Modeling the Current Suitable Habitat Range of the Yellow-Bellied Gecko (<i>Hemidactylus flaviviridis</i> Rüppell, 1835) in Iran by Saman Ghasemian Sorboni, Mehrdad Hadipour, Narina Ghasemian Sorboni

“…We achieved this by combining four machine learning algorithms: Random Forest (RF), the Support Vector Machine (SVM), Maximum Entropy (Maxent), and the Generalized Linear Model (GLM). …”
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Artificial Intelligence-Driven Biological Age Prediction Model Using Comprehensive Health Checkup Data: Development and Validation Study by Chang-Uk Jeong, Jacob S Leiby, Dokyoon Kim, Eun Kyung Choe

“…Our model incorporated 27 clinical factors and employed machine learning algorithms, including linear regression, least absolute shrinkage and selection operator, ridge regression, elastic net, random forest, support vector machine, gradient boosting, and K-nearest neighbors. …”
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A double-layer ensemble framework for rubber plantation mapping using multi-source data in the google earth engine: a case study of the southwestern border region of China by Hui Wang, Jie Li, Jinliang Wang, Yuncheng Deng, Shupeng Gao, Jing Zou, An Chen, Haichao Xu

“…This layer utilizes five machine learning algorithms, namely Random Forest, Maximum Entropy Model, Gradient Tree Boosting, Support Vector Machine, and Classification and Regression Tree, to construct the corresponding PFT-EMs. …”
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Predictive Model to Analyse Real and Synthetic Data for Learners' Performance Prediction Using Regression Techniques by SHABNAM ARA S.J, Tanuja R, Manjula S.H

“…This study presents an empirical comparison of real, synthetic, and mixed (real + synthetic) data sets in forecasting learner performance, deploying an array of regression-based ML algorithms, including Random Forest, Gradient Boosting, XG Boost, K-nearest Neighbor, and Support Vector Regression. …”
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Risk Factors for Gastrointestinal Bleeding in Patients With Acute Myocardial Infarction: Multicenter Retrospective Cohort Study by Yanqi Kou, Shicai Ye, Yuan Tian, Ke Yang, Ling Qin, Zhe Huang, Botao Luo, Yanping Ha, Liping Zhan, Ruyin Ye, Yujie Huang, Qing Zhang, Kun He, Mouji Liang, Jieming Zheng, Haoyuan Huang, Chunyi Wu, Lei Ge, Yuping Yang

“…Propensity score matching was adjusted for demographics, and the Boruta algorithm identified key predictors. A total of 7 ML algorithms—logistic regression, k-nearest neighbors, support vector machine, decision tree, random forest (RF), extreme gradient boosting, and neural networks—were trained using 10-fold cross-validation. …”
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Multiobjective Optimization of Turbine Coolant Collection/Distribution Plenum Based on the Surrogate Model by Junsheng Chai, Zhenyu Wang, Xuanling Zhao, Chunhua Wang

“…Based on these data sampling, least square support vector machine (LS-SVM) was used for the surrogate model, and a kind of chaotic optimization algorithms was used for searching for the Pareto solution set. …”
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Applying an optimized low risk model for fast history matching in giant oil reservoir by Mojtaba karimi, Ali Mortazavi, Mohammad Ahmadi

“…In this paper the latest approaches for automated history matching (AHM) were applied to a real brown field with 14 active wells with multiple responses (production rate, bottom hole pressure and well block pressure) located in south part of Iran. Modified support vector machine was employed to create proxy model in which 44 model parameters were incorporated based on design of experimental. …”
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An Efficient and Fast Model Reduced Kernel KNN for Human Activity Recognition by Zongying Liu, Shaoxi Li, Jiangling Hao, Jingfeng Hu, Mingyang Pan

“…With accumulation of data and development of artificial intelligence, human activity recognition attracts lots of attention from researchers. Many classic machine learning algorithms, such as artificial neural network, feed forward neural network, K-nearest neighbors, and support vector machine, achieve good performance for detecting human activity. …”
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Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives by Marco Cascella, Daniela Schiavo, Arturo Cuomo, Alessandro Ottaiano, Francesco Perri, Renato Patrone, Sara Migliarelli, Elena Giovanna Bignami, Alessandro Vittori, Francesco Cutugno

“…Concerning methods, early studies were conducted by machine learning algorithms such as support vector machine, decision tree, and random forest classifiers. …”
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Identification of the Optimal Model for the Prediction of Diabetic Retinopathy in Chinese Rural Population: Handan Eye Study by Shanshan Jin, Xu Zhang, Hanruo Liu, Jie Hao, Kai Cao, Caixia Lin, Mayinuer Yusufu, Na Hu, Ailian Hu, Ningli Wang

“…Methods. Five algorithms, including multivariable logistic regression (MLR), classification and regression trees (C&RT), support vector machine (SVM), random forests (RF), and gradient boosting machine (GBM), were used to establish DR prediction models with HES data. …”
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Data-driven modeling of the Yld2000 yield criterion and its efficient application in numerical simulation by Xiaomin Zhang, Jianzhong Mao, Zhi Cheng

“…Regression models for the yield stress and its first-order derivatives based on the Yld2000–2d yield criterion are developed using several machine learning algorithms, including Random Forest (RF), Multilayer Perceptron (MLP), Histogram-Based Gradient Boosting (HGB), and Support Vector Machine (SVM). …”
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Predicting Mesothelioma Using Artificial Intelligence: A Scoping Review of Common Models and Applications by Malihe Ram MS, Mohammad Reza Afrash PhD, Khadijeh Moulaei PhD, Erfan Esmaeeli, Mohadeseh Sadat Khorashadizadeh, Ali Garavand PhD, Parastoo Amiri PhD, Azam Sabahi PhD

“…Conclusion Artificial intelligence, particularly machine learning models such as neural networks, decision trees, support vector machines, and random forests, holds promise in predicting and managing mesothelioma, potentially enhancing early detection and improving patient outcomes.…”
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Establishment of Hyperspectral Prediction Model of Water Content in Anshan-Type Magnetite by Xiaoxiao XIE, Yang BAI, Jiuling ZHANG, Yuna JIA

“…In order to further improve the prediction ability of the model, the competitive adaptive reweighting method (CARS) was used to optimize the characteristic band, and a prediction model was established by combining random forest regression (RFR), least squares support vector regression (LSSVR) and particle swarm optimization least squares support vector regression (PSO-LSSVR). …”
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Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers by S. B. G. Tilak Babu, Ch Srinivasa Rao

“…The paper aims to present copy-move forgery detection algorithms with the help of advanced feature descriptors, such as local ternary pattern, local phase quantization, local Gabor binary pattern histogram sequence, Weber local descriptor, and local monotonic pattern, and classifiers such as optimized support vector machine and optimized NBC. …”
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Variational Autoencoder Based Anomaly Detection in Large-Scale Energy Storage Power Stations by Tuo Ji, Pinghu Xu, Dongliang Guo, Lei Sun, Kangji Ma, Yanan Wang, Xuebing Han

“…Compared to classical machine learning algorithms such as Isolation Forest and Support Vector Machine, the detection performance of the VAE-based model demonstrates superiority, indicating its practical value and research significance.…”
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Fault Diagnosis Method for Main Pump Motor Shielding Sleeve Based on Attention Mechanism and Multi-Source Data Fusion by Nengqing Liu, Xuewei Xiang, Hui Li, Zhi Chen, Peng Jiang

“…By comparing it to methods such as the one-dimensional convolutional neural network (1D-CNN), Bagging Ensemble Learning, Random Forest, and Support Vector Machine (SVM), it was found that for the simulation data and experimental data, the accuracy of the AM-MSMDF-CNN is 5–10% and 10–15% higher than that of the other methods, demonstrating the superiority of the method proposed in this paper.…”
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The Fermentation Degree Prediction Model for Tieguanyin Oolong Tea Based on Visual and Sensing Technologies by Yuyan Huang, Jian Zhao, Chengxu Zheng, Chuanhui Li, Tao Wang, Liangde Xiao, Yongkuai Chen

“…In this study, using Tieguanyin oolong tea as the research object, features including the tea water loss rate, aroma, image color, and texture were obtained using weight sensors, a tin oxide-type gas sensor, and a visual acquisition system. Support vector regression (SVR), random forest (RF) machine learning, and long short-term memory (LSTM) deep learning algorithms were employed to establish models for assessing the fermentation degree based on both single features and fused multi-source features, respectively. …”
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Evaluation of Shelf Life Prediction for Broccoli Based on Multispectral Imaging and Multi-Feature Data Fusion by Xiaoshuo Cui, Xiaoxue Sun, Shuxin Xuan, Jinyu Liu, Dongfang Zhang, Jun Zhang, Xiaofei Fan, Xuesong Suo

“…The physicochemical parameters for broccoli shelf life were predicted using three methods: support vector regression (SVR), random forest classification (RF), and 2D convolutional neural network (2D-CNN) models. …”
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Rotor Location During Atrial Fibrillation: A Framework Based on Data Fusion and Information Quality by Miguel A. Becerra, Diego H. Peluffo-Ordoñez, Johana Vela, Cristian Mejía, Juan P. Ugarte, Catalina Tobón

“…Fuzzy inference was applied for situation and risk assessment, followed by IQ mapping using a support vector machine by level. Finally, the IQ criteria were optimized through a particle swarm optimization algorithm. …”
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Prediction of the Quality of Anxi Tieguanyin Based on Hyperspectral Detection Technology by Tao Wang, Yongkuai Chen, Yuyan Huang, Chengxu Zheng, Shuilan Liao, Liangde Xiao, Jian Zhao

“…The characteristic wavelengths were extracted via principal component analysis (PCA), competitive adaptive reweighted sampling (CARS), and the successive projection algorithm (SPA). The contents of free amino acid and tea polyphenol in Tieguanyin tea were predicted by the back propagation (BP) neural network, partial least squares regression (PLSR), random forest (RF), and support vector machine (SVM). …”
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