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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Neuro-fuzzy inference system and white shark optimization of coagulation-flocculation of aquaculture wastewater treatment by A.F. Mohamed, H. Rezk

“…The R-squared values for training and testing are 1.0 and 0.82, respectively, and adaptive neuro-fuzzy inference system reduced the root mean square error from 6.8 with analysis of variance to 1.135 with adaptive neuro-fuzzy inference system achieving an 83.5 percent reduction. …”
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Gait-based Parkinson’s disease diagnosis and severity classification using force sensors and machine learning by Navita, Pooja Mittal, Yogesh Kumar Sharma, Anjani Kumar Rai, Sarita Simaiya, Umesh Kumar Lilhore, Vimal Kumar

“…The crucial evaluation metrics used for evaluating model performance include accuracy, mean absolute error, and root mean square error. The findings indicate that the suggested model significantly surpasses current methodologies. …”
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Machine Learning Using Approximate Computing by Padmanabhan Balasubramanian, Syed Mohammed Mosayeeb Al Hady Zaheen, Douglas L. Maskell

“…Approximate computation has emerged as a promising alternative to accurate computation, particularly for applications that can tolerate some degree of error without significant degradation of the output quality. …”
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Renal replacement therapy in the neonatal intensive care unit by Tze Yee Diane Mok, Min-Hua Tseng, Ming-Chou Chiang, Ju-Li Lin, Shih Ming Chu, Jen-Fu Hsu, Reyin Lien

“…Twelve neonates, including three with inborn errors of metabolism (IEM), received continuous RRT (CRRT), and five neonates underwent peritoneal dialysis (PD). …”
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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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Nanosecond Laser Etching of Surface Drag-Reducing Microgrooves: Advances, Challenges, and Future Directions by Xulin Wang, Zhenyuan Jia, Jianwei Ma, Wei Liu

“…The aim is to control the geometric accuracy error of the prepared surface microgrooves within 5% and to enhance the fatigue life of the substrate by more than 20%, breaking through the technical bottleneck of separating “drag reduction design” from “fatigue resistance manufacturing”, and providing theoretical support for the integrated manufacturing of “drag reduction-fatigue resistance” in aircraft skins.…”
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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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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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High-resolution population mapping based on SDGSAT-1 glimmer imagery and deep learning: a case study of the Guangdong-Hong Kong-Macao Greater Bay Area by Haoxuan Duan, Zhongqi Shi, Ji Ge, Fan Wu, Yuzhou Liu, Hong Zhang, Chao Wang

“…It also outperformed other population spatialization datasets and NTL data by over 30% and 10%, respectively, in terms of error reduction. The results highlight the method’s effectiveness and the value of SDGSAT-1 glimmer imagery for fine population spatialization.…”
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An Effective Hybrid Strategy: Multi-Fuzzy Genetic Tracking Controller for an Autonomous Delivery Van by Mohammad Ghazali, Zaid Samadi, Mehmet Gol, Ali Demir, Kemal Rodoplu, Tarek Kabbani, Emrecan Hatipoğlu, Ahu E. Hartavi

“…The results show that the proposed strategy leads to a reduction of up to 91.2% and 61.1% in tracking error compared to the manually and geometrically weighted alternatives, respectively.…”
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Neural Implicit Monocular Visual SLAM for 3D Reconstruction in Planetary Environments by C. Liu, R. Huang, R. Huang, H. Xie, H. Xie, T. Tao, Y. Feng, Y. Feng, X. Tong, X. Tong

“…The results demonstrate that our method significantly outperforms OV²SLAM in localization accuracy, achieving an 85.16% reduction in absolute trajectory errors and maintaining translation errors within 1 m across the entire trajectory. …”
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A Self-Compensating Non-Intrusive Ring-Type AC Voltage Sensor Based on Capacitive Coupling by Junpeng Wang, Jiacheng Li, Chunrong Peng, Zhengwei Wu, Dengfeng Ju, Qiang Zhang

“…The effects of changes in cable diameter and cable position on the measurement were tested separately. The worst-case error of the sensor output is less than 6.44%, representing a reduction of 21.4% compared to the uncompensated case. …”
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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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Safety Status Prediction Model of Transmission Tower Based on Improved Coati Optimization-Based Support Vector Machine by Xinxi Gong, Yaozhong Zhu, Yanhai Wang, Enyang Li, Yuhao Zhang, Zilong Zhang

“…The predictive outcomes indicate that the proposed ICOA-SVM model exhibits rapid convergence and high prediction accuracy, with a 62.5% reduction in root mean square error, a 59.6% decrease in average relative error, and a 75.0% decline in average absolute error compared to the conventional support vector machine. …”
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