Bayesian optimization-support vector machine (BO-SVM). We collected differential Raman data from 52 paper cup samples using SERDS, with an excitation wavelength range of 784-785 nm, a laser power of 440 mW, an integration time of 10 s, and a spectral range spanning from 280 to 2700 cm-1 . Subsequently, principal component analysis (PCA) was applied to reduce the dimensionality of the data. The SOM clustering outcomes were utilized as the foundation for constructing the discriminant analysis (FDA) and BO-SVM classification models. The primary constituent of the paper cup samples was identified as cellulose, while additional fillers such as talc, calcium carbonate, and kaolin were also present. The SOM clustering categorized the samples into seven distinct groups. The FDA model achieved a classification accuracy of 92.3%, and the BO-SVM model reached a classification accuracy of 96.2%. The SOM clustering effectively discerned samples with different fillers, as evidenced by distinct peak numbers and shapes in the differential Raman spectra, thereby underscoring the practical significance of SOM clustering. In comparison with FDA, BO-SVM exhibited enhanced classification accuracy and exceptional performance in handling outliers and linearly inseparable data, indicating its superior generalization capabilities.