A Supervised Approach for Land Use Identification in Trento Using Mobile Phone Data as an Alternative to Unsupervised Clustering Techniques by Manuel Mendoza-Hurtado, Gonzalo Cerruela-García, Domingo Ortiz-Boyer

“…By analyzing spatiotemporal patterns in CDRs, we trained and evaluated several classification algorithms, including k-nearest neighbors (kNN), support vector machines (SVM), and random forests (RF), to map land use categories, such as home, work, and forest. …”
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Enhancing IoT Security in 5G Networks by Reem Alzhrani, Mohammed Alliheedi

“…We compared the results of these algorithms with three machine learning methods: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Stochastic Gradient Descent (SGD). …”
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A systematic review on sleep stage classification and sleep disorder detection using artificial intelligence by Tayab Uddin Wara, Ababil Hossain Fahad, Adri Shankar Das, Md Mehedi Hasan Shawon

“…At the same time, Long Short-Term Memory, Ensemble Learning, Support Vector Machine, and Random Forest accounted for 15 %, 12 %, 7 %, and 6 % of usage, respectively. …”
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Investigating the Use of Electrooculography Sensors to Detect Stress During Working Activities by Alessandra Papetti, Marianna Ciccarelli, Andrea Manni, Andrea Caroppo, Gabriele Rescio

“…Employing supervised machine learning (ML) algorithms—Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbors (KNN)—the analysis revealed accuracy rates exceeding 80%, with RF leading at 85.8% and 82.4% for two classes and three classes, respectively. …”
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AI predicting recurrence in non-muscle-invasive bladder cancer: systematic review with study strengths and weaknesses by Saram Abbas, Rishad Shafik, Naeem Soomro, Rakesh Heer, Rakesh Heer, Kabita Adhikari

“…Each study was analysed for strengths, weaknesses, performance metrics, and limitations, with emphasis on generalisability, interpretability, and cost-effectiveness. ResultsML algorithms demonstrate significant potential, with neural networks achieving accuracies of 65–97.5%, particularly with multi-modal datasets, and support vector machines averaging around 75%. …”
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Diagnostic accuracy of artificial intelligence for the screening of prostate cancer in biparametric magnetic resonance imaging: a systematic review by Oksana V. Kryuchkova, Elena V. Schepkina, Natalia A. Rubtsova, Boris Y. Alekseev, Anton I. Kuznetsov, Svetlana V. Epifanova, Elena V. Zarya, Ali E. Talyshinskii

“…Moreover, 43% and 33% of the studies were dedicated to transition zone and prostate peripheral zone neoplasms, respectively, and 52% of the authors examined the whole prostate gland, without dividing it into zones. The most common machine-learning algorithms applied by the investigators were as follows: multiple logistic regression (76%), support vector machine (38%), and random forest (24%). …”
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An optimized approach for predicting water quality features and a performance evaluation for mapping surface water potential zones based on Discriminant Analysis (DA), Geographical... by Abhijeet Das

“…Again, this research used a strong methodology by incorporating Machine learning (ML) algorithms, such as: Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Machine (SVM), and Linear Regression Model (LRM), were applied to forecast and confirm the quality of the water. …”
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Subtypes detection of papillary thyroid cancer from methylation assay via Deep Neural Network by Andrea Colacino, Andrea Soricelli, Michele Ceccarelli, Ornella Affinito, Monica Franzese

“…We then compared its performance with 4 machine learning models (logistic regression with elastic net penalty, quadratic discriminant analysis, support vector classifier with RBF kernel, and random forest). …”
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Evaluating multitemporal vegetation indices from Zhuhai-1 hyperspectral images for detecting a rapidly spreading invasive species - Spartina alterniflora by Yuanhui Zhu, Soe W. Myint, Jingjing Cao, Kai Liu, Mei Zeng, Chenxi Diao

“…This study examined multitemporal VIs from nine months using hyperspectral images and common machine learning methods (i.e., K-nearest neighbor, support vector machine, random forest) to compare a variety of VIs' performance in identifying SA invasion in the Guangxi Zhuang Autonomous Region. …”
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FTIR-Based Microplastic Classification: A Comprehensive Study on Normalization and ML Techniques by Octavio Villegas-Camacho, Iván Francisco-Valencia, Roberto Alejo-Eleuterio, Everardo Efrén Granda-Gutiérrez, Sonia Martínez-Gallegos, Daniel Villanueva-Vásquez

“…The study assessed the performance of ML algorithms, such as k-nearest neighbors (k-NN), support vector machines (SVM), naive Bayes (NB), random forest (RF), and artificial neural networks architectures (including convolutional neural networks (CNNs) and multilayer perceptrons (MLPs)). …”
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Improving the accuracy of remotely sensed TSS and turbidity using quality enhanced water reflectance by a statistical resampling technique by Kunwar Abhishek Singh, Dongryeol Ryu, Meenakshi Arora, Manoj Kumar Tiwari, Bhabagrahi Sahoo

“…The statistical resampling approach based on GMM was applied to Sentinel-2 (S2) imagery to produce input to Machine Learning (ML) algorithms to retrieve the TSS and turbidity for target river sections. …”
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Interpretable Multi-Sensor Fusion of Optical and SAR Data for GEDI-Based Canopy Height Mapping in Southeastern North Carolina by Chao Wang, Conghe Song, Todd A. Schroeder, Curtis E. Woodcock, Tamlin M. Pavelsky, Qianqian Han, Fangfang Yao

“…This framework incorporates multitemporal optical observations from Sentinel-2; C-band backscatter and InSAR coherence from Sentinel-1; quad-polarization L-Band backscatter and polarimetric decompositions from the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR); texture features from the National Agriculture Imagery Program (NAIP) aerial photography; and topographic data derived from an airborne LiDAR-based digital elevation model. We evaluated four machine learning algorithms, K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), and eXtreme gradient boosting (XGB), and found consistent accuracy across all models. …”
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Vibration Signal Analysis for Intelligent Rotating Machinery Diagnosis and Prognosis: A Comprehensive Systematic Literature Review by Ikram Bagri, Karim Tahiry, Aziz Hraiba, Achraf Touil, Ahmed Mousrij

“…In the context of fault detection, support vector machines (SVMs), convolutional neural networks (CNNs), Long Short-Term Memory (LSTM) networks, k-nearest neighbors (KNN), and random forests have been identified as the five most frequently employed algorithms. …”
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Computed tomography-based radiomics model for predicting station 4 lymph node metastasis in non-small cell lung cancer by Yanru Kang, Mei Li, Xizi Xing, Kaixuan Qian, Hongxia Liu, Yafei Qi, Yanguo Liu, Yi Cui, Hua Zhang

“…Using least absolute shrinkage and selection operator (LASSO) regression to select features with non-zero coefficients. Four machine learning algorithms—decision tree (DT), logistic regression (LR), random forest (RF), and support vector machine (SVM)—were employed to construct radiomics models. …”
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Analysis of soil salinization and land use change under water conservation retrofit in the Hetao irrigation district by Yi Zhao, Shuya Yang, Haibin Shi, Haoqi Han, Yunlei Dong, Xianyue Li, Jianwen Yan, Yan Yan, Xu Dou, Feng Tian, Qingfeng Miao

“…The soil salinity inversion model constructed using Random Forest demonstrates higher R2 values and lower MAE and RMSE compared to the Support Vector Machine and Gradient Boosting Tree, establishing it as the optimal model for soil salinity inversion. …”
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Artificial intelligence in vaccine research and development: an umbrella review by Rabie Adel El Arab, May Alkhunaizi, May Alkhunaizi, Yousef N. Alhashem, Alissar Al Khatib, Munirah Bubsheet, Salwa Hassanein, Salwa Hassanein

“…Quality assessments were performed using the ROBIS and AMSTAR 2 tools to evaluate risk of bias and methodological rigor.ResultsAmong the 27 reviews, traditional machine learning approaches—random forests, support vector machines, gradient boosting, and logistic regression—dominated tasks from antigen discovery and epitope prediction to supply‑chain optimization. …”
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Hyperspectral imaging for precision nitrogen management: A comparative exploration of two methodological approaches to estimate optimal nitrogen rate in processing tomato by Vito Aurelio Cerasola, Francesco Orsini, Giuseppina Pennisi, Gaia Moretti, Stefano Bona, Francesco Mirone, Jochem Verrelst, Katja Berger, Giorgio Gianquinto

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Noninvasive prediction of meningioma brain invasion via multiparametric MRI⁃based brain⁃tumor interface radiomics by CHENG Xing, WANG Zhi⁃chao, LI Hua⁃ning, WANG Xie⁃feng, YOU Yong⁃ping

“…Following single⁃value elimination and interclass correlation coefficient [ICC (2, k) > 0.90] stability screening, features were selected using five⁃fold cross⁃validated least absolute shrinkage and selection operator (LASSOCV). Six machine learning (ML) algorithms, including light gradient boosting machine (LightGBM), Logistic regression (LR), multilayer perceptron (MLP), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost) were utilized to build predictive models. …”
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An optimal weighting-based hybrid classifier for Children's congenital heart diseases signal processing by Morteza Ebrahimpour, Mehdi Khashei

“…In this paper, a hybrid classifier incorporating Long Short-Term Memory (LSTM), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) is proposed and applied to diagnose congenital heart disease in children. …”
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Predicting postoperative malnutrition in patients with oral cancer: development of an XGBoost model with SHAP analysis and web-based application by Lixia Kuang, Lixia Kuang, Jingya Yu, Yunyu Zhou, Yu Zhang, Yu Zhang, Guangman Wang, Guangman Wang, Fangmin Zhang, Grace Paka Lubamba, Grace Paka Lubamba, Xiaoqin Bi, Xiaoqin Bi

“…Predictive models were developed via four supervised machine learning algorithms: logistic regression (LR), support vector machine (SVM), light gradient boosting machine (LGBM), and extreme gradient boosting (XGBoost). …”
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