Oil Development Engineering Company, Tehran, Iran by Seyed Ali Mohammad Tajalli, Mazda Moattari, Vahid Naghavi, Mohammad Reza Salehizadeh

“…Moreover, machine learning models, including K-nearest neighbors (KNN) and support vector machines (SVM), enhance anomaly detection precision, allowing for early identification and localization of potential leaks.Ultimately, the proposed framework brings several key benefits to pipeline management, including early anomaly detection, real-time data integration, predictive maintenance, and regulatory compliance. …”
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Exploration of Biomarkers of Psoriasis through Combined Multiomics Analysis by Lu Xing, Tao Wu, Li Yu, Nian Zhou, Zhao Zhang, Yunjing Pu, Jinnan Wu, Hong Shu

“…Moreover, the hub genes of psoriasis were screened by the least absolute shrinkage and selection operator (LASSO), Random Forest (RF), and Support Vector Machine (SVM), which were further performed single-gene gene set enrichment analysis (GSEA) to clarify the pathogenesis of psoriasis. …”
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Computational Intelligence-Based Structural Health Monitoring of Corroded and Eccentrically Loaded Reinforced Concrete Columns by Somain Sharma, Harish Chandra Arora, Aman Kumar, Denise-Penelope N. Kontoni, Nishant Raj Kapoor, Krishna Kumar, Arshdeep Singh

“…In this article, an ML-based artificial neural network (ANN), Gaussian process regression (GPR), and support vector machine (SVM) algorithms have been applied to estimate the residual strength of corroded and eccentrically loaded RC columns. …”
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The value of radiomics features of white matter hyperintensities in diagnosing cognitive frailty: a study based on T2-FLAIR imaging by Qinmei Liao, Xihao Hu, Zhiqiong Jiang, Xiaoyun Huang, Jiacheng Guo, Yuanzhong Zhu, Wenjing He

“…Optimal radiomic features were extracted and selected from T2-FLAIR images, and multiple logistic regression analysis was utilized to identify independent risk factors. Three machine learning algorithms—K-Nearest Neighbors (KNN), Logistic Regression (LR), and Support Vector Machine (SVM)—were used to construct radiomic models, clinical models, and combined models. …”
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Design of a Conveyer Trough Bolt Signal Acquisition System and Bayesian Ensemble Identification Method for Working State by Yi Lian, Bangzhui Wang, Meiyan Sun, Kexin Que, Sijia Xu, Zhong Tang, Zhilong Huang

“…Finally, after comparing machine learning algorithms, Support Vector Machine was chosen for its superior performance. …”
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A Big-Data-Driven Framework for Parking Demand Estimation in Urban Central Districts by Yunlin Guan, Yun Wang, Xuedong Yan, Haonan Guo, Yu Zhou

“…The first step is the parking zone division method, which is based on the statistical information grid and multidensity clustering algorithms. The second step is parking demand estimation, which is extracted by support vector machines posed in the form of a machine learning regression problem. …”
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Acoustic Emission-Based Small Leak Detection of Propulsion System Pipeline of Sounding Rocket by Lin Gao, Lili Dong, Jianguo Cao, Shaofeng Wang, Wenjing Liu

“…Finally, a classifier based on the support vector machine (SVM) technology is established, and the qualified features are selected to detect the leak. …”
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Lightweight Deepfake Detection Based on Multi-Feature Fusion by Siddiqui Muhammad Yasir, Hyun Kim

“…Moreover, the features extracted with a histogram of oriented gradients (HOG), local binary pattern (LBP), and KAZE bands were integrated to evaluate using random forest, extreme gradient boosting, extra trees, and support vector classifier algorithms. Our findings show a feature-level fusion of HOG, LBP, and KAZE features improves accuracy to 92% and 96% on FaceForensics++ and Celeb-DF(v2), respectively.…”
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Smart Grid Security: Proactive Prediction of Advanced Persistent Threats by Motahareh Dehghan, Erfan Khosravain

“…Experimental results show that the ProAPT model significantly outperforms traditional machine learning algorithms like Random Forest, Support Vector Machines, and Logistic Regression, achieving 93.8% accuracy, 93.12% precision, 95.2% recall, and 94.15% F1-Score. …”
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Fault Diagnosis in a Four-Arm Delta Robot Based on Wavelet Scattering Networks and Artificial Intelligence Techniques by Claudio Urrea, Carlos Domínguez

“…This study compares time-domain signal features and wavelet scattering networks, applied by classification algorithms including wide neural networks (WNNs), efficient linear support vector machine (ELSVM), efficient logistic regression (ELR), and kernel naive Bayes (KNB). …”
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A Data Decomposition and End-to-End Optimization-Based Monthly Carbon Emission Intensity of Electricity Forecasting Method by Yue Yan, Haoran Feng, Jinwei Song, Shixu Zhang, Shize Zhang, Qi He

“…In Stage I, a monthly carbon emission data generator based on the Denton decomposition method is proposed. In Stage II, support vector machine (SVM), known for their effectiveness in small-sample prediction, are employed for monthly CIF. …”
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Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics. by Lisa M Breckels, Sean B Holden, David Wojnar, Claire M Mulvey, Andy Christoforou, Arnoud Groen, Matthew W B Trotter, Oliver Kohlbacher, Kathryn S Lilley, Laurent Gatto

“…We present a unique transfer learning classification framework that utilises a nearest-neighbour or support vector machine system, to integrate heterogeneous data sources to considerably improve on the quantity and quality of sub-cellular protein assignment. …”
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Artificial Intelligence in Medicine: Transforming The Future of Healthcare by Tashfeen Alam Jan, Shumaila Naz

“…Machine learning (ML) and deep learning (DL) technologies have proved invaluable for the analysis of complex datasets, supporting early diagnosis and optimizing treatment protocols. …”
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Framework Heurístico para la Implementación de Sistemas Activos de Reconocimiento de Objetos by E. González, A. Adán, V. Feliú

“…From the study of the information contained in the cluster are efficiently developed the classification task, selection of sensor positions and calculation of the evidence. The classification algorithm uses a support vector machine (SVM) model to provide robustness to small deformations in the appearance of objects by noise, lighting changes, variations in the point of view. …”
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Fast SVM-based Multiclass Classification in Large Training Sets by M. Yu. Kurbakov, V. V. Sulimova

“…This paper addresses the actual problem of multiclass classification in large training sets. Classical Support Vector Machines (SVM) is a popular, convenient and well-interpreted classification method, but it has a high computational complexity of a training stage in a nonlinear case and a low data parallelism. …”
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Bearing Early Fault Diagnosis Based on an Improved Multiscale Permutation Entropy and SVM by Qunyan Jiang, Juying Dai, Faming Shao, Shengli Song, Fanjie Meng

“…IMPE was applied to feature extraction of early weak fault of rolling bearing and input into Support Vector Machines (SVMs) for faults classification. …”
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Gd-EOB-DTPA-enhanced MRI radiomics and deep learning models to predict microvascular invasion in hepatocellular carcinoma: a multicenter study by Zhu Zhu, Kaiying Wu, Jian Lu, Sunxian Dai, Dabo Xu, Wei Fang, Yixing Yu, Wenhao Gu

“…Clinical, CR, DLR, CR-DLR, and clinical-radiomics (Clin-R) models were built using support vector machines. The predictive capacity of the models was assessed by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. …”
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Speech emotion recognition using long-term average spectrum by Huerta-Hernández Luis David, Meléndez-Acosta Nayeli Joaquinita, Troncoso-Romero David Ernesto, Ramírez-Pacheco Julio César, León-Borges Jose Antonio

“…Our framework is evaluated through a comparative study, where classifiers such as artificial neural network, K-nearest neighbours, logistic regression, Bayesian algorithms, tree-based logistics, and support vector machine were used. …”
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Handling Class Imbalanced Data in Sarcasm Detection with Ensemble Oversampling Techniques by Ya-Han Hu, Ting-Hsuan Liu, Chih-Fong Tsai, Yu-Jung Lin

“…Evaluated across six classifiers – Support Vector Machine, Decision Tree, Random Forest, Extreme Gradient Boosting, Logistic Regression, and BERT – the results demonstrate that the SEO2 framework consistently enhances classifier performance compared to single oversampling techniques. …”
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Comparative Study and Real-World Validation of Vertical Load Estimation Techniques for Intelligent Tire Systems by Ti Wu, Xiaolong Zhang, Dong Wang, Weigong Zhang, Deng Pan, Liang Tao

“…Vertical load prediction algorithms are developed using Support Vector Machine (SVM) and linear regression, considering variables like contact length, vehicle speed, and tire pressure. …”
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