Machine learning models for estimating fetal weight based on ultrasonographic biometry: Development and validation study by Marcos Espinola-Sánchez, Antonio Limay-Rios, Andrés Campaña-Acuña, Silvia Sanca-Valeriano

“…Accuracy was assessed using the coefficient of determination ( R ²) and mean squared error (MSE), comparing the ML models to the Hadlock and Shepard formulas. …”
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Grouped machine learning methods for predicting rock mass parameters in a tunnel boring machine‐driven tunnel based on fuzzy C‐means clustering by Ruirui Wang, Yaodong Ni, Lingli Zhang, Boyang Gao

“…In addition, by combining fuzzy C‐means clustering, the prediction accuracies of support vector regression and random forest are also improved to different degrees, which demonstrates that fuzzy C‐means clustering is helpful for improving the prediction accuracy of machine learning and thus has good applicability. …”
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Prediction of the Ultimate Impact Response of Concrete Strengthened with Polyurethane Grout as the Repair Material by Sadi I. Haruna, Yasser E. Ibrahim, Sani I. Abba

“…Machine learning algorithms (ML) such as Long Short-Term Memory (LSTM), Random Forest (RF), and Wide Neural Network (WNN) models were developed to estimate <i>U</i>s by considering five input parameters: the initial crack strength (<i>C</i>s), thickness of the grouting materials (<i>T</i>), mid-span deflection (<i>λ</i>), and peak applied load (<i>P</i>). …”
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FlickPose: A Hand Tracking-Based Text Input System for Mobile Users Wearing Smart Glasses by Ryo Yuasa, Katashi Nagao

“…A machine learning model trained for hand pose recognition outperforms Random Forest and LightGBM models in accuracy and consistency. …”
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Self-Supervised Learning for Precise Individual Tree Segmentation in Airborne LiDAR Point Clouds by Lama Shaheen, Bader Rasheed, Manuel Mazzara

“…Segmenting individual trees from airborne LiDAR point cloud data is critical for forest management, urban planning, and ecological monitoring but remains challenging due to complex natural environments, diverse tree architectures, and dense canopies. …”
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Development of an Optimal Machine Learning Model to Predict CO₂ Emissions at the Building Demolition Stage by Gi-Wook Cha, Choon-Wook Park

“…., gradient boosting machine [GBM], decision tree, and random forest), based on the information on building features and the equipment used for demolition, as well as energy consumption data. …”
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Assessing Climate Change Impacts on Cropland and Greenhouse Gas Emissions Using Remote Sensing and Machine Learning by Nehir Uyar, Azize Uyar

“…Machine learning algorithms, including gradient boosting trees (GBT), random forest (RF), support vector machines (SVM), and classification and regression trees (CART), were employed to estimate greenhouse gas emissions based on multiple environmental parameters. …”
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The impact of deferred cytoreductive nephrectomy on survival in advanced renal cell carcinoma: A systematic review and meta-analysis by Mohammad Taufiq Alamsyah, Fauriski Febrian Prapiska, Syah Mirsya Warli

“…The fixed-effect and random-effects models were used to obtain pooled estimates using the hazard ratio and standard error, presented using the forest plot with 95% confidence interval. …”
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Detecting Flooded Areas Using Sentinel-1 SAR Imagery by Francisco Alonso-Sarria, Carmen Valdivieso-Ros, Gabriel Molina-Pérez

“…In this study, Random Forest is used to estimate flooded cells after 19 events in Campo de Cartagena, an agricultural area in SE Spain. …”
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Extreme Gradient Boosting Algorithm for Predicting Shear Strengths of Rockfill Materials by Mahmood Ahmad, Ramez A. Al-Mansob, Kazem Reza Kashyzadeh, Suraparb Keawsawasvong, Mohanad Muayad Sabri Sabri, Irfan Jamil, Arnold C. Alguno

“…The XGBoost model was compared against support vector machine (SVM), adaptive boosting (AdaBoost), random forest (RF), and K-nearest neighbor (KNN) models described in the literature. …”
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DEFINING A SUSTAINABLE TOURISM PERSPECTIVES IN EASTERN PART OF BALKHASH-ALAKOL BASIN by Rakhimzhanova GULNUR, Mussina KAMSHAT

“…Analyses show that regression reveals a 38.9% of error of prediction, indicating a moderate level of explanatory power in the model. …”
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LSTM+MA: A Time-Series Model for Predicting Pavement IRI by Tianjie Zhang, Alex Smith, Huachun Zhai, Yang Lu

“…The performance of the LSTM+MA is compared with other state-of-the-art models, including logistic regressor (LR), support vector regressor (SVR), random forest (RF), K-nearest-neighbor regressor (KNR), fully connected neural network (FNN), XGBoost (XGB), recurrent neural network (RNN) and LSTM. …”
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Estimating individual tree DBH and biomass of durable Eucalyptus using UAV LiDAR by Ning Ye, Euan Mason, Cong Xu, Justin Morgenroth

“…Three machine learning (ML) models, including Partial Least Squares Regression (PLSR), Random Forest, and Extreme Gradient Boosting (XGBoost), were trained. …”
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A Bayesian Additive Regression Trees Framework for Individualized Causal Effect Estimation by Lulu He, Lixia Cao, Tonghui Wang, Zhenqi Cao, Xin Shi

“…Compared with mainstream meta-learning methods such as S-Learner, X-Learner and Bayesian Causal Forest, the dual-structure BART-ITE model achieves a favorable balance between root mean square error and bias. …”
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Load aggregator adjustable capability forecasting based on graph convolution neural network by DONG Lingrui, WU Binyuan

“…Taking the mean absolute percentage error (MAPE) index obtained from the example analysis as an example, compared with long short-term memory (LSTM), support vector machine (SVM), and random forest regression (RFR), the forecasting accuracy of GCN model has increased by 1.83%, 2.10% and 2.72% in terms of RMSE.…”
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Study on the quantitative analysis of Tilianin based on Raman spectroscopy combined with deep learning. by Wen Jiang, Wei Liu, Xiaotong Xin, Wei Zhang, Junhui Chen, Jieyu Liu, Yanqi Ma, Cheng Chen, Xiaomei Pan

“…In this paper, five sets of comparison models are set up, including two machine learning models (Random Forest, K-Nearest Neighbors, Artificial Neural Network) and two deep learning models (Convolutional Neural Network and Variational Autoencoder), and the results show that the model in this paper fits the best, obtaining an R2 of 0.9144, as well as a small error.…”
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Normalized Difference Red-Edge Estimation With Modified DiscoGAN Model by Hyeon-Beom Choi, Kwon-Hee Han, Jeongwook Seo

“…Vegetation information is important to study the health and productivity of farmlands and forest ecosystems and investigate the types and severity of threats to them. …”
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Integrating machine learning and spatial clustering for malaria case prediction in Brazil’s Legal Amazon by Kayo Henrique de Carvalho Monteiro, Élisson da Silva Rocha, Luis Augusto Morais, Elton Gino Santos, Sebastião Rogerio da S. Neto, Vanderson Sampaio, Patricia Takako Endo

“…The results demonstrate that the RF model consistently outperformed the other models, achieving the lowest Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) values in most cases, such as in cluster 02 of the state of Acre, with RMSE of 0.00203 and MAE of 0.00133. …”
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Prediction and sensitivity analysis of pressure pulsation in a LDI combustor based on a fully connected neural network by Qian Yao, Shize Tian, Wei Pan, Wu Jin, Jianzhong Li, Li Yuan

“…A fully connected neural network (FCNN) is utilized to predict these pressure parameters, and it demonstrates superior performance compared to support vector regression (SVR) and random forest (RF) models. For test data, the FCNN achieves an average relative error (MRE) of 2.54 % for dominant frequency and a mean absolute error (MAE) of 0.64 % for pressure amplitude, suggesting high accuracy and generalization ability. …”
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A Rice Leaf Area Index Monitoring Method Based on the Fusion of Data from RGB Camera and Multi-Spectral Camera on an Inspection Robot by Yan Li, Xuerui Qi, Yucheng Cai, Yongchao Tian, Yan Zhu, Weixing Cao, Xiaohu Zhang

“…The model based on the LightGBM regression algorithm has the most improvement in accuracy, with a coefficient of determination (R²) of 0.892, a root mean square error (RMSE) of 0.270, and a mean absolute error (MAE) of 0.160. …”
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