Basketball teaching methods based on 3D-Convolutional neural network by Chao Huang, Xian Wu

“…The basketball skilled movement dataset is classified and processed using a support vector machine, while a dual-resolution 3d-convolutional neural network is employed to process action features. …”
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Improving cancer detection through computer-aided diagnosis: A comprehensive analysis of nonlinear and texture features in breast thermograms. by Hamed Khodadadi, Shima Nazem

“…Besides, to optimize feature selection and reduce redundancy, a metaheuristic optimization technique called Non-Dominated Sorting Genetic Algorithm (NSGA III) is applied. The proposed method utilizes various machine learning algorithms, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Pattern recognition Network (Pat net), and Fitting neural Network (Fit net), for classification. ten-fold cross-validation ensures robust performance evaluation. …”
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Collocation ranking: frequency vs semantics by Nikola Ljubešić, Nataša Logar, Iztok Kosem

“…In the experiment, two methods were used: for the quantitative part of the evaluation, we used supervised machine learning with the area-under-the-curve (AUC) ROC score and support-vector machines (SVMs) algorithm, and in the qualitative part the ranking results of the two methods were evaluated by lexicographers. …”
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Predicting Patients’ Revisit Intention Based on Satisfaction Scores: Combination of Penalized Regression and Neural Networks by Farshid Abdi, Shaghayegh Abolmakarem, Amir Karbassi Yazdi, Paul Leger, Yong Tan, Giuliani Coluccio

“…In addition to feature selection models such as Random Forest, Genetic Algorithm, and Lasso Regression, the study employs various methods, including Neural Networks, Support Vector Machines, Decision Trees, k-Nearest Neighbors, Rule-based systems, and Naive Bayes algorithms. …”
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Research on Camouflage Recognition in Simulated Operational Environment Based on Hyperspectral Imaging Technology by Donge Zhao, Shuyan Liu, Xuefeng Yang, Yayun Ma, Bin Zhang, Wenbo Chu

“…According to their similarities and differences between them and referenced spectrum, the models of classification were established by combining the Naive Bayes (NB) algorithm, K-nearest neighbour (KNN) algorithm, random forest (RF) algorithm, and support vector machine (SVM) algorithm. …”
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Optimized Identity Authentication via Channel State Information for Two-Factor User Verification in Information Systems by Chuangeng Tian, Fanjia Li, Xiaomeng Liu, Juanjuan Li

“…For classification, a kernel support vector machine (SVM) model is trained using a randomized hyperparameter search algorithm. …”
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Rolling Bearing Fault Diagnosis Method Based on Improved Variational Mode Decomposition and Information Entropy by Wen FAN, Lian GE, Xiaoting XIAO, Fangji GAN, Xin LAI, Hongxia DENG, Qi HUANG

“…Finally, in order to verify the advantages of the research, the information entropy is extracted from the data of 1000 samples in the bearing database of Case Western Reserve University as the feature set, which is input into support vector machine (SVM) for fault diagnosis test. …”
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Reactor fault diagnosis based on common feature of multivariate vibration sequences by FU Ming, ZHU Ming, MEI Jie, ZHANG Jing, XIAO Li, ZHANG Zongxi

“…Aiming at the limitation that current feature selection algorithms are only available for univariate vibration sequence, this paper proposes a multivariate vibration sequences feature selection algorithm named SVM-RFE-GA based on support vector machine recursive feature elimination algorithm (SVM-RFE) and genetic algorithm (GA). …”
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Guided wave signal‐based sensing and classification for small geological structure by Hongyu Sun, Jiao Song, Shanshan Zhou, Qiang Liu, Xiang Lu, Mingming Qi

“…To achieve multi‐dimensional feature, the two‐dimensional data in the form of a matrix is collected, and a multiplicative update method is introduced to update the algorithm iteratively. Finally, the Support Vector Machine (SVM) multi‐classifier with Gaussian radial basis kernel function is selected for classification of Small Geological Structure. …”
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Interpreting neural decoding models using grouped model reliance. by Simon Valentin, Maximilian Harkotte, Tzvetan Popov

“…The present results confirm previous findings insofar as both random forest and support vector machine models relied on alpha-band activity in most subjects. …”
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Classification of Iranian Wheat Flour by FT-MIR Spectroscopy based on Max-Relevance Min-Redundancy Wavelength Selection Coupled with SVM by Amir Kazemi, Asghar Mahmoudi, Seyyed Hossein Fattahi

“…Then, Principal Component Analysis (PCA) as unsupervised and Support Vector Machine (SVM) as supervised models with Max-Relevance Min-Redundancy (MRMR) feature selection algorithm were applied to investigate the classification of these varieties. …”
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Calculation Methods for the Permeability Coefficient of Concrete Face Rockfill Dam with Cracks by Zhongwen Shi, Zhongru Wu, Chongshi Gu, Bo Chen, Hailong Zhang, Wenzhong Yin, Bangbin Wu

“…Moreover, an inversion algorithm based on particle swarm optimization and support vector machine was proposed and applied. …”
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A New Hybrid Model for Underwater Acoustic Signal Prediction by Guohui Li, Wanni Chang, Hong Yang

“…Support vector regression (SVR) is used to predict the high-frequency subsequence. …”
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Rail Corrugation Detection of High-Speed Railway Using Wheel Dynamic Responses by Jianbo Li, Hongmei Shi

“…A novel method using wheel vibration acceleration is proposed in this paper, in which ensemble empirical mode decomposition (EEMD) is employed to estimate the wavelength, and bispectrum features are extracted to recognize the depth with support vector machine (SVM). Firstly, a vehicle-track coupling model considering the rail corrugation of high-speed railway is established to calculate the wheel vibration acceleration. …”
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Principal Component Analysis and Bacterial Foraging Optimization for Credit Scoring by Jennifer Arjun, Marsudi Wahyu Kisworo, Edi Surya Negara, Usman Ependi

“…This research also uses the Bacterial Foraging Optimization algorithm to optimize qualification results on the Support Vector Machine which uses 4 kernels, namely Linear, RBF, Polynomial and Sigmoid. …”
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Intelligent Optimized Combined Model Based on GARCH and SVM for Forecasting Electricity Price of New South Wales, Australia by Yi Yang, Yao Dong, Yanhua Chen, Caihong Li

“…In this paper, we propose an optimized combined forecasting model by ant colony optimization algorithm (ACO) based on the generalized autoregressive conditional heteroskedasticity (GARCH) model and support vector machine (SVM) to improve the forecasting accuracy. …”
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Bearing fault diagnosis for high-speed train based on improved VMD and APSO-SVM by ZHANG Qingsong, ZHANG Bing, QIN Yi

“…Aiming at the problem that the fault information of high-speed train wheel bearing is weak and difficult to extract, a fault feature extraction and recognition model for vibration signal of high-speed train bearing based on variational mode decomposition and adaptive particle swarm optimization-support vector machine was proposed. To avoid the under-decomposition or over-decomposition of VMD, the selection principle of <italic>k</italic> was suggested from the perspective of energy entropy change rate. …”
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Spam Email Detection using Naïve Bayes classifier by Wang Liansong

“…Spam email detection is still a considerable and ongoing challenge in today’s online environment, as the number of unsolicited emails keeps growing exponentially. Various algorithms such as the tree-based model, support vector machine Algorithm, and Convolutional Neural Network have been explored in prior research to tackle this challenge. …”
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Stock Index Prices Prediction via Temporal Pattern Attention and Long-Short-Term Memory by Xiaolu Wei, Binbin Lei, Hongbing Ouyang, Qiufeng Wu

“…The study’s motivation is based on the notion that datasets of stock index prices involve weak periodic patterns, long-term and short-term information, for which traditional approaches and current neural networks such as Autoregressive models and Support Vector Machine (SVM) may fail. This study applied Temporal Pattern Attention and Long-Short-Term Memory (TPA-LSTM) for prediction to overcome the issue. …”
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Research on fault diagnosis of amorphous alloy transformers by using vibration signals and a PSO-optimized full-process WPT-SVM model by Daosheng Liu, Wentao Yang, Longsheng Liu, Zhe Zhao

“…Therefore, in order to solve the AMT vibration monitoring problem and enhance the diagnostic efficiency, this study proposes an AMT fault diagnosis model based on particle swarm optimization (PSO) to optimize the parameters of wavelet packet transform (WPT) and support vector machine (SVM).The optimal vibration signal acquisition point is determined by finite element analysis to ensure high signal quality. …”
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