Rainfall Prediction in Khorasan Razavi Stations Using a Hybrid Neural Network and Genetic Algorithm Approach by Mahdi Naseri, Mahsa Mardani

“…A convergence analysis of the GA was also provided, as well as histograms of the error distributions, which further validated the superior performance of the proposed NARXGA model. …”
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Uncertainty of Counterfactuals in Deep Learning by Katherine Elizabeth Brown, Doug Talbert, Steve Talbert

“…As part of our analysis, we also measure the extent to which counterfactuals can be considered anomalies in those data sets. …”
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Comparison оf Digital Relief Models by S. A. Antonov, S. V. Peregudov

“…The evaluation of digital elevation models was carried out on the territory of Budennovsky urban district of Stavropol Krai. Error analysis of morphometric indicators was performed based on the comparison of the studied digital elevation models and the data of the State Research and Development Center of Geoformation Systems and Technologies – GosGisCenter, which were presented in the form of topographic maps. …”
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Application of improved clustering algorithm in mixed teaching of modern educational technology by Lei Shu, Guirong Li

“…Abstract This study explores the application of an improved clustering algorithm in blended teaching with modern educational technology. It utilizes data analysis to enhance teaching processes and outcomes. …”
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Risk assessment and prevention in airport security assurance by integrating LSTM algorithm. by Yao Hu, Liguang Qiao, Feng Gu

“…The predicted data was highly consistent with the actual data. …”
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A Regression-Based Method for Monthly Electric Load Forecasting in South Korea by Geun-Cheol Lee

“…These predictor variables were identified through comprehensive data analysis. Comparative experiments were conducted with various existing methods, including univariate time series models and machine learning techniques like Holt–Winters, LightGBM, and Long Short-Term Memory (LSTM). …”
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Uncertainty in Ecohydrological Modeling in an Arid Region Determined with Bayesian Methods. by Junjun Yang, Zhibin He, Jun Du, Longfei Chen, Xi Zhu

“…The need to provide independent descriptions of uncertainty analysis (UA) in the input and output data was demonstrated. …”
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Infer More, Describe Less: More Powerful Survey Conclusions through Easy Inferential Tests. by Christy Hightower, Kerry Scott

“…In this era of reduced budgets, low staffing, stiff competition for new resources, and increasingly complex choices, it is especially important that librarians know how to get strong, statistically reliable direction from the survey data they depend upon. This article focuses on three metrics that are easy to master and will go a long way toward making librarians' survey conclusions more powerful and more meaningful: margin of error (MoE), confidence Level (CL), and cross-tabulation table analysis. …”
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Prediction of Automotive Wire Harness Aging Based on CNN-biLSTM-Attention by Kun Xia, Qi Zhu, Qingqing Yuan, Jingxia Wang

“…Accelerated aging experiments were conducted to obtain wiring harnesses with different degradation levels from new to 720 h aged states, and a dedicated experimental platform was built for data collection and verification. The results show the system achieves a mean absolute error (MAE) of 0.02806, with 32.50% and 62.06% error reduction compared to LSTM and Random Forest models, respectively, demonstrating effective prediction performance.…”
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High Dose Selection for General Toxicity Studies of New Medicines by G. N. Engalycheva, R. D. Subaev

“…It determines the informative value of study results, the compliance with the principles of ethical and rational use of experimental animals, and the accuracy of predicting the safety of new medicines for human use. The literature data and the regulatory experience in evaluating preclinical study results suggest that the selection of an inappropriate high dose is a very common error in planning toxicity studies. …”
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Short-Term Sales Forecasting Using LSTM and Prophet Based Models in E-Commerce by Alp Ecevit, İrem Öztürk, Mustafa Dağ, Tuncay Özcan

“…The performance of the proposed models is compared with the seasonal autoregressive integrated moving average (SARIMA) using real-life data from an e-commerce site. For the comparative analysis of the proposed forecasting models, weighted average absolute percent error (wMAPE), root mean square error (RMSE) and R-squared are selected as performance measures. …”
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Efficient Learning of Long-Range and Equivariant Quantum Systems by Štěpán Šmíd, Roberto Bondesan

“…Recent works have studied the task of predicting the ground state expectation value of sums of geometrically local observables by learning from data. For short-range gapped Hamiltonians, a sample complexity that is logarithmic in the number of qubits and quasipolynomial in the error was obtained. …”
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Optimization of Gear Modification Amount based on Polynomial Response Surface Proxy Model by Yonghua Li, Wusong Wei, Xu Zhang

“…Secondly， according to the model parameters and analysis results， the range of each shape modification parameter is determined， the orthogonal test design on the practice parameter is performed， and the test data is fitted to establish a polynomial response surface model. …”
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The Impact of Using Audio-Visual Interactive Media in Learning Mathematics by Linda Mardiani Setiawati, Mahsup, Abdillah, Syaharuddin

“…The data were analyzed using JASP software by inputting the Effect Size (ES) and Standard Error (SE) values. …”
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Optimizing Methanol Injection Quantity for Gas Hydrate Inhibition Using Machine Learning Models by Mohammed Hilal Mukhsaf, Weiqin Li, Ghassan Husham Jani

“…R²), mean absolute error (MAE), and root mean square error (RMSE), were KNN < DT < RF < XGBoost. …”
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Theoretical and numerical study of profit in agricultural sector model using wavelet method by Yeshwanth R., Kumbinarasaiah S.

“…The CWCM approach generates precise results with better absolute error (Ae) for highly nonlinear scenarios by computing a small number of terms and avoiding data rounding. …”
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Prediction of ultimate load capacity of demountable shear stud connectors using machine learning techniques by Ahmed I. Saleh, Nabil S. Mahmoud, Fikry A. Salem, Mohamed Ghannam

“…Model performance was assessed using R², Mean Absolute Error (MAE), and Mean Squared Error (MSE). Among these, XGBoost and Random Forest delivered the best predictive accuracy, with R² values of 0.9477 and 0.9255, respectively, outperforming other methods across all evaluation metrics. …”
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Recursive feature elimination for summer wheat leaf area index using ensemble algorithm-based modeling: The case of central Highland of Ethiopia by Dereje Biru, Berhan Gessesse, Gebeyehu Abebe

“…Model performance validation analysis was evaluated via R-squared (R2), root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE) statistical models. …”
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Investigation of the Agreement between Glucose Meters Used for Glucose Measurement and Central Laboratory Measurements by Esra Yılmaz, Medeni Arpa, Bayram Şen, Mehmet Çingirt

“…Regression equations were G1 y=1.1235x+3.4662 G2 y=0.8846x+10.0076. In Clarke Error Grid analysis, the criterion of 99% of the data being in zone A or B was met for both glucometers. …”
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Traffic congestion forecasting using machine learning methods by Ramil R. Zagidullin, Almaz N. Khaybullin

“…To identify patterns in the data, additive time series decomposition, spectral analysis based on the fast Fourier transform, and autocorrelation analysis were applied. …”
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