Chemometric and computational modeling of polysaccharide coated drugs for colonic drug delivery by Ahmad Khaleel AlOmari, Khaled Almansour

“…The dataset, consisting of 155 data points with over 1500 spectral features, underwent preprocessing involving normalization, Principal Component Analysis (PCA) for dimensionality reduction, and outlier detection using Cook’s Distance. …”
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Component Prediction of Antai Pills Based on One-Dimensional Convolutional Neural Network and Near-Infrared Spectroscopy by Tuo Guo, Fengjie Xu, Jinfang Ma, Fahuan Ge

“…Convolutional neural networks (CNNs) are widely used for image recognition and text analysis and have been suggested for application on one-dimensional data as a way to reduce the need for preprocessing steps. …”
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Experimental Study of Interphase Heat Transfer with Free Vapor Bubble Condensation Caused by Rapid Pressure Increase by DENG Jiewen1, 2, HUANG Tao3, ZHU Longxiang1, 2, PAN Liangming1, 2, SUN Wan1, 2, , ZHANG Luteng1, 2, MA Zaiyong1, 2

“…The fitted relationship was verified and compared with the experimental data, and the average prediction error was 21.32%. …”

Optimized Demand Forecasting for Bike-Sharing Stations Through Multi-Method Fusion and Gated Graph Convolutional Neural Networks by Hebin Guo, Kexin Li, Yutong Rou

“…Additionally, user characteristics are included as node features, enabling a more comprehensive analysis. The study utilizes the 2020 dataset from Jersey City’s bike-sharing system, starting with the application of the Isolation Forest algorithm to detect and filter anomalous data points. …”
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Clinical efficacy of cataracts surgical treatment in patients after ARK using IOL power calculation basic formulas by E.K. Tsirenzhapova, O.I. Rozanova

“…Results. The analysis of the clinical efficacy of phacoemulsification with IOL implantation in patients after ARK with the refractive error (RE) calculation and refractive prediction error (RPE) calculation according to the basic formulas were performed. …”
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Improving prediction accuracy in agricultural markets through the CIMA-AttGRU model. by Yankun Jiang, Jinhui Liu, Xiaotuan Li

“…While previous studies have primarily relied on historical price data and fundamental financial indicators of agricultural product futures, there is a growing recognition of the value that lies within the vast amounts of textual data generated in the financial domain. …”
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Numerical treatment for Darcy–Forchheimer flow of propylene glycol with carbon nanotubes under the impacts of MHD and activation energy by Hafiz Muhammad Shahbaz, Iftikhar Ahmad

“…The effectiveness of scheme is endorsed through various statistical measures like regression index, error histograms, correlation analysis and convergence analysis showing a minimum level of mean square error (E-12 to E-04) for the comprehensive simulation of the proposed model.…”
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Optimizing Smart Grid Load Forecasting via a Hybrid Long Short-Term Memory-XGBoost Framework: Enhancing Accuracy, Robustness, and Energy Management by Falah Dakheel, Mesut Çevik

“…To assess the performance of the proposed model, we used the Elia Grid dataset from Belgium, which includes load data recorded every 15 min throughout 2022. Results: When compared to individual models, this hybrid approach outperformed them, achieving a Root Mean Square Error (RMSE) of 106.54 MW, a Mean Absolute Percentage Error (MAPE) of 1.18%, and a coefficient of determination (R²) of 0.994. …”
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Machine learning and response surface methodology forecasting comparison for improved spray dry scrubber performance with brine sludge-derived sorbent by B.J. Chepkonga, L. Koech, R.S. Makomere, H.L. Rutto

“…Model performance was evaluated through generalization and accuracy measurements, including the coefficient of determination (R²), root mean square error (RMSE), mean square error (MSE), and mean squared logarithmic error (MSLE). …”
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Harnessing multimodal approaches for depression detection using large language models and facial expressions by Misha Sadeghi, Robert Richer, Bernhard Egger, Lena Schindler-Gmelch, Lydia Helene Rupp, Farnaz Rahimi, Matthias Berking, Bjoern M. Eskofier

“…Our findings show the best results are achieved by enhancing text data with speech quality assessment, with a mean absolute error of 2.85 and root mean square error of 4.02. …”
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Retinal Fibre Layer Thickness Measurement in Normal Paediatric Population in Sweden Using Optical Coherence Tomography by Marcelo Ayala, Evangelia Ntoula

“…Results. 138 eyes were included in the analysis. The average refractive error (SE) was +1.7 D (range −5.25 to +7.25 D). …”
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Efficient multi-station air quality prediction in Delhi with wavelet and optimization-based models. by Lakshmi Sankar, Krishnamoorthy Arasu

“…The AquaWave-BiLSTM framework demonstrated exceptional predictive accuracy, with a Mean Squared Error (MSE) of 0.00065, a Mean Absolute Error (MAE) of 0.04566, a Root Mean Square Error (RMSE) of 0.02523, and an R² value of 0.9494, surpassing conventional methodologies. …”
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An integrated IKOA-CNN-BiGRU-Attention framework with SHAP explainability for high-precision debris flow hazard prediction in the Nujiang river basin, China. by Hao Yang, Tianlong Wang, Nikita Igorevich Fomin, Shuoting Xiao, Liang Liu

“…The IKOA-CNN-BiGRU-Attention framework consistently outperforms 13 benchmark models, achieving a root mean square error of 2.33 × 10-6, mean absolute error of 1.51 × 10-6, and mean absolute percentage error of 0.006%. …”
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Animation Pose Generation Model Based on Kinect Depth Image and Occlusion-Robust Pose-Maps Algorithm by Zhenxi Yu, Ting Wang, Peng Tian, Xuejie Li

“…In the evaluation of registration error, the research model showed the lowest error on both datasets, and the error values did not exceed 2. …”
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Predicting patient visits at the psychiatric polyclinic of a public hospital in Bali, Indonesia: A forecasting approach using single exponential smoothing by Ni Made Ratih Comala Dewi Dewi, Putu Cintariasih, Ni Wayan Suryani, Luh Gde Nita Sri Wahyuningsih

“…Methods: A retrospective analysis was conducted using patient visit data from January 2021 to December 2023. …”
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Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking by Gang Yu, Shuang Zhang, Min Hu, Y. Ken Wang

“…This paper (1) establishes an MTSS prediction model with heterogeneous stacking of eXtreme gradient boosting (XGBoost), the artificial neural network (ANN), random forest (RF), ridge regression, and support vector regression (SVR) component learners after exploratory data analysis (EDA); (2) proposes a method based on multiple time series feature extraction to accurately predict the pavement performance change trend, using the highway segment as the minimum computing unit and considering multiple factors; (3) uses grid search with the k-fold cross validation method to optimize hyperparameters to ensure the robustness, stability, and generalization ability of the prediction model; and (4) constructs a digital twin for pavement performance prediction to realize the real-time dynamic evolution of prediction. …”
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Downscaling, bias correction, and spatial adjustment of extreme tropical cyclone rainfall in ERA5 using deep learning by Guido Ascenso, Andrea Ficchì, Matteo Giuliani, Enrico Scoccimarro, Andrea Castelletti

“…Our results show how RA-Ucmpd improves ERA5 in almost all metrics by 3-28%—more than the other models we used for comparison which actually worsen the total rainfall bias of ERA5—at the cost of a slightly increased (3%) error on the magnitude of the peak. We analyse the behaviour of RA-Ucmpd by visualizing accumulated maps of four particularly wet tropical cyclones and by dividing our data according to the Saffir-Simpson scale and to whether they made landfall, and we perform an error analysis to understand under what conditions our model performs best.…”
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Intra‐ and inter‐session reliability and repeatability of 1H magnetic resonance spectroscopy for determining total creatine concentrations in multiple brain regions by Jedd Pratt, James McStravick, Aneurin J. Kennerley, Craig Sale

“…We provide new minimum detectable change data for tCr concentrations, a detailed discussion of the inherent error sources in repeated 1H MRS, including the substantial effect of the analysis package on tCr quantification, and suggestions for how these should be managed to improve the interpretability and clinical value of future research. …”
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A hybrid LSTM-transformer model for accurate by Tianren Zhao, Yanhui Zhang, Minghao Wang, Wei Feng, Shengxian Cao, Gong Wang

“…Data preprocessing is performed using standardization and Yeo-Johnson transformation. …”
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Method comparison and overview of refractive measurements in children: implications for myopia management by Lihua Li, Arne Ohlendorf, Siegfried Wahl, Jonas Müller, Xiaoqin Chen

“…The observed bias increased with the amount of myopic refractive error for comparisons 2 and 3, which needs to be considered when interpreting wavefront-refraction data.Trial registration number NCT05288335.…”
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