Class Balancing for Soil Data: Predictive Modeling Approach for Crop Recommendation Using Machine Learning Algorithms by Sapkal Kranti G., Kadam Avinash B.

“…Several classification algorithms, including Support Vector Classifier (SVC), Logistic Regression, Decision Tree, Random Forest, and XGBoost, were employed to predict soil characteristics. …”
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Supervised machine learning classification algorithms for detection of fracture location in dissimilar friction stir welded joints by Akshansh Mishra, Apoorv Vats

“…The obtained results showed that the Support Vector Machine (SVM) algorithm classified the fracture location with a good accuracy score of 0.889 in comparison to the other algorithms…”
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Comparative Study of Cell Nuclei Segmentation Based on Computational and Handcrafted Features Using Machine Learning Algorithms by Rashadul Islam Sumon, Md Ariful Islam Mozumdar, Salma Akter, Shah Muhammad Imtiyaj Uddin, Mohammad Hassan Ali Al-Onaizan, Reem Ibrahim Alkanhel, Mohammed Saleh Ali Muthanna

“…In contrast, the Random Forest, Support Vector Machine, and K-means algorithms yielded lower segmentation performance metrics. …”
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Machine Learning Accurately Predicts Need for Critical Care Support in Patients Admitted to Hospital for Community-Acquired Pneumonia by George S. Chen, BSc, Terry Lee, PhD, Jennifer L.Y. Tsang, MD, Alexandra Binnie, MD, Anne McCarthy, MD, Juthaporn Cowan, MD, Patrick Archambault, MD, Francois Lellouche, MD, Alexis F. Turgeon, MD, MSc, Jennifer Yoon, MD, Francois Lamontagne, MD, Allison McGeer, MD, Josh Douglas, MD, Peter Daley, MD, Robert Fowler, MD, David M. Maslove, MD, Brent W. Winston, MD, Todd C. Lee, MD, Karen C. Tran, MD, Matthew P. Cheng, MD, Donald C. Vinh, MD, John H. Boyd, MD, Keith R. Walley, MD, Joel Singer, PhD, John C. Marshall, MD, James A. Russell, MD, for the Community-Acquired Pneumonia: Toward InnoVAtive Treatment (CAPTIVATE) Investigators

“…This study aimed to develop a machine learning (ML) model that predicts the need for such interventions and compare its accuracy to that of logistic regression (LR). DESIGN:. This retrospective observational study trained separate models using random-forest classifier (RFC), support vector machines (SVMs), Extreme Gradient Boosting (XGBoost), and multilayer perceptron (MLP) to predict three endpoints: eventual use of invasive ventilation, vasopressors, and RRT during hospitalization. …”
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An Educational Approach to Higgs Boson Hunting Using Machine Learning Classification Algorithms on ATLAS Open Data by Ayşe Bat

“…In order to discover a solution to the binary classification problem that was discussed earlier, six distinct classification algorithms were utilized. This article also compares the performance of these classification algorithms, including Linear Support Vector Machines (SVM), Radical SVM, Logistic Regression, K-Nearest Neighbours, XGBoost Classifier, and the AdaBoost Classifier. …”
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Inversion of Aerosol Chemical Composition in the Beijing–Tianjin–Hebei Region Using a Machine Learning Algorithm by Baojiang Li, Gang Cheng, Chunlin Shang, Ruirui Si, Zhenping Shao, Pu Zhang, Wenyu Zhang, Lingbin Kong

“…We then established models of aerosol chemical composition based on a machine learning algorithm. By comparing the inversion accuracies of single models—namely MLR (Multivariable Linear Regression) model, SVR (Support Vector Regression) model, RF (Random Forest) model, KNN (K-Nearest Neighbor) model, and LightGBM (Light Gradient Boosting Machine)—with that of the combined model (CM) after selecting the optimal model, we found that although the accuracy of the KNN model was the highest among the other single models, the accuracy of the CM model was higher. …”
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RETRACTED ARTICLE: Detection of hate: speech tweets based convolutional neural network and machine learning algorithms by Hameda A. Sennary, Ghada Abozaid, Ashraf Hemeida, Alexey Mikhaylov

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Predicting Irrigation Water Quality Indices Based on Data-Driven Algorithms: Case Study in Semiarid Environment by Dimple Dimple, Jitendra Rajput, Nadhir Al-Ansari, Ahmed Elbeltagi

“…To achieve this objective, five machine learning (ML) models, namely linear regression (LR), random subspace (RSS), additive regression (AR), reduced error pruning tree (REPTree), and support vector machine (SVM), have been developed and tested for predicting of six irrigation water quality (IWQ) indices such as sodium adsorption ratio (SAR), percent sodium (%Na), permeability index (PI), Kelly ratio (KR), soluble sodium percentage (SSP), and magnesium hazards (MH) in groundwater of the Nand Samand catchment of Rajasthan. …”
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Red Fox Optimization-Based Estimation Algorithms for Splitting Tensile Strength of Basalt Fiber Reinforced Concrete by Xiao Wu, Daoyong Zhu, Qin Yan

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A Comparative Study between Different Machine Learning Algorithms for Estimating the Vehicular Delay at Signalized Intersections by Yazan Alatoom, Abdallah Al-Hamdan

“…Consequently, this study aimed to compare a wide array of machine learning algorithms, including Artificial Neural Networks (ANN), Random Forest (RF), decision tree, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), AdaBoost, Gradient Boost, XGBoost, and Partial Least Squares (PLS) regression. …”
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Use of responsible artificial intelligence to predict health insurance claims in the USA using machine learning algorithms by Ashrafe Alam, Victor R. Prybutok

“…The algorithms examined include support vector machine (SVM), decision tree (DT), random forest (RF), linear regression (LR), extreme gradient boosting (XGBoost), and k-nearest neighbors (KNN). …”
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Identifying liver cirrhosis in patients with chronic hepatitis B: an interpretable machine learning algorithm based on LSM by Xueting Bai, Chunwen Pu, Wenchong Zhen, Yushuang Huang, Qian Zhang, Zihan Li, Yixin Zhang, Rongxuan Xu, Zhihan Yao, Wei Wu, Mei Sun, Xiaofeng Li

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Development of machine learning algorithms to predict viral load suppression among HIV patients in Conakry (Guinea) by Degninou Yehadji, Degninou Yehadji, Geraldine Gray, Carlos Arias Vicente, Petros Isaakidis, Petros Isaakidis, Abdourahimi Diallo, Saa Andre Kamano, Thierno Saidou Diallo

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Identifying determinants of malnutrition in under-five children in Bangladesh: insights from the BDHS-2022 cross-sectional study by Tanzila Tamanna, Shohel Mahmud, Nahid Salma, Md. Musharraf Hossain, Md. Rezaul Karim

“…Descriptive statistics were conducted to summarize the key characteristics of the dataset. Boruta algorithm was employed to identify important features related to malnutrition which were then used to evaluate several machine learning models, including K-Nearest Neighbors (KNN), Neural Networks (NN), Classification and Regression Trees (CART), XGBoost (XGBM), Support Vector Machines (SVM), and Random Forest (RF), in addition to the traditional logistic regression (LR) model. …”
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What Influences Low-cost Sensor Data Calibration? - A Systematic Assessment of Algorithms, Duration, and Predictor Selection by Lu Liang, Jacob Daniels

“…This study comprehensively assessed ten widely used data techniques, namely AdaBoost, Bayesian ridge, gradient tree boosting, K-nearest neighbors, Lasso, multivariable linear regression, neural network, random forest, ridge regression, and support vector machine. …”
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NIRS and machine learning algorithms as a non-invasive technique to discriminate and classify cooked broiler and duck meat by Kamrunnahar Khan Bristy, Dip Ghosh, Md. Abul Hashem

“…Various machine learning algorithms, including linear discriminant analysis (LDA), support vector machine (SVM), Logistic Regression, Random Forest, Gradient Boosting, K-Nearest Neighbors, Decision Tree, Naive Bayes, Neural Network, and XGB, were evaluated. …”
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An integrated stacked convolutional neural network and the levy flight-based grasshopper optimization algorithm for predicting heart disease by Syed Muhammad Salman Bukhari, Muhammad Hamza Zafar, Syed Kumayl Raza Moosavi, Majad Mansoor, Filippo Sanfilippo

“…Compared to traditional classifiers, including Regression Trees, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, and standard Neural Networks, the SCNN-LFGOA consistently outperforms these methods. …”
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Adoption of K-means clustering algorithm in smart city security analysis and mythical experience analysis of urban image. by Haotong Han

“…The practical feasibility of the model is assessed using the receiver operating characteristic (ROC) curve, and its performance is compared with that of the Naive Bayes (NB), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM) classification methods. …”
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Accuracy Prediction of Compressive Strength of Concrete Incorporating Recycled Aggregate Using Ensemble Learning Algorithms: Multinational Dataset by Menghay Phoeuk, Minho Kwon

“…Although the random forest regression algorithm performed the least well among the four models, it still outperformed conventional machine learning algorithms such as support vector machines and artificial neural networks. …”
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Robust-tuning machine learning algorithms for precise prediction of permeability impairment due to CaCO3 deposition by Mohammad Javad Khodabakhshi, Masoud Bijani, Masoud Hasani

“…Using machine learning models—Support Vector Regression (SVR), Extra Trees (ET), and Extreme Gradient Boosting (XGB)—the research aims to predict how much permeability is lost due to scaling. …”
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