Predicting climate-driven shift of the East Mediterranean endemic Cynara cornigera Lindl by Heba Bedair, Heba Bedair, Yehia Hazzazi, Asmaa Abo Hatab, Marwa Waseem A. Halmy, Mohammed A. Dakhil, Mohammed A. Dakhil, Mubaraka S. Alghariani, Mubaraka S. Alghariani, Mari Sumayli, A. El-Shabasy, Mohamed M. El-Khalafy

“…Our analysis involved inclusion of bioclimatic variables, in the SDM modeling process that incorporated five algorithms: generalized linear model (GLM), Random Forest (RF), Boosted Regression Trees (BRT), Support Vector Machines (SVM), and Generalized Additive Model (GAM).Results and discussionThe ensemble model obtained high accuracy and performance model outcomes with a mean AUC of 0.95 and TSS of 0.85 for the overall model. …”
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Optimizing flood resilience in China’s mountainous areas: Design flood estimation using advanced machine learning techniques by Xuemei Wang, Ronghua Liu, Chaoxing Sun, Xiaoyan Zhai, Liuqian Ding, Xiao Liu, Xiaolei Zhang

“…This process considered different ML algorithms (random forest, extreme gradient boosting, and support vector regression), model scopes (nation and hydrological zones), and feature input sets (1–14 features) to optimize model development strategies. …”
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Estimation of Reference Crop Evapotranspiration in the Yellow River Basin Based on Machine Learning and Its Regional and Drought Adaptability Analysis by Jun Zhao, Huayu Zhong, Congfeng Wang

“…The study constructed four machine learning models—random forest (RF), a Support Vector Machine (SVM), Gradient Boosting (GB), and Ridge Regression (Ridge)—using the meteorological variables required by the Priestley–Taylor (PT) and Hargreaves (HG) equations as inputs. …”
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ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool by D. Di Santo, C. He, F. Chen, L. Giovannini

“…This tool leverages the strengths of multiple regression-based and probabilistic machine learning methods, including LASSO (see the list of abbreviations in Appendix B), support vector machine, classification and regression trees, random forest, extreme gradient boosting, Gaussian process regression, and Bayesian ridge regression. …”
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Optimal design of high‐performance rare‐earth‐free wrought magnesium alloys using machine learning by Shaojie Li, Zaixing Dong, Jianfeng Jin, Hucheng Pan, Zongqing Hu, Rui Hou, Gaowu Qin

“…Abstract In this study, a small dataset of 370 datapoints of Mg alloys are selected for machine learning (ML), in which each datapoint includes five rare‐earth‐free alloying elements (Ca, Zn, Al, Mn and Sn), three extrusion parameters (extrusion speed, temperature and ratio), and three mechanical properties (yield strength [YS], ultimate tensile strength [UTS] and elongation [EL]). The ML algorithms, including support vector machine regression (SVR), artificial neural network, and other three methods, are employed, and the SVR has the best performance in predicting mechanical properties based on the components, and process parameters, with the mean absolute percentage error of YS, UTS, and EL being 6.34%, 4.19%, and 13.64% in the test set, respectively. …”
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Mortality prediction of inpatients with NSTEMI in a premier hospital in China based on stacking model. by Li Wang, Yu Zhang, Feng Li, Caiyun Li, Hongzeng Xu

“…Finally, a unique double-layer stacking model is designed to improve the performance of the algorithm. Seven classical artificial intelligence methods of Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Adaptive Boosting (ADB), Extra Tree (ET), and Gradient Boosting Decision Tree (GBDT) were selected as candidate models for the base model of the first layer of the model, and extreme gradient enhancement (XGBOOST) was selected as the meta-model for the second layer.…”
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A Meta-Learning-Based Ensemble Model for Explainable Alzheimer’s Disease Diagnosis by Fatima Hasan Al-bakri, Wan Mohd Yaakob Wan Bejuri, Mohamed Nasser Al-Andoli, Raja Rina Raja Ikram, Hui Min Khor, Zulkifli Tahir, The Alzheimer’s Disease Neuroimaging Initiative

“…The methodology involves training an ensemble model that integrates Random Forest, Support Vector Machine, XGBoost, and Gradient Boosting classifiers, with meta-logistic regression used for the final decision. …”
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Biomarkers associated with cell-in-cell structure in kidney renal clear cell carcinoma based on transcriptome sequencing by Zehua Wang, Zhongxiao Zhang

“…Enrichment analyses were performed using the clusterProfiler package. Support vector machine-recursive feature elimination (SVM-RFE) and Least Absolute Shrinkage and Selection Operator (LASSO) regression, implemented via the caret and glmnet packages in R, were used to select biomarkers. …”
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Replicating Current Procedural Terminology code assignment of rhinology operative notes using machine learning by Christopher P. Cheng, Ryan Sicard, Dragan Vujovic, Vikram Vasan, Chris Choi, David K. Lerner, Alfred‐Marc Iloreta

“…The Decision Tree, Support Vector Machine, Logistic Regression and Naïve Bayes (NB) algorithms were used to train and test models on operative notes. …”
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Neurocognition as a major predictor of 8-week response to antipsychotics for drug-naïve first-episode schizophrenia using machine learning by Xianghe Wang, Tianqi Gao, Xiaodong Guo, Bingjie Huang, Yunfei Ji, Wanheng Hu, Xiaolin Yin, Yue Zheng, Chengcheng Pu, Xin Yu

“…Six machine learning algorithms, including random forest, eXtreme gradient boosting (XGBoost), logistic regression, linear support vector machine (SVM), radial basis function SVM and poly SVM were applied and compared to draw the prediction model. …”
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Experimental Study on Evaluation of Organization Collaboration in Prefabricated Building Construction by Dingjing Bao, Yuan Chen, Shuai Wan, Jinlai Lian, Ying Lei, Kaizhe Chen

“…Moreover, the BO-XGBoost model was compared with the random forest, support vector machine, and logistic regression prediction models. …”
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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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A low-cost autonomous portable poultry egg freshness machine using majority voting-based ensemble machine learning classifiers by Jirayut Hansot, Wongsakorn Wongsaroj, Thaksin Sangsuwan, Natee Thong-un

“…The proposed machine learning model is an ensemble machine learning algorithm, which integrates predictions obtained from several individual classifiers like Random Forest, Decision Trees, Support Vector Machine, Naïve Bayes, k-Nearest Neighbors and Logical Regression to make a final prediction. …”
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Stroke prediction in elderly patients with atrial fibrillation using machine learning combined clinical and left atrial appendage imaging phenotypic features by Hao Huang, Yan Xiong, Yuan Yao, Jie Zeng

“…The independent correlations between these phenotypes and stroke risk were subsequently analyzed. Machine learning algorithms—Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting—were selected to develop a predictive model for stroke risk in this patient cohort. …”
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Automatic machine-learning based identification of jogging periods from accelerometer measurements of adolescents under field conditions. by Eftim Zdravevski, Biljana Risteska Stojkoska, Marie Standl, Holger Schulz

“…Four machine learning algorithms were used for classification: Logistic regression, Support Vector Machines, Random Forest and Extremely Randomized Trees. …”
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Signatures of Six Autophagy‐Related Genes as Diagnostic Markers of Thyroid‐Associated Ophthalmopathy and Their Correlation With Immune Infiltration by Qintao Ma, Yuanping Hai, Jie Shen

“…Gene ontology analysis (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to perform the enrichment analysis of AR‐DEGs. LASSO regression, support vector machine recursive feature elimination, and random forest were performed to screen for disease signature genes (DSGs), which were further validated in another independent validation dataset. …”
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Enhanced prediction of ventilator-associated pneumonia in patients with traumatic brain injury using advanced machine learning techniques by Negin Ashrafi, Armin Abdollahi, Kamiar Alaei, Maryam Pishgar

“…Six machine learning models, including Support Vector Machine, Logistic Regression, Random Forest, XGBoost, Artificial Neural Network, and AdaBoost, were trained using extensive hyperparameter tuning. …”
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Comparing the Potential of Near- and Mid-Infrared Spectroscopy in Determining the Freshness of Strawberry Powder from Freshly Available and Stored Strawberry by Da Wang, Wenwen Wei, Yanhua Lai, Xiangzheng Yang, Shaojia Li, Lianwen Jia, Di Wu

“…Furthermore, partial least squares regression and least squares support vector machines (LS-SVM) models were established based on NIR, MIR, and combination of NIR and MIR data with full variables or optimal variables of strawberry powder to predict the storage days of strawberries that produced the powder. …”
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Combination of skin sympathetic nerve activity and urine biomarkers in improving diagnostic accuracy for urge urinary incontinence by Yu-Chen Chen, Hao-Wei Chen, Tzu-Yu Liu, Yung-Shun Juan, Yu-Peng Liu, Shiou-Lan Chen, Chien-Hung Lee, Wei-Chung Tsai, Wen-Jeng Wu

“…All participants underwent measurements of SKNA and evaluations of nine urine biomarkers, both with and without urinary creatinine correction. Logistic regression and support vector machine with L1 penalty were applied to SKNA and urine biomarker measurements. …”
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Rapid screening and optimization of CO2 enhanced oil recovery operations in unconventional reservoirs: A case study by Shuqin Wen, Bing Wei, Junyu You, Yujiao He, Qihang Ye, Jun Lu

“…Three different methods, namely random forest (RF), support vector regression (SVR), and artificial neural network (ANN), were used to establish proxy models using the data from a specific unconventional reservoir, and the RF model demonstrated a preferable performance. …”
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