Artificial intelligence in predicting pathogenic microorganisms’ antimicrobial resistance: challenges, progress, and prospects by Yan Li, Xiaoyan Cui, Xiaoyan Yang, Guangqia Liu, Juan Zhang

“…Subsequently, we highlight the main AI and ML models used in resistance prediction, including but not limited to Support Vector Machines, Random Forests, and Deep Learning networks. …”
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A warning model for predicting patient admissions to the intensive care unit (ICU) following surgery by Li Li, Hongye He, Linjun Xiang, Yongxiang Wang

“…Subsequently, the effectiveness of logistic regression, random forest, support vector machine, and multi-layer perceptron algorithms was compared using ROC curves. …”
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An improved multiclass classification of acute lymphocytic leukemia using enhanced glowworm swarm optimization by Saranya N, Kalamani M

“…Popular classifiers -Decision tree, Random Forest, Multi-Layer Perceptron, Naive Bayes and Linear, Polynomial, Radial basis function, sigmoid kernels of Support Vector Machine were used for multiclass classification. …”
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A novel deep learning-based spider wasp optimization approach for enhancing brain tumor detection and physical therapy prediction by Suleiman Daoud, Ahmad Nasayreh, Khalid M.O. Nahar, Wlla k. Abedalaziz, Salem M. Alayasreh, Hasan Gharaibeh, Ayah Bashkami, Amer Jaradat, Sultan Jarrar, Hammam Al-Hawamdeh, Absalom E. Ezugwu, Raed Abu Zitar, Aseel Smerat, Vaclav Snasel, Laith Abualigah

“…We compared the proposed SWO+KNN model with other deep learning architectures such as MobileNetV2, Resnet50V2, and machine learning algorithms such as KNN, Support Vector Machine SVM, and Random Forest (RF). …”
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Comparison of Satellite-based PM2.5 Estimation from Aerosol Optical Depth and Top-of-atmosphere Reflectance by Heming Bai, Zhi Zheng, Yuanpeng Zhang, He Huang, Li Wang

“…In this study, satellite observations of TOA reflectance and AOD from the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite in 2016 over Yangtze River Delta (YRD) and meteorological data are used to estimate hourly PM2.5 based on four different machine learning algorithms (i.e., random forest, extreme gradient boosting, gradient boosting regression, and support vector regression). …”
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COMPARATIVE ANALYSIS OF CLASSIFICATION MODELS FOR DETERMINING THE QUALITY OF WINE BY ITS CHEMICAL COMPOSITION by Vladimir S. Repkin, Artemy V. Li, Grigory Yu. Semenov, Nikita I. Sermavkin, Alexander S. Kovalenko, Nikolai S. Egoshin

“…Objects: classification models, including the support vector machine, decision tree, random forest algorithm, neural network, multiple regression and their application for automated wine quality assessment. …”
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Multi-Objective Parameter Optimization of Rotary Screen Coating Process for Structural Plates in Spacecraft by Yanhui Guo, Yanpeng Chen, Peibo Li, Xinfu Chi, Yize Sun

“…In order to solve the optimization problem, 108 sets of process experiments were designed, and then the experimental data were used to train a Back Propagation Neural Network (BPNN), a Least Squares Support Vector Machine (LSSVM), and Random Forest (RF) to obtain the best prediction model for the process parameters. …”
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Quantitative Prediction of Protein Content in Corn Kernel Based on Near-Infrared Spectroscopy by Chenlong Fan, Ying Liu, Tao Cui, Mengmeng Qiao, Yang Yu, Weijun Xie, Yuping Huang

“…Near-infrared spectral data from different varieties of maize grain powder were collected, and quantitative analysis of protein content was conducted using Partial Least Squares Regression (PLSR), Support Vector Machine (SVM), and Extreme Learning Machine (ELM) models. …”
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Enhanced dry SO₂ capture estimation using Python-driven computational frameworks with hyperparameter tuning and data augmentation by Robert Makomere, Hilary Rutto, Alfayo Alugongo, Lawrence Koech, Evans Suter, Itumeleng Kohitlhetse

“…The data-driven models executed were multilayer perceptron, support vector regressor, random forest, categorical boosting, and light gradient boosting machine. …”
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Thirty-day mortality risk prediction for geriatric patients undergoing non-cardiac surgery in the surgical intensive care unit by Mengke Ma, Jiatong Liu, Caiyun Li, Yingxue Chen, Huishu Jia, Aijie Hou, Hongzeng Xu

“…Five predictive models were established: categorical boosting (CatBoost), logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM). External validation was performed utilizing data from 153 patients in the MIMIC-III database. …”
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Analysis of Microbiome for AP and CRC Discrimination by Alessio Rotelli, Ali Salman, Leandro Di Gloria, Giulia Nannini, Elena Niccolai, Alessio Luschi, Amedeo Amedei, Ernesto Iadanza

“…Subsequently, the synthesised data quality was evaluated using a logistic regression model in parallel with an optimised support vector machine algorithm (polynomial kernel). …”
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Data driven models for predicting pH of CO2 in aqueous solutions: Implications for CO2 sequestration by Mohammad Rasool Dehghani, Moein Kafi, Hamed Nikravesh, Maryam Aghel, Erfan Mohammadian, Yousef Kazemzadeh, Reza Azin

“…To fill this research gap, this study developed 15 models comprising five machine learning methods: regression trees, support vector regression, Gaussian process regression, bagged trees, and boosted trees, and three optimization algorithms: random search, grid search, and Bayesian optimization. …”
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Radiomics in pediatric brain tumors: from images to insights by Pranjal Rai, Sabha Ahmed, Abhishek Mahajan

“…Recent studies combining radiomics with machine learning algorithms — including support vector machines, random forests, and deep learning CNNs — have demonstrated promising performance, with AUCs ranging from 0.75 to 0.98 for tumor classification and 0.77 to 0.88 for molecular subgroup prediction, across cohorts from 50 to over 450 patients, with internal cross-validation and external validation in some cases. …”
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Concrete Dam Deformation Prediction Model Based on Attention Mechanism and Deep Learning by ZHANG Hongrui, CAO Xin, JIANG Chao, ZU Anjun, XU Mingxiang

“…Traditional statistical methods based on hydrostatic-season-time (HST) theory, while having clear physical meanings and being easy to implement, are limited by their inherent linear assumptions, resulting in constrained prediction accuracy. Machine learning models such as random forest, support vector regression, and extreme learning machine (ELM) extend statistical approaches but still lack the ability to establish temporal dependencies due to their static input-output mapping relationships. …”
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Data-driven thrust prediction in applied-field magnetoplasmadynamic thrusters for space missions using artificial intelligence-based models by Tarik Pinaffo Almeida, Shahin Alipour Bonab, Mohammad Yazdani-Asrami

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AI-Based model for site-selecting earthquake emergency shelters by Amirmasoud Amiran, Behrouz Behnam, Sanaz Seyedin

“…Support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), gaussian processes classifier (GPC), and artificial neural network (ANN) methods are used to develop the model here. …”
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Evaluation of the Decision Tree Model for Air Condition Classification on the Global Air Pollution Dataset by Cindy Dinda Sabella, Yoga Pristyanto

“…Decision Tree, Random Forest, and Support Vector Machine (SVM) algorithms are applied to perform classification, with a focus on hyperparameter tuning to increase model accuracy. …”
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Sex estimation with ensemble learning: an analysis using anthropometric measurements of piriform aperture by Muhammed Emin Parlak, Yasin Etli, Murat Beyhan, Kubilay Kanat, Hüseyin Alper Kızıloğlu

“…After sex estimation was performed using discriminant analysis, K-nearest neighbor, Gaussian Naive Bayes, multilayer perceptron neural networks, decision trees, support vector machines, and random forest algorithms, a random forest model that accepted the results of these seven methods as predictors was created, and sex estimation was performed again with ensemble learning. …”
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Multi-Target Mechanism of Compound Qingdai Capsule for Treatment of Psoriasis: Multi-Omics Analysis and Experimental Verification by Qiao Y, Li C, Chen C, Wu P, Yang Y, Xie M, Liu N, Gu J

“…The ingredients of CQC were detected by UPLC-MS/MS, and target prediction was performed by systems pharmacology. Machine learning, including Lasso regression, Random Forest, and Support Vector Machine (SVM), were utilized to screen core targets of psoriasis. …”
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Evidential Reasoning Approach for Predicting Popularity of Instagram Posts by L. Rivadeneira, I. Loor

“…MAKER’s performance is compared with decision tree (DT), support vector machine (SVM), and k-nearest neighbours (KNN) algorithms. …”
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