Effective Machine Learning Techniques for Dealing with Poor Credit Data by Dumisani Selby Nkambule, Bhekisipho Twala, Jan Harm Christiaan Pretorius

“…Additionally, the average error rate for these classifiers is over 9%, which implies that the estimates are not far from the actual values. …”
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Aligning Evapotranspiration from MOD16A2.061 Product to Ground Estimates in Piemonte (NW Italy): an analysis of temporal and spatial biases by E. Ronco, E. C. Borgogno Mondino

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Linking land value to indicators of soil quality and land use pressure by John J. Drewry, Stephen J. McNeill, Richard W. McDowell, Richard Law, Bryan A. Stevenson

“…The most important explanatory variable in predicting land valuation per hectare in the random forest model was catchment elevation (mean decrease in the mean square error; 0.92), followed by catchment potential evapotranspiration (0.78). …”
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A Comparative Study of Downscaling Methods for Groundwater Based on GRACE Data Using RFR and GWR Models in Jiangsu Province, China by Rihui Yang, Yuqing Zhong, Xiaoxiang Zhang, Aizemaitijiang Maimaitituersun, Xiaohan Ju

“…This study based on GRACE employs Random Forest Regression (RFR) and Geographically Weighted Regression (GWR) methods in order to obtain high-resolution information on groundwater storage change. …”
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Artificial intelligence aided microwave coagulation therapy: Analysis of heat transfer to tumor tissue via hybrid modeling by Zheng Yang, KeWei Dai, Wujun Zhang, Rui Zhou, QingBin Wu, Liang Liu, HuaiRong Qu

“…Obtaining a score of 0.9991 by R2 criterion (Coefficient of Determination), an MSE (Mean Squared Error) of 0.1526, and an MAE (Mean Absolute Error) of 0.2545, the results show that the LGBM is the best-fit model. …”
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Machine learning-based estimation of crude oil-nitrogen interfacial tension by Safia Obaidur Rab, Subhash Chandra, Abhinav Kumar, Pinank Patel, Mohammed Al-Farouni, Soumya V. Menon, Bandar R. Alsehli, Mamata Chahar, Manmeet Singh, Mahmood Kiani

“…The evaluation study proved that Random Forest is the most accurate developed intelligent model as it was characterized with acceptable R-squared (0.959), mean square error (1.65), average absolute relative error (6.85%) of unseen test datapoints as well as with correct trend prediction of IFT with regard to all input parameters of pressure, temperature and crude oil API. …”
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Forecasting invasive mosquito abundance in the Basque Country, Spain using machine learning techniques by Vanessa Steindorf, Hamna Mariyam K. B., Nico Stollenwerk, Aitor Cevidanes, Jesús F. Barandika, Patricia Vazquez, Ana L. García-Pérez, Maíra Aguiar

“…Forecasting models, including random forest (RF) and seasonal autoregressive integrated moving average (SARIMAX), were evaluated using root mean squared error (RMSE) and mean absolute error (MAE) metrics. …”
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VIS/NIR Spectroscopy as a Non-Destructive Method for Evaluation of Quality Parameters of Three Bell Pepper Varieties Based on Soft Computing Methods by Meysam Latifi Amoghin, Yousef Abbaspour-Gilandeh, Mohammad Tahmasebi, Mohammad Kaveh, Hany S. El-Mesery, Mariusz Szymanek, Maciej Sprawka

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Weight trimming and propensity score weighting. by Brian K Lee, Justin Lessler, Elizabeth A Stuart

“…In a simulation study, the authors examined the performance of weight trimming following logistic regression, classification and regression trees (CART), boosted CART, and random forests to estimate propensity score weights. Results indicate that although misspecified logistic regression propensity score models yield increased bias and standard errors, weight trimming following logistic regression can improve the accuracy and precision of final parameter estimates. …”
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Hybrid Model for 6G Network Traffic Prediction and Wireless Resource Optimization by Mohammed Anis Oukebdane, A. F. M. Shahen Shah, Md Baharul Islam, John Ekoru, Milka Madahana

“…The results of the proposed hybrid model are presented and compared with baseline methods, including LSTM, GRU, random forest, and XGBoost. Our model obtains a Root Mean Squared Error (RMSE) of 0.0049, an Mean Absolute Error (MAE) of 0.0034, a mean absolute percentage error (MAPE) of 0.46%, and a coefficient of determination <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> of 0.9970 according to experimental findings on a whole dataset. …”
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Design of upper limb muscle strength assessment system based on surface electromyography signals and joint motion by Siqi Wang, Wei Lai, Yipeng Zhang, Junyu Yao, Xingyue Gou, Hui Ye, Jun Yi, Dong Cao

“…Model performance was evaluated using Root Mean Squared Error (RMSE), R-Square (R2), Mean Absolute Error (MAE), and Mean Bias Error (MBE), to identify the most accurate regression prediction algorithm.ResultsThe system effectively collected and analyzed the sEMG from the deltoid muscles and shoulder joint motion data. …”
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A Sensor Data Prediction and Early-Warning Method for Coal Mining Faces Based on the MTGNN-Bayesian-IF-DBSCAN Algorithm by Mingyang Liu, Xiaodong Wang, Wei Qiao, Hongbo Shang, Zhenguo Yan, Zhixin Qin

“…Experimental results indicate that the MTGNN outperforms comparative algorithms, such as CrossGNN and FourierGNN, in prediction accuracy, with the mean absolute error (MAE) being as low as 0.00237 and the root mean square error (RMSE) maintained below 0.0203 across different sensor locations (T0, T1, T2). …”
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Solar Energy Forecasting Framework Using Prophet Based Machine Learning Model: An Opportunity to Explore Solar Energy Potential in Muscat Oman by Mazhar Baloch, Mohamed Shaik Honnurvali, Adnan Kabbani, Touqeer Ahmed, Sohaib Tahir Chauhdary, Muhammad Salman Saeed

“…The model evaluation metrics used in this study include the mean absolute error (MAE), the root mean squared error (RMSE), R², and mean bias deviation (MBD). …”
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Leveraging Synthetic Data to Develop a Machine Learning Model for Voiding Flow Rate Prediction From Audio Signals by Marcos Lazaro Alvarez, Alfonso Bahillo, Laura Arjona, Diogo Marcelo Nogueira, Elsa Ferreira Gomes, Alipio M. Jorge

“…Results show that random forest achieved the best performance in both regression and classification tasks, with a mean absolute error (MAE) of 0.9, 0.7 and 0.9 ml/s and quadratic weighted kappa (QWK) of 0.99, 1.0 and 1.0 for the three devices. …”
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Making the most out of timeseries symptom data: A machine learning study on symptom predictions of internet-based CBT by Nils Hentati Isacsson, Kirsten Zantvoort, Erik Forsell, Magnus Boman, Viktor Kaldo

“…., Linear Regression and Random Forest models) and 2) time-dependent methods (i.e., multilevel model regression, mixed-effects random forest, and a Long Short-Term Memory model) to predict symptoms during treatment, including the final outcome. …”
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Automated Stock Volume Estimation Using UAV-RGB Imagery by Anurupa Goswami, Unmesh Khati, Ishan Goyal, Anam Sabir, Sakshi Jain

“…Forests play a critical role in the global carbon cycle, with carbon storage being an important carbon pool in the terrestrial ecosystem with tree crown size serving as a versatile ecological indicator influencing factors such as tree growth, wind resistance, shading, and carbon sequestration. …”
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Predicting student self-efficacy in Muslim societies using machine learning algorithms by Mohammed Ba-Aoum, Mohammed Ba-Aoum, Mohammed Alrezq, Jyotishka Datta, Konstantinos P. Triantis

“…Model performance was assessed using root mean square error (RMSE) and r-squared (R2) metrics to ensure reliability and validity.ResultsThe results showed that Random Forest outperformed the other models in accuracy, as measured by R2 and RMSE metrics. …”
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Handheld lidar sensors can accurately measure aboveground biomass by David H. Atkins, Ryan C. Blackburn, Daniel C. Laughlin, Margaret M. Moore, Andrew J. Sánchez Meador

“…We compared the capability of iPad and MLS sensors to estimate AGB via minimization of model normalized root mean square error (NRMSE). This process was performed on predictor subsets describing structural, spectral, and field‐based characteristics across a suite of modeling approaches including simple linear, stepwise, lasso, and random forest regression. …”
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Guiding Field Measurement of Pine Tree Crowns: A Geometric Shape Comparison Using Drone Imagery by A. Hosingholizade, Y. Erfanifard, S. K. Alavipanah, V. García Millan, S. Pirasteh, S. Pirasteh

“…The study was conducted in an Eldarica pine plantation forest, which was digitally mapped using RGB images captured by a Phantom 4 Pro drone. …”
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Narrow band based and broadband derived vegetation indices using Sentinel-2 Imagery to estimate vegetation biomass by A.B. Imran, K. Khan, N. Ali, N. Ahmad, A. Ali, K. Shah

“…Forest’s ecosystem is one of the most important carbon sink of the terrestrial ecosystem. …”
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