Impact of Climate Change on Groundwater Level Changes: An Evaluation Based on Deep Neural Networks by Stephen Afrifa, Tao Zhang, Peter Appiahene, Xin Zhao, Vijayakumar Varadarajan, Thomas Atta-Darkwah, Yanzhang Geng, Daniel Gyamfi, Rose-Mary Owusuaa Mensah Gyening

“…The study advances environmental modeling by exhibiting methodological complexity and emphasizes the importance of comprehensive data analysis in water resource management.…”
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Computationally Efficient Impact Estimation of Coil Misalignment for Magnet-Free Cochlear Implants by Samuelle Boeckx, Pieterjan Polfliet, Lieven De Strycker, Liesbet Van der Perre

“…The MATLAB model is verified with FEA software with a median 8% relative error on the coupling coefficient for various misalignments, ensuring that it can be used to study the feasibility of various magnet-free implants and wireless power and data transmission systems in general. …”
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Flood hazard mapping in an urban area using combined hydrologic-hydraulic models and geospatial technologies by B.A.M. Talisay, G.R. Puno, R.A.L. Amper

“…On the other hand, the performance of hydraulic model during error computation was “intermediate fit” using F measure analysis with a value of 0.56, using confusion matrix with 80.5% accuracy and the Root Mean Square Error of 0.47 meters. …”
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Investigation of cadmium removal using tin oxide nanoflowers through process optimization, isotherms and kinetics by Selim Gürsoy, Miray Bombom, Buse Tuğba Zaman, Fatma Turak, Sezgin Bakırdere, Elif Öztürk Er

“…The adsorption equilibrium process was elucidated by Langmuir, Freundlich, Sips and Toth isotherm models using nonlinear regression. In addition, error functions such as Chi-square (X 2, Average Relative Error (ARE), Root Mean Squared Error (RMSE) and HYBRID were used to test the validity of the nonlinear models. …”
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Hybrid quadratic diagonal algorithm for thinning contour lines by Mamatov Narzullo, Jalelova Malika, Fayziev Vohid, Samijonov Abdurashid, Samijonov Boymirzo

“…One of the main issues of image analysis is the separation of contour lines. Nowadays, many effective methods for dividing contour lines have been developed. …”
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Funding the future: Nigeria's battle against poverty through government expenditure by Temitope Adebayo

“…Using time series data and cointegration analysis, the study reveals a significant long-run relationship between government expenditure and poverty reduction, with a 1 % increase corresponding to a 0.05 percentage point reduction in poverty incidence. …”
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Predicting steady degradation in ship power system: A deep learning approach based on comprehensive monitoring parameters by Xingshan Chang, Xiaojian Xu, Bohua Qiu, Muheng Wei, Xinping Yan, Jie Liu

“…Compared to baseline models, YC2Model demonstrates superior performance on key performance indicators, including the highest coefficient of determination (R2) of 0.960717, and the lowest symmetric mean absolute percentage error of 0.015500, mean square error of 0.707211 × 10−4, root mean square error of 0.008410, and mean absolute error of 0.006519, proving its superior predictive accuracy. …”
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Energy consumption prediction using modified deep CNN-Bi LSTM with attention mechanism by Adel Binbusayyis, Mohemmed Sha

“…Based on regression outcomes from analysis taken in hourly, daily and monthly time intervals, enhanced prediction accuracy is estimated through evaluation metrics such as MSE (Mean Square Error), MAPE (Mean Absolute Percentage Error) and RMSE (Root Mean Square Error) which determines the efficacy of the system, where Specifically, the proposed model achieves MSE of 0.123, MAE of 0.22, and MAPE of 324.12. …”
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Suitability of Mechanics-Based and Optimized Machine Learning-Based Models in the Shear Strength Prediction of Slender Beams Without Stirrups by Abayomi B. David, Oladimeji B. Olalusi, Paul O. Awoyera, Lenganji Simwanda

“…Among the ML models, XGB and GBR demonstrated the highest predictive accuracy, with coefficients of determination (R²) of 0.974 and 0.966, respectively, indicating strong correlation with experimental data. Performance metrics such as mean absolute error (MAE) and root mean squared error (RMSE) showed that XGB and GBR consistently outperformed other models, yielding lower error margins. …”
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Damage detection of beam by natural frequencies: General theory and procedure by Nguyen Tien Khiem

“…Then, measurement data are corrected based on the updated model. Finally, the damage parameters are identified using updated model and corrected measurement data. …”
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Population pharmacokinetic study of colistin for dose optimization in patients with acute on chronic liver failure (ACLF) patients by Dr Ritika Bhandari, Prof. Nusrat Shafiq, Dr. Naveen Murali, Dr. Soumya Vij, Dr. Nipun Verma, Dr. Avaneesh Kumar Pandey, Prof. Vikram Prakash Gota, Dr. M Surulivel Rajan, Prof. Ajay Kumar Duseja, Prof. Samir Malhotra

“…The combined error model (additive and proportional) provided the best fit for the data. …”
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Bridge Deflection Separation Prediction Model Based on DWT-LSTM and Its Engineering Application by ZHENG Shuai, JIANG He, WANG Zhongchang, DING Jia, YANG Yi

“…Prediction performance was assessed using three metrics: correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE). Fourthly, in order to demonstrate the necessity of the combined model for predicting bridge deflection, a comparative analysis was conducted between the proposed model and the single LSTM prediction model. …”
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Strawberry Freshness Assessment by Hyperspectral Imaging by Georgiy V. Nesterov, Anastasia V. Guryleva, Milana O. Sharikova, Svetlana A. Sukhanova, Alexander S. Machikhin

“…Assessment of individual stages, including sample preparation, hyperspectral imaging, digital data processing, and statistical analysis will be beneficial to advance methods for spectral diagnostics of food products. …”
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Photovoltaic Generation Prediction of CCIPCA Combined with LSTM by E. Zhu, D. Pi

“…Then, it uses CCIPCA to reduce the dimension of PV super large-scale data to the factor dimension, avoiding the complex calculation of covariance matrix of algorithms such as Principal Component Analysis (PCA) and to some extent eliminating the influence of noise made by PV generation data acquisition equipment and transmission equipment such as sensors. …”
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Inverse Identification of Constituent Elastic Parameters of Ceramic Matrix Composites Based on Macro–Micro Combined Finite Element Model by Sheng Huang, Le Rong, Zhuoqun Jiang, Yuriy V. Tokovyy

“…Under four different degrees of deviation in the initial iteration conditions, the inversion error of all parameters was within 1%, and the maximum inversion error was only 2.16% under a 10% high noise level.…”
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An ensemble deep learning framework for energy demand forecasting using genetic algorithm-based feature selection. by Mohd Sakib, Tamanna Siddiqui, Suhel Mustajab, Reemiah Muneer Alotaibi, Nouf Mohammad Alshareef, Mohammad Zunnun Khan

“…The proposed model demonstrated exceptional precision, achieving a Root Mean Square Error (RMSE) of 130.6, a Mean Absolute Percentage Error (MAPE) of 0.38%, and a Mean Absolute Error (MAE) of 99.41 for weekday data. …”
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Research on Ginger Price Prediction Model Based on Deep Learning by Fengyu Li, Xianyong Meng, Ke Zhu, Jun Yan, Lining Liu, Pingzeng Liu

“…Based on an in-depth analysis of actual data on ginger prices over the past decade, the STL-LSTM-ATT-KAN model demonstrated excellent performance in terms of prediction accuracy: its mean absolute error (MAE) was 0.111, mean squared error (MSE) was 0.021, root mean squared error (RMSE) was 0.146, and the coefficient of determination (R²) was 0.998. …”
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Reliable scheme for the cluster-based communication protocol in wireless sensor networks by ZHOU Zu-de, HU Peng, LI Fang-min

“…Simulation results demonstrate REECP can prolong the effective lifetime of the network and mitigate the error propagation in data aggrega-tion of sensing tasks,reinforcing the reliability of WSNs with cluster-based communication protocols.…”
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Role of regional and global datasets in the simulation of intense tropical cyclones over Bay of Bengal region in a convection‐permitting scale by Thatiparthi Koteshwaramma, Kuvar Satya Singh

“…Simulations with FNL data exhibit the least mean track errors of 70, 126, 121, and 204 km for days 1–4, respectively. …”
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The neural network for measuring IOP by Maklakov method: comparison between neuronal net and experts by A.A. Rascheskov, I.A. Frolychev, N.A. Pozdeeva

“…Polyak (groups II1, II2, II3), the experts’ data were averaged in order to create a «comparison standard» (group IIM) for analysis. …”
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