Analysis of factors influencing clinical pregnancy rates in frozen-thawed embryo transfer cycles by Junqiang Wang, Zexing Yang, Ying Chen, Ying Chen, Fengchen Gao, Wenxiu Zhao, Shuxuan Cao, Yixi Li, Limei He, Limei He

“…Baseline characteristics were compared between groups. A random forest algorithm was applied to rank the importance of variables, followed by dimensionality reduction using a sliding window sequential forward selection (SWSFS) method. …”
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Evaluating war-induced damage to agricultural land in the Gaza Strip since October 2023 using PlanetScope and SkySat imagery by He Yin, Lina Eklund, Dimah Habash, Mazin B. Qumsiyeh, Jamon Van Den Hoek

“…Third, we assessed the damage to greenhouses by classifying PlanetScope imagery using a random forest model. We performed accuracy assessments on a generated tree crop fields damage map using 1,200 randomly sampled 3 × 3-m areas, and we generated error-adjusted area estimates with a 95% confidence interval. …”
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Enhancing solar irradiance prediction precision: A stacked ensemble learning-based correction paradigm by Bo Tian, Ningbo Wang, Yuanxin Lin, Shuangquan Shao

“…Experimental results demonstrate significant improvements over the original NWP forecasts: the corrected model reduces the mean absolute error (MAE) and root mean square error (RMSE) by 47% and 41%, respectively, while increasing the R² determination coefficient by 11%. …”
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Understanding the environmental health implications of tourism on carbon emissions in China by Jinhua Shao, Sheng Fang, Meiling Zhao, Wanxin Qian, Cai Wang

“…Our findings demonstrate that sparrow search algorithm and random forest (SSA-RF) hybrid model can model the relationship between carbon emissions and tourism factors with low error. …”
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Pioneering machine learning techniques to estimate thermal conductivity of carbon-based phase change materials: A comprehensive modeling framework by Raouf Hassan, Alireza Baghban

“…Extensive machine learning algorithms were explored; however, CatBoost, XGBoost, ANN, Random Forest, and Gradient Boosting emerged as the most accurate models. …”
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Machine learning‐based investigations of the effect of surface texture geometry on the wear behaviour of UHMWPE bearings in hip joint implants by Vipin Kumar, Ravi Prakash Tewari, Anubhav Rawat

“…With a performance evaluation of 0.06 mean absolute error (MAE), 0.17 Root Mean Square Error (RMSE), and 0.96 R2, the Random Forest Regression is found to be the best model. …”
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Visual–Inertial Autonomous UAV Navigation in Complex Illumination and Highly Cluttered Under-Canopy Environments by Leyang Zhao, Weixi Wang, Qizhi He, Li Yan, Xiaoming Li

“…The objective is to enhance the reliability and safety of UAV operations beneath the forest canopy, thereby establishing a technical foundation for surveying vertically stratified natural resources.…”
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Enhanced SOC estimation method for lithium-ion batteries using Bayesian-optimized TCN–LSTM neural networks by Taotao Hu, Xiting Zhu, Maokai Tian

“…Experimental results demonstrate the effectiveness of the proposed method, achieving a coefficient of determination (R2) of 0.996, a mean absolute error of 0.146%, and a root mean square error of 0.207%. …”
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Interactive online learning method for students based on artificial intelligence by Cizhang Li, Wenfen Yin

“…The model was evaluated through experimental analysis using key regression and classification metrics, including Mean Absolute Error, Root Mean Square Error, R2 Score, Accuracy, Precision, Recall, Sensitivity, Specificity, and F1-Score with training time. …”
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Machine Learning Impact on Modern Business Intelligence by Raziyeh Moghaddas, Farinaz Tanhaei, Maryam Al Moqbali, Solmaz Safari

“…Finally, we assessed the performance of each model using standard evaluation metrics, i.e., Mean Absolute Error (MAE), Mean Squared Error (MSE), and the R-squared (R²) score. …”
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Day-Ahead Energy Price Forecasting with Machine Learning: Role of Endogenous Predictors by Chibuike Chiedozie Ibebuchi

“…This involved training random forest, support vector regression, XGBoost, and feed forward neural network models, followed by stacking and voting ensembles. …”
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FEPP: Advancing Software Risk Prediction in Requirements Engineering Through Innovative Rule Extraction and Multi-Class Integration by Muhammad Binsawad, Bilal Khan

“…The accuracy analysis of the models is assessed critically using the precision, F-measure (FM), and Mathew’s correlation coefficient (MCC), as well as the error rate using the Kappa Statistic (KS) and Mean Absolute Error (MAE). …”
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Early Prediction of Battery Lifetime Using Centered Isotonic Regression with Quantile-Transformed Features by Muhammad Arslan Khan, Yixing Wang, Benben Jiang

“…To validate our proposed method, we have done a comprehensive comparison among several different benchmarks, including elastic net, gradient boosting regression tree, decision tree, support vector machine, random forest, and Gaussian process regression. In contrast to existing methods, our CIR model has shown the best performance, with an average percentage error of 9.8% and a root mean square error of 149 cycles. …”
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Impact of Data Balancing and Feature Engineering on Accident Severity Models by Fayez ALANAZI, Aminu SULEIMAN

“…Evaluation metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Log Loss, Area under the Curve (AUC), and Area under the Precision-Recall Curve (AUCPR) are employed. …”
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HEIGHT GROWTH MODELS FOR Populus nigra STANDS GROWN AT ZAKHO REGION by Muzaham Saeed

“…Data analyzed and mathematical models driven for many equations connect the studied variable of stands with the height by using regression system then constructing on the resulted precision measurements of the equation , the followings were selected: H =-12.12 + 8.50(A) 0.5 H = -2.08 + 1.74(A) + 0.14(SP ) 2 H = 1.27 + 2.132(D) – 0.75EXP(SP) The coefficient determination coefficient (R2) of above equations were (90.3, 91.02 ,91.33) with standard error (0.88,0.86,0.84) respectively. Resulted equations enable us to estimate average stand height at any age or density then different tables and graphics prepared to illustrate the development of stand height growth which is widely used by forest managers in order to predict the mean stand height also to estimate average growth annual growth in the height of stand through first driven of equations links the height with the age .…”
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Data-Driven Pavement Performance: Machine Learning-Based Predictive Models by Mohammad Fahad, Nurullah Bektas

“…This study utilizes a range of machine learning algorithms, including linear regression, decision tree, random forest, gradient boosting, K-nearest neighbour, Support Vector Regression, LightGBM and CatBoost, to analyse their effectiveness in predicting pavement performance. …”
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Performance Evaluation of Machine Learning Algorithms for Detecting Gas Leakage System by Kondireddy Muni Sankar, Dr. B. Booba

“…The ML based models used in the research work are Linear Regression, Logistic Regression, RF (Random Forest) and KNN (K-Nearest Neighbor). The following ML based algorithms are compared and the performance of the model was evaluated using assorted metrics in accordance with different types of damages. …”
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Ghost hunting in the nonlinear dynamic machine. by Jonathan E Butner, Ascher K Munion, Brian R W Baucom, Alexander Wong

“…Applying dynamical systems theory to the machine learning solution further provides a pathway to interpret the results. Using random forest models as an illustrative example, these models were able to recover the temporal dynamics of time series data simulated using a modified Cusp Catastrophe Monte Carlo. …”
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Analysis of drug crystallization by evaluation of pharmaceutical solubility in various solvents by optimization of artificial intelligence models by Wael A. Mahdi, Adel Alhowyan, Ahmad J. Obaidullah

“…The novelty of the work is to maximize the performance of the model by using methods including the isolation forest for anomaly detection and the tree-structured Parzen estimator for hyperparameter fine-tuning. …”
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A Data-Driven Approach for Urban Heat Island Predictions: Rethinking the Evaluation Metrics and Data Preprocessing by Berk Kıvılcım, Patrick Erik Bradley

“…The trained models with Random Forest and XGBoost methods which are capable of predicting the spatial distribution of air temperature by using building volume information are compared. …”
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