Visible, near-infrared, and shortwave-infrared spectra as an input variable for digital mapping of soil organic carbon by Vahid Khosravi, Asa Gholizadeh, Radka Kodešová, Prince Chapman Agyeman, Mohammadmehdi Saberioon, Luboš Borůvka

“…Accordingly, two SOC modeling approaches were used in three agricultural sites in Czech Republic: i) machine learning (ML) including partial least squares regression (PLSR), cubist, random forest (RF), and support vector regression (SVR), and ii) regression kriging (RK) by the combination of ordinary kriging (OK) and PLSR (PLSR-K), cubist (cubist-K), RF (RF-K), and SVR (SVR-K). …”
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Mitochondrial insights: key biomarkers and potential treatments for diabetic nephropathy and sarcopenia by Yi Wei Chen, Shan He, Yu Wang, Lian Ying Hu, Qin Kai Chen, Si Yi Liu

“…Using Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), Extreme Gradient Boosting (XGB), and Random Forest (RF) algorithms, we identified three key mitochondrial hub genes. …”
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Prediction of Rice Chlorophyll Index (CHI) Using Nighttime Multi-Source Spectral Data by Cong Liu, Lin Wang, Xuetong Fu, Junzhe Zhang, Ran Wang, Xiaofeng Wang, Nan Chai, Longfeng Guan, Qingshan Chen, Zhongchen Zhang

“…Subsequently, CHI prediction models were developed using four machine learning algorithms: support vector regression (SVR), random forest (RF), back-propagation neural network (BPNN), and k-nearest neighbors (KNNs). …”
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Multimodal Visualization and Explainable Machine Learning–Driven Markers Enable Early Identification and Prognosis Prediction for Symptomatic Aortic Stenosis and Heart Failure With... by Jun Wang, Jiajun Zhu, Hui Li, Shili Wu, Siyang Li, Zhuoya Yao, Tongjian Zhu, Bi Tang, Shengxing Tang, Jinjun Liu

“…A total of 5 ML model-decision trees, K-nearest neighbors, random forest, support vector machine, and extreme gradient boosting were used to construct a visualization and explainable predictive framework to elucidate model decision-making processes. …”
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Identification and validation of diagnostic biomarkers and immune cell abundance characteristics in Staphylococcus aureus bloodstream infection by integrative bioinformatics analys... by Junhong Shi, Li Shen, Yanghua Xiao, Cailing Wan, Bingjie Wang, Peiyao Zhou, Jiao Zhang, Weihua Han, Rongrong Hu, Fangyou Yu, Hongxiu Wang

“…Subsequently, the hub genes including DRAM1, PSTPIP2, and UPP1 were identified via three machine-learning algorithms: random forest, support vector machine-recursive feature elimination, and least absolute shrinkage and selection operator. …”
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Detection of multiple pesticide residues on the surface of broccoli based on hyperspectral imaging by GUI Jiangsheng, GU Min, WU Zixian, BAO Xiao’an

“…Mahalanobis distance (MD), least square support vector machine (LSSVM), artificial neural networks (ANN) and extreme learning machine (ELM) models were created to predict the pesticide residues from full spectra and characteristic wavelengths. …”
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Mapping the global risk of chikungunya virus endemicity and autochthonous transmission following importation by Yu-Feng Yang, Yun-Bo Qiu, Qiang Xu, Rui-Cheng Gao, Tian Tang, Yao Tian, Yan-He Wang, Sheng-Hong Lin, Yun-Dong Shi, Long-Tao Chen, Yan Zhang, Jun Ma, Chen-Long Lv, Guo-Lin Wang, Hai-Feng Pan, Wei Liu, Li-Qun Fang

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Escalate Prognosis of Parkinson’s Disease Employing Wavelet Features and Artificial Intelligence from Vowel Phonation by Rumana Islam, Mohammed Tarique

“…For classification purposes, two popular machine learning models, namely, support vector machine (SVM) and k-nearest neighbors (kNNs), are trained to distinguish patients with PD. …”
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Preliminary Development of a Database for Detecting Active Mounting Behaviors Using Signals Acquired from IoT Collars in Free-Grazing Cattle by Miguel Guarda-Vera, Carlos Muñoz-Poblete

“…The resulting database comprises 415 labeled events associated with various behaviors, containing acceleration signals in both the Body and World Frame of reference and gyroscope signals. A Support Vector Machine (SVM) algorithm is implemented to evaluate the effectiveness of the dataset in detecting active mounts and to compare training performance using both frames. …”
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Zero-Shot Learning for Accurate Project Duration Prediction in Crowdsourcing Software Development by Tahir Rashid, Inam Illahi, Qasim Umer, Muhammad Arfan Jaffar, Waheed Yousuf Ramay, Hanadi Hakami

“…Bidirectional Encoder Representations from Transformers (BERT) are employed to convert textual information into vectors, which are then analyzed using various machine learning algorithms. …”
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The Role of Performance Metrics in Estimating Market Values of Footballers in Europe's Top Five Leagues by Murat Işık, Mehmet Ali Yalçınkaya

“…In the regression analysis, seven models (Adaboost, Decision Tree, Gradient Boosting, K Nearest Neighbors, Random Forest, Ridge Regression, and Support Vector Machine) predicted players' market values. …”
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Enhancing acute leukemia classification through hybrid fuzzy C means and random forest methods by K. Lakshmi Narayanan, R. Santhana Krishnan, Y. Harold Robinson, S. Vimal, Tarik A. Rashid, Chetna Kausha, Md. Mehedi Hassan

“…In this proposed method the classification is tested with two Machine Learning algorithms which are Hybrid Fuzzy C Means (FCM) and Random Forest algorithm (RF) and Support Vector Machine for the detection and classification of Acute Leukemia disease and their performance was evaluated. …”
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Liquid chromatography-mass spectrometry-based metabolic panels characteristic for patients with prostate cancer and prostate-specific antigen levels of 4–10 ng/mL by Chen Wang, Ting Chen, Teng-Fei Gu, Sheng-Ping Hu, Yong-Tao Pan, Jie Li

“…Based on the identified metabolites, LASSO regression was applied for variable selection, and logistic regression and support vector machine models were developed. Results: The LASSO algorithm’s ability to select variables effectively reduced redundant features and minimized model overfitting. …”
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A model-free method to detect the risk and locate the sources of sub-synchronous oscillations in a large-scale renewable power system by Yufan He, Wenjuan Du, Qiang Fu, H.F. Wang

“…The proposed method applies the deep learning support vector data description and label spreading approaches. …”
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Prediction of Traction Energy Consumption for Urban Rail Transit Trains in Relative Speed Mode by GUO Tuansheng

“…［Objective］It is aimed to accurately predict the traction energy consumption of urban rail transit trains operating in relative speed mode using support vector machine（SVM）regression and genetic algorithms, ultimately enhancing energy efficiency during train operation. …”
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Optimization and prediction of corporate credit rating through advanced feature selection based on AI and deep learning by Jumanah Ahmed Darwish

“…This study offers a comprehensive evaluation of six machine learning algorithms—Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Support Vector Machine One-vs-One (SVM OVO), Support Vector Machine One-vs-All (SVM OVA), and Multi-Layer Perceptron (MLP)—in the context of corporate credit rating classification. …”
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Risk prediction and effect evaluation of complicated appendicitis based on XGBoost modeling by Sunmeng Chen, Jianfu Xia, Beibei Xu, Yi Huang, Miaomiao Teng, Juyi Pan

“…An integrated learning algorithm, Extreme Gradient Boosting (XGBoost), was introduced to predict the risk of CAP and compared with Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (CART) algorithms. …”
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Integrated diagnostics and time series sensitivity assessment for growth monitoring of a medicinal plant (Glycyrrhiza uralensis Fisch.) based on unmanned aerial vehicle multispectr... by Ao Zhang, Haibin Guan, Zhiheng Dong, Xin Jia, Yan Xue, Fengyu Han, Lingjiang Meng, Xiuling Yu, Xiaoqin Wang, Yang Cao

“…Models were constructed using backpropagation neural network (BP), support vector machine (SVM), and random forest (RF) to evaluate PIs to predict yield and monitor growth dynamics. …”
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Autophagy crosstalk with the immune microenvironment in chronic myeloid leukemia and serves as a biomarker for diagnosis and progression by Fangmin Zhong, Fangyi Yao, Jing Liu, Qun Fang, Xiajing Yu, Bo Huang, Xiaozhong Wang

“…Three diagnostic ARGs (FOXO1, TUSC1, and ATG4A) were identified by support vector machine recursive feature elimination, least absolute shrinkage selection operator, and random forest algorithms, and the combined diagnostic efficiency of the three was further improved. …”
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A novel early stage drip irrigation system cost estimation model based on management and environmental variables by Masoud Pourgholam-Amiji, Khaled Ahmadaali, Abdolmajid Liaghat

“…Support vector machine (SVM) and optimization algorithms (Wrapper) were found to be the best learner and feature selection techniques, respectively, out of all the available feature selection algorithms. …”
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