Parameters Of Support Vector Machine Research Articles

Sentiment Analysis for IMDb Movie Review Using Support Vector Machine (SVM) Method

Many researchers currently employ supervised, machine learning methods to study sentiment analysis. Analysis can be done on movie reviews, Twitter reviews, online product reviews, blogs, discussion forums, Myspace comments, and social networks. Support Vector Machines (SVM) classifiers are used to analyze the Twitter data set using different parameters. The analysis and discussion were undertaken to allow for the conclusion that SVM has been successfully implemented utilizing the IMDb data for this study (Support Vector Machine). To complete this study, the preprocessing phase, which consisted of filtering and classifying data using SVM with a total of 50.000 data points, was completed after collecting up to 40.000 reviews to use as training data and 10.000 reviews to use as testing data. 25.000 positive and 25.000 negative points make up the view. In this study, we adopted an evaluation matrix including accurate, precision, recall, and F1-score. According to the experiment report, our model achieved SVM with Bags of Word (BoW) used to get results for the highest accuracy test, which was 88,59% accurate. Then, using grid-search, optimize against the SVM parameters to find the best parameters that SVM models can use. Our model achieved Term Frequency–inverse Document Frequency (TF-IDF) was used to get results for the highest accuracy test, which was 91,27% accurate.

A Relief-PGS algorithm for feature selection and data classification

As a supervised learning algorithm, Support Vector Machine (SVM) is very popularly used for classification. However, the traditional SVM is error-prone because of easy to fall into local optimal solution. To overcome the problem, a new SVM algorithm based on Relief algorithm and particle swarm optimization-genetic algorithm (Relief-PGS) is proposed for feature selection and data classification, where the penalty factor and kernel function of SVM and the extracted feature of Relief algorithm are encoded as the particles of particle swarm optimization-genetic algorithm (PSO-GA) and optimized by iteratively searching for optimal subset of features. To evaluate the quality of features, Relief algorithm is used to screen the feature set to reduce the irrelevant features and effectively select the feature subset from multiple attributes. The advantage of Relief-PGS algorithm is that it can optimize both feature subset selection and SVM parameters including the penalty factor and the kernel parameter simultaneously. Numerical experimental results indicated that the classification accuracy and efficiency of Relief-PGS are superior to those of other algorithms including traditional SVM, PSO-GA-SVM, Relief-SVM, ACO-SVM, etc.

A Novel Marine Predators Optimization based Deep Neural Network for Quality and Shelf-Life Prediction of Shrimp

Consumer satisfaction and food safety are prime concerns for seafood retailers and wholesalers. Shrimp and its products are popular all over the world and play a significant part in maintaining a healthy diet by delivering a variety of nutrients and health benefits. Fresh shrimp and shrimp products, on the other hand, are very perishable and vulnerable to the rapid formation of disagreeable scents and tastes, as well as a rapid decay process. Shrimp freshness has previously been determined using a variety of techniques, but earlier techniques lacked adequate precision. To overcome this issue, in this manuscript, a Hybrid Convolutional Neural Network (Hyb-CNN) and Support Vector Machine (SVM) optimized with Marine Predators Algorithm (MPA) for shrimp freshness detection (SFD-Hyb-CNN-SVM-MPA) is proposed for classifying the freshness shrimp and non-freshness shrimp. The real time dataset is given to mean curvature Flow (MCF) filtering method and its pre-processed images are given to the Hybrid Convolutional Neural Network and Support Vector Machine classifier for classifying the freshness shrimp and non-freshness shrimp. Generally, The Hyb-CNN- does not demonstrate the implementation of any optimization techniques for identifying the ideal parameters and assuring correct classification. The proposed Marine Predators Algorithm (MPA) is considered for optimizing the hyper parameter of Hyb-CNN and SVM which is executed in MATLAB and certain performance measures are used to assess the effectiveness of the proposed approach such as precision, recall, f-measure, accuracy, computation time. The proposed method attains lower computational time39.89%, 43.78%, and 52.67%, higher accuracy 21.35%, 18.56%, and 13.56% compared with the existing methods, like shrimp freshness detection using Deep shrimp Net (SFD-D-SHNet), shrimp freshness detection using artificial neural network and k-neighbour network (SFD-ANN-KNN), shrimp freshness detection using convolutional neural network (SVM-FCCD) respectively.

A New Hybrid Fault Diagnosis Method for Wind Energy Converters

Fault diagnostic techniques can reduce the requirements for the experience of maintenance crews, accelerate maintenance speed, reduce maintenance cost, and increase electric energy production profitability. In this paper, a new hybrid fault diagnosis method based on multivariate empirical mode decomposition (MEMD), fuzzy entropy (FE), and an artificial fish swarm algorithm (AFSA)-support vector machine (SVM) is proposed to identify the faults of a wind energy converter. Firstly, the measured three-phase output voltage signals are processed by MEMD to obtain three sets of intrinsic mode functions (IMFs). The multi-scale analysis tool MEMD is used to extract the common modes matching the timescale. It studies the multi-scale relationship between three-phase voltages, realizes their synchronous analysis, and ensures that the number and frequency of the modes match and align. Then, FE is calculated to describe the IMFs’ complexity, and the IMFs-FE information is taken as fault feature to increase the robustness to working conditions and noise. Finally, the AFSA algorithm is used to optimize SVM parameters, solving the difficulty in selecting the penalty factor and radial basis function kernel. The effectiveness of the proposed method is verified in a simulated wind energy system, and the results show that the diagnostic accuracy for 22 fault modes is 98.7% under different wind speeds, and the average accuracy of 30 running can be maintained above 84% for different noise levels. The maximum, minimum, average, and standard deviation are provided to prove the robust and stable performance. Compared with the other methods, the proposed hybrid method shows excellent performance in terms of high accuracy, strong robustness, and computational efficiency.

Predicting customer churn using grey wolf optimization‐based support vector machine with principal component analysis

AbstractCustomer churn is a challenging problem that can lead to a loss of organizational assets. Organizations need to predict customer churn successfully in order to get rid of potential damages and gain a competitive advantage. The aim of this study is to provide a churn prediction model by including feature selection and optimization in classification. The study performs principal component analysis (PCA) to select the best features, support vector machine (SVM) to predict customer churn, and grey wolf optimization (GWO) to optimize the parameters of SVM. In other words, this study proposes a novel hybrid model called PCA‐GWO‐SVM to enhance the prediction ability in customer churn. A comparison experiment is carried out, evaluating the proposed model with the other classification algorithms. Experimental results show that the proposed PCA‐GWO‐SVM hybrid model produces higher accuracy, recall, and F1‐score than other machine learning algorithms such as logit, k‐nearest neighbors, naive Bayes, decision tree, and SVM.

Retrieval of soil salinity based on multi-source remote sensing data and differential transformation technology

ABSTRACT Rapid and accurate assessments of soil salinity information surrounding saline lakes are crucial for agricultural development and ecological security in arid regions. The Support Vector Machine (SVM) algorithm is currently utilized to derive the relationship between environmental covariates and soil salinity to perform remote sensing inversion of regional soil salinity; however, there is still potential for improvement in the existing SVM algorithm. Therefore, this study aims to improve the remote sensing-based soil salinity content (SSC) extraction from the Landsat 8, DEM and HJ-1A CCD satellite data using the Cuckoo Search Algorithms-Support Vector Machines (CS-SVM) model. In addition, the correlation and principal component analysis were conducted to determine the principal components of environmental covariates. The results show that the differential transformation effectively separates the land and water, which helps to reduce the noise in the raw remote sensing image. The analysis of soil and vegetation factors shows that the first three principal components cumulative variance contributed 99.69% on the raw remote sensing image, while the first two principal components cumulative variance contributed 88.01% and 85.28% on the first- and second-order differential transformation remote sensing images, respectively. Interestingly, L-S2 is the only factor correlated with SSC in the third order differential transform remote sensing image, with the R value of 0.325. The slope direction and plane curvature under the topographic factors had negative correlations with SSC, with the R values of −0.521 and −0.325, respectively. Finally, the SSC inversion model was developed using the first order differential transformation remote sensing images, which has high accuracy and good stability (R2 = 0.68 and RMSE = 3.80 g−1). The cuckoo algorithm is helpful for determining the best support vector machine parameters and offers new perspectives in improving the reliability of remote sensing-based soil salinity inversion in arid regions.

Research on transformer fault diagnosis based on ISOMAP and IChOA‐LSSVM

AbstractOil‐immersed transformers play an important role in the stable operation of power systems. In order to improve the accuracy of transformer fault diagnosis, a transformer fault diagnosis method based on Isomap and IChOA‐LSSVM is proposed. Firstly, Isomap is used to reduce the dimensionality of the 14‐dimensional transformer fault characteristics data to eliminate redundant data between variables. In addition, IChOA is proposed to optimise the Least Squares Support Vector Machine (LSSVM) parameters and establish an optimal diagnosis model based on LSSVM. For ChOA, three improvement methods are proposed. Circle mapping is used instead of the original population initialisation to improve population diversity. In addition, the location weighting strategy with proportional weights and the Gaussian Corsi variation strategy is proposed to improve the optimization accuracy and efficiency of ChOA. The improved ChOA is compared with the original ChOA, PSO and GWO algorithms by five benchmark test functions. Finally, the improved Chimpanzee Optimisation algorithm was used to find the parameters of the LSSVM to obtain the fault diagnosis model combining Isomap and IChOA‐LSSVM. The model is compared with PSO‐LSSVM, ChOA‐LSSVM and GWO‐LSSVM. The diagnostic accuracy is 90.83%, 81.67%, 83.33% and 80%, respectively. The results demonstrate that the proposed method can effectively improve the performance of transformer fault diagnosis.

Study on fault diagnosis algorithms of EHA based on CNN-SVM

Contrapose the highly integrated, complex working conditions and many kinds of faults of aircraft electro hydrostatic actuator(EHA), to diagnose the typical fault of EHA effectively, a fault diagnosis algorithm based on convolutional neural networks (CNN) and support vector machine(SVM) was proposed. Firstly, the fault date sets are entered on CNN for adaptive feature extraction, then the output of the fully connected layer of CNN are classified by using SVM. To improve the performance of SVM, dynamic inertia weight adaptive particle swarm optimization (IWAPSO) was used to optimize the SVM parameters. Finally, the sensitivity of SVM to noise was reduced by introducing ramp loss function. The results show that the accuracy of SVM after parameter optimization is 12.6% higher than that of standard SVM and 17.3% higher than CNN. The SVM based on the ramp loss function showed better robustness when using noisy test sets.

Optimized and Efficient Image-Based IoT Malware Detection Method

With the widespread use of IoT applications, malware has become a difficult and sophisticated threat. Without robust security measures, a massive volume of confidential and classified data could be exposed to vulnerabilities through which hackers could do various illicit acts. As a result, improved network security mechanisms that can analyse network traffic and detect malicious traffic in real-time are required. In this paper, a novel optimized machine learning image-based IoT malware detection method is proposed using visual representation (i.e., images) of the network traffic. In this method, the ant colony optimizer (ACO)-based feature selection method was proposed to get a minimum number of features while improving the support vector machines (SVMs) classifier’s results (i.e., the malware detection results). Further, the PSO algorithm tuned the SVM parameters of the different kernel functions. Using a public dataset, the experimental results showed that the SVM linear function kernel is the best with an accuracy of 95.56%, recall of 96.43%, precision of 94.12%, and F1_score of 95.26%. Comparing with the literature, it was concluded that bio-inspired techniques, i.e., ACO and PSO, could be used to build an effective and lightweight machine-learning-based malware detection system for the IoT environment.

Predicting Potential Geographical Distribution of Tobacco Blue Mold in China

Tobacco blue mold is a serious tobacco disease, which has high risk of invasion in many areas, therefore, it is of particular importance to strengthen the quarantine work and to make a good prediction. Since each species has its own particular relatively stable ecological niche, one can predict the potential geographical distribution of tobacco blue mold in target areas like China. Based on the known distribution data of tobacco blue mold, 20 environmental factors were selected and combined support vector machine (SVM) with ecological niche model was used to predict the potential spatial distribution of tobacco blue mold and to obtain the optimal combination of SVM parameters through the brute force search algorithm. Search algorithm overcame the shortage of empirical method and improved the accuracy of prediction. The method was proved to be effective and feasible by cross validation which provided a robust theoretical basis for preventing the intrusion and spread of tobacco blue mold. Bangladesh J. Bot. 51(4): 883-888, 2022 (December) Special

Tuning Support Vector Machines and Boosted Trees Using Optimization Algorithms

Statistical learning methods have been growing in popularity in recent years. Many of these procedures have parameters that must be tuned for models to perform well. Research has been extensive in neural networks, but not for many other learning methods. We looked at the behavior of tuning parameters for support vector machines, gradient boosting machines, and adaboost in both a classification and regression setting. We used grid search to identify ranges of tuning parameters where good models can be found across many different datasets. We then explored different optimization algorithms to select a model across the tuning parameter space. Models selected by the optimization algorithm were compared to the best models obtained through grid search to select well performing algorithms. This information was used to create an R package, EZtune, that automatically tunes support vector machines and boosted trees.

Imbalanced Data Classification Method Based on LSSASMOTE

Imbalanced data exist extensively in the real world, and the classification of imbalanced data is a hot topic in machine learning. In order to classify imbalanced data more effectively, an oversampling method named LSSASMOTE is proposed in this paper. First, the kernel function parameters and penalty parameters of the support vector machine (SVM) were optimized using levy sparrow search algorithm (LSSA), and a fitness function was correspondingly designed. Then, during the optimization process, SMOTE sampling rate was combined, and LSSA iteration was used to select the best combination of SVM parameters and SMOTE sampling rate. In addition, the oversampled samples were noise processed by Tomek Link. In this case, the LSSASMOTE+SVM classification model was constructed to classify the imbalanced data.Eight of the datasets used in the experiments were obtained on UCI and KEEL, and the other three datasets were created manually. The experimental results confirm that the model can effectively improve the classification accuracy of imbalanced data and can be used as a new imbalanced data classification method.

Prediction of Flood Discharge Using Hybrid PSO-SVM Algorithm in Barak River Basin

A crucial necessity in integrated water resource management is flood forecasting. Climate forecasts, specifically flood prediction, comprise multifaceted tasks as they are dependant on several parameters for predicting the dependant variable, which varies from time to time. Calculation of these parameters also changes with geographical location. From the time when Artificial Intelligence was first introduced to the field of hydrological modelling and prediction, it has produced enormous attention in research aspects for additional developments to hydrology. This study investigates the usability of support vector machine (SVM), back propagation neural network (BPNN), and integration of SVM with particle swarm optimization (PSO-SVM) models for flood forecasting. Performance of SVM solely depends on correct assortment of parameters. So, PSO method is employed in selecting SVM parameters. Monthly river flow discharge for a period of 1969 - 2018 of BP ghat and Fulertal gauging sites from Barak River flowing through Barak valley in Assam, India were used. For obtaining optimum results, different input combinations of Precipitation (Pt), temperature (Tt), solar radiation (Sr), humidity (Ht), evapotranspiration loss (El) were assessed. The model results were compared utilizing coefficient of determination (R2) root mean squared error (RMSE), and Nash–Sutcliffe coefficient (NSE). The most important results are highlighted below.•First, the inclusion of five meteorological parameters improved the forecasting accuracy of the hybrid model.•Second, model comparison specifies that hybrid PSO-SVM model executed superior performance with RMSE- 0.04962 and NSE- 0.99334 compared to BPNN and SVM models for monthly flood discharge forecasting.•Third, applied optimization algorithm has easy implementation, simple theory, and high computational efficacy.Results revealed that PSO-SVM could be utilised as an improved alternate method for flood forecasting as it provided a higher degree of reliability and accurateness.

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.

Exploring Pattern Recognition for Bearing Fault Diagnosis

Traditional bearing sensory diagnostic include touching and hearing rely on personal experience, and for more complex system are unable to meet the needs of equipment fault diagnosis. The research on bearing fault diagnosis is developing significantly. Bearings are used in rotating machinery and most machinery failures are caused by bearing failures. The fault diagnosis of bearings is an important research area. The core of bearing fault diagnosis is the pattern recognition of fault features. The key of pattern recognition is to develop a reasonable classifier. Intelligent pattern recognition has been developed such as principal components, support vector machine, neural network. In this study, a bearing fault diagnosis based on exploring pattern recognition is proposed. The key to pattern recognition is to design a significant classifier. A number of features from bearing vibration of normal and fault bearing are extracted and processed using principal components of correlation matrix. Plot of principal components shows the visualization of normal and fault bearing and the classifier is chosen subjectively. The principal components exploration will be confirmed using least squares support vector machine. The parameter of support vector machine estimated using heuristic optimization particle swarm optimization. The proposed method can be applied in the detection of faults of bearing

IMPROVED SUPPORT VECTOR MACHINE PERFORMANCE USING PARTICLE SWARM OPTIMIZATION IN CREDIT RISK CLASSIFICATION

In Classification using Support Vector Machine (SVM), each kernel has parameters that affect the classification accuracy results. This study examines the improvement of SVM performance by selecting parameters using Particle Swarm Optimization (PSO) on credit risk classification, the results of which are compared with SVM with random parameter selection. The classification performance is evaluated by applying the SVM classification to the Credit German benchmark credit data set and the private credit data set which is a credit data set issued from a local bank in North Sumatra. Although it requires a longer execution time to achieve optimal accuracy values, the SVM+PSO combination is quite effective and more systematic than trial and error techniques in finding SVM parameter values, so as to produce better accuracy. In general, the test results show that the RBF kernel is able to produce higher accuracy and f1-scores than linear and polynomial kernels. SVM classification with optimization using PSO can produce better accuracy than classification using SVM without optimization, namely the determination of parameters randomly. Credit data classification accuracy increased to 92.31%.

Prediction of Supply Chain Financial Credit Risk Based on PCA-GA-SVM Model

Supply Chain Finance (SCF) is a new type of financing business carried out by commercial banks on the basis of supply chain management, which effectively promotes the healthy development of the supply chain. As the most typical mode of SCF, accounts receivable financing mode can use the part of accounts receivable occupying working capital for financing, which is widely used. In order to effectively manage the credit risk in the Supply Chain Finance and maintain the healthy operation of the supply chain, this paper proposes a supply chain financial credit risk prediction model based on PCA-GA-SVM. First, principal component analysis (PCA) is used to reduce the dimension of the original index system, and then genetic algorithm (GA) is used to optimize the parameters of support vector machine (SVM). Finally, the principal components selected by PCA are input into the GA-SVM model for training, and the final prediction model is established. The running results show that the prediction performance of PCA-GA-SVM model is better than that of SVM and GA-SVM models. It has a good generalization ability, which can be used as a reference for commercial banks to improve the credit risk management ability of Supply Chain Finance and is conducive to the sustainable development of supply chain finance business.

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.

COMPARISON OF MACHINE LEARNING TECHNIQUES FOR PREDICTING NLR PROTEINS

The nucleotide-binding domain leucine-rich repeat-containing (NLR) proteins plays significant role in the intestinal tissue repair and innate immunity. It recently added to the members of innate immunity effectors molecules. It also plays an essential role in intestinal microbiota and recently emerged as a crucial hit for developing ulcerative colitis (UC) and colitis-associated cancer (CAC). A machine learning-based approach for predicting NLR proteins has been developed. In this study, we present a comparison of three supervised machine learning algorithms. Using ProtR and POSSUM Packages, the features are extracted for the dataset used in this work. The models are trained with the input compositional features generated using dipeptide composition, amino acid composition, etc., as well as Position Specific Scoring Matrix (PSSM) based compositions. The dataset consists of 390 proteins for the negative and positive datasets. The five-fold cross-validation (CV) is used to optimize Sequential Minimal Optimization (SMO) library of Support Vector Machine (LIBSVM) and Random Forest (RF) parameters, and the best model was selected. The proposed work performs rationally well with an accuracy of 90.91% and 93.94% for RF as the best classifier for the Amino Acid Composition (AAC) and PSE_PSSM-based model. We believe that this method is a reliable, rapid and useful prediction method for NLR Protein.

Detection of sub-healthy apples with moldy core using deep-shallow learning for vibro-acoustic multi-domain features

The apples with early moldy-core symptoms are in the sub-healthy status and still keep commodity value. The accurate recognition of sub-healthy fruit with moldy core is a valuable and challenging task for the apple industry. In this study, the well-trained IResNet50 network was used as a feature extractor to automatically mine the sensitive features from pixel grid images for time and frequency domains, and the time-frequency images. The extracted multi-domain deep features were fused by adaptive weighting and then fed into two shallow machine learning algorithms including support vector machine (SVM) and extreme learning machine (ELM) for classification. The parameters of SVM and ELM classifiers were optimized by a particle swarm optimization algorithm (PSO). The constructed IResNet50-PSO-ELM hybrid model achieved the overall classification accuracy of 96.7 %, and the recall value of 100 % for healthy fruit, 94.1 % for sub-healthy fruit, 96.2 % for diseased fruit. Also, the F1 values of the IResNet50-PSO-ELM hybrid model were over 95 % for the three-class classification. A comparison with existing studies demonstrated the potential of the proposed method for detecting the sub-healthy apples with moldy core.