Optimal Support Vector Machine Model Research Articles

Acoustic tunnel lining cavity detection using cepstral coefficients with optimized filter bank

Tunnels are an essential component of modern transportation infrastructure, and their structural health is critical to traffic safety, which can be seriously affected by tunnel lining cavities. In this paper, an acoustic-based detection approach for assessing the integrity of tunnel linings is studied. By tapping the tunnel lining surface, acoustic signals are sampled and analyzed using a novel feature parameter extraction algorithm-the energy-frequency cepstral coefficient, which uses wavelet packet decomposition to obtain energy distribution statistics in the frequency domain of the signal, and constructs a signal-dependent filter bank to achieve the cepstral coefficient extraction. Compared with the traditional Mel filter bank, this method can adaptively adjust the resolution of the filter bank according to the frequency characteristics of the classified samples. This allows for higher frequency resolution in regions where the energy distribution is concentrated. As a result, the extracted feature parameters achieve both dimensional compression and superior information retention. Experimental results show that the proposed energy-frequency cepstral coefficient feature outperforms the traditional Mel-frequency cepstral coefficient feature, resulting in a higher accuracy of tunnel lining detection. The convolutional neural network model achieves an accuracy of 99.2%, with a 78.9% reduction in error rate compared with the traditional Mel-frequency cepstral coefficient feature parameters. Additionally, a particle swarm optimization support vector machine model is trained to achieve an accuracy rate of 99.6% and an error rate reduction of 76.5%.

Methods for Identifying Effective Microseismic Signals in a Strong-Noise Environment Based on the Variational Mode Decomposition and Modified Support Vector Machine Models

The environment for acquiring microseismic signals is always filled with complex noise, leading to the presence of abundant invalid signals in the collected data and greatly disturbing effective microseismic signals. Regarding the identification of effective microseismic signals with a low signal-to-noise ratio, a method for identifying effective microseismic signals in a strong-noise environment by using the variational mode decomposition (VMD) and genetic algorithm (GA)-based optimized support vector machine (SVM) model is proposed. Microseismic signals with a low signal-to-noise ratio are adaptively decomposed into several intrinsic mode functions (IMFs) by using VMD. The characteristics of such IMFs are extracted and used as a basis for the determination of signal validity. The SVM model is optimized by utilizing GA to obtain the optimal penalty factor c and the kernel function parameter g. The availability of IMF components is judged by the optimized SVM model, based on which the effectiveness of microseismic signals is further identified. By applying the algorithm to the microseismic signals with artificially added noise, the effective microseismic signals and ineffective noise are discriminated, verifying the feasibility of the algorithm. After processing the microseismic records collected in the field, we effectively judge the effectiveness of microseismic signals, suppress the interfering noise in the data and greatly improve the signal-to-noise ratio of the seismic records. The results show that the method for identifying effective microseismic signals based on VMD and GA-SVM can well discriminate between effective and ineffective microseismic signals, which is very significant and provides technical support for microseismic monitoring in a strong-noise environment.

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.

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.

Crack classification and quantitative evaluation based on dimensionality reduction optimization model of multifeature weak magnetic signal

In this paper, we propose a classification and quantitative method based on the optimization model of dimensionality reduction for detecting crack defects in superalloy turbine disks using multifeature weak magnetic signals. First, the experiment on magnetic weak nondestructive testing was conducted on the defects, resulting in the acquisition of 112 sets of preset magnetic anomaly eigenvalue data. The dimensionality of the multidimensional weak magnetic signal features was then reduced using the principal component analysis method to construct the weak magnetic feature sample library. On this basis, a support vector machine (SVM) model was established to classify and quantitatively evaluate the defects. Genetic algorithm and grid search method were used to optimize the model. Finally, the validity of the optimized SVM model was verified by adding artificial and natural crack defect specimens outside of the sample library. Results show that compared with the original model, the feature dimension reduction and optimization of the two methods enhance the classification accuracy of superalloy surface defects and the quantitative accuracy of defect length, width, and depth. The genetic algorithm significantly improved the classification accuracy, increasing it by 39.33 %. Specifically, the genetic algorithm improved the quantitative accuracy of length and width by 17.9 % and 28.56 % respectively. On the other hand, the grid search method improved the quantitative accuracy of depth the most, with an improvement of 49.83 %. For specimens with natural cracks, the optimized SVM model still demonstrated good classification and quantitative effectiveness in detecting defects, with an accuracy rate of over 85 %.

Landslide susceptibility prediction by gray wolf optimized support vector machine model under different factor states

Landslide susceptibility prediction by gray wolf optimized support vector machine model under different factor states

Research on Three-Phase Asynchronous Motor Fault Diagnosis Based on Multiscale Weibull Dispersion Entropy.

Three-phase asynchronous motors have a wide range of applications in the machinery industry and fault diagnosis aids in the healthy operation of a motor. In order to improve the accuracy and generalization of fault diagnosis in three-phase asynchronous motors, this paper proposes a three-phase asynchronous motor fault diagnosis method based on the combination of multiscale Weibull dispersive entropy (WB-MDE) and particle swarm optimization-support vector machine (PSO-SVM). Firstly, the Weibull distribution (WB) is used to linearize and smooth the vibration signals to obtain sharper information about the motor state. Secondly, the quantitative features of the regularity and orderliness of a given sequence are extracted using multiscale dispersion entropy (MDE). Then, a support vector machine (SVM) is used to construct a classifier, the parameters are optimized via the particle swarm optimization (PSO) algorithm, and the extracted feature vectors are fed into the optimized SVM model for classification and recognition. Finally, the accuracy and generalization of the model proposed in this paper are tested by adding raw data with Gaussian white noise with different signal-to-noise ratios and the CHIST-ERA SOON public dataset. This paper builds a three-phase asynchronous motor vibration signal experimental platform, through a piezoelectric acceleration sensor to discern the four states of the motor data, to verify the effectiveness of the proposed method. The accuracy of the collected data using the WB-MDE method proposed in this paper for feature extraction and the extracted features using the optimization of the PSO-SVM method for fault classification and identification is 100%. Additionally, the proposed model is tested for noise resistance and generalization. Finally, the superiority of the present method is verified through experiments as well as noise immunity and generalization tests.

Retracted: Application of Optimized Support Vector Machine Model in Tax Forecasting System

Retracted: Application of Optimized Support Vector Machine Model in Tax Forecasting System

CRDI Engine Emission Prediction Models with Injection Parameters Based on ANN and SVM to Improve the SOOT-NOx Trade-Off

Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been widely used to solve non-linear problems. In the current study, based on 112 groups of experimental data, ANN and SVM models were established and compared to improve the trade-off relationship between SOOT and NOx emissions of a Common Rail Diesel Injection (CRDI) engine fueled with Fischer-Tropsch (F-T) diesel under different operating conditions and injection parameters. The model parameters for the different predictive targets were selected by evaluating the mean square error (MSE) and determination coefficient. Compared to the number of network iterations, the number of implied nodes had a greater effect on the MSE of the ANN model. Compared to the penalty parameter, the width coefficient had a weaker impact on the SVM performance. A comparative analysis showed that the SVM had better predictive accuracy and generalization ability than the ANN, with a maximum error not exceeding five percent and a determination coefficient of over 0.9. Subsequently, the optimal SVM model was combined with the NSGA-II algorithm to determine the optimal injection parameters for the CRDI engine, resulting in solutions to simultaneously decrease the SOOT and NOx emissions. The optimized injection parameters resulted in a 3.7–7.1% reduction in SOOT emission and a 1.2–2.6% reduction in NOx emissions compared to the original engine operating conditions. Based on limited experimental samples, SVM is inferred to be a useful tool for predicting the exhaust emissions of engines fueled with F-T diesel and can provide support for optimizing injection parameters.

The Rapid Detection of Trash Content in Seed Cotton Using Near-Infrared Spectroscopy Combined with Characteristic Wavelength Selection

Herein, we propose a new method based on Fourier-transform near-infrared spectroscopy (FT-NIR) for detecting impurities in seed cotton. Based on the spectral data of 152 seed cotton samples, we screened the characteristic wavelengths in full-band spectral data with regard to potential correlation with the trash content of seed cotton. Then, we applied joint synergy interval partial least squares (siPLS) and combinatory algorithms with the competitive adaptive reweighted sampling method (CARS) and the successive projection algorithm (SPA). In addition, we used the sparrow search algorithm (SSA), gray wolf algorithm (GWO), and eagle algorithm (BES) to optimize parameters for support vector machine (SVM) analysis. Finally, the feature wavelengths optimized via the six feature wavelength extraction algorithms were modeled and analyzed via partial least squares (PLS), SSA-SVM, GWO-SVM, and BES-SVM, respectively. The correlation coefficients, Rc and Rp, of the calibration and prediction sets were subsequently used as model evaluation indices; comparative analysis highlighted that the preferred option was the inverse estimation model as this could accurately predict the trash content of seed cotton. Subsequently, we found that the accuracy of predicting the content of impurities in seed cotton when applying the optimized SVM model of SSA combined with the feature wavelengths screened via siPLS-SPA was optimal. Thus, the optimal modeling method for inverse impurity content was siPLS-SPA-SSA-SVM, with an Rc value of 0.9841 and an Rp value of 0.9765. The rapid application development (RPD) value was 6.7224; this is >3, indicating excellent predictive ability. The spectral inversion model for determining the impurity rate of mechanized harvested seed cotton samples established herein can, therefore, determine the impurity rate in a highly accurate manner, thus providing a reference for the subsequent construction of a portable spectral detector of impurity rate. This will help objectively and quantitatively characterize the impurity rate of mechanized harvested seed cotton and provide a new tool for rapidly detecting impurities in mechanized harvested wheat. Our findings are limited by the small sample size and the fact that the model developed for estimating the impurity content of seed cotton was specific to a local experimental field and certain varieties of cotton.

Assessment of compensated advanced chronic liver disease based on serum bile acids in chronic hepatitis B patients

Patients with chronic liver disease progressed to compensated advanced chronic liver disease (cACLD), the risk of liver-related decompensation increased significantly. This study aimed to develop prediction model based on individual bile acid (BA) profiles to identify cACLD. This study prospectively recruited 159 patients with hepatitis B virus (HBV) infection and 60 healthy volunteers undergoing liver stiffness measurement (LSM). With the value of LSM, patients were categorized as three groups: F1 [LSM ≤ 7.0 kilopascals (kPa)], F2 (7.1 < LSM ≤ 8.0 kPa), and cACLD group (LSM ≥ 8.1 kPa). Random forest (RF) and support vector machine (SVM) were applied to develop two classification models to distinguish patients with different degrees of fibrosis. The content of individual BA in the serum increased significantly with the degree of fibrosis, especially glycine-conjugated BA and taurine-conjugated BA. The Marco-Precise, Marco-Recall, and Marco-F1 score of the optimized RF model were all 0.82. For the optimized SVM model, corresponding score were 0.86, 0.84, and 0.85, respectively. RF and SVM models were applied to identify individual BA features that successfully distinguish patients with cACLD caused by HBV. This study provides a new tool for identifying cACLD that can enable clinicians to better manage patients with chronic liver disease.

Quantitative analysis of aflatoxin B1 of peanut by optimized support vector machine models based on near-infrared spectral features

This study designs a chemometric framework for quantitatively evaluating aflatoxin B1 (AFB1) in peanuts based on near-infrared (NIR) spectroscopy technique. The NIR spectra of peanut samples exhibiting diverse fungal contamination levels were acquired using a portable NIR spectrometer. Subsequently, appropriate pre-processing techniques were employed for data refinement. To streamline the analysis, the iterative variable subset optimization (IVSO) technique was employed to conduct an initial screening of the pre-processed NIR spectra, eliminating numerous irrelevant variables. Building upon this screening process, the beluga whale optimization (BWO) algorithm was utilized to optimize the selected feature variables further. Subsequently, support vector machine (SVM) models were developed using the refined near-infrared spectral features to test AFB1 in peanuts quantitatively. The results indicate that the SVM model significantly improves detection performance and generalization proficiency, particularly after secondary optimization using BWO-IVSO. Among the different models considered, the SVM model established after BWO-IVSO optimization exhibited the most extraordinary level of generalization, with a root mean square error of prediction of 24.6322 μg∙kg−1, a correlation coefficient of 0.9761, and a relative percent deviation of 4.6999. Overall, this investigation highlights the effectiveness of the proposed NIR spectroscopy model based on BWO-IVSO-SVM for quantitatively analyzing AFB1 in peanuts. The study contributes valuable technical and methodological insights that can serve as a reference for rapidly determining mycotoxins in cereal crops.

Analyst and machine learning opinions in fire debris analysis

The principles of subjective logic are applied to the competing propositions that ignitable liquid residue (ILR) is present, or is not present, in a fire debris sample. Analysts’ estimates of the strength of evidence coupled with their perceived levels of uncertainty combine to define a “fuzzy category” that is mapped to an opinion triangle. The opinion is expressed as a tuple consisting of the belief mass, disbelief mass, uncertainty and base rate. A workflow is introduced to guide the analyst through the fuzzy category formulation. Opinion tuples are also generated from a set of machine learning (ML) models trained on an ensemble of data sets. A set of 20 single-blind fire debris samples were analyzed by each of the authors, and by an ensemble of optimized support vector machine models. The opinions of each analyst and the ML ensemble were compared and combined to obtain an opinion representing a consensus of each analyst and the ML. The opinions of the analysts and ML were projected onto the zero-uncertainty axis and the projected opinion probabilities were used as scores to construct an receiver operating characteristic (ROC) curve. The area under the ROC curves for each analyst were greater than or equal to 0.90 and the area under the ML ROC curve was 0.96. The methodology is widely applicable to forensic problems that can be represented as a pair of mutually exclusive and exhaustive hypotheses.

Research on eye movement data classification using support vector machine with improved whale optimization algorithm

When performing eye movement pattern classification for different tasks, support vector machines are greatly affected by parameters. To address this problem, we propose an algorithm based on the improved whale algorithm to optimize support vector machines to enhance the performance of eye movement data classification. According to the characteristics of eye movement data, this study first extracts 57 features related to fixation and saccade, then uses the ReliefF algorithm for feature selection. To address the problems of low convergence accuracy and easy falling into local minima of the whale algorithm, we introduce inertia weights to balance local search and global search to accelerate the convergence speed of the algorithm and also use the differential variation strategy to increase individual diversity to jump out of local optimum. In this paper, experiments are conducted on eight test functions, and the results show that the improved whale algorithm has the best convergence accuracy and convergence speed. Finally, this paper applies the optimized support vector machine model of the improved whale algorithm to the task of classifying eye movement data in autism, and the experimental results on the public dataset show that the accuracy of the eye movement data classification of this paper is greatly improved compared with that of the traditional support vector machine method. Compared with the standard whale algorithm and other optimization algorithms, the optimized model proposed in this paper has higher recognition accuracy and provides a new idea and method for eye movement pattern recognition. In the future, eye movement data can be obtained by combining it with eye trackers to assist in medical diagnosis.

Development of a Reliable Machine Learning Model to Predict Compressive Strength of FRP-Confined Concrete Cylinders

The degradation of reinforced concrete (RC) structures has raised major concerns in the concrete industry. The demolition of existing structures has shown to be an unsustainable solution and leads to many financial concerns. Alternatively, the strengthening sector has put forward many sustainable solutions, such as the retrofitting and rehabilitation of existing structural elements with fiber-reinforced polymer (FRP) composites. Over the past four decades, FRP retrofits have attracted major attention from the scientific community, thanks to their numerous advantages such as having less weight, being non-corrodible, etc., that help enhance the axial, flexural, and shear capacities of RC members. This study focuses on predicting the compressive strength (CS) of FRP-confined concrete cylinders using analytical models and machine learning (ML) models. To achieve this, a total of 1151 specimens of cylinders have been amassed from comprehensive literature studies. The ML models utilized in the study are Gaussian process regression (GPR), support vector machine (SVM), artificial neural network (ANN), optimized SVM, and optimized GPR models. The input parameters that have been used for prediction include the geometrical characteristics of specimens, the mechanical properties of FRP composite, and the CS of concrete. The results of the five ML models are compared with nineteen analytical models. The results evaluated from the ML algorithms imply that the optimized GPR model has been found to be the best among all other models, demonstrating a higher correlation coefficient, root mean square error, mean absolute percentage error, mean absolute error, a-20 index, and Nash–Sutcliffe efficiency values of 0.9960, 3.88 MPa, 3.11%, 2.17 MPa, 0.9895, and 0.9921, respectively. The R-value of the optimized GPR model is 0.37%, 0.03%, 5.14%, and 2.31% higher than that of the ANN, GPR, SVM, and optimized SVM models, respectively, whereas the root mean square error value of the ANN, GPR, SVM, and optimized SVM models is, respectively, 81.04%, 12.5%, 471.77%, and 281.45% greater than that of the optimized GPR model.

Disaster Risk Regionalization and Prediction of Corn Thrips Combined with Cloud Model: A Case Study of Shandong Province, China

Corn thrips do serious harm to the yield and quality of corn. In this paper, the Shandong Province of China was taken as the study area. Based on the data of the occurrence of corn thrips in Shandong Province, a risk regionalization model was established by using eight indicators under four categories of hazard, sensitivity, vulnerability and the disaster prevention and mitigation capacity of diseases and pests on a monthly time scale. Firstly, the cloud model was introduced to determine the weight of each indicator, and then the risk regionalization of the corn thrips disaster in Shandong Province was carried out using the weighted percentage method, the weighted comprehensive evaluation method and the natural disaster risk index method. Finally, combined with the collected data, the disaster prediction of corn thrip occurrence degree was realized based on multiple linear regression, genetic algorithm optimized back-propagation neural network and genetic algorithm optimized support vector machine methods. The results show that: (1) the risk of Corn thrips disaster is mainly concentrated in the central and western parts of Shandong Province. Heze City is a high-risk area. Liaocheng City, Dezhou City, Jinan City and Weifang City are relatively high-risk areas. (2) By comparing the prediction accuracy of the three models, it was determined that the genetic algorithm optimized support vector machine model has the best effect, with an average accuracy of 79.984%, which is 7.013% and 22.745% higher than that of the multiple linear regression and genetic algorithm optimized back-propagation neural network methods, respectively. The results of this study can provide a scientific basis for fine prevention of corn thrips in Shandong Province.

Identification of chronic mild traumatic brain injury using resting state functional MRI and machine learning techniques.

[clinicaltrials.gov], identifier [NCT03241732].

Optimized Classification Model for Biomedical Data Analysis

In recent years, medical data analysis has become a well-known research topic in healthcare in recent years. Smart healthcare will use a new generation of information technologies, like artificial intelligence, the Internet of Things, cloud computing, and big data, to transform the conventional medical system in an all-around way, making healthcare highly effective, more personalized, and more convenient to support healthcare practitioners in their decision-making. Due to the huge amount of medical data, appropriate clustering methods have been proven useful to implement an efficient medical data classification process. In addition, the development of machine learning models effectively aids the classification process. With this motivation, this paper focuses on a unit-wide search algorithm with an optimal support vector machine model for medical data analysis. The purpose of the proposed model is to classify health data using clustering and classification models. In addition, the proposed technique includes a clustering method for medical data, which helps to improve the classification performance. In addition, the model used examines accumulated medical data to perform the classification process. In addition, an optimization algorithm is used to improve the classification results of medical data. Extensive benchmarking and a wide range of simulations can highlight the promising performance of the research technique.

Research on the Gearbox Fault Diagnosis Method Based on Multi-Model Feature Fusion

The gearbox is an important component of rotating machinery and is of great significance for gearbox fault diagnosis. In this paper, a gearbox fault diagnosis model based on multi-model feature fusion was proposed that addressed the limitations of a single or few features reflecting the gearbox’s fault state. The time–frequency feature of the vibration signal was extracted, and the sensitive feature was selected. The sensitive features were extracted using a one-dimensional convolutional neural network. The parallel fusion method was used to fuse the two domain features as inputs to the support vector machine model. The radial basis kernel function and penalty factor of the support vector machine were optimized by improving the particle swarm optimization algorithm. Finally, the gearbox states were identified using the optimized support vector machine model. The results show that the recognition rate of the proposed model is 98.3%, which is higher than that of other models.

Intelligent COVID-19 screening platform based on breath analysis

The spread of coronavirus disease 2019 (COVID-19) results in an increasing incidence and mortality. The typical diagnosis technique for severe acute respiratory syndrome coronavirus 2 infection is reverse transcription polymerase chain reaction, which is relatively expensive, time-consuming, professional, and suffered from false-negative results. A reliable, non-invasive diagnosis method is in urgent need for the rapid screening of COVID-19 patients and controlling the epidemic. Here we constructed an intelligent system based on the volatile organic compound (VOC) biomarkers in human breath combined with machine learning models. The VOC profiles of 122 breath samples (65 of COVID-19 infections and 57 of controls) were identified with a portable gas chromatograph-mass spectrometer. Among them, eight VOCs exhibited significant differences (p < 0.001) between the COVID-19 and the control groups. The cross-validation algorithm optimized support vector machine (SVM) model was employed for the prediction of COVID-19 infection. The proposed SVM model performed a powerful capability in discriminating COVID-19 patients from healthy controls, with an accuracy of 97.3%, a sensitivity of 100%, a specificity of 94.1%, and a precision of 95.2%, and an F1 score of 97.6%. The SVM model was also compared with other common machine models, including artificial neural network, k-nearest neighbor, and logistic regression, and demonstrated obvious superiority in the prediction of COVID-19 infection. Furthermore, user-friendly software was developed based on the optimized SVM model. The developed intelligent platform based on breath analysis provides a new strategy for the point-of-care screening of COVID and shows great potential in clinical application.