DETERMINATION OF CHEWING EFFICIENCY IN PATIENTS WITH RESTORED TOOTH CROWN PART by V.V. Nikolov, D M. Korol, D.D. Kindiy, M.D. Korol

“…In a week after the fixation of fixed porcelain fused metal dentures, the reduction of the mean value of mastication test index by 0.2 was fixed. …”
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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

“…In the context of aging populations, it has become necessary to develop new methods and devices for the daily home-based self-rehabilitation of elderly people. …”
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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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Understanding the Role of Robotic Process Automation in Healthcare by Octavian Constantin IORDACHE, Sebastian Aurelian ŞTEFĂNIGĂ

“…The study follows a design and implementation approach, evaluating the system's efficiency in terms of error reduction, processing speed, and data integrity compared to manual processing methods. …”
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Red Fox Optimization-Based Estimation Algorithms for Splitting Tensile Strength of Basalt Fiber Reinforced Concrete by Xiao Wu, Daoyong Zhu, Qin Yan

“…Also, the models’ results indicate that RFORF outperforms another model, with −34% lower values in symmetric mean absolute percentage error (SMAPE) and a significant −80% reduction in mean squared logarithmic error (MSLE). …”
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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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Hydrodynamic Performance Enhancement of Torpedo-Shaped Underwater Gliders Using Numerical Techniques [version 2; peer review: 1 approved, 2 approved with reservations, 1 not approv... by Srinivas G, Sudheendra Prabhu K

“…While a 0.19m nose length resulted in a 1.67% reduction. This study helps researchers in hydrodynamics by optimizing geometry for drag reduction.…”
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Predicting Ship Waiting Times Using Machine Learning for Enhanced Port Operations by Min-Hwa Choi, Woongchang Yoon

“…Shapley additive explanation (SHAP)-based feature selection is typically applied to enhance interpretability, and its effect is compared with principal component analysis-based dimensionality reduction and nonselection methods. The XGBoost Regressor (XGBR) is optimized using genetic-algorithm-based hyperparameter tuning, reducing mean squared error (RMSE) from 20.9531 to 19.6387, mean absolute error (MAE) from 13.6821 to 12.6753, and improving coefficient of determination (R2) from 0.2791 to 0.2949. …”
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Estimation of Ambient Air PM2.5 Concentration Using MLP and RBF by Ali Mohammadi Bardshahi, Nematollah Jaafarzadeh, Tayebeh Tayebeh, Fazel Amiri

“…The dataset was divided into three subsets: 70% for training, 15% for testing, and 15% for validation.Results: The results showed that the average concentration of PM2.5 was 26.5 μg/m3. The root mean square error (RMSE) was estimated as 6.49 μg/m3. Increasing the input data resulted in a slight reduction in network error, with the RBF model, utilizing 1450 inputs and an RMSE of 6.47, achieving the same accuracy as the MLP model with 10 inputs.Conclusion: Given that the PM2.5 concentration estimates from the RBF and MLP models deviated by less than 23 and 25%, respectively, compared to the observed concentrations, both MLP and RBF can be regarded as reliable tools for predicting PM2.5 levels.…”
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A Near-Infrared Imaging System for Robotic Venous Blood Collection by Zhikang Yang, Mao Shi, Yassine Gharbi, Qian Qi, Huan Shen, Gaojian Tao, Wu Xu, Wenqi Lyu, Aihong Ji

“…Results show that, compared to U-Net, the BYOL+U-Net+ResNet18 method achieves an 8.31% reduction in Binary Cross-Entropy (BCE), a 5.50% reduction in Hausdorff Distance (HD), a 15.95% increase in Intersection over Union (IoU), and a 9.20% increase in the Dice coefficient (Dice), indicating improved image segmentation quality. …”
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A deep neural network framework for estimating coastal salinity from SMAP brightness temperature data by Yidi Wei, Qing Xu, Qing Xu, Xiaobin Yin, Xiaobin Yin, Yan Li, Yan Li, Kaiguo Fan

“…The framework leverages machine learning interpretability tools (Shapley Additive Explanations, SHAP) to optimize input feature selection and employs a grid search strategy for hyperparameter tuning.Results and discussionSystematic validation against independent in-situ measurements demonstrates that the baseline DNN model constructed for the entire region and time period outperforms conventional algorithms including K-Nearest Neighbors, Random Forest, and XGBoost and the standard SMAP SSS product, achieving a reduction of 36.0%, 33.4%, 40.1%, and 23.2%, respectively in root mean square error (RMSE). …”
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Super-resolution reconstruction of the 1 arc-second Australian coastal DEM dataset by Bo Zhang, Zekai Shi, Danfeng Hong, Qi Wang, Jian Yang, Haoyuan Ren, Meng Zhang

“…Through experiments using bathymetric (ocean) data around Australia, the proposed method demonstrated superior performance compared to naive bicubic interpolation, achieving a 11.64% reduction in root mean square error (RMSE) and a 1.93% increase in peak signal-to-noise ratio (PSNR) averages. …”
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Advancing Evapotranspiration Modeling With Optimized Soil and Canopy Resistance Combinations by Jinfeng Zhao, Shikun Sun, Yali Yin, Yihe Tang, Chong Li, Yongshan Liang, Yubao Wang, Alexander Winkler, Shijie Jiang

“…The performance of best‐performing but unexplored combinations (S2‐C1, S2‐C2, S2‐C5) is consistent with PML‐V2, GLEAM4, and underlying water use efficiency model, explaining 56% of the variation in daily ET and achieving an root mean square error as low as 1.02 mm day−1. However, these models show reduced accuracy in arid zones, where prolonged water stress led to a 38% reduction in R2. …”
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Comparative investigation of bagging enhanced machine learning for early detection of HCV infections using class imbalance technique with feature selection. by Ekramul Haque Tusher, Mohd Arfian Ismail, Abdullah Akib, Lubna A Gabralla, Ashraf Osman Ibrahim, Hafizan Mat Som, Muhammad Akmal Remli

“…The performance of these ensemble methods is evaluated using metrics such as accuracy, recall, precision, F1 score, G-mean, balanced accuracy, cross-validation (CV), area under the curve (AUC), standard deviation, and error rate. …”
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Harmonizing remote sensing and ground data for forest aboveground biomass estimation by Ying Su, Zhifeng Wu, Xiaoman Zheng, Yue Qiu, Zhuo Ma, Yin Ren, Yanfeng Bai

“…The effectiveness of this approach was demonstrated by a 0.67 increase in the correlation coefficient R2, a 43.57 % reduction in the root mean square error (RMSE), and a 68.00 % reduction in the mean square error (MSE) achieved through the optimal combination of data sources. …”
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Hierarchical Multi-Scale Decomposition and Deep Learning Ensemble Framework for Enhanced Carbon Emission Prediction by Yinuo Sun, Zhaoen Qu, Zhuodong Liu, Xiangyu Li

“…Experiments on four real-world datasets (133,225 observations) demonstrate that our CEEMDAN–CNN–Transformer framework outperforms 12 state-of-the-art methods, achieving a 13.3% reduction in root mean square error (RMSE) to 0.117, 12.7% improvement in mean absolute error (MAE) to 0.088, and 13.0% improvement in continuous ranked probability score (CRPS) to 0.060. …”
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Nonlinear time domain and multi-scale frequency domain feature fusion for time series forecasting by Kejiang Xiao, Yefeng Li, Yaning Dong, Wenqi Yang, Binting Yao, Liang Chen

“…Experimental results show that the WTConv-iKransformer achieves an additional 3% error reduction compared with individual enhanced models and realizes an average 25% error decrease over mainstream methods (e.g., Informer, LSTM) on ETTh1, ETTm1, Electricity, and Traffic datasets.…”
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Harmonizing measurement tools: examining the concurrent validity of the Daily Activity Behaviors Questionnaire compared to the ActiGraph to assess 24-hour movement behaviors among... by Iris Willems, Vera Verbestel, Dorothea Dumuid, Patrick Calders, Bruno Lapauw, Marieke De Craemer

“…Interaction effects between sociodemographic variables and the measurement methods were explored in mixed models. All analyses were compared by four commonly used data processing methods for ActiGraph data (cut-points and data reduction method-specific). …”
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A hybrid variational mode decomposition framework for enhanced cardiac output estimation using impedance cardiography by Priya Darshini Kumari, Ksh Milan Singh, Zefree Lazarus Mayaluri, Prabodh Kumar Sahoo, Satyabrata Lenka, Ganapati Panda, Sujeevan Kumar Agir

“…Experimental results demonstrate that the proposed VMD-NLM-DWT approach achieves a maximum of 1.2 dB improvement in signal-to-noise ratio (SNR), an average 13% reduction in mean squared error (MSE), and 9% lower percent root mean square difference (PRD) compared to leading two-stage denoising methods. …”
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Optimization of Sensor Targeting Configuration for Intelligent Tire Force Estimation Based on Global Sensitivity Analysis and RBF Neural Networks by Yu Zhang, Guolin Wang, Haichao Zhou, Jintao Zhang, Xiangliang Li, Xin Wang

“…Variance-based global sensitivity analysis combined with dimensional reduction methods was used to evaluate the sensitivity of acceleration, strain, and displacement responses to variations in longitudinal and lateral forces. …”
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