Assessing and Forecasting Natural Regeneration in Mediterranean Landscapes After Wildfires by Paraskevi Oikonomou, Vassilia Karathanassi, Vassilis Andronis, Ioannis Papoutsis

“…To predict vegetation regrowth, two time series models (ARMA, VARIMA) and two machine learning-based ones (random forest, XGBoost) were tested. Their performance was evaluated by comparing the predicted and actual numerical values, calculating error metrics (RMSE, MAPE), and analyzing how the predicted patterns align with the observed ones. …”
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Filtering airborne LiDAR data based on multi-view window and multi-resolution hierarchical cloth simulation by Shangshu Cai, Yong Pang, Sisi Yu, Xiangguo Lin, Xinlian Liang

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Ensemble Learning for Precise State-of-Charge Estimation in Electric Vehicles Lithium-Ion Batteries Considering Uncertainty by Aya Haraz, Khalid Abualsaud, Ahmed M. Massoud

“…The ETR-GBM consistently outperforms the individual models (ETR, LightGBM, XGBoost, CatBoost, Support Vector Regression (SVR), Random Forest (RF), and Bayesian) when noise is added to the training dataset. …”
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Tribological behavior of PLA reinforced with boron nitride nanoparticles using Taguchi and machine learning approaches by Harishbabu Sundarasetty, Santosh Kumar Sahu

“…The Relative Root Mean Square Error (RRSME) values decisively confirm that Random Forest Regression (23.86 % wear rate and 18.32 % COF) is more accurate than traditional linear regression approaches. …”
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The role of hybrid models in financial decision-making: Forecasting stock prices with advanced algorithms by Xiaoyi Zhu

“…Then, the high sequences underwent processing using the optimized random forest algorithm, and the remaining sequences were subjected to processing utilizing optimized bidirectional long short-term memory. …”
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MODELING HOUSE SELLING PRICES IN JAKARTA AND SOUTH TANGERANG USING MACHINE LEARNING PREDICTION ANALYSIS by Sugha Faiz Al Maula, Nicoletta Almira Dyah Setiawan, Elly Pusporani, Sa'idah Zahrotul Jannah

“…Results showed that LGBM and Random Forest outperformed others with 0.8 R2 and low MSE, with building and land area as the most significant factors influencing prices. …”
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Machine Learning-Based Prediction Performance Comparison of Marshall Stability and Flow in Asphalt Mixtures by Muhammad Farhan Zahoor, Arshad Hussain, Afaq Khattak

“…We used three feature importance analysis techniques (Random Forest, Permutation Importance, and Lasso Regression) to determine which parameters were the most significant. …”
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Meta-analysis of the effect of black soldier fly larvae meal in diet on broiler performance and prediction of its metabolisable energy value by Andrés L. Martínez Marín, Marta Gariglio, Ilaria Biasato, Laura Gasco, Achille Schiavone

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Regression models for the prediction of the influence of magnesium ions on primary endothelial cell (HUVEC) proliferation and migration by Heike Helmholz, Redon Resuli, Marius Tacke, Jalil Nourisa, Sven Tomforde, Roland Aydin, Regine Willumeit-Römer, Berit Zeller-Plumhoff

“…We compared linear regression, random forests, support vector machines, neural networks and large language models for the prediction of HUVEC proliferation for a number of scenarios. …”
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Integration of Convolutional Neural Network and Image Processing for Pulp Fibril Detection and Measurement by Tanachot Chirakitsakul, Pakaket Wattuya, Phichit Somboon, Panthira Jansakra, Chakrit Watcharopas

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Micro hole drilling and multi criteria optimization of soda lime glass via ultrasonic assisted rotary electrochemical discharge drilling by Sahil Grover, Viveksheel Rajput, Sanjay Kumar Mangal, Sarbjit Singh, Sandeep Singh, Shubham Sharma, Ehab El Sayed Massoud, Dražan Kozak, Jasmina Lozanovic

“…Machine learning-based algorithms are also used to predict the responses using Random Forest and Gradient Boost approaches. Comparative results indicated that the Random Forest predicts the responses with reduced error in comparison to the Gradient Boost method. …”
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The Role of Country- and Firm-Level Factors in Determining Firms’ Environmental, Social, and Governance (ESG) Performance: A Machine Learning Approach by Eman Abdelfattah, Mahfuja Malik, Syed Muhammad Ishraque Osman

“…For the random forests regressor, the coefficient of determination (R2) was 30% and the mean absolute error (MAE) was 1.52. …”
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Addressing Spatial Variability in Estimating Cover Management Factor of Soil Erosion Models using Geoinformatics: A Case Study of Netravati Catchment, Karnataka, India by Waleed Makhdumi, Shwetha H. R., G. S. Dwarakish, Jagadeesha B. Pai

“…To address this, a high-resolution Land Use Land Cover (LULC) map was generated using the Random Forest algorithm and in situ C factor values were assigned to LULC classes. …”
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A stacking ensemble model for food demand forecasting: A preventative approach to food waste reduction by Asmaa Seyam, Sujith Samuel Mathew, Bo Du, May El Barachi, Jun Shen

“…The experimental results reveal that the proposed stacking model outperforms random forest and eXtreme gradient boosting while consistently outperforming support vector regression and long short-term memory model, achieving a coefficient of determination score of 0.99, mean absolute error of 0.63, mean absolute percentage error of 1.8, and prediction accuracy of 98.2%. …”
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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

“…Among these algorithms, the RFR model demonstrated outstanding performance, yielding an excellent result compared to existing techniques, achieving an R-squared (R²) value of 0.79 and a root mean square error (RMSE) of 3.93 t/ha (per 10 m × 10 m pixel). …”
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Integrating AI predictive analytics with naturopathic and yoga-based interventions in a data-driven preventive model to improve maternal mental health and pregnancy outcomes by Neha Irfan, Sherin Zafar, Kashish Ara Shakil, Mudasir Ahmad Wani, S. N. Kumar, A. Jaiganesh, K. M. Abubeker

“…In regression tasks, the Random Forest Regressor achieved near-perfect predictions with a Mean Squared Error (MSE) of 4.5767 × 10−8 and an R2 score of 1.000, underscoring its superior predictive capabilities. …”
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An An accurate molecular method to sex elephants using PCR amplification of Amelogenin gene by George Lohay

“…This discrepancy observed was due to observational errors in the field, where high grass  reduces the ability to accurately sex young individuals. …”
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Development of a Noninvasive Genotyping‐In‐Thousands (GTseq) Panel for Long‐Term Conservation of Western Great Lakes Gray Wolves (Canis lupus) by Samuel D. Hervey, Bridgett M. vonHoldt, Mark C. Romanski, Tyler J. Wheeldon, Brent R. Patterson, Kristin E. Brzeski

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Research on Support Vector Regression Short-Time Traffic Flow Prediction Model for Secondary Roads Based on Associated Road Analysis by Ganglong Duan, Yutong Du, Yanying Shang, Hongquan Xue, Ruochen Zhang

“…In comparison, other models tested in our study, such as LSTM, Random Forest, and Gradient Boosting Decision Tree (GBDT), had higher error values. …”
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Plane tree risk assessment in urban space using Artificail Neural Network by Mozdeh Nafian, Mohsen Bahmani, Elham Ghehsareh Ardestani, Ali Soltani

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