Continuous Estimation of Swallowing Motion With EMG and MMG Signals by Zhenhui Guo, Ziyang Wang, Yue Wang, Weiguang Huo, Jianda Han

“…For the healthy subjects, the mean correlation coefficient (CC) is about 0.90 and the normalized root mean square error (NRMSE) is less than 0.15. For the PD patient, the CC is 0.804 and the NRMSE is 0.205 when using RF. …”
Get full text

Chronic Kidney Disease Prediction Based On Machine Learning Algorithms by Kethineni Likitha., Nithinchandra, Kumar Narendra, Sk Sajida Sultana.

“…KNN Basic model obtained a root mean square error of 0.5007 with an accuracy of 44.50. The NMF model gave a better result than the first one with RMSE 0.4999 and accuracy 51.50. …”
Get full text

Assessing dengue forecasting methods: a comparative study of statistical models and machine learning techniques in Rio de Janeiro, Brazil by Xiang Chen, Paula Moraga

“…Employing a dynamic window approach, various statistical methods and machine learning techniques were used to generate weekly forecasts at several time horizons. Error measures, uncertainty intervals, and computational efficiency obtained with each method were compared. …”
Get full text

Optimizing photocatalytic dye degradation: A machine learning and metaheuristic approach for predicting methylene blue in contaminated water by Yunus Ahmed, Keya Rani Dutta, Sharmin Nahar Chowdhury Nepu, Meherunnesa Prima, Hamad AlMohamadi, Parul Akhtar

“…It reached a very high R² of 0.9998 on the training set and 0.9915 on the test set, coupled with low error metrics, showcasing its strong generalization capability. …”
Get full text

Evaluating Supervised Learning Classifier Performance for OFDM Communication in AWGN-Impacted Systems by Lavanya Vaishnavi D A, Anil Kumar C

“…., Peak Signal to Noise Ratio (PSNR), Power to Average Power Ratio (PAPR) and Bit Error Ratio (BER) and Accuracy, in the perspective of the AWGN channel Mean and Variance are considered as variables from 0.4 to 1 and 0.7 ± 0.3 respectively. …”
Get full text

Predicting mechanical ventilation duration in ICU patients: A data-driven machine learning approach for clinical decision-making by Shivi Mendiratta, Vinay Gandhi Mukkelli, Esha Baidya Kayal, Puneet Khanna, Amit Mehndiratta

“…Results The least-squares boosting regression model achieved root mean squared error (RMSE) of 4.66 days and coefficient of Determination (R²) of 0.65 using 34-SHAP-selected features, with tracheostomy (53.66% importance) being the top predictor. …”
Get full text

Multi-scale machine learning model predicts muscle and functional disease progression by Silvia S. Blemker, Lara Riem, Olivia DuCharme, Megan Pinette, Kathryn Eve Costanzo, Emma Weatherley, Jeff Statland, Stephen J. Tapscott, Leo H. Wang, Dennis W. W. Shaw, Xing Song, Doris Leung, Seth D. Friedman

“…After training, the models predicted fat fraction change with a root mean square error (RMSE) of 2.16% and lean volume change with a RMSE of 8.1 ml in a holdout testing dataset. …”
Get full text

A machine-learning-based approach for active monitoring of blade pitch misalignment in wind turbines by S. Milani, J. Leoni, S. Cacciola, A. Croce, M. Tanelli

“…Additionally, regression analysis proves the capability of the framework to detect misalignments as low as 0.1° with a root mean square error of 5.48 %. The methodology relies on features extracted from a limited set of sensors already integrated into modern wind turbine systems. …”
Get full text

Rapid diagnosis of drug resistance to fluoroquinolones, amikacin, capreomycin, kanamycin and ethambutol using genotype MTBDRsl assay: a meta-analysis. by Yan Feng, Sijun Liu, Qungang Wang, Liang Wang, Shaowen Tang, Jianming Wang, Wei Lu

“…From these calculations, forest plots and summary receiver operating characteristic (SROC) curves were produced.…”
Get full text

Strategies for Soil Salinity Mapping Using Remote Sensing and Machine Learning in the Yellow River Delta by Junyong Zhang, Xianghe Ge, Xuehui Hou, Lijing Han, Zhuoran Zhang, Wenjie Feng, Zihan Zhou, Xiubin Luo

“…Notably, under Strategy IX, the SVR model achieved the best predictive performance, with a coefficient of determination (R²) of 0.62 and a root mean square error (RMSE) of 0.38 g/kg. Analysis based on SHapley Additive exPlanations (SHAP) values and feature importance indicated that Vegetation Type Factors contributed significantly and consistently to the model’s performance, maintaining higher importance than traditional salinity indices and playing a dominant role. …”
Get full text

Benchmarking farm-level cotton water productivity using on-farm irrigation measurements and remotely sensed yields by Zitian Gao, Danlu Guo, Dongryeol Ryu, Andrew W. Western

“…The results showed that field-scale cotton yield could be predicted with the best accuracy using the GBR model (R2 = 0.7, RMSE = 235 kg/ha, mean absolute error = 176 kg/ha and Pearson correlation = 0.84) when applied to the period of training. …”
Get full text

Development and Validation of a Nomogram to Predict Ventricular Fibrillation During Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction by Ruifeng Liu, Xiangyu Gao, Jihong Fan, Huiqiang Zhao

“…The calibration curve showed a strong alignment between predicted probabilities and observed outcomes, with a mean absolute error of 0.033. …”
Get full text

A Learning-Based Dual-Scale Enhanced Confidence for DSM Fusion in 3-D Reconstruction of Multiview Satellite Images by Shuting Yang, Hao Chen, Fachuan He, Wen Chen, Ting Chen, Jianjun He

“…The proposed method achieves an average MAE of 1.14 m, RMSE of 2.16 m, median height error of 0.47 m, and COMP of 75.13%, outperforming several mainstream methods.…”
Get full text

Estimation of the air conditioning energy consumption of a classroom using machine learning in a tropical climate by Liliana Ortega-Diaz, Julian Jaramillo-Ibarra, German Osma-Pinto

“…The evaluation of the models was performed using RMSE, MAE and R2 metrics, to different characteristics and approaches to error measurement. During the training phase, the RFR model achieved a coefficient of determination (R2) of 0.97, while the SVR obtained an R2 of 0.78 in the test phase. …”
Get full text

Investigating the effect of land use type on surface water quality in the Talar watershed in Mazandaran by Fatemeh Shokrian, Karim solaimani, Aref Saberi

“…To produce the land use map, atmospheric, geometric and radiometric errors were first corrected, then a land use map was produced using false colour combinations and training samples separately for each year. …”
Get full text

Enhancing Pear Tree Yield Estimation Accuracy by Assimilating LAI and SM into the WOFOST Model Based on Satellite Remote Sensing Data by Zehua Fan, Yasen Qin, Jianan Chi, Ning Yan

“…The coefficients of determination (R²) were 0.73, 0.72, 0.76, and 0.77 for the four periods, respectively, and the root-mean-square errors (RMSE) were 0.21 m²/m², 0.24 m²/m², 0.18 m²/m², and 0.16 m²/m², respectively. …”
Get full text

Enhancing burned area monitoring with VIIRS dataset: A case study in Sub-Saharan Africa by Boris Ouattara, Michael Thiel, Barbara Sponholz, Heiko Paeth, Marta Yebra, Florent Mouillot, Patrick Kacic, Kwame Hackman

“…Based on a stratified random sampling, the validation results demonstrate varying levels of accuracy for the VIIRS-BA product across different confidence levels. The commission error (CE) ranges from 7.8% to 23.4%, while the omission error (OE) falls between 29.4% and 58.8%. …”
Get full text

A global daily seamless 9 km vegetation optical depth (VOD) product from 2010 to 2021 by D. Hu, Y. Wang, H. Jing, L. Yue, Q. Zhang, L. Fan, Q. Yuan, Q. Yuan, Q. Yuan, H. Shen, L. Zhang

“…Results show that the seamless SMOS (SMAP) dataset is evaluated with a coefficient of determination (<span class="inline-formula"><i>R</i>²</span>) of 0.855 (0.947) and a root mean squared error (RMSE) of 0.094 (0.073) for the simulated real missing masks. …”
Get full text

Spatial-temporal distribution patterns change of grassland formation in Inner Mongolia since the 1980s by Anan Zhang, Jiakui Tang, Na Zhang, Xuefeng Xu, Wuhua Wang, Xiaofan Li, Maojin Li, Kaihui Li, Mengquan Wu, Shuohao Cai

“…Formation boundaries were the primary areas of classification errors; excluding the 2 km buffer zones significantly enhanced the classification performance. …”
Get full text

Developing a Framework for Building Condition Assessment of Schools in Osijek-Baranja County by Hana Begić Juričić, Hrvoje Krstić

“…Performance was evaluated using <i>R</i>², mean squared error (<i>MSE</i>), root mean squared error (<i>RMSE</i>), coefficient of variation of <i>RMSE</i> (<i>CVRMSE</i>), and mean absolute percentage error (<i>MAPE</i>). …”
Get full text