Machine Learning-Based Prediction of Postoperative Pneumonia Among Super-Aged Patients With Hip Fracture by Tang M, Zhang M, Dang Y, Lei M, Zhang D

“…The majority of patients were used to train models, which was tuned using a series of algorithms, including decision tree (DT), random forest (RF), extreme gradient boosting machine (eXGBM), support vector machine (SVM), neural network (NN), and logistic regression (LR).Results: The incidence of postoperative pneumonia was 7.2% (40/555). …”
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A Novel Algorithm for Feature Level Fusion Using SVM Classifier for Multibiometrics-Based Person Identification by Ujwalla Gawande, Mukesh Zaveri, Avichal Kapur

“…A support-vector-machine-based learning algorithm is used to train the system using the feature extracted. …”
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Diagnosing prostate cancer in the PSA gray zone through machine learning and transrectal ultrasound video by Qin Wu, Chengyi Wu, Maoliang Zhang, Jie Yang, Junxiang Zhang, Yun Jin, Yanhong Du, Xingbo Sun, Liyuan Jin1, Kai Wang, Zhengbiao Hu, Xiaoyang Qi1, Jincao Yao, Zhengping Wang, Dong Xu

“…The selected features were employed to construct radiomics models based on four machine learning algorithms support vector machine (SVM), random forest (RF), adaptive boosting (ADB) and gradient boosting machine (GBM). …”
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Red Fox Optimization-Based Estimation Algorithms for Splitting Tensile Strength of Basalt Fiber Reinforced Concrete by Xiao Wu, Daoyong Zhu, Qin Yan

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Methodology for Estimating the Cost of Construction Equipment Based on the Analysis of Important Characteristics Using Machine Learning Methods by Nataliya Boyko, Oleksii Lukash

“…The study built and analyzed models using machine learning methods (linear and polynomial regression, decision trees, random forest, support vector machine, and neural network). …”
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Enhanced multivariate data fusion and optimized algorithm for comprehensive quality profiling and origin traceability of Chinese jujube by Peng Chen, Xiaoli Wang, Rao Fu, Xiaoyan Xiao, Yu Li, Tulin Lu, Tao Wang, Qiaosheng Guo, Peina Zhou, Chenghao Fei

“…The multivariate statistical analysis and support vector machine (SVM) classification were also combined to develop a novel artificial intelligence algorithm. …”
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Differences in emotional expression among college students: a study on integrating psychometric methods and algorithm optimization by Xiaozhu Chen

“…In order to improve the precision of data analysis, random forests, support vector machines, and neural network machine learning algorithms were applied, and the variance analysis was used to calculate and compare the emotional differences of different genders and academic backgrounds in different grades. …”
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Improved Black-Winged Kite Algorithm with Multi-Strategy Optimization for Identifying <i>Dendrobium huoshanense</i> by Chaochuan Jia, Ting Yang, Maosheng Fu, Yu Liu, Xiancun Zhou, Zhendong Huang, Fang Wang, Wenxia Li

“…Furthermore, the support vector machine (SVM) model was optimized by BKAIM for grade identification of <i>Dendrobium huoshanense</i> based on near-infrared spectral data, thereby confirming its effectiveness in practical applications.…”
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Enhancing flood susceptibility mapping in Meghna River basin by introducing ensemble Naive Bayes with stacking algorithms by Abu Reza Md. Towfiqul Islam, Md. Uzzal Mia, Nourin Akter Nova, Rabin Chakrabortty, Md. Sanjid Islam Khan, Bonosri Ghose, Subodh Chandra Pal, A. B. M. Mainul Bari, Edris Alam, Md Kamrul Islam, Mohammed Ali Alshehri, Hazem Ghassan Abdo, Romulus Costache

“…This article intends to assess flood susceptibility mapping in Meghna River basin (MRB) and identified flood susceptible regions using three benchmark models including random forest (RF), support vector machine (SVM) and bagging with Naïve Bayes (NB) stacking ensemble algorithms (e.g. …”
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Predicting central lymph node metastasis in papillary thyroid microcarcinoma: a breakthrough with interpretable machine learning by Weijun Zhou, Lijuan Li, Xiaowen Hao, Lanying Wu, Lifu Liu, Binyu Zheng, Yangzheng Xia, Yong Liu

“…Eight ML algorithms, namely Decision Tree, Random Forest (RF), K-nearest neighbors, Support vector machine, Extreme Gradient Boosting, Naive Bayes, Logistic regression, and Light Gradient Boosting machine, were developed for the prediction of CLNM. …”
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Komparasi Machine Learning Berbasis Pso Untuk Prediksi Tingkat Keberhasilan Belajar Berbasis E-Learning by Elin Panca Saputra, Siti Nurajizah, Mawadatul Maulidah, Nadiyah Hidayati, Taufik Rahman

“…Then the application key using the PSO-based Naïve Bayes (NB) algorithm can get performance results with a weight of 94.40% and an-AUC number of 94.50%, then the PSO-based Support Vectore Machine (SVM) Algorithm with a performance result of 88.20 and an AUC value of 91.10%, and Artificial Neural Network-(NN) based on Particle Swarm Optimizatio (PSO) produces an accuracy performance score with a weight of 99.20% and an accuracy value of 98.50%. …”
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Recognition of Handwritten Persian Two-digit Numerals Using a Novel Hybrid SVM/HMM algorithm by Mahsa Aliakbarzadeh, Farbod Razzazi, Alireza Behrad

“…The proposed method is composed of a combinational structure of Support Vector Machines (SVM) and a Hidden Markov Models (HMM). …”
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Machine Learning-Based Modelling and Predictive Maintenance of Turning Operation under Cooling/Lubrication for Manufacturing Systems by Gurpreet Singh, Jothi Prabha Appadurai, Varatharaju Perumal, K. Kavita, T. Ch Anil Kumar, DVSSSV Prasad, A. Azhagu Jaisudhan Pazhani, K. Umamaheswari

“…This current work focuses on developing the machine learning algorithm by using three different types of regression processes, namely, polynomial regression process (PR), support vector regression (SVR), and gaussian process regression (GPR). …”
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Prediction of water inrush from coal seam floor based on machine learning with small sample data by Chenxi LI, Haifeng LU

“…The influence rule of sample number on prediction accuracy was discussed, and the comparison study was conducted with the commonly used particle swarm, support vector machine, BP neural network, random forest and convolutional neural network. …”
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Predicting Intensive Care Unit Admissions in COVID-19 Patients: An AI-Powered Machine Learning Model by A. M. Mutawa

“…We employed various ML classifiers, including support vector machines (SVM). The SVM model surpasses all other models in terms of precision (0.99) and area under curve (AUC, 0.91). …”
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Wearable sensor-based fall detection for elderly care using ensemble machine learning techniques by Ch Gangadhar, P Pavithra Roy, R. Dinesh Kumar, Janjhyam Venkata Naga Ramesh, S. Ravikanth, N. Akhila

“…Meanwhile, the research's offsite evaluation of a combined structure utilizing the Random Forest technique (RF), Supporting Vectors Machines (SVM), and available information were used to illustrate this idea. …”
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Improving detection of Parkinson’s disease with acoustic feature optimization using particle swarm optimization and machine learning by Elmoundher Hadjaidji, Mohamed Cherif Amara Korba, Khaled Khelil

“…Performance evaluation encompasses four classification algorithms: support vector machine, Gradient Boosting (GB), k-nearest neighbors (KNN), and Naïve Bayes. …”
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Non-destructive identification of commercial jerky types based on multi-band hyperspectral imaging with machine learning by Yuanxi Han, Liang Li, Siyuan Jiang, Pengpeng Sun, Wenliang Wu, Zhendong Liu

“…With hyperparameter optimization, Support Vector Machine with linear kernel had highest accuracy (89.29 % and 95.29 % in two bands). …”
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Generalizability of machine learning models for diabetes detection a study with nordic islet transplant and PIMA datasets by Dinesh Chellappan, Harikumar Rajaguru

“…Evaluated the performance of a system by using the following classifiers as Non-Linear Regression—NLR, Linear Regression—LR, Gaussian Mixture Model—GMM, Expectation Maximization—EM, Bayesian Linear Discriminant Analysis—BLDA, Softmax Discriminant Classifier—SDC, and Support Vector Machine with Radial Basis Function kernel—SVM-RBF classifier on two publicly available datasets namely the Nordic Islet Transplant Program (NITP) and the PIMA Indian Diabetes Dataset (PIDD). …”
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A Diagnostic Model of Breast Cancer Based on Digital Mammogram Images Using Machine Learning Techniques by Farouk A. K. Al-Fahaidy, Belal Al-Fuhaidi, Ishaq AL-Darouby, Faheem AL-Abady, Mohammed AL-Qadry, Abdurhman AL-Gamal

“…The followed step-by-step procedure of the proposed method is performed by passing the Mammographic Image Analysis Society (MIAS) through five steps of image preprocessing, image segmentation using seeded region growing (SRG) algorithm, feature extraction using different feature’s extraction classes, and important and effectiveness feature selection using the Sequential Forward Selection (SFS) technique, and finally, the Support Vector Machine (SVM) algorithm is used as a binary classifier in two classification levels. …”
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