A Fault Diagnosis Method for Planetary Gearboxes Based on IFMD by Fengfeng Bie, Xueping Ding, Qianqian Li, Yuting Zhang, Xinyue Huang

“…Subsequently, a convolutional neural network integrated with the support vector machine model (CNN-SVM) is established, leveraging the convolutional neural network for feature extraction. …”
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Fingernail analysis management system using microscopy sensor and blockchain technology by Shih Hsiung Lee, Chu Sing Yang

“…Histogram of oriented gradients and local binary patterns are used to capture the characteristic value. It uses support vector machine and random forest tree for classification. …”
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Histogram of Maximal Optical Flow Projection for Abnormal Events Detection in Crowded Scenes by Ang Li, Zhenjiang Miao, Yigang Cen, Tian Wang, Viacheslav Voronin

“…Following the extraction of the HMOFP of the training frames, the one-class support vector machine (SVM) classification method is utilized to detect the abnormality of the testing frames. …”
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Detecting unknown vulnerabilities in smart contracts using opcode sequences by Peiqiang Li, Guojun Wang, Xiaofei Xing, Xiangbin Li, Jinyao Zhu

“…Finally, we test the effectiveness of our method with four machine learning models: the K-Nearest Neighbor algorithm (KNN), Support Vector Machine (SVM), Logistic Regression (LR), and Decision Tree (DT). …”
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Forecasting water quality indices using generalized ridge model, regularized weighted kernel ridge model, and optimized multivariate variational mode decomposition by Marjan Kordani, Mohsen Bagheritabar, Iman Ahmadianfar, Arvin Samadi-Koucheksaraee

“…Statistical metrics confirmed that the proposed OMVMD-GRKR model, concerning the best efficiency in the Ahvaz (R = 0.987, RMSE = 0.761, and U95% = 2.108) and Molasani (R = 0.963, RMSE = 1.379, and U95% = 3.828) stations, outperformed the OMVMD and simple-based methods such as ridge regression (Ridge), least squares support vector machine (LSSVM), deep random vector functional link (DRVFL), and deep extreme learning machine (DELM). …”
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Lightweight Anomaly Detection for Wireless Sensor Networks by Pu Cheng, Minghua Zhu

“…In this paper two lightweight anomaly detection algorithms LADS and LADQA are proposed for WSNs. Both algorithms utilize the one-class quarter-sphere support vector machine (QSSVM) and convert the linear optimization problem of QSSVM to a sort problem for the reduced computational complexity. …”
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Research and Application of Personalized Modeling Based on Individual Interest in Mining by Baocheng Huang, Guang Yu

“…First, the interest classification problem called multiclass classification algorithm is proposed based on improving support vector machine of binary tree. …”
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Intelligent policy framework: Natural resource conservation, knowledge and big data analytics by Nina Xiao, Xianhe Qu

“…To enhance the reliability of the results, this study employed two algorithms: Bayesian inference and a weighted Support Vector Machine (SVM) algorithm based on the grey relational analysis. …”
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Hybrid feature learning framework for the classification of encrypted network traffic by S. Ramraj, G. Usha

“…The focus of this research is to evaluate the performance of the Support Vector Machine (SVM) in classifying network packets by application type, as well as classifying the type of data communicated within an application. …”
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Emotion Recognition in the Eye Region Using Textural Features, IBP and HOG by Laura Jalili, Josue Espejel, Jair Cervantes, Farid Lamont

“…Subsequently, we assessed the performance of multiple classifiers, including Support Vector Machine (SVM), Logistic Regression, Bayesian Regression, and Decision Trees, to identify the most effective approach. …”
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A Hybrid Forecasting System Based on Comprehensive Feature Selection and Intelligent Optimization for Stock Price Index Forecasting by Xuecheng He, Jujie Wang

“…At last, an enhanced least squares support vector machine (LSSVM) algorithm is established to obtain high-precision point prediction results, and the Gaussian process regression (GPR) algorithm is used to obtain reasonable interval prediction results. …”
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Adaptive Anomaly Detection Framework Model Objects in Cyberspace by Hasan Alkahtani, Theyazn H. H. Aldhyani, Mohammed Al-Yaari

“…The deep learning based on Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) and machine learning algorithms namely Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) algorithms were implemented to classify the Denial-of-Service attack (DoS) and Distributed Denial-of-Service (DDoS) attacks. …”
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Research on Path Optimization of Vehicle-Drone Joint Distribution considering Customer Priority by Xiaoye Zhou, Yuhao Feng

“…Second, a two-stage hybrid algorithm is proposed for the problem model. In the first stage, the deep neural network and the grid search improved support vector machine algorithm (DNN-GSM-SVM) are used to screen and classify customer priority features, and in the second stage, adaptive large-scale neighborhood search improved genetic algorithms (ALNS-GA) are used to solve the problem of vehicle-drone joint distribution path planning problem. …”
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Evaluating and Forecasting the Probability of Lightning Occurrence in Rasht City by Afsaneh Ghasemi, Jamil Amanollahi

“…From the evaluation of the CHIAD tree and the Support Vector Machine model, the Support Vector Machine model with a slight difference in utility indices of accuracy = 0.773, precision = 0.855, harmonic mean = 0.813, root mean square error = 0.475. …”
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Enhancing power transformer health assessment through dimensional reduction and ensemble approaches in Dissolved Gas Analysis by Abdelmoumene Hechifa, Saurabh Dutta, Abdelaziz Lakehal, Hazlee Azil Illias, Arnaud Nanfak, Chouaib Labiod

“…The method involves fusing reduced dimensional input features through Principal Component Analysis with Ensemble techniques such as Bagging, Decorate, and Boosting. Various machine learning algorithms, including Decision Tree (DT), K‐Nearest Neighbours, Radial Basis Function Network, and Support Vector Machine, are employed in conjunction with Ensemble techniques. …”
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UAV imaging for spectral characterization of Coffee Leaf Miner (<i>Leucoptera coffeella</i>) infestation in the Cerrado Mineiro region by V. S. W. Orlando, M. D. L. B. T. Galo, G. D. Martins, A. M. Lingua, V. Andaló

“…The performances of the three machine learning algorithms were compared. Determining the number of mines in the group of most infested plants demonstrated slightly higher precision, achieving an RMSE of 22.69% using the Support Vector Machine algorithm. …”
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Komparasi Algoritma Klasifikasi Dengan Menggunakan Anaconda untuk Memprediksi Ramai Penonton Film di Bioskop by Rano Agustino

“…By using CART classification, NBC (Naive Bayes Classifier) algorithm, SVM (Support Vector Machine), LR (Logis Text Regression) and LDA (Linear Discriminant Analysis) which will be compared which accuracy is the best for predicting the absence or absence of the audience. …”
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Effect of Wireless Channels on Detection and Classification of Asthma Attacks in Wireless Remote Health Monitoring Systems by Orobah Al-Momani, Khaled M. Gharaibeh

“…This paper aims to study the performance of support vector machine (SVM) classification in detecting asthma attacks in a wireless remote monitoring scenario. …”
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Fault diagnosis of nonlinear analog circuits using generalized frequency response function and LSSVM. by Jialiang Zhang, Yaowang Yang

“…A fault diagnosis method of nonlinear analog circuits is proposed that combines the generalized frequency response function (GFRF) and the simplified least squares support vector machine (LSSVM). In this study, the harmonic signal is used as an input to estimate the GFRFs. …”
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Electrical Grounding Fault Prediction of EMU Traction Motor by Xuemiao PANG, Chunxing PEI, Chunguang YAN, Dongxing WANG, Jie JIANG

“…It shows that the prediction accuracy of the three algorithms is higher than 84% at all. And comparing with RBF neural network and support vector machine, decision tree has higher prediction accuracy and reaches 85.6%. …”
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