Bearing Fault Diagnosis in the Mixed Domain Based on Crossover-Mutation Chaotic Particle Swarm by Tongle Xu, Junqing Ji, Xiaojia Kong, Fanghao Zou, Wilson Wang

“…To solve this problem, a new fault diagnosis technique is proposed in the mixed domain, based on the crossover-mutation chaotic particle swarm optimization support vector machine. Firstly, fault features are generated using techniques in the time domain, the frequency domain, and the time-frequency domain. …”
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Experimental and Analytical Studies on Improved Feedforward ML Estimation Based on LS-SVR by Xueqian Liu, Hongyi Yu

“…However, when dealing with small sample and low signal-to-noise ratio (SNR), threshold effects are resulted and estimation performance degrades greatly. It is proved that support vector machine (SVM) is suitable for small sample. …”
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Two-stage Non-Intrusive Load Monitoring method for multi-state loads. by Lei Wang, Xia Han, Yushu Cheng, Jiaqi Ma, Xuerui Zhang, Xiaoqing Han

“…This paper proposed an improved NILM method based on the Resnet18 Convolutional Neural Network (CNN) and Support Vector Machine (SVM) algorithm to address the misidentification of multi-state appliances. …”
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Evaluating the Performance of SVM, Isolation Forest, and DBSCAN for Anomaly Detection by Lu Haowen

“…This study evaluates the suitability of three algorithms—Support Vector Machine (SVM), Isolation Forest, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) by comparing their accuracy and time efficiency in detecting outliers in different types of datasets. …”
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Research on Credit Default Prediction Model Based on TabNet-Stacking by Shijie Wang, Xueyong Zhang

“…XGBoost (eXtreme Gradient Boosting), LightGBM (Light Gradient Boosting Machine), CatBoost (Category Boosting), KNN (K-NearestNeighbor), and SVM (Support Vector Machine) are selected as the first-layer base learners, and XGBoost is used as the second-layer meta-learner. …”
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Analisis Perbandingan Algoritma SVM, KNN, dan CNN untuk Klasifikasi Citra Cuaca by Mohammad Farid Naufal

“…There are several popular image classification algorithms, namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). …”
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Abnormal Diagnosis Method of Self-Powered Power Supply System Based on Improved GWO-SVM by Ya jie Li, Shao bing Li, Wei Li

“…In order to solve the problem of low abnormal diagnosis rate of self-powered power supply system, an improved grey wolf optimization-support vector machine (GWO-SVM) algorithm combined with maximal information coefficient (MIC) are proposed. …”
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Fuzzy One-Class Classification Model Using Contamination Neighborhoods by Lev V. Utkin

“…It is shown that an algorithm for computing the classification parameters is reduced to a set of standard support vector machine tasks with weighted data points. …”
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Frequency hopping modulation recognition based on time-frequency energy spectrum texture feature by Hongguang LI, Ying GUO, Ping SUI, Zisen QI

“…For frequency hopping modulation identification,a novel method based on time-frequency energy spectrum texture feature was proposed.Firstly,the time-frequency diagram of the frequency hopping signal was obtained by smoothed pseudo Wigner-Ville distribution,and the background noise of the time-frequency diagram was removed by two-dimensional Wiener filtering to improve the resolution of the time-frequency diagram under low SNR conditions.Then,the connected-domain detection algorithm was used to extract the time-frequency energy spectrum of each hop signal and convert it into a time-frequency gray-scale image.The histogram statistical features and the gray-scale co-occurrence matrix feature were combined to form a 22-dimensional eigenvector.Finally,the feature set was trained,classified and identified by optimized support vector machine classifier.Simulation experiments show that the multi-dimensional feature vector extracted by the algorithm has strong representation ability and avoids the misjudgment caused by the similarity of single features.The average recognition accuracy of the six modulation methods of frequency hopping signals BPSK,QPSK,SDPSK,QASK,64QAM and GMSK is 91.4% under the condition of -4 dB SNR.…”
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An Interior Point Method for L1/2-SVM and Application to Feature Selection in Classification by Lan Yao, Xiongji Zhang, Dong-Hui Li, Feng Zeng, Haowen Chen

“…This paper studies feature selection for support vector machine (SVM). By the use of the L1/2 regularization technique, we propose a new model L1/2-SVM. …”
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An efficient spectrum sensing of mixed kernel SVM based on sampling covariance matrix by Jianyuan NIE, Jianrong BAO, Bin JIANG, Chao LIU, Fang ZHU, Jianhai HE

“…In recent years,with the blind detection algorithms were proposed,more and more blind algorithms based on sampling covariance matrix were applied to spectrum sensing.The detection threshold was an approximation,and the detection performance would be affected for this algorithms.Thus,the mixed kernel function support vector machine (SVM) efficient spectrum sensing based on sampling covariance matrix was proposed.The statistics which were maximum minimum eigenvalue (MME) and covariance absolute value (CAV) of sensing signal sampling covariance matrices were used as the feature vectors of SVM and were trained to generate a spectrum sensing classifier.The advantage of this algorithm was that it needn’t calculate the detection threshold and the extraction of features reduces size of the sample set.The genetic algorithm (GA) was used to optimize the parameters of mixed kernel function SVM algorithm.The experimental results show that the proposed method has higher detection probability than MME and CAV algorithms,and has less sensing time than SVM,which has good practicability.…”
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Anxiety Detection System Based on Galvanic Skin Response Signals by Abeer Al-Nafjan, Mashael Aldayel

“…This study addresses this gap by investigating the performance of three commonly used ML algorithms, support vector machine (SVM), K-nearest neighbor (KNN), and random forest (RF), in classifying anxiety and stress activity using a benchmark dataset. …”
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Modeling of the Power Station Boiler Combustion Efficiency Considering Multiple Work Condition with Feature Selection by TANG Zhenhao, WU Xiaoyan, CAO Shengxian

“…It is difficult for power station boiler efficiency to measure precisely A datadriven modeling method is proposed to establish the boiler combustion efficiency model, according to the machine learning theories A classification and regression trees (CART) algorithm provides correlated variables which have significant relation with the boiler combustion efficiency by data analysis Then, a KNearest Neighbor (KNN) classifies the samples to distinguish the data from different work conditions Based on the classified data, a least square support vector machine (LSSVM) optimized by differential evolution (DE) algorithm is proposed to establish a datadriven model (DDMMF) The parameters of LSSVM are optimized dynamically by DE to improve the model accuracy Finally, the prediction model is corrected dynamically for further improvement of the prediction accuracy The experimental results based on actual production data illustrate that the proposed approach can predict the boiler combustion efficiency accurately, which meets the requirements of boiler control and optimization…”
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Bearing Fault Prediction Based on Mixed Domain Features and GWO-SVM by Xuan Zhou, Ruiyang Xia, Zhaodong Zhang, Sasa Duan, Mao Cheng, Chengjiang Zhou, Min Mao

“…We propose a bearing fault identification algorithm based on grey wolf optimizer (GWO) to address the common problems of high signal noise, inability of a single indicator to accurately reflect the true state of bearings, and optimization of support vector machine (SVM) prediction model parameters in bearing fault identification. …”
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An Analysis Method of Principal Components of Power Quality Based on Fast S-transform by MAN Weishi, ZHANG Zhiyu, XI Lei

“…To handle the problems in power quality analysis such as a large amount of calculation for S-transform, requiring setting feature parameters to the support vector machine classification, etc, it proposed a new approach which combined the fast S-transform (FST) algorithm with the principal component analysis (PCA) to classify the power quality disturbances. …”
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Back Analysis of Geomechanical Parameters in Underground Engineering Using Artificial Bee Colony by Changxing Zhu, Hongbo Zhao, Ming Zhao

“…To the problem without analytical solution, optimal back analysis is time-consuming, and least square support vector machine (LSSVM) was used to build the relationship between unknown geomechanical parameters and displacement and improve the efficiency of back analysis. …”
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Evaluation of Teaching Quality of English Courses in Comprehensive Universities under Multiple Indicators by Haiying Song

“…In this paper, based on the analysis of the evaluation subjects of university English teaching quality, 16 evaluation indexes are constructed from five aspects: teaching content, teaching method, teaching process, teaching literacy, and teaching effect, and the Particle Swarm Optimization-Least Squares Support Vector Machine (PSO-LSSVM) algorithm is used to comprehensively evaluate the teaching quality, and finally English teaching in universities is selected as the object of empirical research. …”
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Multi-center study: ultrasound-based deep learning features for predicting Ki-67 expression in breast cancer by Qishan Cen, Man Wang, Siying Zhou, Hong Yang, Ye Wang

“…In the ultrasound depth feature model for the tumor area, the Support Vector Machine (SVM) algorithm achieved the highest performance, with an accuracy of 0.782, ROAUC of 0.771 (95% CI 0.704–0.838), sensitivity of 0.905, specificity of 0.543, and F1 score of 0.846. …”
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A study on rolling bearing fault diagnosis using RIME-VMD by Zhenrong Ma, Ying Zhang

“…Finally, the sample entropy of the reconstructed signal is calculated as a fault feature and input into a Support Vector Machine (SVM) for rapid identification and diagnosis of various rolling bearing fault types. …”
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基于Shannon熵的LCD-SVM方法在齿轮故障分类中的研究 by 李鹏宇, 邵忍平, 汪亚运

“…Local feature scale decomposition（Local characteristic scale decomposition,LCD）is a new adaptive time-frequency analysis method,which can adaptively decompose a complex signal into a number of ISC（Intrinsic scale component,ISC）components.SVM（support vector machine,SVM）is a kind of intelligent classification methods of machine learning,Shannon entropy is a nonlinear statistical learning methods.The LCD and SVM are introduced to the fault classification of mechanical transmission system.The Hilbert demodulation algorithm is used to strike the envelope signal of ISC components,through which the Shannon entropy is structured.After an in-depth analysis on the basis,it is combined with SVM in good relationship.The result is input vectors of SVM classifier,the trained SVM is used to determine the fault location,the type or degree of the test sample.Through the experimental analysis and verification,using this combining method,the recognition rate of four operating states which contains the normal state,the root crack of tooth,the wear of tooth surface,the composite fault is up to 97.5%.…”
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