Machine Learning-Based Estimation of foF2 and MUF(3000)F2 Using GNSS Ionospheric TEC Observations by Yuhang Zhang, Ming Ou, Liang Chen, Yi Hao, Qinglin Zhu, Xiang Dong, Weimin Zhen

“…For foF2 (MUF(3000)F2) estimation, the root mean square error (RMSE) values at Kunming and Xi’an stations were reduced by approximately 38% (26%) and 18% (11%), respectively, compared to IRI-2020. …”
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Fisher Discriminant Analysis for Extracting Interpretable Phenological Information From Multivariate Time Series Data by Conor T. Doherty, Meagan S. Mauter

“…We find that using multivariate data inputs can reduce prediction root mean squared error (RMSE, in days) by 20% relative to models using only univariate inputs. …”
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Application research and effectiveness evaluation mechanism of hybrid intelligent algorithm integrating cognitive computing and deep learning for dynamically adjusting employee per... by Zhenlin Luo, Kebin Lu

“…The results revealed that the mean square error (MSE) of the hybrid algorithm was significantly lower than that of the KPI (Key Performance Indicators) method across all datasets, with a 43.5% improvement in accuracy. …”
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Predicting Three-Dimensional (3D) Printing Product Quality with Machine Learning-Based Regression Methods by Ahmet Burak Tatar

“…Within this framework, prediction models including Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Gaussian Process Regression (GPR), and Multi-Layer Perceptron (MLP) were developed, and their performances were assessed using metrics such as accuracy (R²), error rates (RMSE, MSE, MAE), and computational time. …”
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Improving the Skill of Subseasonal to Seasonal (S2S) Wind Speed Forecasts Over India Using Statistical and Machine Learning Methods by Aheli Das, Dondeti Pranay Reddy, Somnath Baidya Roy

“…The quality and skill of raw and calibrated forecasts are evaluated using root mean squared error (RMSE), ratio of standard deviation, and continuous ranked probability skill score (CRPSS). …”
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Reconstruction of Sea Surface Chlorophyll-a Concentration in the Bohai and Yellow Seas Using LSTM Neural Network by Qing Xu, Guiying Yang, Xiaobin Yin, Tong Sun

“…Compared with Gated Recurrent Unit, Random Forest, XGBoost, and Extra Trees models, the LSTM model exhibits the highest accuracy. …”
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Energy Demand Forecasting Scenarios for Buildings Using Six AI Models by Khaled M. Salem, Francisco J. Rey-Martínez, A. O. Elgharib, Javier M. Rey-Hernández

“…Model performance is rigorously evaluated using metrics like Squared Mean Root Percentage Error (RMSPE) and Coefficient of Determination (R²), ensuring robust analysis. …”
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Interpretable machine learning models for predicting Ebus battery consumption rates in cold climates with and without diesel auxiliary heating by Kareem Othman, Diego Da Silva, Amer Shalaby, Baher Abdulhai

“…The results indicate that the Random Forest method emerges as the superior choice for predicting the energy consumption rate, with a resulting mean absolute error of 0.09–0.1 ​kWh/km observed across the different models. …”
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Machine Learning-Based Software for Predicting <i>Pseudomonas</i> spp. Growth Dynamics in Culture Media by Fatih Tarlak

“…., a prominent bacterial genus in food spoilage, by applying machine learning regression models, including Support Vector Regression (SVR), Random Forest Regression (RFR) and Gaussian Process Regression (GPR). …”
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Rate of penetration prediction in drilling operations: a comparative study of AI models and meta-heuristic approaches by Fatemeh Mohammadinia, Ali Ranjbar, Fatemeh Ghazi, Seyyed Taha Hosseini

“…Among the tested models, the LSSVM-CSA framework achieved the best results, with a remarkable R-squared (R2) value of 92.55, a Root Mean Square Error (RMSE) of 2.98. These results underscore the superior accuracy, robustness, and adaptability of the proposed methodology. …”
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Structural time series modelling for weekly forecasting of enterovirus outpatient, inpatient, and emergency department visits. by Cathy W S Chen, Leon L Hsieh, Betty X Y Chu

“…Specifically, by accounting for the Lunar New Year holiday within the out-of-sample period, the models attain mean absolute percentage error (MAPE) values of 6.509% for non-ED visits and 12.645% for ED visits.…”
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Evaluating the precision and reliability of real-time continuous glucose monitoring systems in ambulatory settings: a systematic review by Valentina Dávila-Ruales, Laura F. Gilón, Ana M. Gómez, Oscar M. Muñoz, María N. Serrano, Diana C. Henao

“…Most of the devices evaluated with consensus error grids reached values above 99% in zones A and B only in overall accuracy and hyperglycemia. …”
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Monitoring Crop Condition at Field Scales and at a Daily Time Step Using Synthetic Aperture Radar (SAR): Surveiller l’état des cultures à l’échelle du champ et à une étape de temps... by Heather McNairn, Xianfeng Jiao

“…Total power, the first and second eigenvalues and VH backscatter were important in reducing model error. The SARcal-NDVI and Growing Degree Days were then integrated into a Crop Structure Dynamic Model to produce daily estimates of crop condition, at field scales. …”
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Automated Analysis of Vertebral Body Surface Roughness for Adult Age Estimation: Ellipse Fitting and Machine-Learning Approach by Erhan Kartal, Yasin Etli

“…Performance was quantified with the standard error of the estimate (SEE). <b>Results:</b> DS values correlated moderately to strongly with age (peak r = 0.60 at L3–L5). …”
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Next-generation diabetes diagnosis and personalized diet-activity management: A hybrid ensemble paradigm. by Muhammad Sajid, Kaleem Razzaq Malik, Ali Haider Khan, Sajid Iqbal, Abdullah A Alaulamie, Qazi Mudassar Ilyas

“…Leveraging efficient learning and inference techniques, the study achieves a meager error rate of less than 30% using an extensive dataset comprising over 100 million user-rated foods. …”
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An Ensemble Learning Approach for Drought Analysis and Forecasting in Central Bangladesh by Md. Alomgir Hossain, Momotaz Begum, Md. Nasim Akhtar, Md. Alamin Talukder, Nomanur Rahman, Mahfuzur Rahman

“…Its error metrics included MAE (0.055–0.068), MSE (0.0032–0.0052), RMSE (0.056–0.072), and R2 (0.914–0.965) across an 80% training and 20% testing split. …”
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Increasing Neural-Based Pedestrian Detectors’ Robustness to Adversarial Patch Attacks Using Anomaly Localization by Olga Ilina, Maxim Tereshonok, Vadim Ziyadinov

“…The proposed method consists of a Deep Convolutional Neural Network to reconstruct a benign image from the adversarial one; a Calculating Maximum Error block to highlight the mismatches between input and reconstructed images; a Localizing Anomalous Fragments block to extract the anomalous regions using the Isolation Forest algorithm from histograms of images’ fragments; and a Clustering and Processing block to group and evaluate the extracted anomalous regions. …”
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Machine learning models for estimating fetal weight based on ultrasonographic biometry: Development and validation study by Marcos Espinola-Sánchez, Antonio Limay-Rios, Andrés Campaña-Acuña, Silvia Sanca-Valeriano

“…Accuracy was assessed using the coefficient of determination ( R ²) and mean squared error (MSE), comparing the ML models to the Hadlock and Shepard formulas. …”
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Grouped machine learning methods for predicting rock mass parameters in a tunnel boring machine‐driven tunnel based on fuzzy C‐means clustering by Ruirui Wang, Yaodong Ni, Lingli Zhang, Boyang Gao

“…The average percentage error of uniaxial compressive strength and joint frequency (Jf) of the 30 testing samples predicted by the pure back propagation (BP) neural network is 13.62% and 12.38%, while that predicted by the BP neural network combined with fuzzy C‐means is 7.66% and 6.40%, respectively. …”
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Prediction of the Ultimate Impact Response of Concrete Strengthened with Polyurethane Grout as the Repair Material by Sadi I. Haruna, Yasser E. Ibrahim, Sani I. Abba

“…The results indicated that LSTM models, particularly LSTM-M2 and LSTM-M3, demonstrated superior predictive accuracy and consistency in both the calibration and verification phases, as evidenced by high Pearson’s correlation coefficients (PCC = 0.9156 for LSTM-M2) and Willmott indices (WI = 0.7713 for LSTM-M2), and low error metrics (MSE = 0.0017, RMSE = 0.0418). The SHAP (SHapley Additive exPlanations) analysis showed that the thickness of the grouting materials and maximum load were the most significant parameters affecting the ultimate capacity of the composite U-shaped specimen. …”
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