Apple Variety Identification Using Near-Infrared Spectroscopy by Caihong Li, Lingling Li, Yuan Wu, Min Lu, Yi Yang, Lian Li

“…Near-infrared (NIR) spectra of apple samples were submitted in this paper to principal component analysis (PCA) and successive projections algorithm (SPA) to conduct variable selection. Three pattern recognition methods, backpropagation neural network (BPNN), support vector machine (SVM), and extreme learning machine (ELM), were applied to establish models for distinguishing apples of different varieties and geographical origins. …”
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Acoustic Sensor Based Recognition of Human Activity in Everyday Life for Smart Home Services by Jae Mun Sim, Yonnim Lee, Ohbyung Kwon

“…The performance of the proposed algorithm was compared with that of commonly used classification algorithms such as case-based reasoning, k-nearest neighbors, support vector machine, and multiple regression.…”
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Development and Validation of DIANA (Diabetes Novel Subgroup Assessment tool): A web-based precision medicine tool to determine type 2 diabetes endotype membership and predict indi... by Viswanathan Baskar, Mani Arun Vignesh, Sumanth C Raman, Arun Jijo, Bhavadharini Balaji, Nico Steckhan, Lena Maria Klara Roth, Moneeza K Siddiqui, Saravanan Jebarani, Ranjit Unnikrishnan, Viswanathan Mohan, Ranjit Mohan Anjana

“…<h4>Methodology</h4>The DIANA tool determines subgroup membership using a machine learning model (support vector machine) on T2D subgroups in the Asian Indian population. …”
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Research on the optimization method of inventory management of important spare parts of intercity railway. by Dongyan Wang, Ying Sun, Liang Yu, Kun Shen, Junbo Li, Xia Wu

“…To enhance the reliability of intercity railway operations and reduce spare parts management costs, this paper employs the Zebra Optimization Algorithm-Least Squares Support Vector Machine (ZOA-LSSVM) to analyze the reliability of the important Weibull distribution spare parts of the intercity railway and fit the parameters of the reliability function for spare parts. …”
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Research on Fault Diagnosis Method of High-Speed EMU Air Compressor Based on ICEEMDAN and Wavelet Threshold Combined Noise Reduction by Liqiang Peng, Akang Guo, Shuzhao Zhang

“…Finally, vibration and pressure signals from the air compressor are subjected to feature extraction, constructing high-dimensional feature vectors. The t-SNE manifold learning algorithm is applied for secondary feature extraction, creating an MPGA-SVM (Multi-Objective Genetic Algorithm-Support Vector Machine) fault diagnosis model. …”
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Unsupervised Learning With Hybrid Models for Detecting Electricity Theft in Smart Grids by Ali Jaber Almalki

“…By fusing supervised learning models (Random Forest) with unsupervised learning algorithms (Isolation Forest, One-Class Support Vector Machine (SVM), Local Outlier Factor (LOF), and Density-Based Spatial Clustering of Applications with Noise(DBSCAN)), this study presents a unique hybrid technique for identifying power theft. …”
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Natural language processing-based approach for automatically coding ship sensor data by Yunhui Kim, Kwangphil Park, Byeongwoo Yoo

“…Accordingly, the characteristics of the IO descriptions were extracted using Term Frequency-Inverse Document Frequency (TF–IDF) and word embedding, and machine learning techniques such as K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) and deep learning models such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and bidirectional LSTM (BiLSTM) were used to classify them into codes. …”
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An Information Granulated Based SVM Approach for Anomaly Detection of Main Transformers in Nuclear Power Plants by Wenmin Yu, Ren Yu, Cheng Li

“…A condition prediction method based on the online support vector machine (SVM) regression model is proposed, with the input data being preprocessed using the information granulation method, and the parameters of the model are optimized using the particle swarm optimization (PSO) algorithm. …”
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Research on feature extraction and intelligent diagnosis method of reciprocating compressor bearing clearance fault by Feng Yan

“…To overcome the shortcomings of traditional binary tree support vector machine (BTSVM) classifiers—such as slow convergence and error accumulation due to early misclassifications—an enhanced BTSVM model is introduced to reduce error propagation. …”
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FDTD Complex Terrain Modeling Method Based on Papery Contour Map by Meng Chen, Bing Wei, Xinbo He

“…First, the image preprocessing technology is employed to segment, denoise, and number the drawing area to arrive at independent figures and curve matrices. Subsequently, the support vector machine (SVM) classifier is implemented to identify the number and obtain the elevation value pertinent to the curve in the figure. …”
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Dynamic Inversion Analysis of Structural Layer Modulus of Semirigid Base Pavement considering the Influence of Temperature and Humidity by Bei Zhang, Xu Zhang, Yanhui Zhong, Xiaolong Li, Meimei Hao, Jinbo Liu

“…The firefly algorithm was adapted to optimize the selection of support vector machine (SVM) parameters. …”
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Modulation recognition driven by signal enhancement by CHENG Fengyun, ZHOU Jin

“…Secondly, an IQ signal enhancement network based on auxiliary classifier generative adversarial network (ACGAN) was proposed to achieve dual enhancement of sample quantity and quality. Finally, the support vector machine algorithm was employed to achieve modulation recognition and classification. …”
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Electricity Theft Detection in a Smart Grid Using Hybrid Deep Learning-Based Data Analysis Technique by Camille Franklin Mbey, Jacques Bikai, Felix Ghislain Yem Souhe, Vinny Junior Foba Kakeu, Alexandre Teplaira Boum

“…Thus, the proposed hybrid model is based on the support vector machine (SVM) and a particle swarm optimization (PSO) algorithm to detect energy fraudsters in the network. …”
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Integrating Advanced Techniques: RFE-SVM Feature Engineering and Nelder-Mead Optimized XGBoost for Accurate Lung Cancer Prediction by Sarah Ayad, Hamdi A. Al-Jamimi, Ammar El Kheir

“…Our methodology combines Recursive Feature Elimination with Support Vector Machines (RFE-SVM) for effective feature selection and employs the XGBoost ensemble learning algorithm for classification, optimized using the Nelder-Mead algorithm. …”
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A New Semisupervised-Entropy Framework of Hyperspectral Image Classification Based on Random Forest by Mengmeng Sun, Chunyang Wang, Shuangting Wang, Zongze Zhao, Xiao Li

“…The framework is composed of five parts: (1) random samples selection with (2) probabilistic output initial random forest classification processing based on the number of votes; (3) semisupervised classification, which is an improvement of the supervision classification of random forest based on the weighted entropy algorithm; (4) precision evaluation; and (5) a comparison with the traditional minimum distance classification and the support vector machine (SVM) classification. …”
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Using SVM Classifier and Micro-Doppler Signature for Automatic Recognition of Sonar Targets by Abbas Saffari, Seyed Hamid Zahiri, Navid Khozein Ghanad

“…In this paper, we propose using a propeller modulation on the transmitted signal (called sonar micro- Doppler) and different support vector machine (SVM) kernels for automatic recognition of moving sonar targets. …”
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Blind Parameter Identification of MAR Model and Mutation Hybrid GWO-SCA Optimized SVM for Fault Diagnosis of Rotating Machinery by Wenlong Fu, Jiawen Tan, Xiaoyuan Zhang, Tie Chen, Kai Wang

“…Then the multivariate autoregressive (MAR) model of all IMFs was established, whose order was determined by Schwartz Bayes Criterion (SBC), and all parameters of the model were identified blindly through QR decomposition, where key features were subsequently extracted via principal component analysis (PCA) to construct feature vectors of different fault types. Afterwards, a hybrid optimization algorithm combining mutation operator, grey wolf optimizer (GWO), and sine cosine algorithm (SCA), termed mutation hybrid GWO-SCA (MHGWOSCA), was proposed for parameter selection of support vector machine (SVM). …”
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Defect detection method of red globe grapes bunches based on near infrared camera imaging by GAO Sheng

“…Finally, a classification model based on BP neural network and support vector machine was developed to discriminate the defective parts and fruit edges. …”
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Development of a Hybrid Method to Predict the Slope Surface Deformation Utilizing the Time Series and GA-Elman Models by Gang Wang, Zheng Fang, Jiren Xie, Na Du

“…Then, the predicted results of slope surface deformation through GA-Elman were compared with the predicted results through Support Vector Machines (SVM), Extreme Learning Machine (ELM), Back Propagation (BP), and Genetic Algorithm-Back Propagation (GA-BP) models. …”
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Analisa Sentimen Financial Technology Peer To Peer Lending Pada Aplikasi Koinworks by Rousyati Rousyati, Windu Gata, Dany Pratmanto, Nia Kusuma Wardhani

“…This research will analyze the problems related to some reviews about the Fintech P2PL Koinworks application on reviews on the Google Play Store and determine the results of the accuracy of sentiment analysis produced by the Decision Tree algorithm, K-Nearest Neigbor and Support Vector Machine. …”
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