Performance Analysis of Machine Learning Classifiers for Brain Tumor MR Images by Lubna Farhi, Razia Zia, Zain Anwar Ali

“…We have compared Artificial Neural Network (ANN), K-nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM) and Naïve Bayes (NB) classifiers to determine the accuracy of each classifier and find the best amongst them for classification of cancerous and noncancerous brain MR images. …”
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Machine learning assisted estimation of total solids content of drilling fluids by B.T. Gunel, Y.D. Pak, A.Ö. Herekeli, S. Gül, B. Kulga, E. Artun

“…The relationships among various rheological parameters were analyzed using statistical methods and machine learning algorithms. Several machine learning algorithms of diverse classes, namely linear (linear regression, ridge regression, and ElasticNet regression), kernel-based (support vector machine) and ensemble tree-based (gradient boosting, XGBoost, and random forests) algorithms, were trained and tuned to estimate solids content from other readily available drilling fluid properties. …”
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Image Based Detection of Coating Wear on Cutting Tools with Machine Learning by Jan Wolf, Nithin Kumar Bandaru, Martin Dienwiebel, Hans-Christian Möhring

“…For the classification task four machine learning Algorithms consisting of Random Forests, Decision Trees, Support Vector Machines and a Feed Forward Neural Network were implemented. …”
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Optimizing machine learning for enhanced automated ECG analysis in cardiovascular healthcare by Keyi Tang, Shuyuan Ma, Xiaohui Sun, Dongfang Guo

“…This study explores the use of machine learning and deep learning algorithms, including Support Vector Classifier (SVC), RandomForest, XGBoost, and LinearSVC, for ECG classification, aiming to improve accuracy and diagnostic capabilities. …”
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Improving the accuracy of honey bee forage class mapping using ensemble learning and multi-source satellite data in Google Earth Engine by Filagot Mengistu, Binyam Tesfaw Hailu, Temesgen Alemayehu Abera, Janne Heiskanen, Tadesse Terefe Zeleke, Tino Johansson, Petri Pellikka

“…Predictors derived from multi-source satellite data, such as high-resolution Planet imagery (P), Sentinel 1 RADAR (S1), Sentinel 2 multispectral (S2), and Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) were tested and best predictors were identified using Forward Feature Selection (FFS). Four machine learning algorithms (Gradient Tree Boost (GTB), Random Forest (RF), Classification and Regression Trees (CART), and Support Vector Machine (SVM)), all available in GEE, were compared and ensembled for honey bee forage class mapping. …”
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Data-Driven Pavement Performance: Machine Learning-Based Predictive Models by Mohammad Fahad, Nurullah Bektas

“…This study utilizes a range of machine learning algorithms, including linear regression, decision tree, random forest, gradient boosting, K-nearest neighbour, Support Vector Regression, LightGBM and CatBoost, to analyse their effectiveness in predicting pavement performance. …”
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Integrating Machine Learning and Material Feeding Systems for Competitive Advantage in Manufacturing by Müge Sinem Çağlayan, Aslı Aksoy

“…The research employs six machine learning (ML) algorithms—logistic regression (LR), decision trees (DT), random forest (RF), support vector machines (SVM), K-nearest neighbors (K-NN), and artificial neural networks (ANN)—to develop a multi-class classification model for material feeding system selection. …”
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Understanding the flowering process of litchi through machine learning predictive models by SU Zuanxian, NING Zhenchen, WANG Qing, CHEN Houbin

“…Feature engineering of the data, which involved removing irrelevant or redundant features and ensuring that there was no high correlation between the retained features, was used to improve the performance and generalization of the model. The six classical machine algorithms including Classified Regression Tree (CART), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), Stepwise Regression (STR) and Gradient Boosting Machine (GBM) were used for training. …”
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Stock Price Pattern Prediction Based on Complex Network and Machine Learning by Hongduo Cao, Tiantian Lin, Ying Li, Hanyu Zhang

“…Next, the topology characteristic variables for each combination symbolic pattern are used as the input variables for K-nearest neighbors (KNN) and support vector machine (SVM) algorithms to predict the next-day volatility patterns of a single stock. …”
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A machine learning-powered energy consumption prediction system with API by Toyeeb Adekunle Abd’Azeez, Lanre Olatomiwa

“…After comparing different statistical models, including RandomForestRegressor, ExtraTreesRegressor, SupportVectorRegressor, and XGBRegressor algorithms, ExtraTreesRegressor emerged as the optimal model, with an R2 score of 0.7441 and a MAPE of 16.27%. …”
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A review of machine learning and deep learning for Parkinson’s disease detection by Hajar Rabie, Moulay A. Akhloufi

“…Our evaluation included different algorithms such as support vector machines (SVM), random forests (RF), convolutional neural networks (CNN). …”
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Burnout Risk Profiles in Psychology Students: An Exploratory Study with Machine Learning by M. Graça Pereira, Martim Santos, Renata Magalhães, Cláudia Rodrigues, Odete Araújo, Dalila Durães

“…The accuracy of the three machine learning models—Random Forest, XGBoost, and Support Vector Machine—was 95.06%, 93.82%, and 97.53%, respectively. …”
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Enhanced water saturation estimation in hydrocarbon reservoirs using machine learning by Ali Akbari, Ali Ranjbar, Yousef Kazemzadeh, Dmitriy A. Martyushev

“…In this study, a comprehensive dataset consisting of 30,660 independent data points was utilized to develop machine learning (ML) models for Sw prediction. Nine well log parameters—Depth (DEPT), High-Temperature Neutron Porosity, True Resistivity, Computed Gamma Ray, Spectral Gamma Ray, Hole Caliper, Compressional Sonic Travel Time, Bulk Density, and Temperature—were used as input features to train and test five ML algorithms: Linear Regression, Support Vector Machine (SVM), Random Forest, Least Squares Boosting, and Bayesian methods. …”
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Using machine learning for the assessment of ecological status of unmonitored waters in Poland by Andrzej Martyszunis, Małgorzata Loga, Karol Przeździecki

“…Decision Tree, Random Forest, KNN, Support Vector Machine, Multinomial Naive Bayes, XGBoost models have been tested and the results indicated most suitable techniques. …”
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Multi-Camera Machine Learning for Salt Marsh Species Classification and Mapping by Marco Moreno, Sagar Dalai, Grace Cott, Ben Bartlett, Matheus Santos, Tom Dorian, James Riordan, Chris McGonigle, Fabio Sacchetti, Gerard Dooly

“…UAV surveys were conducted with RGB, MSI, and HSI sensors, and the collected data were classified using Random Forest (RF), Spectral Angle Mapper (SAM), and Support Vector Machine (SVM) algorithms. The classification performance was assessed using Overall Accuracy (<i>OA</i>), Kappa Coefficient (<i>k</i>), Producer’s Accuracy (<i>PA</i>), and User’s Accuracy (<i>UA</i>), for both individual sensor datasets and the fused dataset generated via band stacking. …”
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Leveraging machine learning for data-driven building energy rate prediction by Nasim Eslamirad, Mehdi Golamnia, Payam Sajadi, Francesco Pilla

“…Our approach leverages cutting-edge ML techniques, including Decision Trees (DT), Random Forest (RF), K-Nearest Neighbours (KNN), and Support Vector Machines (SVM), to develop highly accurate predictive models. …”
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Fault Detection in Photovoltaic Systems Using a Machine Learning Approach by Jossias Zwirtes, Fausto Bastos Libano, Luis Alvaro de Lima Silva, and Edison Pignaton de Freitas

“…The proposed fault detection solutions rely on analyzing different algorithms, including Support Vector Machine, Artificial Neural Network, Random Forest, Decision Tree, and Logistic Regression. …”
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Prediction of Work-relatedness of Shoulder Musculoskeletal Disorders as by Using Machine Learning by Saemi Jung, Bogeum Kim, Yoon-Ji Kim, Eun-Soo Lee, Dongmug Kang, Youngki Kim

“…In this study, demographic analysis and difference of approval rate by shoulder diseases were performed. Additionally, machine learning algorithms, including logistic regression, support vector machine, decision tree, random forest, and the XGBoost, were utilized to construct prediction models for work-relatedness assessment. …”
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Hyperspectral Imaging for Enhanced Skin Cancer Classification Using Machine Learning by Teng-Li Lin, Arvind Mukundan, Riya Karmakar, Praveen Avala, Wen-Yen Chang, Hsiang-Chen Wang

“…The current study investigates the use of ten different machine learning algorithms for the purpose of classification of AK, BCC, and SK, including convolutional neural network (CNN), random forest (RF), you only look once (YOLO) version 8, support vector machine (SVM), ResNet50, MobileNetV2, Logistic Regression, SVM with stochastic gradient descent (SGD) Classifier, SVM with logarithmic (LOG) Classifier and SVM- Polynomial Classifier, in assessing the capability of the system to differentiate AK from BCC and SK with heightened accuracy. …”
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Resilience evaluation of memristor based PUF against machine learning attacks by Hebatallah M. Ibrahim, Heorhii Skovorodnikov, Hoda Alkhzaimi

“…Our main contribution is a holistic study that focuses on attacking the randomness output resiliency based on building randomness predictors using Logistic Regression (LR), Support Vector Machine (SVM), Gaussian Mixture Models (GMM), K-means, K-means $$++$$ + + , Random Forest, XGBoost and LSTM, within efficient time, and data complexity. …”
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