Construction of a diagnostic model utilizing m7G regulatory factors for the characterization of diabetic nephropathy and the immune microenvironment by Jingying Zhong, Pengli Xu, Xuanyi Li, Meng Wang, Xuejun Chen, Huiyu Liang, Zedong Chen, Jing Yuan, Ya Xiao

“…To construct and validate m7G-related prognostic features and risk scores, we integrated multiple machine learning approaches, including Support Vector Machine-Recursive Feature Elimination, Random Forest, LASSO, Cox regression, and ROC curves analysis. …”
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A Convolutional Neural Network Model for Classifying Resting Tremor Amplitude in Parkinson’s Disease by Augusto Ielo, Serena Dattola, Lilla Bonanno, Paolo De Pasquale, Alberto Cacciola, Angelo Quartarone, Maria Cristina De Cola

“…The proposed model outperformed traditional machine learning techniques as Random Forest, Support Vector Machine (SVM) and Decision Trees, demonstrating superior accuracy in tremor classification. …”
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Developing an Integrative Data Intelligence Model for Construction Cost Estimation by Zainab Hasan Ali, Abbas M. Burhan, Murizah Kassim, Zainab Al-Khafaji

“…In this research, extreme gradient boosting is developed as an advanced input selector algorithm and coupled with three AI models, including random forest (RF), artificial neural network (ANN), and support vector machine (SVM) for cost estimation. …”
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Sensitive Wavelengths Selection in Identification of Ophiopogon japonicus Based on Near-Infrared Hyperspectral Imaging Technology by Zhengyan Xia, Chu Zhang, Haiyong Weng, Pengcheng Nie, Yong He

“…And a nonlinear calibration model, support vector machine (SVM), was also provided for comparison. …”
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Miniaturized Near-Infrared Analyzer for Quantitative Detection of Trace Water in Ethylene Glycol by Qunling Luo, Zhiqiang Guo, Danping Lin, Boxue Chang, Yinlan Ruan

“…The modeling performances of three algorithms including Support Vector Machine Regression (SVMR), Principal Component Regression (PCR), and Partial Least Squares Regression (PLSR) were systematically evaluated, with PLSR identified as the optimal algorithm. …”
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Sentiment Analysis of Public Satisfaction with the 'INFO BMKG' Application using Naive Bayes, SVM, and KNN by Natasya Aditiya, Pratomo Setiaji, Supriyono Supriyono

“…This research employs three classification algorithms—Naive Bayes, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN)—to categorize user reviews into positive, neutral, or negative sentiments. …”
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Obesity Status Prediction Through Artificial Intelligence and Balanced Label Distribution Using SMOTE by Arif Riyandi, Mahazam Afrad, M Yoka Fathoni, Yogo Dwi Prasetyo

“…This study investigated the application of Artificial Intelligence (AI) to predict obesity status using a dataset of 1,610 individuals, including demographic and anthropometric data. Four AI algorithms were analyzed: Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Random Forest, and Support Vector Machine (SVM). …”
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A Novel Aerosol Optical Depth Retrieval Method Based on SDAE from Himawari-8/AHI Next-Generation Geostationary Satellite in Hubei Province by Shiquan Deng, Ting Bai, Zhe Chen, Yepei Chen

“…This approach was tested in the Hubei province of China. Traditional machine learning methods such as Extreme Learning Machines (ELMs), BackPropagation Neural Networks (BPNNs), and Support Vector Machines (SVMs) are also used to evaluate model performance. …”
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Zoning Elastic Modulus Inversion for High Arch Dams Based on the PSOGSA-SVM Method by Bo Chen, Xiao Fu, Xuyuan Guo, Chongshi Gu, Chenfei Shao, Xiangnan Qin

“…The zoning elastic modulus of a high arch dam is inversed by using deformation safety monitoring data in the operation period, based on the particle swarm optimization with gravitation search algorithm for support vector machine (PSOGSA-SVM) method. …”
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In-vehicle localization based on multi-channel Bluetooth Low Energy received signal strength indicator by Gao Yuan, Zhao Ze, Huang Changcheng, Han Chuanqi, Cui Li

“…To achieve higher positioning precision, a hierarchical computation algorithm based on Adaboost and support vector machine is proposed in our method. …”
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Design and Optimization of a Fan-Out Wafer-Level Packaging- Based Integrated Passive Device Structure for FMCW Radar Applications by Jiajie Yang, Lixin Xu, Ke Yang

“…Using this metric as a loss function, we apply the support vector machine (SVM) for electromagnetic simulation and the genetic algorithm (GA) for optimization. …”
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Early Detection of Parkinson's Disease: Ensemble Learning for Improved Diagnosis by Raut Komal, Balpande Vijaya

“…This paper proposed several machine learning algorithms such as Decision Tree, Random Forest, Logistic Regression and Support Vector Machine and design an ensemble of these models to detect and classify Parkinson's disease. …”
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Computer Methods for the Antimicrobial Efficacy of Metal Nanoparticles Against MDR Microbes by Shanza Anzar, Ayesha Sohail, Lubna Sherin, Peter S. Kim

“…To address this issue, metal and metal oxide nanoparticles have emerged as potent nanoantimicrobial agents, offering an alternative to clinically used conventional antibiotics. This study employs support vector machine learning classification algorithms to identify the key attributes of inorganic nanoparticles that influence their antimicrobial efficacy. …”
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Plasma metabolite biomarker identification study for the early detection of gastric cancer by Juan Zhu, Yida Huang, Bin Liu, Xue Li, Li Yuan, Le Wang, Kun Qian, Yingying Mao, Lingbin Du, Xiangdong Cheng

“…Ultra-performance liquid chromatography–mass spectrometry–based metabolomics methods were used to characterize the subjects’ plasma metabolic profiles and to screen and validate the GC biomarkers. Five machine learning algorithms (neural network, support vector machine, ridge regression, lasso regression and Naïve Bayes) were used to build a diagnostic model. …”
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The Role of Time in Facial Dynamics and Challenges in Automatic Emotion Recognition (2019–2024) by Williams Contreras-Higuera, Lucrezia Crescenzi-Lanna

“…Vision transformers (ViTs) show promise but require integration with algorithms to mitigate spatial noise. Conventional machine learning (CML) methods like support vector machine (SVM) remain robust, especially in smaller datasets. …”
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An anomaly detection scheme for data stream in cold chain logistics. by Zhibo Xie, Heng Long, Chengyi Ling, Yingjun Zhou, Yan Luo

“…Subsampling and cross factor are designed and used to overcome the shortcomings of the isolated forest algorithm (iForest). Experiments have shown that as the dimensionality of the data increases, the performance indicators of the new scheme, such as P (precision), R (recall), F1 score, and AUC (area under the curve), become increasingly superior to commonly used support vector machines (SVM), local outlier factors (LOF), and iForests. …”
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Modeling Canopy Height of Forest–Savanna Mosaics in Togo Using ICESat-2 and GEDI Spaceborne LiDAR and Multisource Satellite Data by Arifou Kombate, Guy Armel Fotso Kamga, Kalifa Goïta

“…We tested four methods: Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN). …”
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Enhanced Superpixel-Guided ResNet Framework with Optimized Deep-Weighted Averaging-Based Feature Fusion for Lung Cancer Detection in Histopathological Images by Karthikeyan Shanmugam, Harikumar Rajaguru

“…The optimized features are classified using various machine learning classifiers, including support vector machine (SVM), decision tree (DT), random forest (RF), K-nearest neighbor (KNN), SoftMax discriminant classifier (SDC), Bayesian linear discriminant analysis classifier (BLDC), and multilayer perceptron (MLP). …”
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Real Time 3D Internal Building Directory Map by Zi Yang Chia, Pey Yun Goh

“…These results outperformed the other models, i.e., K-Nearest Neighbors (KNN) and Support Vector Machine (SVM).…”
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A novel deep learning approach for investigating liquid fuel injection in combustion system by Syed Azeem Inam, Abdullah Ayub Khan, Noor Ahmed, Tehseen Mazhar, Tariq Shahzad, Sunawar Khan, Mamoon M. Saeed, Habib Hamam

“…Linear Regression, Polynomial Regression, and Support Vector Regressor are found to be the least-performing algorithms. …”
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