Evaluating machine learning models for supernova gravitational wave signal classification by Y Sultan Abylkairov, Matthew C Edwards, Daniil Orel, Ayan Mitra, Bekdaulet Shukirgaliyev, Ernazar Abdikamalov

“…We test convolutional and recurrent neural networks, as well as six classical algorithms: random forest, support vector machines, naïve Bayes(NB), logistic regression, k -nearest neighbors, and eXtreme gradient boosting. …”
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Water Quality Variations in the Lower Yangtze River Based on GA-RF Model From GF-1, Landsat-8, and Sentinel-2 Images by Wentao Hu, Shuanggen Jin, Yuanyuan Zhang

“…In this article, GF-1, Landsat-8, and Sentinel-2 data are jointly used to develop a genetic algorithm-random forest (GA-RF) water quality inversion model weighted by the entropy method. …”
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A Novel Ensemble Classifier Selection Method for Software Defect Prediction by Xin Dong, Jie Wang, Yan Liang

“…The experimental results demonstrate that the DFD ensemble learning-based software defect prediction model outperforms the ten other models, including five common machine learning (ML) classification algorithms (logistic regression (LR), naïve Bayes (NB), K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM)), two deep learning (DL) algorithms (multi-layer perceptron (MLP) and convolutional neural network (CNN)), and three ensemble learning algorithms (random forest (RF), extreme gradient boosting (XGB), and stacking). …”
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Palmitoylation-related gene ZDHHC22 as a potential diagnostic and immunomodulatory target in Alzheimer’s disease: insights from machine learning analyses and WGCNA by Sanying Mao, Xiyao Zhao, Lei Wang, Yilong Man, Kaiyuan Li

“…This study applied WGCNA along with three machine learning algorithms—random forest, LASSO regression, and SVM–RFE—to further select key PRGs (KPRGs). …”
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Early Detection of Seasonal Outbreaks from Twitter Data Using Machine Learning Approaches by Samina Amin, Muhammad Irfan Uddin, Duaa H. alSaeed, Atif Khan, Muhammad Adnan

“…This work proposes a machine-learning-based approach to detect dengue and flu outbreaks in social media platform Twitter, using four machine learning algorithms: Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Decision Tree (DT), with the help of Term Frequency and Inverse Document Frequency (TF-IDF). …”
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Explainable machine learning model for predicting decline in platelet count after interventional closure in children with patent ductus arteriosus by Song-Yue Zhang, Yi-Dong Zhang, Hao Li, Qiao-Yu Wang, Qiao-Fang Ye, Xun-Min Wang, Tian-He Xia, Yue-E He, Xing Rong, Ting-Ting Wu, Rong-Zhou Wu

“…The extra tree algorithm was used for feature selection and four ML algorithms [random forest (RF), adaptive boosting, extreme gradient boosting, and logistic regression] were established. …”
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Assessing the performance of machine learning and analytical hierarchy process (AHP) models for rainwater harvesting potential zone identification in hilly region, Bangladesh by Md. Mahmudul Hasan, Md. Talha, Most. Mitu Akter, Md Tasim Ferdous, Pratik Mojumder, Sujit Kumar Roy, N.M. Refat Nasher

“…Specifically, four ML algorithms—random forest (RF), boosted regression trees (BRT), k-nearest neighbors (KNN), and naïve bayes (NB)—alongside the analytical hierarchy process (AHP) were employed to delineate potential RWH zones in the Chattogram hilly districts, including Chattogram, Rangamati, Bandarban, Khagrachari, and Cox’s Bazar. …”
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Design of low-carbon planning model for vehicle path based on adaptive multi-strategy ant colony optimization algorithm by Qi Guo, Rui Li, Changjiang Zheng, Gwanggil Jeon

“…Experimental results demonstrate that ACGNN yields significant path planning outcomes on both public and custom-built datasets, surpassing the traditional Dijkstra’s shortest path algorithm, random graph network (RGN), and conventional GNN methodologies. …”
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Compressive strength prediction models for concrete containing nano materials and exposed to elevated temperatures by Hany A. Dahish, Ahmed D. Almutairi

“…In the present study, two machine learning algorithms, random forest (RF) and M5P decision tree, and linear regression were used for developing prediction models for the compressive strength (CS) of concrete containing nano alumina (NA) and carbon nanotubes (CNT) and being subjected to elevated temperatures. …”
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Effect of phosphorus fractions on benthic chlorophyll-a: Insight from the machine learning models by Yuting Wang, Sangar Khan, Zongwei Lin, Xinxin Qi, Kamel M. Eltohamy, Collins Oduro, Chao Gao, Paul J. Milham, Naicheng Wu

“…To address this gap, we applied two machine learning algorithms—random forest (RF), and artificial neural networks (ANN) to predict benthic chl-a concentrations by incorporating these specific P fractions as separate variables. …”
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Inversion of Water Quality Parameters from UAV Hyperspectral Data Based on Intelligent Algorithm Optimized Backpropagation Neural Networks of a Small Rural River by Manqi Wang, Caili Zhou, Jiaqi Shi, Fei Lin, Yucheng Li, Yimin Hu, Xuesheng Zhang

“…To intuitively evaluate the performance of the hybrid optimization algorithm, its prediction accuracy is compared with that of conventional machine learning algorithms (Random Forest, CatBoost, XGBoost, BPNN, GA–BPNN and PSO–BPNN). …”
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A Machine Learning Algorithm to Predict Medical Device Recall by the Food and Drug Administration by Victor Barbosa Slivinskis, Isabela Agi Maluli, Joshua Seth Broder

“…We constructed an ML algorithm (random forest regressor) that automatically searched Google Trends and PubMed for the RMDs and NRMDs. …”
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Improved Estimation of Aboveground Biomass in Rubber Plantations Using Deep Learning on UAV Multispectral Imagery by Hongjian Tan, Weili Kou, Weiheng Xu, Leiguang Wang, Huan Wang, Ning Lu

“…To address this, our study evaluated the performance of three ML algorithms (random forest regression, RFR; XGBoost regression, XGBR; categorical boosting regression, CatBoost) combined with feature selection techniques and a deep convolutional neural network (DCNN) using multispectral imagery obtained from UAV for the AGB estimation of rubber plantations. …”
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Machine-Learning Parsimonious Prediction Model for Diagnostic Screening of Severe Hematological Adverse Events in Cancer Patients Treated with PD-1/PD-L1 Inhibitors: Retrospective... by Seok Jun Park, Seungwon Yang, Suhyun Lee, Sung Hwan Joo, Taemin Park, Dong Hyun Kim, Hyeonji Kim, Soyun Park, Jung-Tae Kim, Won Gun Kwack, Sung Wook Kang, Yun-Kyoung Song, Jae Myung Cha, Sang Youl Rhee, Eun Kyoung Chung

“…Among the tested ML algorithms, random forest achieved the highest accuracy (area under the receiver operating characteristic curve [AUROC] 0.88 for both cohorts). …”
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Time-series forest age estimation in Xinjiang based on forest disturbance and recovery detection by Lili Zhai, Mei Zan, Mao Ye, Jia Zhou, Cong Xue, Shunfa Yang, Yuntong Liu

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CLASSIFICATION AND PREDICTION OF BENTHIC HABITAT FROM SCIENTIFIC ECHOSOUNDER DATA: APPLICATION OF MACHINE LEARNING ALGORITHMS by Baigo HAMUNA, Sri PUJIYATI, Jonson Lumban GAOL, Totok HESTIRIANOTO

“…The classification and prediction process of benthic habitats uses two machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM), in XLSTAT Basic+ software. …”
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Leaf Water Potential in a Mixed Mediterranean Forest from Machine Learning and Unmanned Aerial Vehicle (UAV)-Based Hyperspectral Imaging by Netanel Fishman, Yehuda Yungstein, Assaf Yaakobi, Sophie Obersteiner, Laura Rez, Gabriel Mulero, Yaron Michael, Tamir Klein, David Helman

“…Three machine learning algorithms—random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM)—were used to model <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>ψ</mi></mrow></semantics></math></inline-formula>_leaf. …”
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