Modeling residential property prices in emerging climate-responsive urban markets: a hybrid modeling framework for Baidoa City-Somalia by Mohamed Ibrahim Nor, Shuaib Nour Hussein

“…Compared to the baseline linear hedonic regression model, the ANN achieved approximately a 20% reduction in mean squared error (MSE), with performance improvements validated through 5-fold cross-validation and supported by 95% confidence intervals. …”
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Spatiotemporal Analysis and Anomalous Trends of Asia AOD (2001–2024): Insights from a Deep Learning Fusion Model and EOF Decomposition by Yu Ding, Wenjia Ni, Jiaxin Dong, Jie Yang, Shiyao Meng, Siwei Li

“…The fused dataset demonstrates significant improvements over the original MERRA-2 AOD, with an increase in the coefficient of determination (R²) by 0.1065 and a reduction in root mean square error (RMSE) by 0.0369. …”
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Factors Related to Superior and Inferior Hemifield Defects in Primary Open-Angle Glaucoma by Remi Takeuchi, Nobuko Enomoto, Kyoko Ishida, Ayako Anraku, Goji Tomita

“…Clinical data including visual acuity, refractive error, disc hemorrhage, VF indexes, and medical history were recorded. …”
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A Deep Learning Enabled Chest X-Ray Abnormality Detection Model for Radiology Assistance. by Ssempeebwa, Phillip, Ainembabazi, Patience

“…This difficulty persists even among experienced radiologists, leading to potential errors and delays in diagnosis (Pang et al., 2021). …”
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A Novel Method of Parameter Identification for Lithium-Ion Batteries Based on Elite Opposition-Based Learning Snake Optimization by Wuke Li, Ying Xiong, Shiqi Zhang, Xi Fan, Rui Wang, Patrick Wong

“…The experimental results demonstrate that EOLSO significantly outperforms the SO, achieving reductions of 43.83% in the Sum of Squares Error (SSE), 30.73% in the Mean Absolute Error (MAE), and 25.05% in the Root Mean Square Error (RMSE). …”
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Research on Path-Following Technology of a Single-Outboard-Motor Unmanned Surface Vehicle Based on Deep Reinforcement Learning and Model Predictive Control Algorithm by Bin Cui, Yuanming Chen, Xiaobin Hong, Hao Luo, Guanqiao Chen

“…The results indicate that for straight path tracking, the DDPG-MPC algorithm achieves 37% and 21% reductions in the average cross error and heading angle error, respectively, compared to the ALOS-PID algorithm. …”
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Elastodynamic modeling and analysis of a 4SRRR overconstrained parallel robot by B. Wang, Y. Zhao, C. Yang, X. Hu, Y. Zhao

“…Further increasing the number of rod divisions results in diminishing reductions in the error.</p>…”
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Design and Analysis of a Sowing Depth Detection and Control Device for a Wheat Row Planter Based on Fuzzy PID and Multi-Sensor Fusion by Yueyue Li, Bing Qi, Encai Bao, Zhong Tang, Yi Lian, Meiyan Sun

“…Experimental results demonstrate that under representative test conditions, the system achieves excellent sowing depth control performance; average error reductions of 10.7%, 22.9%, and 9.6% were observed when using fuzzy PID control versus no control. …”
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Periodformer: An Energy Consumption Prediction Model Based on Decomposition of Time Series by CHEN Bowen, DENG Jian, ZHU Qianliu

“…Experimental results showed that Periodformer achieved reductions in both Mean Absolute Error (MAE) and Mean Squared Error (MSE) of 5.56% and 11.85%, respectively, compared to the existing Transformer model, while exhibiting strong robustness against data noise.…”
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GRU–Transformer Hybrid Model for GNSS/INS Integration in Orchard Environments by Peng Gao, Jinzhen Fang, Junlin He, Shuang Ma, Guanghua Wen, Zhen Li

“…Compared with the conventional ES-EKF, the proposed method achieves reductions in position root mean square error (PRMSE) of 48.74% (East), 41.94% (North), and 61.59% (Up), and reductions in velocity root mean square error (VRMSE) of 71.5% (East), 39.31% (North), and 56.48% (Up) in the East–North–Up (ENU) coordinate frame. …”
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Global digital elevation model (GDEM) product generation by correcting ASTER GDEM elevation with ICESat-2 altimeter data by B. Li, B. Li, B. Li, B. Li, H. Xie, H. Xie, S. Liu, Z. Ye, Z. Hong, Q. Weng, Q. Weng, Q. Weng, Y. Sun, Q. Xu, X. Tong

“…The results from the validation comparison show that the elevation accuracy of IC2-GDEM is evidently superior to that of the ASTER GDEM product: (1) the RMSE reduction ratio of the corrected GDEM elevation is between 16 % and 82 %, and the average reduction ratio is about 47 %; and (2) from the analysis of the different topographies and land covers, this error reduction is effective even in areas with high topographic relief (<span class="inline-formula">&gt;15<i>°</i></span>) and high vegetation cover (<span class="inline-formula">&gt;60 %</span>). …”
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Physics-informed transformation toward improving the machine-learned NLTE models of ICF simulations by Min Sang Cho, Paul E. Grabowski, Kowshik Thopalli, Thathachar S. Jayram, Michael J. Barrow, Jayaraman J. Thiagarajan, Rushil Anirudh, Hai P. Le, Howard A. Scott, Joshua B. Kallman, Branson C. Stephens, Mark E. Foord, Jim A. Gaffney, Peer-Timo Bremer

“…By replacing the costly nonlocal thermodynamic equilibrium (NLTE) model with machine-learning models, significant reductions in calculation time have been achieved. However, determining how to optimize machine-learning-based NLTE models in order to match ICF simulation dynamics remains challenging, underscoring the need for physically relevant error metrics and strategies to enhance model accuracy with respect to these metrics. …”
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Spatial interpolation of cropland soil bulk density by increasing soil samples with filled missing values by Aiwen Li, Jinli Cheng, Dan Chen, Wendan Li, Yaruo Mao, Xinyi Chen, Bin Zhao, Wenjiao Shi, Tianxiang Yue, Qiquan Li

“…The RBFNN model, tailored for each sub-watershed, yielded the highest accuracy in filling missing BD, with an increase in coefficient of determination (R 2) by 19.54–37.36% and reductions in mean absolute error (MAE), mean relative error (MRE) and root mean square error (RMSE) by 8.91–14.81%, 9.02–16.22% and 7.71–13.61%, respectively. …”
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Prediction of Aluminum Alloy Surface Roughness Through Nanosecond Pulse Laser Assisted by Continuous Laser Paint Removal by Jingyi Li, Rongfan Liang, Han Li, Junjie Liu, Jingdong Sun

“…The SSA-BPNN model demonstrates high prediction accuracy, with a correlation coefficient (R²) of 0.98628, root mean square error (RMSE) of 0.024, mean absolute error (MAE) of 0.020 and mean absolute percentage error (MAPE) of 1.30% on the test set. …”
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Assessing effective mask use by the public in two countries: an observational study by Peter C Austin, Bory Kea, Bin Chen, Arshia P Javidan, Huong Nguyen, Alex Chang, Allen Li, Clare L Atzema, Ivona Mostarac, Dana Button, Lauren Wintraub, Raumil V Patel, Daniel Dongjoo Lee, Nathaniel P Latham, Eric A Latham, Patrick C M Brown, Rita D Somogyi, Sara Buerk, Tristen Zimmerman, Trevor Funari, Cameron Colbert

“…Subjects aged 81+ years (vs 31–65 years) and those on public transit and at the airport (vs stores) had higher odds of mask errors. Mask-wearers had a large reduction in adjusted mean number of breaches (rate ratio (RR) 0.19; 95% CI 0.17 to 0.20). …”
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Aerosol single-scattering albedo derived by merging OMI/POLDER satellite products and AERONET ground observations by Y. Dong, J. Li, J. Li, Z. Zhang, C. Zhang, Q. Li

“…Cross-validation using independent AERONET observations shows an average increase of 64 <span class="inline-formula">%</span> in correlation, an 11 <span class="inline-formula">%</span> reduction in RMSE, and a 10 <span class="inline-formula">%</span> reduction in MAB for the Merged-OMI dataset, as well as similar – although weaker – improvement for Merged-POLDER mainly due to the smaller sample size. …”
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The Impact of Managerial Overconfidence on Expenses Classification Shifting: The Moderating Role of Comparability of Financial Statements by Farshid Riahi Dorcheh, Iraj Torabi

“…The research model is estimated through panel data by controlling the effects of industry and year by ordinary least squares method with robust standard error.   Finding The findings of the first hypothesis show that managers' overconfidence leads to an increase in expense classification shifting and a piecemeal decrease in classification shifting over time. …”
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Analyzing and forecasting under-5 mortality trends in Bangladesh using machine learning techniques. by Shayla Naznin, Md Jamal Uddin, Ishmam Ahmad, Ahmad Kabir

“…Key metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R-squared, and Mean Absolute Percentage Error (MAPE), were employed to evaluate model performance. …”
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Prediction of mechanical characteristics of shearer intelligent cables under bending conditions. by Lijuan Zhao, Dongyang Wang, Guocong Lin, Shuo Tian, Hongqiang Zhang, Yadong Wang

“…The results show that, compared to other predictive models, the proposed model achieves reductions in Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) to 0.0002, 0.0159, and 0.0126, respectively, with the coefficient of determination (R2) increasing to 0.981. …”
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Are Junior Residents Accurate at Predicting Fetal Weight? An Analysis of Junior Residents' Performance of Estimated Fetal Weight Using Ultrasound and Leopold's Maneuver by Kimberly Huynh, Alicia Lunardhi, Karren Lewis, Trevor Pickering, Hindi E. Stohl

“…Maternal body mass index and actual BW were associated with absolute percentage estimation error. After adjusting for these variables, there was a statistically significant decrease in error between PGY1 and PGY2 for Leopold's method in term births; ultrasound (term and preterm) showed more modest reductions in error during PGY2. …”
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