Design of machine learning-based controllers for speed control of PMSM drive by Ashly Mary Tom, J. L. Febin Daya

“…The ML-based regression techniques like linear regression (LR), support vector machine regression (SVM), feedforward neural network (NN) and advanced NN like Long Short-Term Memory network (LSTM) are explored here in detail. …”
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Machine Learning for the Prediction of Acute Kidney Injury in Critically Ill Patients With Coronary Heart Disease: Algorithm Development and Validation by Yike Li, Mingyang Xiao, Yaqian Li, Lulu Lv, Shanshan Zhang, Yuhui Liu, Juan Zhang

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Application of machine learning algorithms in predicting new onset hypertension: a study based on the China Health and Nutrition Survey by Manhui Zhang, Xian Xia, Qiqi Wang, Yue Pan, Guanyi Zhang, Zhigang Wang

“…We tested and evaluated the performance of four traditional machine learning algorithms commonly used in epidemiological studies: Logistic Regression, Support Vector Machine, XGBoost, LightGBM, and two deep learning algorithms: TabNet and AMFormer model. …”
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Development and validation of a biomarker-based prediction model for metastasis in patients with colorectal cancer: Application of machine learning algorithms by Erfan Ayubi, Sajjad Farashi, Leili Tapak, Saeid Afshar

“…Subsequently, the prediction model was developed and internally validated using five machine learning (ML) algorithms including lasso and elastic-net regularized generalized linear model (glmnet), k-nearest neighbors (kNN), support vector machine (SVM) with Radial Basis Function Kernel, random forest (RF), and eXtreme Gradient Boosting (XGBoost). …”
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Exploration of Machine Learning Models for Prediction of Gene Electrotransfer Treatment Outcomes by Alex Otten, Michael Francis, Anna Bulysheva

“…Support vector machines (SVMs) using selected features based on <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>χ</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> tests were also explored. …”
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Hybrid Deep Learning Approach for Accurate Detection and Multiclass Classification of Broken Conductor Faults in Power Distribution Systems by Firas Saadoon Mohammed Al-Jumaili, Mustafa Onat

“…It is shown that the proposed method has higher fault detection and classification accuracy compared to three traditional classification approaches, namely, Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and three state-of-the-art methods: 1) Stockwell transform +SVM, 2) Fast Fourier Transform + SVM, and 3) Hilbert-Huang transform of vibration data and power spectral density + Artificial Neural Network. …”
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Supervised machine learning algorithms for the classification of obesity levels using anthropometric indices derived from bioelectrical impedance analysis by Rodrigo Yáñez-Sepúlveda, Aldo Vásquez-Bonilla, Rodrigo Olivares, Pablo Olivares, Juan Pablo Zavala-Crichton, Claudio Hinojosa-Torres, Catalina Muñoz-Strale, Frano Giakoni-Ramírez, Josivaldo de Souza-Lima, Jacqueline Páez-Herrera, Jorge Olivares-Arancibia, Tomás Reyes-Amigo, Guillermo Cortés-Roco, Juan Hurtado-Almonacid, Eduardo Guzmán-Muñoz, Nicole Aguilera-Martínez, José Francisco López-Gil, Boryi A. Becerra-Patiño, Juan David Paucar-Uribe, Exal Garcia-Carrillo, Vicente Javier Clemente-Suárez

“…Anthropometric data included BMI, fat mass index (FMI), fat-free mass index (FFMI), skeletal muscle index (SMI), muscle mass index (MM), and others were collected using a validated multifrequency octopolar BIA device (InBody 270). Six supervised machine learning models, random forest, gradient koosting, k-nearest neighbors, logistic regression, support vector machine, and decision tree, were trained and evaluated using accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve (AUC-ROC), and SHapley Additive exPlanations value explanations. …”
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Unveiling the Efficacy of AI-based Algorithms in Phishing Attack Detection by Tajamul Shahzad, Kashif Aman

“…To give the brief knowledge of phishing attacks and their types of the objective of this work is to investigate various AI algorithms. Through a detail literature 14 AI algorithms which are repeatedly used for detection, and these are Random Forests, Convolutional Neural Network, Naïve Bayes, K-Nearest Neighbours algorithm, Decision Trees, long short-term memory, gated recurrent unit, Artificial Neural Network, AdaBoost, Logistic Regression, Gradient Boost, Multi-layer perceptron, Recurrent Neural Network, Extreme gradient boosting, and Support Vector Machine to detect phishing attacks. …”
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Prediction of winter wheat nitrogen nutrition index using high-resolution satellite and machine learning by Po-Ting Pan, Yamine Bouzembrak, Miguel Quemada, Bedir Tekinerdogan

“…Therefore, this study integrated PlanetScope satellite images with weather data while adopting three ML algorithms, including random forest (RF), support vector machine (SVM), and artificial neural network (ANN) to predict NNI in Spain from 2018 to 2019. …”
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Real-time prediction of the rate of penetration via computational intelligence: a comparative study on complex lithology in Southwest Iran by Mohammad Najafi, Yousef Shiri

“…In this study, five methodologies, including three artificial intelligence models (artificial neural networks [ANNs], support vector regression [SVR], random forest [RF]), a physical model, and a hybrid model, were evaluated for their ability to estimate the ROP on the basis of drilling data from a complex lithological area. …”
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A Novel SOH Estimation Method for Lithium-Ion Batteries Based on the PSO–GWO–LSSVM Prediction Model with Multi-Dimensional Health Features Extraction by Xu He, Zhengpu Wu, Jinghan Bai, Junchao Zhu, Lu Lv, Lujun Wang

“…With strong generalization and robustness, least squares support vector machine (LSSVM) is widely applied to nonlinear computations and function approximation. …”
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Estimating Canopy Chlorophyll Content of Potato Using Machine Learning and Remote Sensing by Xiaofei Yang, Hao Zhou, Qiao Li, Xueliang Fu, Honghui Li

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Comparative Analysis of Machine Learning Models for Android Malware Detection by Adem Korkmaz, Selma Bulut

“…Traditional models, such as random forest (RF) and support vector machines (SVM), alongside advanced approaches, such as convolutional neural networks (CNN) and XGBoost, were evaluated. …”
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Machine learning ensemble technique for exploring soil type evolution by Xiangyuan Wu, Kening Wu, Shiheng Hao, Er Yu, Jinghui Zhao, Yan Li

“…Ensemble models address this challenge by combining the strengths of multiple algorithms. This study applies a voting-based ensemble model (VEM), integrating Random Forest (RF), Support Vector Machine (SVM), and XGBoost (XGB), to gain a deeper understanding of soil type evolution, which is crucial for land management and soil conservation. …”
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Pixel and region-oriented classification of Sentinel-2 imagery to assess LULC dynamics and their climate impact in Nowshera, Pakistan by Farnaz, Nuthammachot Narissara, Shabbir Rabia, Iqbal Benazeer

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RARE: right algorithm for the right errand; a multi-model machine learning-based approach for tourism routes and spots recommendation by Ling Luo

“…The framework employs long short-term memory (LSTM) for spot relevance prediction, support vector machine (SVM) for spot name classification, and depth first search (DFS) for optimal route generation. …”
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Analysis of Possibilities to Automate Detection of Unscrupulous Microfinance Organizations based on Machine learning Methods by Yu. M. Beketnova

“…The author carried out a comparative analysis of the results obtained by classification methods — the logistic regression method, decision trees (algorithms of two-class decision forest, Adaboost), support vector machine (algorithm of two-class support vector machine), neural network methods (algorithm of two-class neural network), Bayesian networks (algorithm of two-class Bayes network). …”
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A Novel Capacity Estimation Method for Lithium-Ion Batteries Based on the Adam Algorithm by Yingying Lian, Dongdong Qiao

“…In this paper, we propose multiple machine learning algorithms to estimate the capacity using the incremental capacity (IC) curve features, including the adaptive moment estimation (Adam) model, root mean square propagation (RMSprop) model, and support vector regression (SVR) model. …”
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Identification and validation of HOXC6 as a diagnostic biomarker for Ewing sarcoma: insights from machine learning algorithms and in vitro experiments by Yonghua Pang, Jiahui Liang, Jiahui Liang, Yakai Deng, Weinan Chen, Yunyan Shen, Jing Li, Xin Wang, Zhiyao Ren

“…To identify key diagnostic genes, we applied three machine learning algorithms: least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF).ResultsHOXC6 was identified as a key diagnostic biomarker for ES. …”
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An Adaptive SVD-Based Approach to Clutter Suppression for Slow-Moving Targets by Yuhao Hou, Baixiao Chen

“…To address this limitation, this paper proposes an adaptive singular value decomposition (A-SVD) method utilizing support vector machines (SVM). The proposed approach leverages the augmented implicitly restarted Lanczos bidiagonalization (AIRLB) algorithm to decompose echo matrices into different subspaces, which are then characterized in relation to Doppler frequency, energy, and correlation. …”
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