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

“…In comparison to other methods, the proposed model demonstrated superior performance on the S&P 500, with an R2 of 0.99 and low error metrics. This model’s adaptability and reliability in diverse and volatile market conditions are emphasized by this robust framework, which renders it a potent financial forecasting tool.…”
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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

“…The analysis focused on key predictors like land area, building area, bedrooms, and carports, with R² and Mean Squared Error (MSE) metrics evaluating model performance. …”
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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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Machine Learning Framework for Early Detection of Chronic Kidney Disease Stages Using Optimized Estimated Glomerular Filtration Rate by Samit Kumar Ghosh, Namareq Widatalla, Ahsan H. Khandoker

“…The application of GWO for hyperparameter tuning has resulted in a 37.3% reduction in root mean square error (RMSE), a 37.4% drop in mean absolute percentage error (MAPE), and a 2.06% improvement in <inline-formula> <tex-math notation="LaTeX">$\text {R}^{2}$ </tex-math></inline-formula> to improve the precision of prediction. …”
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Automated seafood freshness detection and preservation analysis using machine learning and paper-based pH sensors by B. Kumaravel, A. L. Amutha, T. P. Milintha Mary, Aryan Agrawal, Akshat Singh, S. Saran, Nagamaniammai Govindarajan

“…The model’s performance was evaluated using standard metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). …”
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A New Frontier in Wind Shear Intensity Forecasting: Stacked Temporal Convolutional Networks and Tree-Based Models Framework by Afaq Khattak, Jianping Zhang, Pak-wai Chan, Feng Chen, Abdulrazak H. Almaliki

“…The proposed TCN-XGBoost model outperformed these alternatives, achieving a lower Root Mean Squared Error (RMSE: 1.95 for training, 1.97 for testing), Mean Absolute Error (MAE: 1.41 for training, 1.39 for testing), and Mean Absolute Percentage Error (MAPE: 7.90% for training, 7.89% for testing). …”
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Load Optimization for Connected Modern Buildings Using Deep Hybrid Machine Learning in Island Mode by Seyed Morteza Moghimi, Thomas Aaron Gulliver, Ilamparithi Thirumarai Chelvan, Hossen Teimoorinia

“…For a four-bedroom Connected Smart Green Townhouse (CSGT), the Mean Absolute Percentage Error (MAPE) is 4.43%, the Root Mean Square Error (RMSE) is 3.49 kWh, the Mean Absolute Error (MAE) is 3.06 kWh, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> is 0.81. …”
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Prediction of splitting tensile strength of fiber-reinforced recycled aggregate concrete utilizing machine learning models with SHAP analysis by Md Al Adnan, Muhammad Babur, Faisal Farooq, Mursaleen Shahid, Zamiul Ahmed, Pobithra Das

“…The machine learning models demonstrated high reliability in predicting splitting tensile strength, including robust values for R-squared (R2), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). …”
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Fetal Birth Weight Prediction in the Third Trimester: Retrospective Cohort Study and Development of an Ensemble Model by Jing Gao, Xu Jie, Yujun Yao, Jingdong Xue, Lei Chen, Ruiyao Chen, Jiayuan Chen, Weiwei Cheng

“…The models were compared using accuracy, mean squared error, root mean squared error, and mean absolute error. …”
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Machine Learning Models Informed by Connected Mixture Components for Short- and Medium-Term Time Series Forecasting by Andrey K. Gorshenin, Anton L. Vilyaev

“…For geophysical spatiotemporal data, the decrease in Root Mean Square Error (RMSE) was up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>27.7</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and the reduction in Mean Absolute Percentage Error (MAPE) was up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>45.7</mn><mo>%</mo></mrow></semantics></math></inline-formula> compared with ML models without probability informing. …”
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Maize and soybean yield prediction using machine learning methods: a systematic literature review by Ramandeep Kumar Sharma, Jasleen Kaur, Gary Feng, Yanbo Huang, Chandan Kumar, Yi Wang, Sandhir Sharma, Johnie Jenkins, Jagmandeep Dhillon

“…In the utilized models, the most used performance assessment measures were noted as the coefficient of determination (R2), root absolute error (RAE), root mean square error (RMSE), and mean absolute error (MAE). …”
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Linking Electrocardiogram and Echocardiogram: Comparing Classical Machine Learning and Deep Learning Neural Networks for the Detection of Regional Wall Motion Abnormalities by Shantanu M. Joshi, Hana R. Shaik, Shivam Rai Sharma, Philip Strong, Uma Srivatsa, Imo Ebong, Hyoyoung Jeong, Chen-Nee Chuah, Lihong Mo

“…Historically, electrocardiogram (ECG) datasets have been created based on physicians&#x2019; interpretation of the ECG, which may introduce human biases and errors. Nightingale Open Science provides an open-source ECG dataset linking to an imaging marker, regional wall motion abnormality (RWMA), that is primarily associated with myocardial ischemia or infarction. …”
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Uncertainty‐Aware Machine Learning Bias Correction and Filtering for OCO‐2: 1 by Steffen Mauceri, William Keely, Josh Laughner, Christopher W. O’Dell, Steven Massie, Robert Nelson, David Baker, Matthäus Kiel, Otto Lamminpää, Jonathan Hobbs, Abhishek Chatterjee, Tommy Taylor, Paul Wennberg, Sean Crowell, Britton Stephens, Vivienne H. Payne

“…However, biases are present in the retrieved XCO2 due to sensor calibration errors and discrepancies between the physics‐based retrieval and nature. …”
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Revolutionizing Chinese medicine granule placebo with a machine learning four-color model by Tingting Teng, Jingze Zhang, Peiqi Miao, Lipeng Liang, Xinbo Song, Dailin Liu, Junhua Zhang

“…However, due to the diverse colors and complex color gamut of these particles, existing simulation methods rely on manual comparison and color mixing, leading to high subjectivity and errors. This study addresses this issue by developing a prediction model to accurately simulate the colors of Chinese medicine granules. …”
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Machine Learning-Driven Rapid Flood Mapping for Tropical Storm Imelda Using Sentinel-1 SAR Imagery by Reda Amer

“…The proposed approach eliminates the need for manual threshold selection, thereby reducing misclassification errors due to speckle noise and land cover heterogeneity. …”
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A Neighborhood Approach for Using Remotely Sensed Data to Estimate Current Ranges for Conservation Assessments by Bethany A. Johnson, Gonzalo E. Pinilla‐Buitrago, Robert P. Anderson

“…We implement its use for a forest‐dwelling species (Handleyomys chapmani) considered threatened by the IUCN. …”
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Assessing SWOT interferometric SAR altimetry for inland water monitoring: insights from Lake Léman by Henri Bazzi, Nicolas Baghdadi, Yen-Nhi Ngo, Cassandra Normandin, Frédéric Frappart, Cecile Cazals

“…To explore the sources of errors in SWOT WSE, a random forest analysis showed that atmospheric perturbations had the most significant impact on the SWOT WSE estimation accuracy. …”
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A framework for modelling whole-lung and regional transfer factor of the lung for carbon monoxide using hyperpolarised xenon-129 lung magnetic resonance imaging by Jemima H. Pilgrim-Morris, Laurie J. Smith, Helen Marshall, Bilal A. Tahir, Guilhem J. Collier, Neil J. Stewart, Jim M. Wild

“…The random forest model was applied voxel-wise to 129Xe images to yield regional TL maps. …”
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