Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors. by Montaser Abdelsattar, Mohamed A Ismeil, Karim Menoufi, Ahmed AbdelMoety, Ahmed Emad-Eldeen

“…Using a dataset of 40,000 observations, the models were assessed based on R-squared, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). ET achieved the highest performance among ML models, with an R-squared value of 0.7231 and a RMSE of 0.1512. …”
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Recycled Aggregate Concrete Incorporating GGBS and Polypropylene Fibers Using RSM and Machine Learning Techniques by Anjali Jaglan, Rati Ram Singh

“…The Distributed Random Forest model also provided strong performance but slightly higher error rates and lower R² values than GBM. …”
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Machine Learning-Based Recommender System for Pulsed Laser Ablation in Liquid: Recommendation of Optimal Processing Parameters for Targeted Nanoparticle Size and Concentration Usin... by Anesu Nyabadza, Dermot Brabazon

“…Multiple ML models were evaluated, including K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Random Forest, and Decision trees. The DT model achieved the best performance for predicting the NP size with a mean percentage error (MPE) of 10%. …”
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A Novel Hybrid Machine Learning Framework for Wind Speed Prediction by Rhafes Mohamed Yassine, Moussaoui Omar, Raboaca Maria Simona, Mihaltan Traian Candin

“…The performance of the models is evaluated using the R² score, Mean Absolute Error, and Root Mean Squared Error. The dataset for this study was generated from a numerical simulation conducted at a location with a latitude of 22.55° N and a longitude of -14.33° E. …”
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Three Environments, One Problem: Forecasting Water Temperature in Central Europe in Response to Climate Change by Mariusz Ptak, Mariusz Sojka, Katarzyna Szyga-Pluta, Teerachai Amnuaylojaroen

“…The framework integrates Bayesian Model Averaging (BMA), Random Sample Consensus (RANSAC) regression, Gradient Boosting Regressor (GBR), and Random Forest (RF) machine learning models. To assess the performance of the models, the coefficient of determination (R2), mean absolute error (<i>MAE</i>), and root mean square error (<i>RMSE</i>) were used. …”
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Short-term Power Load Forecasting for a 33/11 KV Sub-Station by Utilizing Attention-Based Hybrid Deep Learning Architectures by Mukkamala R.

“…The performance of these models measured using several key metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²). …”
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Deep learning framework for interpretable supply chain forecasting using SOM ANN and SHAP by Khandakar Rabbi Ahmed, Md Eahia Ansari, Md. Naimul Ahsan, Arafat Rohan, Md Borhan Uddin, Mir Araf Hossain Rivin

“…The R 2 was found to be 0.92, Root Mean Squared Error (RMSE) 0.936, and Mean Absolute Error (MAE) 0.8459 of the SOM+ANN model estimated the shipping duration. …”
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Do Machine Learning and Business Analytics Approaches Answer the Question of ‘Will Your Kickstarter Project be Successful? by Murat Kılınç, Can Aydın, Çiğdem Tarhan

“…F1-Score, Recall, Precision, Mean Squared Error (MSE), Kappa and AUC values were analyzed to determine the most successful models. …”
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Optimizing Renewable Energy Integration Using IoT and Machine Learning Algorithms by Orken Mamyrbayev, Ainur Akhmediyarova, Dina Oralbekova, Janna Alimkulova, Zhibek Alibiyeva

“…Three ML models (Random Forest, XGBoost, and Long Short-Term Memory networks) were developed and compared against a traditional persistence model for energy generation forecasting. …”
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Metal loss defect detection and depth estimation using multi-spectral image analysis of cooling excited steel specimen with corrosion by Shamendra Egodawela, Amirali K. Gostar, H. A. D. Samith Buddika, W. A. N. I. Harischandra, A. J. Dhammika, Mojtaba Mahmoodian

“…Support Vector Regression, Decision Tree Regressor, Random Forest Regressor, Gradient Boosting Regressor, and a Feedforward Neural Network (FNN) were tested for this task. …”
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Adaptive Smart System for Energy-Saving Campus by Ziling Chen, Ray-I Chang, Quincy Wu

“…Additionally, by incorporating a random forest classifier, the system learns users’ electricity usage habits to create a tailored energy-saving environment. …”
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Synergistic application of artificial intelligence and response surface methodology for predicting and enhancing in vitro tuber production of potato (Solanum tuberosum). by Rajermani Thinakaran, Ecenur Korkmaz, Başak Ünver, Seyid Amjad Ali, Zeshan Iqbal, Muhammad Aasim

“…Results analyzed by Machine learning (ML) models revealed maximum predictive accuracy for tuberization by Random Forest (RF) model with an R2 of 0.379. However, all other models also faced challenges with high error rates, indicating the need for improved feature engineering. …”
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Computational fluid dynamics analysis and machine learning study of heat transfer in solar air heaters with distinct ribs configuration by Eid S. Alatawi

“…Critically, the developed Convolutional Neural Network (CNN) model significantly outperformed Random Forest Regression and Support Vector Regression, achieving a Mean Square Error (MSE) of 0.004 and an R2 value of 0.96 in predicting heat transfer coefficients. …”
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A Method to Obtain Remotely Sensed Grain Size Distributions From Granular Deposits With Complex Surfaces by H. L. Jacobson, G. Walton, K. R. Barnhart, F. K. Rengers

“…This approach combines an existing random forest machine learning method with a novel iterative clustering algorithm. …”
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Batch evaluation of collective owned commercialised construction land using machine learning by Wenzhu Zhang, Licheng Huang, Shengquan Lu, Shiyu Deng, Bin Wu, Yanfei Wei

“…Focusing on Beiliu City, a representative reform pilot area, we implemented three models—Random Forest (RF), Back Propagation Neural Network (BPNN), and Support Vector Machine (SVM)—to develop a tailored indicator system for price prediction. …”
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Optimising the Selection of Input Variables to Increase the Predicting Accuracy of Shear Strength for Deep Beams by Mohammed Majeed Hameed, Faidhalrahman Khaleel, Mohamed Khalid AlOmar, Siti Fatin Mohd Razali, Mohammed Abdulhakim AlSaadi

“…The LWLR-GAITH model showed 29.15% to 47.88% higher performance accuracy in terms of root mean square error (RMSE) than the other hybrid models during the test phase. …”
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Predicting Oil Price Trends During Conflict With Hybrid Machine Learning Techniques by Hicham Boussatta, Marouane Chihab, Mohamed Chiny, Younes Chihab

“…Using advanced machine learning techniques, we developed a hybrid system combining Random Forest, ElasticNet, K-Nearest Neighbors, Gradient Boosting, and Support Vector Regressor models. …”
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Long-term patterns of forearm asymmetry in females of three syntopic bat species and its effects on individual fitness by Tobias Süess, Gerald Kerth

“…Here, we analyzed up to 27 years of mark-recapture data from 894 RFID tagged individuals of three forest-living bat species in southern Germany to investigate the degree of fluctuating asymmetry in forearm length. …”
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Travel time prediction for Two-Lane Two-Way undivided carriageway road Section- A case study by Sanjay Luitel, Pradeep Kumar Shrestha, Hemant Tiwari

“…Furthermore, statistical error tests demonstrate that the random forest method outperforms other approaches in predicting travel time. …”
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Combined Prediction of Dust Concentration in Opencast Mine Based on RF-GA-LSSVM by Shuangshuang Xiao, Jin Liu, Yajie Ma, Yonggui Zhang

“…Initially, the random forest (RF) algorithm is employed to identify key features from the meteorological and dust concentration data collected on site, ultimately selecting five indicators—temperature, humidity, stripping amount, wind direction, and wind speed—as the input variables for the prediction model. …”
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