Evaluating Sugarcane Yield Estimation in Thailand Using Multi-Temporal Sentinel-2 and Landsat Data Together with Machine-Learning Algorithms by Jaturong Som-ard, Savittri Ratanopad Suwanlee, Dusadee Pinasu, Surasak Keawsomsee, Kemin Kasa, Nattawut Seesanhao, Sarawut Ninsawat, Enrico Borgogno-Mondino, Filippo Sarvia

“…Moreover, in order to generate the sugarcane yield estimation maps, only 75 sampling plots were selected and surveyed to provide training and validation data for several powerful machine-learning algorithms, including multiple linear regression (MLR), stepwise multiple regression (SMR), partial least squares regression (PLS), random forest regression (RFR), and support vector regression (SVR). …”
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Comparative Performance of Autoencoders and Traditional Machine Learning Algorithms in Clinical Data Analysis for Predicting Post-Staged GKRS Tumor Dynamics by Simona Ruxandra Volovăț, Tudor Ovidiu Popa, Dragoș Rusu, Lăcrămioara Ochiuz, Decebal Vasincu, Maricel Agop, Călin Gheorghe Buzea, Cristian Constantin Volovăț

“…Traditional ML models, such as Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Extra Trees, Random Forest, and XGBoost, were trained and evaluated. …”
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A Novel Damage Inspection Method Using Fluorescence Imaging Combined with Machine Learning Algorithms Applied to Green Bell Pepper by Danial Fatchurrahman, Noelia Castillejo, Maulidia Hilaili, Lucia Russo, Ayoub Fathi-Najafabadi, Anisur Rahman

“…Chlorophyll fluorescence imaging was combined with machine learning algorithms—including logistic regression (LR), artificial neural networks (ANN), random forests (RF), k-nearest neighbors (kNN), and the support vector machine (SVM) to classify damaged and sound fruit. …”
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Predictive model establishment for forward-head posture disorder in primary-school-aged children based on multiple machine learning algorithms by Hongjun Tao, Yang Wen, Rongfang Yu, Yining Xu, Fangliang Yu

“…Six machine learning models—K-nearest neighbor (KNN), light gradient boosting machine (LGBM), extreme gradient boosting (XGBoost), random forest (RF), linear model (LM), and support vector machine (SVM)—were developed to predict risk. …”
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Development of a prediction model for acute respiratory distress syndrome in ICU patients with acute pancreatitis based on machine learning algorithms by REN Xia*，LIU Luojie，ZHA Junjie，YE Ye，XU Xiaodan，YE Hongwei，ZHANG Yan

“…Feature selection was performed using least absolute shrinkage and selection operator（LASSO）regression. Predictive models were constructed using seven machine learning algorithms：random forest（RF），extreme gradient boosting（XGBoost），light gradient boosting machine（LightGBM），decision tree（DT），logistic regression（LR），support vector machine（SVM），and K⁃nearest neighbors（KNN）. …”
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Comparing the effect of pre-anesthesia clonidine and tranexamic acid on intraoperative bleeding volume in rhinoplasty: a machine learning approach by Zahra Asghari Varzaneh, Akram Hemmatipour, Hadi Kazemi-Arpanahi

“…The data were preprocessed and analyzed using various regression models, including Linear regression, random forest (RF), support vector regression (SVR), Extreme Gradient Boosting (XGBoost), Gradient Boosting, Ridge, and least absolute shrinkage and selection operator (LASSO), to forecast blood loss associated with the use of clonidine and TXA. …”
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Application of machine learning algorithms for predicting the life-long physiological effects of zinc oxide Micro/Nano particles on Carum copticum by Maryam Mazaheri-Tirani, Soleyman Dayani, Majid Iranpour Mobarakeh

“…In this study, nine ML algorithms [Support-Vector Regression (SVR), Linear, Bagging, Stochastic Gradient Descent (SGD), Gaussian Process, Random Sample Consensus (RANSAC), Partial Least Squares (PLS), Kernel Ridge, and Random Forest] were applied to evaluate their efficiency in predicting the effects of zinc oxide nanoparticles (ZnO NPs: 0.5, 1, 5, 25, and 125 µM) and microparticles (ZnO MPs: 1, 5, 25, and 125 µM) on Carum copticum. …”
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Genetic algorithm optimization of ensemble learning approach for improved land cover and land use mapping: Application to Talassemtane National Park by Ali Azedou, Aouatif Amine, Isaya Kisekka, Said Lahssini

“…Multiple Machine Learning (ML) classifiers including Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), Classification and Regression Tree (CART), Minimum Distance (MinD), and Gradient Tree Boost (GTB), and a Grid Search (GS)-optimized ensemble-were evaluated. …”
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Intelligent algorithm-based model for predicting mass transfer performance in CO2 absorption within a rotating packed bed by Wei Zhang, Hao Chen, Ke Huang, Xing Shu, Cheng Fu, Bin Huang

“…Using dimensional analysis, key factors are transformed into dimensionless numbers, which are then input into models integrating least squares support vector machine (LSSVM) with genetic algorithm (GA), particle swarm optimization (PSO), and a hybrid GA-PSO (HGAPSO). …”
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Machine learning algorithms to predict the risk of admission to intensive care units in HIV-infected individuals: a single-centre study by Jialu Li, Yi Ding, Yiwei Hao, Chengyu Gao, Jinjing Xiao, Ying Liu, Yining Zhao, Qinlan Li, Lulu Xing, Hongyuan Liang, Liang Ni, Fang Wang, Sa Wang, Di Yang, Guiju Gao, Jiang Xiao, Hongxin Zhao

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Predicting Surface Roughness and Grinding Forces in UNS S34700 Steel Grinding: A Machine Learning and Genetic Algorithm Approach to Coolant Effects by Mohsen Dehghanpour Abyaneh, Parviz Narimani, Mohammad Sadegh Javadi, Marzieh Golabchi, Samareh Attarsharghi, Mohammadjafar Hadad

“…This research study adds value by applying algorithms and various machine learning techniques—such as support vector regression, Gaussian process regression, and artificial neural networks—on a dataset related to the grinding process of UNS S34700 steel. …”
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Production and optimization of affordable artificial geopolymer aggregates containing crumb rubber, plastic waste, and granulated cork based on machine learning algorithms by Mohamed Abdellatief, Abedulgader Baktheer, Mohamed Shahin, Aref A. Abadel, Ashraf M. Heniegal

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A novel lightweight deep learning framework using enhanced pelican optimization for efficient cyberattack detection in the Internet of Things environments by Yaozhi Chen, Yan Guo, Yun Gao, Baozhong Liu

“…The model achieves 98.1% accuracy on Bot-IoT, 97.4% on NSL-KDD, and 97.9% on CICIDS2018, outperforming conventional approaches like long short-term memory (LSTM), gated recurrent unit (GRU), support vector machine (SVM), logistic regression (LR), artificial neural network (ANN), and recurrent neural network (RNN). …”
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Fractional-Order System Identification: Efficient Reduced-Order Modeling with Particle Swarm Optimization and AI-Based Algorithms for Edge Computing Applications by Ignacio Fidalgo Astorquia, Nerea Gómez-Larrakoetxea, Juan J. Gude, Iker Pastor

“…These optimized parameters then serve as training data for several AI-based algorithms—including neural networks, support vector regression (SVR), and extreme gradient boosting (XGBoost)—to evaluate their inference speed and accuracy. …”
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Optimized CNN-LSTM with hybrid metaheuristic approaches for solar radiation forecasting by İrem Fatma Şener, İhsan Tuğal

“…The performance of several machine learning and deep learning models, including Long Short-Term Memory, Autoregressive Integrated Moving Average, Multilayer Perceptron, Random Forest, XGBoost, Support Vector Regression, and a hybrid CNN-LSTM model, is evaluated for daily solar radiation forecasting. …”
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A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques by Cong Peng, Cheng Gong, Xiaoya Zhang, Duxian Liu

“…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …”
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Optimizing Outdoor Micro-Space Design for Prolonged Activity Duration: A Study Integrating Rough Set Theory and the PSO-SVR Algorithm by Jingwen Tian, Zimo Chen, Lingling Yuan, Hongtao Zhou

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Quantitative Prediction of Low-Permeability Sandstone Grain Size Based on Conventional Logging Data by Deep Neural Network-Based BP Algorithm by Hongjun Fan, Xiaoqing Zhao, Zongjun Wang, Zheqing Zhang, Ao Chang

“…The best model was obtained by using decision tree, support vector machine, shallow and deep neural networks to model the median rock grain size and predict neighboring wells, and a comparative analysis showed that for the problem of predicting the median rock grain size in low-permeability sandstone reservoirs, the deep neural network improved significantly over the shallow one and was much stronger than other machine learning methods. …”
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COMPARATIVE ANALYSIS OF CLASSIFICATION MODELS FOR DETERMINING THE QUALITY OF WINE BY ITS CHEMICAL COMPOSITION by Vladimir S. Repkin, Artemy V. Li, Grigory Yu. Semenov, Nikita I. Sermavkin, Alexander S. Kovalenko, Nikolai S. Egoshin

“…Objects: classification models, including the support vector machine, decision tree, random forest algorithm, neural network, multiple regression and their application for automated wine quality assessment. …”
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Data-driven thrust prediction in applied-field magnetoplasmadynamic thrusters for space missions using artificial intelligence-based models by Tarik Pinaffo Almeida, Shahin Alipour Bonab, Mohammad Yazdani-Asrami

“…Results indicate that the supervised ensemble algorithm, eXtreme Gradient Boosting (XGBoost), outperforms all other utilized techniques such as random forest, Gradient Boosting Regressor, support vector regression, kernel ridge regression, K-nearest neighbors, and Gaussian process regression. …”
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