Machine Learning-based for Automatic Detection of Corn-Plant Diseases Using Image Processing by Geofrey Prudence Baitu, Y. Benal Öztekin, Omsalma Alsadig Adam Gadalla, Khaled Adil Dawood Idress

“…The highest accuracy scores were obtained with support vector machine and artificial neural networks, respectively.…”
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Machine Learning and Artificial Intelligence Techniques in Smart Grids Stability Analysis: A Review by Arman Fathollahi

“…It includes support vector machines, decision trees, artificial neural networks, extreme learning machines and probabilistic graphical models, as well as reinforcement strategies like dynamic programming, Monte Carlo methods, temporal difference learning and Deep Q-networks, etc. …”
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Integrating Handcrafted Features with Machine Learning for Hate Speech Detection in Albanian Social Media by Fetahi Endrit, Hamiti Mentor, Susuri Arsim, Zenuni Xhemal, Ajdari Jaumin

“…We utilized several machine-learning algorithms, including Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), and Logistic Regression (LR), and extracted a considerable number of handcrafted features. …”
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A machine learning and neural network approach for classifying multidrug-resistant bacterial infections by Preeda Mengsiri, Ratchadaporn Ungcharoen, Sethavidh Gertphol

“…We compared several algorithms, including Logistic Regression, Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), and Light Gradient Boosting Machine (LightGBM), with the MLP model exhibiting the highest accuracy and specificity. …”
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Comparative Analysis of Machine Learning Models for Predicting Innovation Outcomes: An Applied AI Approach by Marko Martinović, Kristian Dokic, Dalibor Pudić

“…Methods included random forests, gradient boosting frameworks, support vector machines, neural networks, and logistic regression, each with hyperparameters optimized through Bayesian search routines and evaluated using corrected cross-validation techniques. …”
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Empowering machine learning for robust cyber-attack prevention in online retail: an integrative analysis by Kamran Razzaq, Mahmood Shah, Mohammad Fattahi, Jing Tang

“…The review revealed that the research on ML prevention algorithms in e-tailing is an emerging field with a growing number of articles in recent years, and significant emphasis has been placed on supervised and unsupervised methods, with a particular focus on classification techniques, e.g., support vector machine and naive Bayes for prevention of cybercrimes in e-tailing. …”
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Remote Sensing of Grasslands: Performance Comparison of Radar and Optical Data in Machine Learning Classification by K. Christofi, C. Chrysostomou, I. Tsardanidis, M. Mavrovouniotis, G. Guerrisi, C. Kontoes, D. G. Hadjimitsis, D. G. Hadjimitsis

“…Both datasets from Sentinel-1 and Sentinel-2 satellites were used to train and evaluate a variety of machine learning models including Random Forest, Support Vector Machines, Logistic Regression, XGBoost and Deep Neural Networks. …”
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Intelligent System for Reducing Waste and Enhancing Efficiency in Copper Production Using Machine Learning by Bagdaulet Kenzhaliyev, Timur Imankulov, Aksultan Mukhanbet, Sergey Kvyatkovskiy, Maral Dyussebekova, Nurdaulet Tasmurzayev

“…Using a combination of real-world and synthetic data, we developed models capable of both forward prediction, estimating slag and matte compositions from ore characteristics, and backward prediction, inferring ore compositions from output characteristics. Five ML algorithms were evaluated, with Gradient Boosting and Support Vector Regression demonstrating superior performance in capturing complex, non-linear relationships. …”
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Prediction of alkali-silica reaction expansion of concrete using explainable machine learning methods by Yasitha Alahakoon, Hirushan Sajindra, Ashen Krishantha, Janaka Alawatugoda, Imesh U. Ekanayake, Upaka Rathnayake

“…In this study, we developed four different machine learning models – extreme gradient boosting (XGBoost), random forest (RF), support vector regression (SVR), and k-nearest neighbor (KNN) to predict the ASR expansion in concrete using a comprehensive dataset with 1896 data points. …”
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Classifying social and physical pain from multimodal physiological signals using machine learning by Eun-Hye Jang, Young-Ji Eum, Daesub Yoon, Sangwon Byun

“…The electrocardiogram, electrodermal activity, photoplethysmogram, respiration, and finger temperature were recorded, and 12 physiological features were extracted. Three machine learning algorithms—logistic regression, support vector machine, and random forest—were employed to classify the input data into baseline versus painful states and physical versus social pain. …”
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A Machine Learning Framework for Student Retention Policy Development: A Case Study by Sidika Hoca, Nazife Dimililer

“…For the case study, various machine learning algorithms—including Support Vector Classifier, K-Nearest Neighbors, Logistic Regression, Naïve Bayes, Artificial Neural Network, Random Forest, Classification and Regression Trees, and Categorical Boosting—were trained for dropout prediction using data available at the end of the students’ second semester. …”
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Machine learning frameworks to accurately estimate the adsorption of organic materials onto resin and biochar by Raouf Hassan, Mohammad Reza Kazemi

“…The dataset was split into training (1225 data points), testing (262), and validation (263). Various machine learning methods were evaluated, including Linear Regression, Ridge Regression, Lasso Regression, Elastic Net, Support Vector Regression (SVR), k-Nearest Neighbors (KNN), Decision Trees, Random Forests, Gradient Boosting Machines, Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Gaussian Processes, as well as ensemble algorithms such as XGBoost, LightGBM, and CatBoost. …”
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Evaluation of Smart Building Integration into a Smart City by Applying Machine Learning Techniques by Mustafa Muthanna Najm Shahrabani, Rasa Apanaviciene

“…Six optimised machine learning algorithms (K-Nearest Neighbours (KNNs), Support Vector Regression (SVR), Random Forest, Adaptive Boosting (AdaBoost), Decision Tree (DT), and Extra Tree (ET)) were employed to train the model and perform feature engineering and permutation importance analysis. …”
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A predictive healthcare model using machine learning and psychological factors for medication adherence by Junwu Dong, Minyi Chu, Yirou Xu

“…Based on the Meta-Theoretic Model of Motivation and Personality (3M Model), data from 428 chronic disease patients, included dark triad traits (narcissism, Machiavellianism, psychopathy), general self-efficacy, doctor-patient trust, and demographic variables. Five machine learning algorithms – multiple logistic regression, decision tree, adaptive boosting, random forest and support vector machine (SVM) – were utilized to identify MAB levels and assess feature importance. …”
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Screening of serum biomarkers in patients with PCOS through lipid omics and ensemble machine learning. by Ji-Ying Chen, Wu-Jie Chen, Zhi-Ying Zhu, Shi Xu, Li-Lan Huang, Wen-Qing Tan, Yong-Gang Zhang, Yan-Li Zhao

“…PI (18:0/20:3)-H and PE (18:1p/22:6)-H were identified as candidate biomarkers. Three machine learning models, logistic regression, random forest, and support vector machine, showed that screened biomarkers had better classification ability and effect. …”
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Enhancing Machine Learning Models Through PCA, SMOTE-ENN, and Stochastic Weighted Averaging by Youngjin Han, Inwhee Joe

“…An ensemble model combining seven machine learning algorithms—Logistic Regression, Support Vector Machine, KNN, Random Forest, XGBoost, LightGBM, and CatBoost—was applied to predict survival outcomes. …”
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Enhanced Network Traffic Classification Using Bayesian-Optimized Logistic Regression and Random Forest Algorithm by Manisankar Sannigrahi, R. Thandeeswaran

“…The research evaluates four key machine learning algorithms: Random forest, logistic regression, support vector machine (SVM), and K-nearest neighbors (K-NN). …”
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Machine Learning-Based Alfalfa Height Estimation Using Sentinel-2 Multispectral Imagery by Hazhir Bahrami, Karem Chokmani, Saeid Homayouni, Viacheslav I. Adamchuk, Rami Albasha, Md Saifuzzaman, Maxime Leduc

“…We utilized Sentinel-2 data to obtain satellite imagery corresponding to the same timeframe and location as the ground measurements. Three machine learning algorithms were employed to estimate plant height from satellite images: random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGB). …”
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Machine-Learning-Driven Analysis of Wear Loss and Frictional Behavior in Magnesium Hybrid Composites by Barun Haldar, Hillol Joardar, Arpan Kumar Mondal, Nashmi H. Alrasheedi, Rashid Khan, Murugesan P. Papathi

“…Five distinct machine learning algorithms, Artificial Neural Network (ANN), Random Forest (RF), K-Nearest Neighbor (KNN), Gradient Boosting Machine (GBM), and Support Vector Machine (SVM), were employed to analyze experimental tribological data for predicting wear loss and coefficients of friction (COFs). …”
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Machine learning-based detection of DDoS attacks on IoT devices in multi-energy systems by Hesham A. Sakr, Mostafa M. Fouda, Ahmed F. Ashour, Ahmed Abdelhafeez, Magda I. El-Afifi, Mohamed Refaat Abdellah

“…Using the CICDDOS2019 and KDD-CUP datasets, a comprehensive analysis was conducted on several classifiers, including Decision Tree (DT), Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest. …”
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