Validity of a Single Inertial Measurement Unit to Measure Hip Range of Motion During Gait in Patients Undergoing Total Hip Arthroplasty by Noor Alalem, Xavier Gasparutto, Kevin Rose-Dulcina, Peter DiGiovanni, Didier Hannouche, Stéphane Armand

“…Multiple regression was the best-performing model, with limits of agreement (LoA) of ±13° and a systematic bias of 0. Random forest, RNN, GRU and LSTM models yielded LoA ranges > 27.8°, exceeding the threshold of acceptable error. …”
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Improving the evapotranspiration estimation by coupling soil moisture and atmospheric variables in the relative evapotranspiration parameterization by Elisabet Walker, Virginia Venturini

“…Accurate monthly evapotranspiration (ET) estimation is essential for many forest, climate, and hydrological applications, as well as for some agricultural uses. …”
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Enhanced dry SO₂ capture estimation using Python-driven computational frameworks with hyperparameter tuning and data augmentation by Robert Makomere, Hilary Rutto, Alfayo Alugongo, Lawrence Koech, Evans Suter, Itumeleng Kohitlhetse

“…Results obtained evidence that random forest obtained the strongest accuracy, and generalizability from the high coefficient of determination, and lowest error scores. …”
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Functional Disability and Psychological Impact in Headache Patients: A Comparative Study Using Conventional Statistics and Machine Learning Analysis by Jong-Ho Kim, Hye-Sook Kim, Jong-Hee Sohn, Sung-Mi Hwang, Jae-Jun Lee, Young-Suk Kwon

“…Frequent analgesic medication emerged as a significant predictor of poorer life quality (Headache Impact Test-6, root mean squared error = 7.656) and increased depression (Patient Health Questionnaire-9, root mean squared error = 5.07) and anxiety (Generalized Anxiety Disorder-7, root mean squared error = 4.899) in the Random Forest model. …”
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Prediction of particulate matter PM2.5 level in the air of Islamabad, Pakistan by using machine learning and deep learning approaches by Muhammad Waqas, Shahid Noor Jan, Basir Ullah, Afed Ullah Khan, Ateeq Ur Rauf, Bakht Niaz Khan

“…Each model's performance was assessed by using statistical indicators including coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Relative Root Mean Square Error (RRMSE). …”
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COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers by Syed Ali Jafar Zaidi, Indranath Chatterjee, Samir Brahim Belhaouari

“…The CNN and AdaBoost, on the other hand, have been taught to detect the mean square error, root mean square error, and mean absolute error. …”
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PSOA-LSTM: a hybrid attention-based LSTM model optimized by particle swarm optimization for accurate lung cancer incidence forecasting in China (1990–2021) by Nannan Xu, Guang Yang, Linlin Ming, Jiefei Dai, Kun Zhu

“…The proposed model was compared against traditional models including ARIMA, standard LSTM, Support Vector Regression (SVR), and Random Forest (RF).ResultsThe PSOA-LSTM model achieved superior performance across five key evaluation metrics: mean squared error (MSE) = 0.023, coefficient of determination (R2) = 0.97, mean absolute error (MAE) = 0.152, normalized root mean squared error (NRMSE) = 0.025, and mean absolute percentage error (MAPE) = 0.38%. …”
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Machine learning models for predicting tibial intramedullary nail length by Sercan Capkin, Ali Ihsan Kilic, Hakan Cici, Mehmet Akdemir, Mert Kahraman Marasli

“…The performance of the models was evaluated using the mean squared error (MSE) and the R-squared values. Results The linear regression model demonstrated superior performance compared to the random forest, decision tree, and XGBoost models, with an R-squared value of 0.89, an MSE of 117.53, and a root mean squared error (RMSE) of 10.84 mm. …”
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A Soft Sensor Based Inference Engine for Water Quality Assessment and Prediction by Micheal A Ogundero, Theophilus A Fashanu, Foluso O Agunbiade, Kehinde Orolu, Ahmed A Yinusa, Usman A Daudu, Muhammed O H Amuda

“…Similarly, the Dissolved Oxygen was estimated by the Random Forest model with a mean squared error (MSE) of 1.0335 for training and 0.7150 for validation. …”
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Navigating cognitive boundaries: the impact of CognifyNet AI-powered educational analytics on student improvement by Mrim M. Alnfiai, Faiz Abdullah Alotaibi, Mona Mohammed Alnahari, Nouf Abdullah Alsudairy, Asma Ibrahim Alharbi, Saad Alzahrani

“…Evaluated through rigorous 5-fold cross-validation on a comprehensive dataset of 1200 anonymized student records and validated across multiple educational platforms, including UCI Student Performance and Open University Learning Analytics datasets, CognifyNet demonstrates superior performance over conventional approaches, achieving 10.5% reduction in mean squared error and 83% reduction in mean absolute error compared to baseline random forest models, with statistical significance confirmed through paired t-tests (p < 0.01). …”
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Early Warning System for Debt Group Migration: The Case of One Commercial Bank in Vietnam by Nguyen Quoc Hung, Trinh Hoang Viet, Phuong Truong Viet, Ly Truong Thi Minh

“…However, if the priority is to reduce Type I errors, SVM performs better with a recall of 91.48%, although the accuracy drops to 46.62%. …”
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Electric vehicle charging station demand prediction model deploying data slotting by A.V. Sreekumar, R.R. Lekshmi

“…The article recommends Categorical Boosting Regression model with least mean absolute error, mean square error and root mean square error of 0.0726, 0.0112, and 0.1059 respectively. …”
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Impact of morphological traits and irrigation levels on fresh herbage yield of sorghum x sudangrass hybrid: Modelling data mining techniques. by Halit Tutar, Senol Celik, Hasan Er, Erdal Gönülal

“…Model fit statistics, including coefficient of determination (R2), adjusted R2, root of mean square error (RMSE), mean absolute percentage error (MAPE), standard deviation ratio (SD ratio), Mean Absolution Error (MAE) and Relative Absolution Error (RAE), were used to evaluate the prediction abilities of the fitted models. …”
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Monitoring active fires in Borneo from Sentinel-2 MSI images by Xiaoxiao Guo, Yongxue Liu, Peng Liu, Huize Wang, Wei Wu

“…While moderate-resolution sensors offer unprecedented opportunities for detecting small and subtle fires, they face the dilemma of high commission errors (CE). To address this problem, we propose an object-oriented method to effectively detect AFs from Sentinel-2 MSI images, which focuses on suppressing the interference of various CEs through object-level inter-spectral criteria cloud filtering, seamline exclusion based on granule footprints, and false positive refinement based on random forest classification model. …”
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Simulation and Experimental Study on the Shrub-Cutting Performance of Quasi-Planetary Cutter by Zikai Song, Xibin Dong, Chi Teng, Ben Guo, Jiawang Zhang, Yuchen Zhang

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Machine learning models for accurately predicting properties of CsPbCl3 Perovskite quantum dots by Mehmet Sıddık Çadırcı, Musa Çadırcı

“…Although all models performed highly accurate results, SVR and NND demonstrated the best accurate property prediction by achieving excellent performance on the test and training datasets, with high R2, low Root Mean Squared Error (RMSE) and low Mean Absolute Error (MAE) metric values. …”
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An Upscaling-Based Strategy to Improve the Ephemeral Gully Mapping Accuracy by Solmaz Fathololoumi, Daniel D. Saurette, Harnoordeep Singh Mann, Naoya Kadota, Hiteshkumar B. Vasava, Mojtaba Naeimi, Prasad Daggupati, Asim Biswas

“…The errors for EGs maps at various resolutions revealed an increase in identification error with higher spatial resolution. …”
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Prediction of the monthly river water level by using ensemble decomposition modeling by Chaitanya Baliram Pande, Lariyah Mohd Sidek, Bijay Halder, Okan Mert Katipoğlu, Jitendra Rajput, Fahad Alshehri, Rabin Chakrabortty, Subodh Chandra Pal, Norlida Mohd Dom, Miklas Scholz

“…Finally, the CEEMDAN-RF hybrid model is best model based on the lowest observed errors of Root mean square error (RMSE): 0.13, Mean square error (MSE): 0.02 and high R2: 0.94, hence this model is appropriate for prediction of river water level. …”
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FORECASTING STOCK MARKET LIQUIDITY WITH MACHINE LEARNING: AN EMPIRICAL EVALUATION IN THE GERMAN MARKET by Bogdan Ionut ANGHEL

“…Empirical testing shows that the two gradient-boosting ensembles consistently outperform both Random Forest and the LSTM model, tracking sudden liquidity swings more accurately and delivering the tightest forecast errors. …”
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Retrieval of Chinese fir tree parameters under different understory conditions with the integration of handheld and airborne Lidar data by Yunhe Li, Guiying Li, Sirong Wang, Dengsheng Lu

“…Accurate extraction of tree parameters is vital for the calculation of high-quality forest volume or biomass. The Light Detection and Ranging (Lidar) technology with its ability to acquire three-dimensional forest stand structures is an important data source for extracting tree parameters. …”
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