Maturity Classification and Quality Determination of Cherry Using VNIR Hyperspectral Images and Comprehensive Chemometrics by Yuzhen Wei, Siyi Yao, Feiyue Wu, Qiangguo Yu

“…Based on the spectral principal components, three classifiers were built to classify the maturity level: support vector machine, backpropagation neural network, and radial basis function neural network. …”
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Diagnosis of citrus leaf canker disease based on naive Bayesian classification by SHU Meiyan, WEI Jiaxi, ZHOU Yeying, DONG Qizhou, CHEN Haochong, HUANG Zhigang, MA Yuntao

“…The results showed that the method based on naive Bayesian classification was effective in the segmentation of citrus leaf canker disease, and the incorrect segmentation rate was only 3.58%, which was far better than the threshold methods and support vector machine. In terms of performance efficiency, the time order of the four algorithms was fixed threshold method＜adaptive threshold＜naive Bayesian＜support vector machine, all of which were within a reasonable range. …”
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A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning by Xibin Wang, Zhenyu Dai, Hui Li, Jianfeng Yang

“…In the collaborative filtering (CF) recommendation applications, the sparsity of user rating data, the effectiveness of cold start, the strategy of item information neglection, and user profiles construction are critical to both the efficiency and effectiveness of the recommendation algorithm. In order to solve the above problems, a personalized recommendation approach combining semisupervised support vector machine and active learning (AL) is proposed in this paper, which combines the benefits of both TSVM (Transductive Support Vector Machine) and AL. …”
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Fault Diagnosis of Gearbox based on Multi-fractal and PSO-SVM by Li Sha, Pan Hongxia, Zhang Jundong, Zhao Weiwei

“…Aiming at the non-stationary and nonlinear of gearbox vibration signals,a fault diagnosis method based on the multi-fractal and particle swarm optimization support vector machine（PSO-SVM）is put forward.Firstly,the fractal filter with short-time fractal dimension as fuzzy control parameters is used to filtering noise reduction the gearbox vibration signals with bigger background noises.Secondly,the multi-fractal spectrum algorithm is applied to analyze the signal after filtering,the results show that the characteristic parameters:Δa（q）、f（a（q））maxand box dimensions Dbcan give a good presentation for gearbox working condition.Finally,the particle swarm optimization（PSO）is applied to optimize the parameters of support vector machine（SVM）.Taking the multi-fractal characteristic vectors as input parameters of PSO-SVM and SVM to recognize the fault types of the gearbox.The results show that SVM based on particle swarm optimization can improve the classification accuracy.Meanwhile,the validity of gearbox fault diagnosis based on muti-fractal and PSO-SVM is proved.…”
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Estimation of the Time of Occurrence of the Maximum Electrical Demand by Selecting the Optimal Classification Model and Making Use of Unbalanced Data by César Aristóteles Yajure, Valesca M. Fuenzalida Sánchez

“…To predict the time of maximum demand, supervised machine learning algorithms were used: random forests, K nearest neighbors, support vector machine, and logistic regression. …”
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Classification of wheat flour levels in powdered spices using visual imaging by Kamran Kheiralipour, Mohammad Hossein Nargesi

“…During the experiment, these results were better than the performance of the support vector machine, (93.33, 100.00, and 98.88 %, respectively). …”
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Quantum resonant dimensionality reduction by Fan Yang, Furong Wang, Xusheng Xu, Pan Gao, Tao Xin, ShiJie Wei, Guilu Long

“…We demonstrate the performance of our algorithm combining with two types of quantum classifiers, quantum support vector machines and quantum convolutional neural networks, for classifying underwater detection targets and quantum many-body phase, respectively. …”
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Sentiment Analysis of Short Texts Using SVMs and VSMs-Based Multiclass Semantic Classification by K. Suresh Kumar, A.S. Radha Mani, T. Ananth Kumar, Ahmad Jalili, Mehdi Gheisari, Yasir Malik, Hsing-Chung Chen, Ata Jahangir Moshayedi

“…In our approach, a hybrid machine learning model is proposed which uses Enhanced Vector Space Model (EVSM) along with Hybrid Support Vector Machine (HSVM) classifier. …”
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Speech Recognition using Wavelets and Improved SVM by Emad Ahmed Hussien, Mohannad Abid Shehab Ahmed, Haithem Abd Al-Raheem Taha

“…This paper is based on text independent speaker recognition system that makes use of Discrete Wavelet Transform (DWT) as a feature extraction and kernel Support Vector Machine (SVM) approach as a classification tool for taking the decision through applying simplified-Class Support Vector Machine approach.   …”
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APPLICATION OF IMPROVED EXPERIENCE WAVELET TRANSFORM IN FAULT DIAGNOSIS OF WIND TURBINE GEARBOX by HU Xuan, LI Chun, YE KeHua

“…The CASNEWT method is used to decompose the fault signal of the wind turbine gearbox bearing, and then the obtained components are filtered and reconstructed by the spectrum negentropy criterion, and the reconstructed signal is analyzed by envelope analysis to accurately extract the fault characteristics. Finally, a feature vector set is formed and input to the support vector machine for fault diagnosis. …”
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Innovative segmentation technique for aerial power lines via amplitude stretching transform by Pengfei Xu, Nor Anis Asma Sulaiman, Yafei Ding, Jiangwei Zhao

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A Hybrid Fault Diagnosis Model for Rolling Bearing With Optimized VMD and Fuzzy Dispersion Entropy by Xin Xia, Xiaolu Wang, Weilin Chen

“…To improve the efficiency of feature extraction and fault diagnosis, a hybrid model based on optimized variational mode decomposition (VMD), fuzzy dispersion entropy (FDE), and a support vector machine (SVM) is proposed. Firstly, a parameter optimization method using the sparrow search algorithm (SSA) was applied to VMD to improve the decomposition ability. …”
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基于MIE和SVM算法的无级变速器故障诊断研究 by 蒋强, 柳洪义, 郝建军, 唐毅锋

“…The fault inspection and diagnosis of vehicle transmission plays a important role of safety traffic and knocking the traffic accidents down.For the fault character of vehicle continuously variable transmission（CVT）,the fault signal character extract entropy method based on the theory of mutual information entropy and the multiclass classification support vector machine（SVM） algorithm to be easily implemented to deal with the problem of classification for fault state are proposed.Experimental results show that the combination mutual information entropy with multiclass classification SVM algorithm in fault diagnosis of vehicle CVT is the feasibility and efficiency,and the reliability of fault inspection is improved.…”
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Intelligent Data Processing Methods for the Atypical Values Correction of Stock Quotes by T. V. Zolotova, D. A. Volkova

“…The mathematical basis of machine learning methods is the Z-score method, the isolation forest method, support vector method for outlier detection, and winsorization and multiple imputation methods for outlier correction. …”
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An Adaptive Data-Driven Approach to Solve Real-World Vehicle Routing Problems in Logistics by Emir Žunić, Dženana Đonko, Emir Buza

“…A comparison of the acquired results has been made using the decision support system with predictive models: generalized linear models (GLMs) and support vector machine (SVM). …”
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基于QPSO-SVM的轴承故障诊断方法 by 杨光春, 蹇清平

“…Due to the importance of rolling bearing as one of the most widely used in rotating machines,bearing failures have adverse effects on the safe operation of the equipment,in order to diagnosing the fault of rolling bearing effectively,a fault diagnosis model of support vector machine（SVM）optimized by quantum particle swarm optimization（QPSO）algorithm is proposed.First,fault vibration signals are decomposed into several intrinsic mode functions（IMFs）using empirical mode decomposition（EMD）method,then the instantaneous amplitudes of the IMFs that have the fault characteristics are extracted and regarded as the features vector,finally the SVM model optimized by QPSO is used for the failure mode identification.The experimental results indicate that the proposed bearing fault diagnosis method has good capability for adaptive features extraction as well as high fault diagnostic accuracy.…”
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Prediction method of sugarcane important phenotype data based on multi-model and multi-task. by Jihong Sun, Chen Sun, Zhaowen Li, Ye Qian, Tong Li

“…In this study, we employed six key phenotypic traits of sugarcane, specifically plant height, stem diameter, third-node length (internode length), leaf length, leaf width, and field brix, along with eight machine learning methods: logistic regression, linear regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Backpropagation Neural Network (BPNN), Decision Tree, Random Forest, and the XGBoost algorithm. …”
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Research on Identification Technology of Explosive Vibration Based on EEMD Energy Entropy and Multiclassification SVM by Huayuan Ma, Xinghua Li, Qiang Liu, Xie Xingbo, Chong Ji, Changxiao Zhao

“…Compared with BP (backpropagation) neural network algorithm, SVM (support vector machine) algorithm has higher training efficiency. …”
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Brain activity patterns reflecting security perceptions of female cyclists in virtual reality experiments by Mohammad Arbabpour Bidgoli, Arian Behmanesh, Navid Khademi, Phromphat Thansirichaisree, Zuduo Zheng, Sara Saberi Moghadam Tehrani, Sajjad Mazloum, Sirisilp Kongsilp

“…Subsequently, four supervised machine learning methods, random forest, support vector machine, logistic regression, and multilayer perceptron, are utilized to classify influential factors on security perception using clustered EEG data. …”
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Mid-Infrared Spectroscopy for Coffee Variety Identification: Comparison of Pattern Recognition Methods by Chu Zhang, Chang Wang, Fei Liu, Yong He

“…These methods were classified as highly effective methods (soft independent modelling of class analogy, support vector machine, back propagation neural network, radial basis function neural network, extreme learning machine, and relevance vector machine), methods of medium effectiveness (partial least squares-discrimination analysis, K nearest neighbors, and random forest), and methods of low effectiveness (Naive Bayes classifier) according to the classification accuracy for coffee variety identification.…”
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