Bi-modal ultrasound radiomics and habitat analysis enhanced the pre-operative prediction of axillary lymph node burden in patients with early-stage breast cancer by Jing Xu, Pan Qi, Xiaoyan Ou, Qiaoxin Zhong, Zhen-Wen Chen, Yin Wang, Aijiao Yi, Bin Wang

“…These modality-specific features were then combined. Eleven machine learning models were used to build models, including support vector machines (SVM), k-nearest neighbor (KNN), RandomForest (RF), ExtraTrees, XGBoost, light gradient boosting machine (LGB), NaiveBayes, AdaBoost, GradientBoosting, LR and MLP. …”
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Optimization of classifier ensemble diversity by Leif E. Peterson

“…Diversity was optimized using a genetic algorithm and particle swarm optimization. Classifiers with the greatest contribution to ensemble diversity were learning vector quantization, naïve Bayes classifier, and supervised neural gas, whereas the supervised artificial neural network (SANN) and support vector machines (SVM) classifiers were among the least informative for diversity. …”
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Developing a Transparent Anaemia Prediction Model Empowered With Explainable Artificial Intelligence by Muhammad Sajid Farooq, Muhammad Hassan Ghulam Muhammad, Oualid Ali, Zahid Zeeshan, Muhammad Saleem, Munir Ahmad, Sagheer Abbas, Muhammad Adnan Khan, Taher M. Ghazal

“…The proposed model utilizes machine learning algorithms such as Support Vector Machine (SVM), Decision Trees, K-Neighbors Classifier, and Gradient Boosting Classifier, enhanced with Explainable AI (XAI) techniques like SHAP and LIME. …”
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Application of Data Mining Technology on Surveillance Report Data of HIV/AIDS High-Risk Group in Urumqi from 2009 to 2015 by Dandan Tang, Man Zhang, Jiabo Xu, Xueliang Zhang, Fang Yang, Huling Li, Li Feng, Kai Wang, Yujian Zheng

“…The k-nearest neighbors algorithm came out second, with 91.5258% diagnostic accuracy on MSM dataset, 96.3083% diagnostic accuracy on FSW dataset, and 90.8287% diagnostic accuracy on IDU dataset, followed by support vector machine (94.0182%, 98.0369%, and 91.3571%). …”
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Data Mining Classification Techniques for Diabetes Prediction by Hindreen Rashid Abdulqadir, Adnan Mohsin Abdulazeez, Dilovan Assad Zebari

“…RF offers 75,7813 greater precisions than Support Vector Machine (SVM).and may assist medical professionals in making care decisions. …”
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Tracking a Subset of Skeleton Joints: An Effective Approach towards Complex Human Activity Recognition by Muhammad Latif Anjum, Stefano Rosa, Basilio Bona

“…The position of the selected joints is tracked for the duration of the activity and is used to construct feature vectors for each activity. Once the feature vectors have been constructed, we use a Support Vector Machines (SVM) multiclass classifier for training and testing the algorithm. …”
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Research on the Inversion Method of Dust Content on Mining Area Plant Canopies Based on UAV-Borne VNIR Hyperspectral Data by Yibo Zhao, Shaogang Lei, Xiaotong Han, Yufan Xu, Jianzhu Li, Yating Duan, Shengya Sun

“…Various regression models, including extreme learning machine (ELM), random forest (RF), partial least squares regression (PLSR), and support vector machine (SVM), were utilized to establish dust inversion models. …”
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Enhanced Multilinear PCA for Efficient Image Analysis and Dimensionality Reduction: Unlocking the Potential of Complex Image Data by Tianyu Sun, Lang He, Xi Fang, Liang Xie

“…In image classification and face recognition experiments, EMPCA significantly enhances classifier efficiency, achieving comparable or superior accuracy to algorithms such as Support Vector Machines (SVMs). Additionally, EMPCA preprocessing exploits latent information within tensor structures, leading to improved segmentation performance. …”
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A novel classification method for balance differences in elite versus expert athletes based on composite multiscale complexity index and ranking forests. by Yuqi Cheng, Dawei Wu, Ying Wu, Youcai Guo, Xinze Cui, Pengquan Zhang, Jie Gao, Yanming Fu, Xin Wang

“…Data were recorded during 30-second trials on both soft and hard support surfaces, with eyes open and closed. We calculated the CMCI and used four machine learning algorithms-Logistic Regression, Support Vector Machine(SVM), Naive Bayes, and Ranking Forest-to combine these features and assess each participant's balance ability. …”
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Effective Parallel Processing Social Media Analytics Framework by Ravindra Kumar Singh, Harsh Kumar Verma

“…Moreover, this research additionally provides a comparison of Support Vector Machines (SVM), Light GBM (LGBM) and Long Short Term Memory (LSTM) supervised machine learning techniques for sentiment analysis and concluded LGBM is the most effective model.…”
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H-DSAE: a hybrid technique to recognize heart disease by K. Uma Maheswari, A. Valarmathi

“…H-DSAE technique utilize Deep Belief Network (DBN), Support Vector Machine (SVM), and Stacked Auto-Encoder (SAE). …”
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Using A One-Class SVM To Optimize Transit Detection by Jakob Roche

“…While having approximately 5% lower accuracy than CNNs, the results of this study show that One-Class Support Vector Machines (SVMs) can be fitted to data up to 84 times faster than simple CNNs and make predictions over 3 times faster on the same datasets using the same hardware. …”
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GreyWolfLSM: an accurate oil spill detection method based on level set method from synthetic aperture radar imagery by Nastaran Aghaei, Gholamreza Akbarizadeh, Abdolnabi Kosarian

“…The oil-suspicious areas are also discriminated by the supported vector machine (SVM) classifier, and their boundaries are applied as an initial contour to a new hierarchical region-based level-set method (HRLSM). …”
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Improving thyroid disorder diagnosis via innovative stacking ensemble learning model by Ayesha Hassan, Shabana Ramzan, Ali Raza, Muhammad Munwar Iqbal, Aseel Smerat, Norma Latif Fitriyani, Muhammad Syafrudin, Seung Won Lee

“…The synthetic minority over-sampling technique technique is utilized to overcome the problem of class imbalance. Five advanced machine learning (ML) algorithms, logistic regression, support vector machine, decision tree, random forest, and gradient boosting are employed to develop predictive models. …”
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Nasopharyngeal Carcinoma Subtype Discovery via Immune Cell Scores from Tumor Microenvironment by Yanbo Sun, Yun Liu, Hanqi Chu

“…The hub genes were selected from subtype-specific genes by bioinformatics analysis. Machine learning models, including random forest (RF) and support vector machine (SVM) algorithms, were constructed to predict the immune subtype. …”
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AIoT-Driven Human Activity Recognition for Versatile Framework on Multipurpose Applications by Nanik Triwahyuni, Eni Wardihani, Aminuddin Rizal, Samuel Beta, Ricky Sambora, Rindang Oktaviani

“…The five models include Naïve Bayes, Support Vector Machine (SVM), AdaBoost, ZeroR, and Random Forest. …”
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Aerodynamic Drag Coefficient Prediction of a Spike Blunt Body Based on K-Nearest Neighbors by Jonathan Arturo Sánchez Muñoz, Christian Lagarza-Cortés, Jorge Ramírez-Cruz

“…In the case of CFD, many models have been explored, such as support vector regression, ensemble methods, and artificial neural networks. …”
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Soil Parameter Inversion in Dredger Fill Strata Using GWO-MLSSVR for Deep Foundation Pit Engineering by Changrui Chen, Sifan Li, Jinbi Ye, Fangjian Chen, Yibin Wu, Jin Yu, Yanyan Cai, Jinna Lin, Xianqi Zhou

“…This study presents an inverse analysis method using Multioutput Least-Squares Support Vector Regression (MLSSVR) optimized by the Gray Wolf Optimization (GWO) algorithm to invert key parameters of the Hardening Soil (HS) model. …”
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A multimodal approach for ADHD with coexisting ASD detection for children by Jungpil Shin, Sota Konnai, Md. Maniruzzaman, Yoichi Tomioka, Yong Seok Hwang, Akiko Megumi, Akira Yasumura

“…The potentiality of these features was evaluated using Sequential Forward Floating Selection (SFFS)-based algorithm and support vector machine (SVM) was employed to evaluate the performance of ZL and PL tasks. …”
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Implementation of laser-light backscattering imaging for authentication of the geographic origin of Indonesia region citrus by Muhammad Achirul Nanda, S. Rosalinda, Rahmat Budiarto, Inna Novianty, Taufik Ibnu Salim, Pradeka Brilyan Purwandoko, Dimas Firmanda Al Riza

“…Furthermore, a combination of the gray-level co-occurrence matrix (GLCM) method and support vector machine (SVM) algorithm was applied to extract texture features and build a classification model, respectively. …”
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