Machine learning methods for landslide mapping studies: A comparative study of SVM and RF algorithms in the Oued Aoulai watershed (Morocco) by Ladel Latifa, Mastere Mohamed, Kader Shuraik, Spalević Velibor, Dudic Branislav

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LASSO-mCGA: Machine Learning and Modified Compact Genetic Algorithm-Based Biomarker Selection for Breast Cancer Subtype Classification by Nimisha Ghosh, Sankar Kumar Mridha, Rourab Paul

“…To identify such biomarkers, initially LASSO in association with four machine learning models such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbours (KNN) and Naive Bayes (NB) are applied on the dataset to find the initial reduced set of genes as well as the best learning model based on classification accuracy; SVM in this case. …”
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Machine learning algorithms that predict the risk of prostate cancer based on metabolic syndrome and sociodemographic characteristics: a prospective cohort study by Tao Thi Tran, Jeonghee Lee, Junetae Kim, Sun-Young Kim, Hyunsoon Cho, Jeongseon Kim

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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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Comparative Analysis of Machine Learning Algorithms for Potential Evapotranspiration Estimation Using Limited Data at a High-Altitude Mediterranean Forest by Stefanos Stefanidis, Konstantinos Ioannou, Nikolaos Proutsos, Ilias Karmiris, Panagiotis Stefanidis

“…This study evaluates the performance of four machine learning (ML) regression algorithms—Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and K-Nearest Neighbors (KNN)—in predicting daily PET using limited meteorological data from a high-altitude in Central Greece. …”
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Comparative Analysis of Supervised Classification Algorithms for Residential Water End Uses by Zahra Heydari, Ashlynn S. Stillwell

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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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Establishing a differential diagnosis model between primary membranous nephropathy and non-primary membranous nephropathy by machine learning algorithms by Shangmei Cao, Shaozhe Yang, Bolin Chen, Xixia Chen, Xiuhong Fu, Shuifu Tang

“…Using the decision tree, random forest, support vector machine, and extreme gradient boosting (Xgboost) to establish a differential diagnosis model for PMN and non-PMN. …”
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APPLICATION OF SEMI SUPERVISED LAPLACE SCORE IN ROLLING BEARING FAULT DIAGNOSIS (MT) by LIANG Chuang, CHEN ChangZheng, LIU Ye, JIA XinYing

“…At the same time, particle swarm optimization-based support vector machine（PSO-SVM） algorithm is used for fault classification. …”
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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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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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Theoretical and computational investigations on estimation of viscosity of ionic liquids for green adsorbent: Effect of temperature and composition by Zhaoxiong Han

“…The viscosity of ionic liquids versus temperature and composition are estimated in this study via machine learning. The models used are Gaussian Process Regression (GPR), Multilayer Perceptron (MLP), and Support Vector Regression (SVR). …”
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A dynamic template adaptation approach for noise-robust sound classification and distance determination in single-channel audio by Rezaul Tutul, André Jakob, Ilona Buchem

“…Our method integrates a low-pass filter for noise reduction and uses an online support vector machine (SVM) to dynamically update the sound templates based on real-time audio inputs. …”
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Using machine learning algorithms to predict risk factors of heart failure after complete mesocolic excision in colorectal cancer patients by Yuan Liu, Yuankun Liu, Yu Zhang, Pengpeng Zhang, Jiaheng Xie, Ning Zhao, Yi Xie, Chao Cheng, Songyun Zhao

“…We compiled 37 feature variables, spanning patient demographic traits, foundational medical histories, preoperative examination characteristics, surgery types, and intraoperative details. Four distinct machine learning algorithms—extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor algorithm (KNN)—were employed to construct the model. …”
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A novel hybrid sand and dust storm detection method using MODIS data on GEE platform by Wei Wang, Alim Samat, Jilili Abuduwaili, Yongxiao Ge, Philippe De Maeyer, Tim Van de Voorde

“…To overcome this challenge, we propose a novel hybrid SDS detection method that combines the support vector machine (SVM) algorithm implemented on the Google Earth Engine (GEE) cloud computing platform with a spectral index to aid the automatic labelling of training samples. …”
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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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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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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

“…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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Machine Learning Applications for Predicting High-Cost Claims Using Insurance Data by Esmeralda Brati, Alma Braimllari, Ardit Gjeçi

“…This study aimed to empirically evaluate the performance of classification algorithms, including Logistic Regression, Decision Tree, Random Forest, XGBoost, K-Nearest Neighbors, Support Vector Machine, and Naïve Bayes to predict high insurance claims. …”
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Method on intrusion detection for industrial internet based on light gradient boosting machine by Xiangdong HU, Lingling TANG

“…Intrusion detection is a critical security protection technology in the industrial internet, and it plays a vital role in ensuring the security of the system.In order to meet the requirements of high accuracy and high real-time intrusion detection in industrial internet, an industrial internet intrusion detection method based on light gradient boosting machine optimization was proposed.To address the problem of low detection accuracy caused by difficult-to-classify samples in industrial internet business data, the original loss function of the light gradient boosting machine as a focal loss function was improved.This function can dynamically adjust the loss value and weight of different types of data samples during the training process, reducing the weight of easy-to-classify samples to improve detection accuracy for difficult-to-classify samples.Then a fruit fly optimization algorithm was used to select the optimal parameter combination of the model for the problem that the light gradient boosting machine has many parameters and has great influence on the detection accuracy, detection time and fitting degree of the model.Finally, the optimal parameter combination of the model was obtained and verified on the gas pipeline dataset provided by Mississippi State University, then the effectiveness of the proposed mode was further verified on the water dataset.The experimental results show that the proposed method achieves higher detection accuracy and lower detection time than the comparison model.The detection accuracy of the proposed method on the gas pipeline dataset is at least 3.14% higher than that of the comparison model.The detection time is 0.35s and 19.53s lower than that of the random forest and support vector machine in the comparison model, and 0.06s and 0.02s higher than that of the decision tree and extreme gradient boosting machine, respectively.The proposed method also achieved good detection results on the water dataset.Therefore, the proposed method can effectively identify attack data samples in industrial internet business data and improve the practicality and efficiency of intrusion detection in the industrial internet.…”
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