Dry sliding tribological characteristics evaluation and prediction of TiB2-CDA/Al6061 hybrid composites exercising machine learning methods by Amit Aherwar, Anamika Ahirwar, Vimal Kumar Pathak

“…Additionally, scanning electron microscopy (SEM) was employed to examine dominant wear mechanisms under extreme wear conditions, revealing adhesion, abrasion, oxidation, and delamination as primary degradation processes. Furthermore, machine learning techniques, including Random Forest (RF), Support Vector Machines (SVM), Gaussian Process Regression (GPR), and Gradient Boosted Trees (GBTA), were leveraged to develop predictive models for wear loss and COF. …”
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Development and validation of machine learning models for osteoporosis prediction in chronic kidney disease patients: Data from National Health and Nutrition Examination survey by Hui Li, Ya Zhang, Chong Zhang

“…Separate models for male and female CKD patients were developed using 59 potential predictors, with key variables selected through the Least Absolute Shrinkage and Selection Operator and Boruta algorithms. Seven single-base models, including logistic regression, support vector machine, extreme gradient boosting, K-nearest neighbors, gradient boosting decision tree, random forest (RF), and neural network, were trained. …”
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Optimized Application of CGA-SVM in Tight Reservoir Horizontal Well Production Prediction by Chao Wang, Ruogu Wang, Yuhan Lin, Jiafei Zhang, Xiaofei Xie, Zidan Zhao, Yunlin Xu

“…In this paper, chaotic genetic algorithm is used to optimize the traditional support vector machine, and the problems of slow convergence and local convergence are solved by chaotic genetic algorithm, and an improved support vector machine horizontal well production prediction method is established. …”
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Real defect partial discharge identification method for power cables joints based on integrated PJS-M and GA-SVM algorithm with multi-source fusion by Ling-Xuan Zhang, Yi-Yang Zhou, Shen-Jiong Yao, Jia-Luo Chai, Ying-Jing Chen, Zhou-Sheng Zhang

“…These features were used to train a novel Genetic Algorithm Weighted Support Vector Machine (GAW-SVM) model, which incorporates an adaptive PJS-M weighting coefficient and a correlation-analysis–based dynamic correction mechanism into the conventional GA-SVM framework. …”
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Development and validation of an interpretable machine learning model for retrospective identification of suspected infection for sepsis surveillance: a multicentre cohort studyRes... by Renée A.M. Tuinte, Luuk P.J. Smolenaers, Bram T. Knoop, Konstantin Föhse, Tamar J. van der Aart, Hjalmar R. Bouma, Mihai G. Netea, Katrijn Van Deun, Jaap ten Oever, Jacobien J. Hoogerwerf

“…Seven ML methods, including gradient boosting, random forest, logistic regression, decision trees, support vector machines, K nearest neighbours and stochastic gradient descent, were trained to identify sepsis with manual chart review as reference standard. …”
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Advancement of Artificial Intelligence in Cost Estimation for Project Management Success: A Systematic Review of Machine Learning, Deep Learning, Regression, and Hybrid Models by Md. Mahfuzul Islam Shamim, Abu Bakar bin Abdul Hamid, Tadiwa Elisha Nyamasvisva, Najmus Saqib Bin Rafi

“…Key AI techniques, including support vector machines (SVMs) (7.90% of studies), decision trees, and gradient-boosting models, offer substantial improvements in cost prediction and resource optimization. …”
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Machine learning analysis of gene expression profiles of pyroptosis-related differentially expressed genes in ischemic stroke revealed potential targets for drug repurposing by Changchun Hei, Xiaowen Li, Ruochen Wang, Jiahui Peng, Ping Liu, Xialan Dong, P. Andy Li, Weifan Zheng, Jianguo Niu, Xiao Yang

“…Leveraging three distinct machine learning algorithms (LASSO, Random Forest, and Support Vector Machine), models were developed to differentiate between the Control and the IS patient samples. …”
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Machine learning's model-agnostic interpretability on the prediction of students' academic performance in video-conference-assisted online learning during the covid-19 pandemic by Eka Miranda, Mediana Aryuni, Mia Ika Rahmawati, Siti Elda Hiererra, Albert Verasius Dian Sano

“…The prediction model was developed using Random Forest (RF), Support Vector Machine (SVM), and Gaussian Naive Bayes (GNB). …”
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Mortality Risk Prediction in Patients With Antimelanoma Differentiation–Associated, Gene 5 Antibody–Positive, Dermatomyositis–Associated Interstitial Lung Disease: Algorithm Develo... by Hui Li, Ruyi Zou, Hongxia Xin, Ping He, Bin Xi, Yaqiong Tian, Qi Zhao, Xin Yan, Xiaohua Qiu, Yujuan Gao, Yin Liu, Min Cao, Bi Chen, Qian Han, Juan Chen, Guochun Wang, Hourong Cai

“…The primary endpoint was 3-month mortality due to all causes. Six ML algorithms (Extreme Gradient Boosting [XGBoost], logistic regression (LR), Light Gradient Boosting Machine [LightGBM], random forest [RF], support vector machine [SVM], and k-nearest neighbor [KNN]) were applied to construct and evaluate the model. …”
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Integrating LoRaWAN sensor network and machine learning models to classify beef cattle behavior on arid rangelands of the southwestern United State by Andres Perea, Sajidur Rahman, Huiying Chen, Andrew Cox, Shelemia Nyamuryekung’e, Mehmet Bakir, Huping Cao, Richard Estell, Brandon Bestelmeyer, Andres F. Cibils, Santiago A. Utsumi

“…Logistic regression, support vector machine, multilayer perceptron, XGBoost and random forest algorithms were trained and tested. …”
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A machine learning-based framework for predicting metabolic syndrome using serum liver function tests and high-sensitivity C-reactive protein by Bahareh Behkamal, Fatemeh Asgharian Rezae, Amin Mansoori, Rana Kolahi Ahari, Sobhan Mahmoudi Shamsabad, Mohammad Reza Esmaeilian, Gordon Ferns, Mohammad Reza Saberi, Habibollah Esmaily, Majid Ghayour-Mobarhan

“…The framework integrated diverse ML algorithms, including Linear Regression (LR), Decision Trees (DT), Support Vector Machine (SVM), Random Forest (RF), Balanced Bagging (BG), Gradient Boosting (GB), and Convolutional Neural Networks (CNNs). …”
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Enhancing liver disease diagnosis with hybrid SMOTE-ENN balanced machine learning models—an empirical analysis of Indian patient liver disease datasets by Ritu Rani, Garima Jaiswal, Nancy, Lipika, Shashi Bhushan, Fasee Ullah, Prabhishek Singh, Manoj Diwakar, Manoj Diwakar

“…Immediate action is necessary for timely diagnosis of the ailment before irreversible damage is done.MethodsThe work aims to evaluate some of the traditional and prominent machine learning algorithms, namely, Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Gaussian Naïve Bayes, Decision Tree, Random Forest, AdaBoost, Extreme Gradient Boosting, and Light GBM for diagnosing and predicting chronic liver disease. …”
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Machine-learning-based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data by N. Mandal, P. Das, K. Chanda, K. Chanda

“…Climate indices, like the Oceanic Niño Index and Dipole Mode Index, are selected as optimal predictors for a large number of grid cells globally, along with TWSAs from LSM outputs. The most effective machine learning (ML) algorithms among convolutional neural network (CNN), support vector regression (SVR), extra trees regressor (ETR) and stacking ensemble regression (SER) models are evaluated at each grid cell to achieve optimal reproducibility. …”
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A Closest Resemblance Classifier with Feature Interval Learning and Outranking Measures for Improved Performance by Nabil Belacel

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Data fusion-based improvements in empirical regression and machine learning for global daily ∼ 8 km resolution sea surface nitrate estimation and interpretation by Aifen Zhong, Difeng Wang, Fang Gong, Jingjing Huang, Zhuoqi Zheng, Xianqiang He, Qing Zhang, Qiankun Zhu

“…After adding SSN-related physical variables, high-accuracy regional empirical models are developed, with root mean square deviations (RMSDs) of 1.641, 2.701, 1.221, 1.298, and 2.379 μmol/kg for the studied regions. For the machine learning models, seven algorithms, namely, extremely randomized trees (ET), multilayer perceptron (MLP), stacking random forest (SRF), Gaussian process regression (GPR), support vector machine (SVM), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost) algorithms, were tested. …”
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Linguistic Markers of Pain Communication on X (Formerly Twitter) in US States With High and Low Opioid Mortality: Machine Learning and Semantic Network Analysis by ShinYe Kim, Winson Fu Zun Yang, Zishan Jiwani, Emily Hamm, Shreya Singh

“…Tweets from 2 high-opioid mortality states (Ohio and Florida) and 2 low opioid mortality states (South and North Dakota) were selected, resulting in 31,994 tweets from high-death states (HDS) and 750 tweets from low-death states (LDS). Six machine learning algorithms (random forest, k-nearest neighbor, decision tree, naive Bayes, logistic regression, and support vector machine) were applied to predict state-level opioid mortality risk based on linguistic features derived from Linguistic Inquiry and Word Count. …”
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Analisis Sentimen untuk Evaluasi Reputasi Merek Motor XYZ Berkaitan dengan Isu Rangka Motor di Twitter Menggunakan Pendekatan Machine Learning by Ferdian Maulana Akbar, Robby Hermansyah, Sofian Lusa, Dana Indra Sensuse, Nadya Safitri, Damayanti Elisabeth

“…We used sentiment analysis with word clouds, trend and distribution analysis, and compared five machine learning algorithms: Naïve Bayes, Decision Tree, Support Vector Machine, Logistic Regression, and Random Forest. …”
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Interpretable machine learning model for identification and risk factor of premature rupture of membranes (PROM) and its association with nutritional inflammatory index: a retrospe... by Meng Zheng, Xiaowei Zhang, Haihong Wang, Ping Yuan, Qiulan Yu

“…The research group adopted four machine learning algorithms: Extreme Gradient Boost (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF). …”
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A Comparison of Machine Learning-Based Models and a Simple Clinical Bedside Tool to Predict Morbidity and Mortality After Gastrointestinal Cancer Surgery in the Elderly by Barbara Frezza, Mario Cesare Nurchis, Gabriella Teresa Capolupo, Filippo Carannante, Marco De Prizio, Fabio Rondelli, Danilo Alunni Fegatelli, Alessio Gili, Luca Lepre, Gianluca Costa

“…In a multicenter analysis of 937 patients aged ≥65 years, the performance of various predictive models including Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), Stepwise Regression, K-Nearest Neighbors, Neural Network, and Support Vector Machine algorithms were evaluated. …”
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Development of machine learning models for predicting non-remission in early RA highlights the robust predictive importance of the RAID score-evidence from the ARCTIC study by Gaoyang Li, Shrikant S. Kolan, Franco Grimolizzi, Joseph Sexton, Giulia Malachin, Guro Goll, Tore K. Kvien, Tore K. Kvien, Nina Paulshus Sundlisæter, Manuela Zucknick, Siri Lillegraven, Espen A. Haavardsholm, Espen A. Haavardsholm, Bjørn Steen Skålhegg

“…The model employed a super learner algorithm that combine three base algorithms of elastic net, random forest and support vector machine. …”
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