Optimized Support Vector Machine Research Articles

Predicting soil moisture content of tea plantation using support vector machine optimized by arithmetic optimization algorithm

Soil moisture content (SMC) is an important parameter that affects tea growth. Reasonable soil moisture content improves tea quality and ensures tea yield. Therefore, it is necessary to regularly monitor the soil water content. However, the traditional soil moisture content prediction algorithm has the problems of low accuracy and low efficiency. This paper constructs and evaluates the performance of a hybrid arithmetic optimization algorithm (AOA) and support vector machine (SVM) prediction model (AOA-SVM) for predicting SMC in tea plantations. Grey relation analysis (GRA) and Pearson correlation analysis are adopted to select features for soil moisture content prediction model, and then the correlation of SMC with soil temperature (ST), atmospheric temperature (AT), and soil electrical conductivity (SEC) was analyzed. The optimal penalty parameter ( c) and the parameter of the kernel function ( g) of SVM model are determined by AOA. The mean square error (MSE) and coefficient of determination ([Formula: see text]), mean absolute error (MAE), and mean error (ME) were calculated to evaluate the performance of the model. Meanwhile, the performance of AOA-SVM model is compared with SVM optimized by the sparrow search algorithm (SSA-SVM), extreme learning machine (ELM), support vector machine (SVM), and convolutional neural networks (CNN). The results showed that AOA-SVM, SSA-SVM and CNN are better, with the coefficient of determination above 93%, the coefficient of determination of SVM and ELM is 81.69% and 89.61%, respectively. The AOA-SVM model has the best R2 of 95.03%. This indicates that the AOA-SVM model has significant performance with higher R2, smaller MSE than other models, which has potential implications in precision agriculture.

A Hybrid Machine Learning algorithm for Heart and Liver Disease Prediction Using Modified Particle Swarm Optimization with Support Vector Machine

Machine learning is now extensively applied in a variety of fields. Machine learning is employed like an efficient assistance mechanism in clinical diagnostics since vast amounts of data are readily available. Owing to heavy alcohol consumption, inhalation of contaminated gas, narcotics, food contamination, unhealthy life style people suffering from heart and liver disease has been significantly growing. Both heart and liver disease cause high mortality rate worldwide. It is critical to discover these diseases at an early stage in order to save people's lives. Incorporating machine learning classification algorithms into health-care organizations yields remarkable outcomes, allowing health-care practitioners to diagnose diseases more quickly and accurately. Machine learning techniques and tools aid in the extraction of useful information from datasets, resulting in more exact findings. In this study for heart and liver data classification, a hybrid model is created by combining support vector machine (SVM) approach and modified particle swarm optimization model. The data sets are collected from UCI machine learning repository. The results are calculated based on classification accuracy, error, correctness, recall as well as F1 score. The results obtained is compared with SVM, hybrid particle swarm optimization support vector machine algorithm (PSOSVM), hybrid Crazy particle swarm optimization support vector machine algorithm (CPSOSVM).

Time-Distributed Non-Convex Optimized Support Vector Machine for Vehicular Tracking Systems

Time-Distributed Non-Convex Optimized Support Vector Machine for Vehicular Tracking Systems

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.

DPMSLM Demagnetization Fault Detection Based on Texture Feature Analysis of Grayscale Fusion Image

This study investigates a novel image morphology texture feature extraction method to realize the demagnetization fault location and severity detection of double-sided permanent magnet synchronous linear motor (DPMSLM). Initially, according to the constraints of DPMSLMs topology structure, the three lines magnetic density signal in motor air gap is extracted by finite element analysis as effective fault signal. Then, the grayscale fusion image (GFI) method is introduced to transform 1D data signal to 2D fused grayscale image which can better describe the demagnetization fault information. The unique features are visualized using the image enhancement techniques, and the image morphology texture features such as the area, Euler number, perimeter operator, correlation of binary image and so on can be extracted to constitute the demagnetization fault indexes. In addition, fisher score (FS) is used for feature optimization which can reduce the feature dimension. Furthermore, the two-level multiverse optimization support vector machine (MVO-SVM) algorithm is established to conduct demagnetization fault classification. Comparison experiments with other classifiers show that the MVO-SVM has a high fault identification accuracy of more than 98.3% and low running time less than 2.57s. Finally, the motor prototype experiment results show that the proposed method can accurately identify the location and severity of DPMSLM demagnetization faults, and it is an effective and feasible method which can be applied in DPMLM batch demagnetization inspection before delivery.

Automatic detection method of epileptic seizures based on IRCMDE and PSO-SVM.

Multi-scale dispersion entropy (MDE) has been widely used to extract nonlinear features of electroencephalography (EEG) signals and realize automatic detection of epileptic seizures. However, information loss and poor robustness will exist when MDE is used to measure the nonlinear complexity of the time sequence. To solve the above problems, an automatic detection method for epilepsy was proposed, based on improved refined composite multi-scale dispersion entropy (IRCMDE) and particle swarm algorithm optimization support vector machine (PSO-SVM). First, the refined composite multi-scale dispersion entropy (RCMDE) is introduced, and then the segmented average calculation of coarse-grained sequence is replaced by local maximum calculation to solve the problem of information loss. Finally, the entropy value is normalized to improve the robustness of characteristic parameters, and IRCMDE is formed. The simulated results show that when examining the complexity of the simulated signal, IRCMDE can eliminate the issue of information loss compared with MDE and RCMDE and weaken the entropy change caused by different parameter selections. In addition, IRCMDE is used as the feature parameter of the epileptic EEG signal, and PSO-SVM is used to identify the feature parameters. Compared with MDE-PSO-SVM, and RCMDE-PSO-SVM methods, IRCMDE-PSO-SVM can obtain more accurate recognition results.

A Hybrid Fault Diagnosis Method Using Translation Invariant Wavelet Denoising, Hierarchical Entropy, and Support Vector Machine with PSO Algorithm

The rolling element bearing is used in various machinery and produces vibration due to imperfections, surface irregularities during manufacture, damaged bearings, and inaccuracies in the allied element. Also, the rolling element bearing vibration generally shows non-linear dynamic characteristics and is masked with heavy background noise. This noble investigation advances a hybrid technique for removing background noise from the vibration signal and detecting bearing defects. Translation invariant wavelet denoising is the initial stage in this hybrid method for noise removal from the signal. The second phase uses Hierarchical Entropy (HE) for defect feature frequency extraction. Hierarchical entropy at scale four and SampEns of eight hierarchical decomposition nodes was utilized to determine the defect feature vector. In particular, low-frequency components are investigated through multi-scale entropy (MSE), but hierarchical entropy (HE) incorporates low-frequency and high-frequency components and can extract more defective information. Implemented a multi-class support vector machine (SVM) for extracting Hierarchical entropy as feature vectors. These feature vectors are trained by utilizing particle swarm optimization (PSO). To accomplish a prediction model, examine the optimal SVM parameters and then various bearing conditions with the variation of type, size, speed, and load severity identified by SVM. The investigation results show that hierarchical entropy can adequately and more precisely express the features of bearing vibration signals. It is beyond MSE, and the proposed Nobel hybrid Translation invariant wavelet denoising and Hierarchical entropy-based method will effectively remove the noisy background signal. Also, it distinguishes different bearings successfully, indicates the bearing conditions correctly, and is more prominent than those found on MSE.

DPP IV Inhibitors Activities Prediction as An Anti-Diabetic Agent using Particle Swarm Optimization-Support Vector Machine Method

Diabetes mellitus is a chronic illness that can affect anyone, while the medicine that can entirely cure diabetes has not been discovered yet. Dipeptidyl Peptidase IV (DPP IV) inhibitor is one of the agents with potency as an anti-diabetic treatment. In this work, we utilized the machine learning method to predict the activity of DPP IV as an anti-diabetic agent. We combined Particle Swarm Optimization (PSO) method for features selection and the Support Vector Machine (SVM) for the prediction model. Three SVM kernels, i.e., radial basis function (RBF), polynomial, and linear, were utilized, and their performance was compared. A Hyperparameter tuning procedure was conducted to improve the performance of models. According to the results, we found that the best model obtained from SVM with RBF kernel with the value R2 of train and test set are 0.79 and 0.85, respectively.

A New Approach to Optimize SVM for Insulator State Identification Based on Improved PSO Algorithm.

The failure of insulators may seriously threaten the safe operation of the power system, where the state detection of high-voltage insulators is a must for the normal and safe operation of the power system. Based on the data of insulators in aerial images, this work explored an enhanced particle swarm algorithm to optimize the parameters of the support vector machine. A support vector machine model was therefore established for the identification of the normal and defective states of the insulators. This methodology works with the structure minimization principle of SVM and the characteristics of particle swarm fast optimization. First, the aerial insulator image was segmented as a target by way of the seed region growth based on double-layer cascade morphological improvements, and then, HOG features plus GLCM features were extracted as sample data. Finally, an ameliorated PSO-SVM classifier was designed to realize insulator state identification. Comparisons were made between PSO-SVM and conventional machine learning algorithms, SVM and Random Forest, and an optimization algorithm, Gray Wolf Optimization Support Vector Machine (GWO-SVM), and advanced neural network CNN. The experimental results showed that the performance of the algorithm proposed in this paper touched the top level, where the recognition accuracy rate was 92.11%, the precision rate 90%, the recall rate 94.74%, and the F1-score 92.31%.

Recognition of Corrosion State of Water Pipe Inner Wall Based on SMA-SVM under RF Feature Selection

To solve the problem of low detection accuracy of water supply pipeline internal wall damage, a random forest algorithm with simplified features and a slime mold optimization support vector machine detection method was proposed. Firstly, the color statistical characteristics, gray level co-occurrence matrix, and gray level run length matrix features of the pipeline image are extracted for multi-feature fusion. The contribution of the fused features is analyzed using the feature simplified random forest algorithm, and the feature set with the strongest feature expression ability is selected for classification and recognition. The global search ability of the slime mold optimization algorithm is used to find the optimal kernel function parameters and penalty factors of the support vector machine model. Finally, the optimal parameters are applied to support the vector machine model for classification prediction. The experimental results show that the recognition accuracy of the classification model proposed in this paper reaches 94.710% on the data sets of different corrosion forms on the inner wall of the pipeline. Compared with the traditional Support Vector Machines (SVM) classification model, the SVM model based on differential pollination optimization, the SVM model based on particle swarm optimization, and the back propagation (BP) neural network classification model, it is improved by 4.786%, 3.023%, 4.030%, and 0.503% respectively.

Optimised distributed sensor system for covid protocol monitoring using multiprocessor architecture

Covid Protocol Monitoring with Multiprocessor Architecture (CPMMA) is proposed in this study as an approach for implementing a Distributed Sensor System (DSS) for covid protocol monitoring utilising multiprocessor architecture. OpenMP and MPI were used to implement the distributed system's parallel programmes, with the OpenMP code working best when used with 60–100 threads in use. CPMMA distributed sensor data was efficiently processed by a multiprocessor with 16 cores. According to the results, using a multithreaded-multiprocessing architecture and optimised Support Vector Machine classifier, the proposed design greatly enhances computing efficiency. The results of our experiments suggest that our approach may significantly enhance computing performance while also delivering adequate outcomes in a short period of time.

Physiological Status Prediction Based on a Novel Hybrid Intelligent Scheme.

Physiological status plays an important role in clinical diagnosis. However, the temporal physiological data change dynamically with time, and the amount of data is large; furthermore, obtaining a complete history of data has become difficult. We propose a hybrid intelligent scheme for physiological status prediction, which can be effectively utilized to predict the physiological status of patients and provide a reference for clinical diagnosis. Our proposed scheme initially extracted the attribute information of nonlinear dynamic changes in physiological signals. The maximum discriminant feature subset was selected by employing conditional relevance mutual information feature selection. An optimal subset of features was fed into the particle swarm optimization-support vector machine classifier to perform classification. For the prediction task, the proposed hybrid intelligent scheme was tested on the Sleep Heart Health Study dataset for sleep status prediction. Experimental results demonstrate that our proposed intelligent scheme outperforms the conventional machine learning classification methods.

Predicting daily global solar radiation in various climatic regions of China based on hybrid support vector machines with meta-heuristic algorithms

Accurate prediction of global solar radiation (Rs) is vital for investment decisions and solar energy distribution. In this study, three hybrid models (ACO-SVM, CS-SVM, and GWO-SVM) based on ant colony optimization (ACO), cuckoo search (CS) and grey wolf optimization (GWO) algorithms were proposed to optimize support vector machine (SVM) for predicting Rs in four climate zones of China (temperate continental zone TCZ, mountain plateau zone MPZ, temperate monsoon zone TMZ, and subtropical monsoon zone SMZ). They were compared with the standalone backpropagation neural network model, decision tree, and support vector machines. The results demonstrated that among the standalone models, support vector machines performed best with the highest accuracy in Rs estimation in each climate zone of China, followed by the decision tree and backpropagation neural network models, with a coefficient of determination (R2) in 0.707–0.882, 0.694–0.881, and 0.681–0.850, respectively. In contrast, the hybrid models exhibited higher accuracy than standalone support vector machines in four climatic regions of China, with the coefficient of determination (R2) increasing by 5.361%, 5.476%, 7.382%, and 10.965%, respectively. Among hybrid models, GWO-SVM performed better than CS-SVM, and both had higher accuracy than ACO-SVM, with the coefficient of determination (R2) in 0.809–0.927, 0.804–0.926, and 0.793–0.930, respectively. Therefore, the hybrid models (ACO-SVM, CS-SVM, and GWO-SVM), especially GWO-SVM and CS-SVM, can significantly improve the accuracy for predicting Rs in various regions of China.

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.

Assessing future technological impacts of patents based on the classification algorithms in machine learning: The case of electric vehicle domain.

Identifying the technologies that will drive technological changes over the coming years is important for the optimal allocation of firms' R&D resources and the deployment of innovation strategies. The citation frequency of a patent is widely recognized as representative of the patent's value. Thus, identifying potential highly cited patents is an important goal. A number of studies have attempted to distinguish highly cited patents from others based on statistical models, but a more effective and applicable method needs to be further developed. This paper treats the prediction of later patent citations as a classification problem and proposes a novel framework based on machine learning methods. First, a indices system to identify highly cited patents is constructed using multiple factors that are believed to influence citation frequency. Second, various machine learning models are utilized to identify highly cited patents. The optimized model with the best generalization capability is selected to predict the future impacts of newly applied patents, which may be representative of emerging significant technologies. Finally, we select the electric vehicle (EV) domain as a case study to empirically test the validity of this framework. The optimized support vector machine (SVM) model performs well in identifying highly cited EV patents. Technological frontiers in the EV domain are identified, which are related to the topics of information systems, batteries, stability control, wireless charging, and vehicle operation. The good performance in prediction accuracy and generalization capability of the method proposed in this paper verifies its effectiveness and feasibility.

A two‐stage transformer fault diagnosis method based multi‐filter interactive feature selection integrated adaptive sparrow algorithm optimised support vector machine

AbstractThe scarcity of samples and disunity of feature inputs hinder the enhancement of transformer fault diagnosis performance, and there are mutual influences between model construction and feature selection, which cannot only consider a single process. Therefore, this study proposes a novel two‐stage transformer fault diagnosis strategy, which includes a multi‐filter interactive feature selection method (MIFS) constructed, and a diagnosis model ASSA‐SVM based on the adaptive sparrow algorithm (ASSA) optimised support vector machine (SVM). Firstly, the proposed MIFS incorporates ReliefF and mRMR to establish a comprehensive criterion ReliefF‐mRMR for feature importance ranking, and then performs dimension‐by‐dimension input classifier interaction selection based on the ranking results to obtain the optimal feature subset. Secondly, ASSA was proposed to optimise the kernel parameters of SVM. A two‐stage integration model MIFS‐ASSA‐SVM was developed. Finally, Experiments were conducted using real fault data, and the diagnostic performance of different feature inputs, optimisation algorithms and classifiers were compared. The results show that the proposed method performs well on parameter optimisation, can dynamically and interactively select feature subsets with few dimensions and good generalisation performance, its overall diagnosis accuracy reached 92.47%, and the diagnosis performance of each fault type has good performance in multiple evaluation metrics.

Predicting cardiac arrhythmia on ECG signal using an ensemble of optimal multicore support vector machines

Predicting cardiac arrhythmia on ECG signal using an ensemble of optimal multicore support vector machines

Hybrid features and ensembles of convolution neural networks for weed detection

<span lang="EN-US">Weeds compete with plants for sunlight, nutrients and water. Conventional weed management involves spraying of herbicides to the entire crop which increases the cost of cultivation, decreasing the quality of the crop, in turn affecting human health. Precise automatic spraying of the herbicides on weeds has been in research and use. This paper discusses automatic weed detection using hybrid features which is generated by extracting the deep features from convolutional neural network (CNN) along with the texture and color features. The color and texture features are extracted by color moments, gray level co-occurrence matrix (GLCM) and Gabor wavelet transform. The proposed hybrid features are classified by Bayesian optimized support vector machine (BO-SVM) classifier. The experimental results read that the proposed hybrid features yield a maximum accuracy of 95.83%, higher precision, sensitivity and F-score. A performance analysis of the proposed hybrid features with BO-SVM classifier in terms of the evaluation parameters is made using the images from crop weed field image dataset.</span>

Tensile properties and prediction of DP780 treated with rare earth Ce based on DE-SVM-Ⅱ

In order to solve the weak tensile properties of DP780 welded joints, rare earth Ce was added to improve the tensile properties of DP780 welded joints. In this paper, the microstructure of DP780 was characterized by metallographic microscopy, scanning electron microscopy and energy spectrum analyzer. An improved differential evolution algorithm optimized support vector machine prediction model (DE-SVM-II) was established to predict the tensile properties of DP780. The results show that when the content of Ce was 0.05%, the tensile strength increased by 378 MPa, the microstructure of molten metal was uniform and the grain boundary of austenite was tight. In the heat affected zone, the size of ferrite decreases, the fracture morphology shows equiaxial depression, and the fiber zone accounts for a large proportion. In addition, the fitness of the mean square error (MSE) of the tensile strength curve in the DE-SVM-Ⅱ prediction was lower than that of the SVM prediction error, and begin to converge in the 10th generation. The average relative error of the DE-SVM model prediction of elongation was within 3%. Compared with the predicted value of BP neural network, the error of predicting tensile strength and elongation of DE-SVM-II was reduced by 1.36% and 15.10%, respectively, which had high stability and accuracy.

Potential of vibrational spectroscopy coupled with machine learning as a non-invasive diagnostic method for COVID-19

Background and ObjectiveEfforts to alleviate the ongoing coronavirus disease 2019 (COVID-19) crisis showed that rapid, sensitive, and large-scale screening is critical for controlling the current infection and that of ongoing pandemics. MethodsHere, we explored the potential of vibrational spectroscopy coupled with machine learning to screen COVID-19 patients in its initial stage. Herein presented is a hybrid classification model called grey wolf optimized support vector machine (GWO-SVM). The proposed model was tested and comprehensively compared with other machine learning models via vibrational spectroscopic fingerprinting including saliva FTIR spectra dataset and serum Raman scattering spectra dataset. ResultsFor the unknown vibrational spectra, the presented GWO-SVM model provided an accuracy, specificity and F1_score value of 0.9825, 0.9714 and 0.9778 for saliva FTIR spectra dataset, respectively, while an overall accuracy, specificity and F1_score value of 0.9085, 0.9552 and 0.9036 for serum Raman scattering spectra dataset, respectively, which showed superiority than those of state-of-the-art models, thereby suggesting the suitability of the GWO-SVM model to be adopted in a clinical setting for initial screening of COVID-19 patients. ConclusionsProspectively, the presented vibrational spectroscopy based GWO-SVM model can facilitate in screening of COVID-19 patients and alleviate the medical service burden. Therefore, herein proof-of-concept results showed the chance of vibrational spectroscopy coupled with GWO-SVM model to help COVID-19 diagnosis and have the potential be further used for early screening of other infectious diseases.