Prediction of uniaxial compressive strength of limestone from ball mill grinding characteristics using supervised machine learning techniques by Sahas V. Swamy, Bijay Mihir Kunar, Karra Ram Chandar, Mamdooh Alwetaishi, Shashikumar Krishnan, Sudhakar Reddy

“…Model robustness was further assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Variance Accounted For (VAF). …”
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The Correlation of Microscopic Particle Components and Prediction of the Compressive Strength of Fly-Ash-Based Bubble Lightweight Soil by Yaqiang Shi, Hao Li, Hongzhao Li, Zhiming Yuan, Wenjun Zhang, Like Niu, Xu Zhang

“…The Bayesian-optimized Random Forest model performed optimally in terms of the mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE), and the prediction performance was best. …”
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Low-variance estimation of live fuel moisture content using VIIRS data through radiative transfer model by Shuai Yang, Rui Chen, Binbin He, Yiru Zhang

“…Results showcase that VIIRS-based LFMC inversion yields marginally superior accuracy (R2= 0.57, R2= 0.32) for both grassland and forest types, with VIIRS-based inversion demonstrating a lower relative root mean square error (rRMSE = 5.84%), compared to results from the MODIS. …”
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Modeling the Land Surface Phenological Responses of Dominant Miombo Tree Species to Climate Variability in Western Tanzania by Siwa E. Nkya, Deo D. Shirima, Robert N. Masolele, Henrik Hedenas, August B. Temu

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Temporal Parasitemia Trends Predict Risk and Timing of Experimental Cerebral Malaria in Mice Infected by <i>Plasmodium berghei</i> ANKA by Peyton J. Murin, Cláudio Tadeu Daniel-Ribeiro, Leonardo José Moura Carvalho, Yuri Chaves Martins

“…Early increases in parasitemia were strong predictors for the development of ECM, with an increase in parasitemia equal to or greater than 0.05 between day 1 and day 3 predicting the development of ECM with 97% sensitivity. The Random Forest model predicted the day of ECM onset with high precision (mean absolute error: 0.43, R²: 0.64). …”
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Snow Depth Extraction From Time‐Lapse Imagery Using a Keypoint Deep Learning Model by C. M. Breen, W. R. Currier, C. Vuyovich, Z. Miao, L. R. Prugh

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Comparison of Satellite-based PM2.5 Estimation from Aerosol Optical Depth and Top-of-atmosphere Reflectance by Heming Bai, Zhi Zheng, Yuanpeng Zhang, He Huang, Li Wang

“…For both reflectance-based and AOD-based approaches, our cross validated results show that random forest algorithm achieves the best performance, with a coefficient of determination (R2) of 0.75 and root-mean-square error (RMSE) of 18.71 µg m−3 for the former and R2 = 0.65 and RMSE = 15.69 µg m−3 for the later. …”
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Photos, tweets, and trails: Are social media proxies for urban trail use? by Xinyi Wu, Spencer A. Wood, David Fisher, Greg Lindsey

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Optimizing potato yield predictions in Uttar Pradesh, India: a comparative analysis of machine learning models by Ahmad Alsaber, Anurag Satpathi, Mariam Alsabah, Parul Setiya

“…The ANN model demonstrated superior performance with the highest R2 values and the lowest error metrics, establishing it as the most reliable model for potato yield prediction. …”
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Development of Non-Invasive Continuous Glucose Prediction Models Using Multi-Modal Wearable Sensors in Free-Living Conditions by Thilini S. Karunarathna, Zilu Liang

“…Feature selection was conducted using random-forest-based importance analysis to select the most relevant features for model training. …”
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Hybridize Machine Learning Methods and Optimization Techniques to Analyze and Repair Welding Defects via Digital Twin of Jidoka Simulator by Ahmed M. Abed, Tamer S. Gaafar

“…Hybridising the Random-Forest algorithm with Dingo optimisation and called Regulated Random Forest (RRF) to precisely identify defect clusters and then predict the welding defect growth rate (<inline-formula> <tex-math notation="LaTeX">$\boldsymbol {{R}_{s}}$ </tex-math></inline-formula>) using the Cat-boost optimiser, which is enhanced by a beetle search mechanism called CatBAS. …”
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Using machine learning to identify key predictors of maternal success in sheep for improved lamb survival by Ebru Emsen, Bahadir Baran Odevci, Muzeyyen Kutluca Korkmaz

“…The Random Forest model achieved the highest accuracy (67.2%) and demonstrated the best overall performance with a 0.41 Kappa statistic and the lowest mean absolute error (0.59). …”
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Improving air quality prediction using hybrid BPSO with BWAO for feature selection and hyperparameters optimization by Mohamed S. Sawah, Hela Elmannai, Alaa A. El-Bary, Kh. Lotfy, Osama E. Sheta

“…The Random Forest model achieved the best performance after feature selection with an MSE of 53.93, R² of 0.9710, and reduced fitted time. …”
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A high-resolution map of coastal vegetation for two Arctic Alaskan parklands: An object-oriented approach with point training data. by Celia J Hampton-Miller, Peter N Neitlich, David K Swanson

“…A unified, study area-wide Random Forest model for both parklands produced the highest accuracy of various models attempted. …”
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Prediction method of gas emission in working face based on feature selection and BO-GBDT by MA Wenwei

“…Comparison with random forest, support vector machine, and neural network models showed that the BO-GBDT model achieved the highest accuracy and generalization, with an average relative error of 2.61%. …”
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Intelligent Forecasting of Air Quality and Pollution Prediction Using Machine Learning by D. Kothandaraman, N. Praveena, K. Varadarajkumar, B. Madhav Rao, Dharmesh Dhabliya, Shivaprasad Satla, Worku Abera

“…In this scenario, the authors proposed various machine learning models such as linear regression, random forest, KNN, ridge and lasso, XGBoost, and AdaBoost models to predict PM2.5 pollutants in polluted cities. …”
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Survival Prediction in Brain Metastasis Patients Treated with Stereotactic Radiosurgery: A Hybrid Machine Learning Approach by Tuğçe Öznacar, İpek Pınar Aral, Hatice Yağmur Zengin, Yılmaz Tezcan

“…Random Forest performed the weakest, with the highest MSE (14.39%), MAE (30.23%), and MAPE (33.58%). …”
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Smart IoT-driven precision agriculture: Land mapping, crop prediction, and irrigation system. by Gourab Saha, Fariha Shahrin, Farhan Hasin Khan, Mashook Mohammad Meshkat, Akm Abdul Malek Azad

“…Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”
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A robot process automation based mobile application for early prediction of chronic kidney disease using machine learning by Md. Hasan Imam Bijoy, Md. Jueal Mia, Md. Mahbubur Rahman, Mohammad Shamsul Arefin, Pranab Kumar Dhar, Tetsuya Shimamura

“…The models’ performance was assessed using accuracy, precision, recall, F1-Score, error rate, AUC, and computational time. Among the tested algorithms, MKR Stacking achieved the highest accuracy of 99.50%, outperforming Random Forest (98.75%) and MKR Voting (98%). …”
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Snow Distribution Patterns Revisited: A Physics‐Based and Machine Learning Hybrid Approach to Snow Distribution Mapping in the Sub‐Arctic by R. L. Crumley, C. L. Bachand, K. E. Bennett

“…When the hybrid results were compared with the physics‐based method, the hybrid method more accurately depicted the spatial patterns of the snowpack, areas of drifting snow, and years when no in‐situ observations were used in the random forest ML training data set. The hybrid method also showed improvements in root mean squared error at 61% of locations where time‐series estimations of snow depth were observed. …”
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