Radiative heat and mass transfer significance through a permeable vertical plate with rotational effects: An artificial approach using the Levenberg–Marquardt algorithm by R. Kavitha, Kavikumar Jacob, Ahmad Haji Zadeh, Nagarajan Deivanayagampillai

“…Model accuracy is validated through mean squared error graphs, regression analysis, and error histograms, demonstrating reliable fluid dynamics predictions under varying conditions.…”
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Analysis of corn price forecast in China based on Lasso-XGBoost-SHAP by Wenming Cheng, Fangyuan Li

“…Results demonstrate that the Lasso-XGBoost model outperforms traditional linear models (LM) and other algorithms, including SVM (Support Vector Machine) and MLP (Multilayer Perceptron), with root mean squared error (RMSE) of 0.094, coefficient of determination (R2) of 0.973, mean absolute error (MAE) of 0.072, representing a 7.84% reduction in RMSE compared to standalone XGBoost. …”
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Spoofing Evident and Spoofing Deterrent Localization Using Ultrawideband (UWB) Active–Passive Ranging by Haige Chen, Ashutosh Dhekne

“…Our experimental evaluation shows a 30-cm <inline-formula><tex-math notation="LaTeX">$\text {75}{\text{th}}$</tex-math></inline-formula> percentile error for ToF-based honest tag localization and a submeter error for TDoA-based localization for spoofing tags. …”
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Isotherms and thermodynamic properties of water adsorption in ‘Cumari-do-Pará’ pepper seeds by Karen C. Rodrigues, Hellismar W. da Silva, Isneider L. Silva, Samuel G. F. dos Santos, Daniel P. da Silva, Renato S. Rodovalho

“…The Chen-Clayton model is the one that best represents the water adsorption isotherms in ‘Cumari-do-Pará’ pepper seeds under the studied conditions, with 9.94% mean relative error, 0.40 mean estimated error and random distribution of residuals. …”
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An Algorithm for Simplifying 3D Building Models with Consideration for Detailed Features and Topological Structure by Zhenglin Li, Zhanjie Zhao, Wujun Gao, Li Jiao

“…Compared to the QEM algorithm and the other two comparison algorithms selected in this paper, the simplified model resulting from this algorithm exhibit a reduction in Hausdorff distance, mean error, and mean square error to varying degrees. …”
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A Sparse CoSaMP Channel Estimation Algorithm With Adaptive Variable Step Size for an OFDM System by Ning Xiaoling, Chen Yangyi, Zhang Linsen

“…Additionally, the proposed algorithm was superior over the SAMP algorithm in terms of reconstruction, mean square error (MSE), and bit error ratio (BER).…”
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SP-RF-ARIMA: A sparse random forest and ARIMA hybrid model for electric load forecasting by Kamran Hassanpouri Baesmat, Farhad Shokoohi, Zeinab Farrokhi

“…This methodology, termed SP-RF-ARIMA, is evaluated against existing approaches; it demonstrates more than 40% reduction in mean absolute error and root mean square error compared to the second-best method.…”
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A Hybrid Learning Framework for Enhancing Bridge Damage Prediction by Amal Abdulbaqi Maryoosh, Saeid Pashazadeh, Pedram Salehpour

“…The proposed model demonstrates exceptional performance, achieving accuracy rates ranging from 98.27% to 100%, with error rates between 1.73% and 0% across multiple bridge damage datasets. …”
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One-Instant Flux Observer design for Three-Phase induction motor with reduced bound active load rejection speed controller by AKRAM HASHIM HAMEED, SHIBLY A. AL-SAMARRAIE, AMJAD JALEEL HUMAIDI

“…Abstract Latency in flux observation has an adverse effect on the performance of observer-based field-oriented speed control for three-phase induction motor (IM). The reduction of the convergent rate of estimation errors could improve the performance of speed-controlled IM based on flux observers. …”
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A modification design method for the angular misalignment of the spline drum shaping by YUAN Xichuan, LIU Qi, TANG Jinyuan, LI Yuan, SU Hui

“…Finite element simulation and test verification were subsequently conducted to validate the accuracy and effectiveness of this method.ResultsThe analytical results demonstrate that regarding computational efficiency, the proposed method reduces finite element simulations from 6-10 iterations required by traditional trial-and-error approaches to a single simulation for determining optimal modification, achieving over 80% efficiency improvement; in performance enhancement, the optimized modification demonstrates 30.22% reduction in contact stress and 50% decrease in maximum tooth wear volume under misalignment conditions, effectively eliminating edge contact while significantly extending service life.…”
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Handheld motorized injection system with fiber-optic distance sensors and adaptive time-delay controller by Jintaek Im, Sukho Park, Cheol Song

“…Also, TDC-based position regulation is designed to adaptively apply motor inputs by estimating disturbances during the handheld task and achieving fast system responses with minor control errors. Phantom studies show a maximum reduction of 26.5% in root-mean-square error (RMSE) compared to the existing approach. …”
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Transforming Prediction into Decision: Leveraging Transformer-Long Short-Term Memory Networks and Automatic Control for Enhanced Water Treatment Efficiency and Sustainability by Cheng Qiu, Qingchuan Li, Jiang Jing, Ningbo Tan, Jieping Wu, Mingxi Wang, Qianglin Li

“…Experimental validation on NH₃-N datasets from the SBR system reveals that the proposed model significantly outperforms existing advanced methods in terms of root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). …”
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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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Design of an Iterative Method for Time Series Forecasting Using Temporal Attention and Hybrid Deep Learning Architectures by Yuvaraja Boddu, A. Manimaran

“…This configuration adeptly extracts both spatial and temporal features, yielding a 15% reduction in prediction error across various datasets. …”
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ACSAformer: A crime forecasting model based on sparse attention and adaptive graph convolution by Zhenkai Qin, Baozhong Wei, Baozhong Wei, Caifeng Gao, Caifeng Gao, Feng Zhu, Feng Zhu, Weiqi Qin, Weiqi Qin, Qian Zhang

“…Specifically, on the DS1 dataset, the proposed model achieved a 17.6% reduction in Mean Squared Error (MSE) and a 9.2% reduction in Mean Absolute Error (MAE).DiscussionThese findings confirm that ACSAformer not only improves predictive accuracy and robustness but also offers better computational efficiency, showcasing its potential for application in complex spatiotemporal tasks such as crime forecasting.…”
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Daily runoff forecasting using novel optimized machine learning methods by Peiman Parisouj, Changhyun Jun, Sayed M. Bateni, Essam Heggy, Shahab S. Band

“…In the Carson River, the GB model achieves the highest forecasting accuracy, which is significantly improved by ARO, resulting in a 24.8 % reduction in root mean square error (RMSE). The MLP model also benefits notably from ARO, with RMSE improvements of 4.8 % and a substantial 48.9 % reduction in mean absolute error (MAE). …”
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Explainable AutoML models for predicting the strength of high-performance concrete using Optuna, SHAP and ensemble learning by Muhammad Salman Khan, Tianbo Peng, Tianbo Peng, Muhammad Adeel Khan, Asad Khan, Mahmood Ahmad, Mahmood Ahmad, Kamran Aziz, Mohanad Muayad Sabri Sabri, N. S. Abd EL-Gawaad

“…For compressive strength, it reduced the Mean Absolute Error (MAE) by 87.69% and the Root Mean Squared Error (RMSE) by 71.93%. …”
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Convergence rates of eigenvalue problems in perforated domains: the case of small volume by Shen Zhongwei, Zhuge Jinping

“…We obtain the optimal quantitative error estimates independent of the spectral gaps for an asymptotic expansion, with two leading terms, of Dirichlet eigenvalues. …”
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Elbow Joint Angle Estimation Using a Low-Cost and Low-Power Single Inertial Device for Daily Home-Based Self-Rehabilitation by Manon Fourniol, Rémy Vauché, Guillaume Rao, Eric Watelain, Edith Kussener

“…Moreover, its power consumption can be reduced by more than the increase in the error when reducing the rate of the data output by the sensor. …”
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Short-term and long-term inertia forecasting with low-inertia event prediction in IBR-integrated power systems using a deep learning approach by Santosh Diggikar, Arunkumar Patil, Katkar Siddhant Satyapal, Kunal Samad

“…The proposed hybrid model achieves superior predictive performance, with a mean absolute percentage error (MAPE) of 2.74%, mean absolute error (MAE) of 4.55 GVAs, root mean square error (RMSE) of 6.65 GVAs, mean squared error (MSE) of 44.22 GVAs2, and combined accuracy (CA) of 3.70 GVAs. …”
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