Time-Series Interval Forecasting with Dual-Output Monte Carlo Dropout: A Case Study on Durian Exports by Unyamanee Kummaraka, Patchanok Srisuradetchai

“…Empirical distributions of predictive means and variances from the MCDO demonstrate the results of the dual-output MCDO DNNs. The proposed method achieves a significant improvement in forecast accuracy, with an RMSE reduction of about 10% compared to the seasonal autoregressive integrated moving average model. …”
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Speech Intelligibility Prediction Using Binaural Processing for Hearing Loss by Xiajie Zhou, Candy Olivia Mawalim, Masashi Unoki

“…Experimental results show that, compared to the baseline system of the second Clarity Prediction Challenge (CPC2) dataset, the proposed method achieves an 8.3% reduction in root mean squared error (RMSE). …”
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A novel multiscale feature enhancement network using learnable density map for red clustered pepper yield estimation by Chenming Cheng, Chenming Cheng, Jin Lei, Jin Lei, Zicui Zhu, Lijian Lu, Lijian Lu, Zhi Wang, Zhi Wang, Jiali Tao, Jiali Tao, Xinyan Qin, Xinyan Qin

“…First, the kernel-based density map (KDM) method was improved by integrating the Swin Transformer (ST), resulting in LDM method, which produces higher quality density maps. …”
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Radar-equivalent snowpack: reducing the number of snow layers while retaining their microwave properties and bulk snow mass by J. Meloche, N. R. Leroux, B. Montpetit, V. Vionnet, C. Derksen

“…A reduction in the mathematical complexity of SWE retrieval cost functions and a reduction in computation of up to 80 % can be gained by using fewer layers in the SWE retrieval.…”
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An Optimal Approach for Assessing Weibull Parameters and Wind Power Potential for Six Coastal Cities in Pakistan by Ghulam Abbas, Arshad Ali, Faizan Rashid, Naveed Ashraf, Zohaib Mushtaq, Muhammad Zubair

“…An enormous reduction in wind power density-based percentage error (for example, 15.3531% than 51.7205% for Gwadar at 10 m height) was observed in NEPFM-SSA compared to NEPFM. …”
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Pressure-Related Discrepancies in Landsat 8 Level 2 Collection 2 Surface Reflectance Products and Their Correction by Santosh Adhikari, Larry Leigh, Dinithi Siriwardana Pathiranage

“…For the green band, the reduction in error was much less due to the significantly lesser impact of aerosol on this band. …”
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Aberration Effects of Planar Layers in Near-Field Multistatic Millimeter-Wave Imaging by Hakseok Ko, Mooseok Jang

“…Furthermore, based on the simulation results, we provide the relation between the thickness and refractive indices of aberrating layers and the positional error and brightness reduction in a reconstructed image. …”
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A Wavelet-based Filtering Algorithm for Enhancing Signal Processing in Coriolis Flow Meters by Khoo David Wee Yang, Ng Zhi Qun, Seah Leong Keey

“…Applying signal processing method effectively for a Coriolis flow meter (CFM) requires robust filtering strategies. …”
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Mental chronometry in big noisy data. by Edmund Wascher, Fariba Sharifian, Marie Gutberlet, Daniel Schneider, Stephan Getzmann, Stefan Arnau

“…In the present study, we systematically evaluated two different approaches to latency estimation (peak latencies and fractional area latencies) with respect to their data quality and the application of noise reduction by jackknifing methods. Additionally, we tested the recently introduced method of Standardized Measurement Error (SME) to prune the dataset. …”
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Predictability Limit of the 2021 Pacific Northwest Heatwave From Deep‐Learning Sensitivity Analysis by P. Trent Vonich, Gregory J. Hakim

“…The advent of deep‐learning frameworks enables a new approach using backpropagation and gradient descent to iteratively optimize initial conditions, minimizing forecast errors. We apply this approach to the June 2021 Pacific Northwest heatwave using the GraphCast model, yielding over 90% reduction in 10‐day forecast errors over the Pacific Northwest. …”
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Water quality prediction model based on improved long short-term memory neural network and empirical mode decomposition by Feng Lin, Xu Li, Yang Su, Jun Yan

“…The improved model achieved a 31% reduction in mean absolute error and a 50% reduction in mean squared error for different ammonia nitrogen concentrations. …”
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TerraWind: A Deep Learning‐Based Near‐Surface Winds Downscaling Model for Complex Terrain Region by Jie Lian, Sirong Huang, Jiahao Shao, Peiyan Chen, Shengming Tang, Yi Lu, Hui Yu

“…Experimental results in Eastern China demonstrate that TerraWind reduces wind speed Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by an average of 42.6% and 33.3%, respectively, compared to three interpolation methods (bicubic, bilinear, and Inverse Distance Weighting). …”
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Improvement of point cloud feature extraction and alignment algorithms and lidar slam in coal mine underground by Guanghui XUE, Zhenghao ZHANG, Guiyi ZHANG, Ruixue LI

“…Compared to the traditional LIO-SAM algorithm, the improved algorithm showcases higher accuracy in pose estimation and point cloud registration, with a 6.52% improvement in average relative position error and an 18.84% reduction in maximum absolute position error. …”
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Sound-Based Unsupervised Fault Diagnosis of Industrial Equipment Considering Environmental Noise by Jeong-Geun Lee, Kwang Sik Kim, Jang Hyun Lee

“…The proposed method minimizes the impact of environmental noise and maintains the fault diagnosis performance in altered environments. …”
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STL-DCSInformer-ETS: A Hybrid Model for Medium- and Long-Term Sales Forecasting of Fast-Moving Consumer Goods by Yecheng Ma, Lili He, Junhong Zheng

“…Through numerical experiments, the method demonstrates excellent performance by achieving a 35.9% reduction in Mean Squared Error and a 21.4% decrease in Mean Absolute Percentage Error, significantly outperforming traditional methods. …”
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An Improved Galerkin Framework for Solving Unsteady High-Reynolds Navier–Stokes Equations by Jinlin Tang, Qiang Ma

“…Numerical experiments are presented to verify the effectiveness of the proposed method, demonstrating its ability to achieve stable, high-fidelity solutions on adaptively refined grids with a substantial reduction in computational cost.…”
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Multi-feature stock price prediction by LSTM networks based on VMD and TMFG by Zhixin Zhang, Qingyang Liu, Yanrong Hu, Hongjiu Liu

“…(sh600009), the VMD–TMFG–LSTM model achieves a 69.76% reduction in Root Mean Squared Error (RMSE), a 71.41% reduction in Mean Absolute Error (MAE), a 46.28% reduction in runtime, and an improvement of 0.2184 in R-squared (R2), indicating significantly higher prediction accuracy. …”
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OT-PCA: New Key-Recovery Plaintext-Checking Oracle Based Side-Channel Attacks on HQC with Offline Templates by Haiyue Dong, Qian Guo

“…Extensive simulations demonstrate that our new attack method significantly reduces the required number of oracle calls, achieving a 2.4-fold decrease for hqc-128 and even greater reductions for hqc-192 and hqc-256 compared to current state-of-the-art methods. …”
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Bayesian Uncertainty Quantification of Reflooding Model With PSO–Kriging and PCA Approach by Ziyue Zhang, Dong Li, Nianfeng Wang, Meng Lei

“…To improve the process of best estimate plus uncertainty (BEPU) for nuclear safety assessment and calibration of thermal–hydraulic models for error reduction, inverse uncertainty quantification (IUQ) is proposed in recent years to quantify the uncertainty of model parameters in reactor program. …”
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A high-accuracy exponential time integration scheme for the Darcy–Forchheimer Williamson fluid flow with temperature-dependent conductivity by Arif Muhammad Shoaib

“…Quantitative validation demonstrates that the scheme achieves second-order accuracy in time and sixth-order convergence in space, with a reduction in error norm of up to 18% compared to the classical second-order Runge–Kutta method. …”
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