Antistray, Learning Smart: Creating Indoor Positioning Learning Environment for Augmenting Self-Regulated Learning by Tien-Chi Huang

“…The maximum error is improved by 51.22%. From the application aspect, we elaborate the implications of this study which present the substantial contributions and educational values of the system. …”
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Climate Change Analysis in Malaysia Using Machine Learning by Anishalache Subramanian, Naveen Palanichamy, Kok-Why Ng, Sandhya Aneja

“…Performance measures like Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) are used to assess three ML models: Support Vector Regression (SVR), Random Forest Regression (RFR) and Linear Regression (LR). …”
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Analysis of Seismic Ionospheric Effects and Prediction of TEC During Earthquakes Occurred in Indonesia Based on GPS Data by R. Mukesh, Sarat C. Dass, M. Vijay, S. Kiruthiga, Vijanth Sagayan Asirvadam

“…Additionally, the average sMAPE values, ranging from 0.11 to 0.21, demonstrate the models’ effectiveness in minimizing percentage-based errors. While variations exist across different earthquakes, these average metrics collectively suggest promising predicting capabilities.…”
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Levenberg-Marquardt recurrent neural network for heat transfer in ternary hybrid nanofluid flow with nonlinear heat source-sink by Ibrahim Mahariq, Kashif Ullah, Mehreen Fiza, Aasim Ullah Jan, Hakeem Ullah, Saeed Islam, Seham M. Al-Mekhlafi

“…Validation is conducted using error histograms (EH) and regression (RG) tests, ensuring high accuracy ranging from E-2 to E-7. …”
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Lake Water Storage and Long-Term Variation of Nganga Rinco on the Tibetan Plateau Revealed by ICESat-2 and Satellite Imagery by Yunmei Li, Qianqian Chen, Shiqiang Zhang, Yuanzheng Cui, Chang Huang

“…When compared to <italic>in situ</italic> bathymetry, the derived bathymetry showed an average error of 3.64 m, with errors concentrated in deeper regions. …”
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Digital and artificial intelligence-assisted cephalometric training effectively enhanced students’ landmarking accuracy in preclinical orthodontic education by Jiayu Lin, Zhihao Liao, Jingtao Dai, Manyi Wang, Ruixue Yu, Hong Yang, Chufeng Liu

“…Results Digital cephalometric training, through real-time feedback and visual error-correction mechanisms, enabled students to quickly identify and correct errors in landmarking, significantly improving their accuracy. …”
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Addressing the challenges of estimating the target population in calculation of routine infant immunization coverage in Kenya. by Christine Karanja-Chege, Ambrose Agweyu, Fred Were, Michael Boele van Hensbroek, William Ogallo

“…We assessed the accuracy of the estimates for 2003-2018 by computing the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and the Pearson Correlation Coefficient (r), excluding outliers. …”
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Forecasting Temperature Time Series Data Using Combined Statistical and Deep Learning Methods: A Case Study of Nairobi County Daily Temperature by John Kamwele Mutinda, Amos Kipkorir Langat, Samuel Musili Mwalili

“…Evaluation metrics, including root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R-squared, highlighted the superiority of hybrid models over individual approaches, particularly those combining VMD with ARIMA, GRU, LSTM, and Transformer. …”
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What diameter? What height? Influence of measures of average tree size on area-based allometric volume relationships by Yilin Wang, John A. Kershaw, Mark J. Ducey, Yuan Sun, James B. McCarter

“…The overall best equation used quadratic mean diameter, Lorey’s height, and density (root mean square error ​= ​5.26 ​m3⋅ha−1; 1.9 % relative error). The best equation without density used mean diameter of the largest trees needed to calculate a stand density index of 400 and the mean height of the tallest 400 trees per ha (root mean square error ​= ​32.08 ​m3⋅ha−1; 11.8 % relative error). …”
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Dirac-Schwinger quantization for emergent magnetic monopoles? by A. Farhan, M. Saccone, B.F.L. Ward

“…We show that, within the experimental errors, the respective magnetic charges obey the Dirac-Schwinger quantization condition. …”
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Was geschieht mit Semantik und Interaktion in automatischen Transkriptionen einer Software? by Marine Kneubühler

“…This article addresses the implications of transcriptions for scientific knowledge by focusing on automatic transcription software called speech-to-text. …”
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What happens to semantics and interaction in a software’s automatic transcriptions? by Marine Kneubühler

“…This article addresses the implications of transcriptions for scientific knowledge by focusing on automatic transcription software called speech-to-text. …”
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FORECASTING AUTOMOBILE DEMAND AND SALES IN THE NIGERIAN MARKET: A MACHINE LEARNING APPROACH TO URBAN MOBILITY, MARKET COMPETITION, AND POLICY INSIGHTS by Emmanuel Imuede Oyasor

“…Ensemble methods (RF and GB) showed moderate performance, with GB slightly outperforming RF in terms of relative error (MAPE = 0.01). The RegressionTree modelalsoperformed well,balancingaccuracyand interpretability.Thefindingsoffer valuable insights for both policymakers and industry stakeholders in Nigeria, emphasizing the importance of model selection in automotive demand estimation and the strategic implications for infrastructure and investment planning. …”
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Architecture Level Safety Analyses for Safety-Critical Systems by K. S. Kushal, Manju Nanda, J. Jayanthi

“…This helps in validating the system architecture with the detection of the error event in the model and its impact in the operational environment. …”
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To What Extent Does Discounting ‘Hot’ Climate Models Improve the Predictive Skill of Climate Model Ensembles? by Abigail McDonnell, Adam Michael Bauer, Cristian Proistosescu

“…This stands in sharp contrast with the broad regional pattern of error reduction in future temperature projections, though we do find regions where error is not significantly reduced. …”
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Structural Equation Modeling Approaches to Estimating Score Dependability Within Generalizability Theory-Based Univariate, Multivariate, and Bifactor Designs by Walter P. Vispoel, Hyeryung Lee, Tingting Chen

“…Generalizability theory (GT) provides an all-encompassing framework for estimating accuracy of scores and effects of multiple sources of measurement error when using measures intended for either norm- or criterion-referencing purposes. …”
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Exploring AI to automate EFL corrective written feedback in the first language by Rob Hirschel, Kayoko Horai

“…The classroom intervention on which this study is based had four main steps: a) individual or collective brainstorming and vocabulary search (5 minutes), b) subsequent free-writing activity in an online browser (10 minutes), c) reading the ChatGPT feedback in L1 Japanese (5 minutes), d) completing paper error logs to process feedback (5 minutes). Data was collected from students’ written submissions as well as the ChatGPT-generated feedback, students’ error logs, and surveys administered to students after each activity and at the end of the semester. …”
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Is This Reliable Enough? Examining Classification Consistency and Accuracy in a Criterion-Referenced Test by Susanne Alger

“…For criterion-referenced tests, and in particular certification tests, where students are classified into performance categories, primary focus need not be on the size of error but on the impact of this error on classification. …”
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Reconstruction of 12-lead ECG: a review of algorithms by Ekenedirichukwu N. Obianom, G. André Ng, G. André Ng, G. André Ng, G. André Ng, Xin Li, Xin Li, Xin Li

“…Both linear and nonlinear algorithms can achieve high correlations, and minimal root means square errors. Hence, planned steps are needed when deciding how to manipulate the data and build the models to achieve high accuracies.…”
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Development of grammatical and lexical skills in argumentative EFL writing at upper secondary level in Germany and Switzerland by Flavio Lötscher, Ruth Trüb, Julian Lohmann, Jens Möller, Thorben Jansen, Stefan Daniel Keller

“…In this longitudinal study, we investigate how grammatical and lexical skills develop in two educational systems among learners at upper secondary schools (operationalized as number of grammatical and lexical errors). Based on texts from n = 470 learners at two time points at the beginning and end of Year 11, it shows that learners in both countries made more lexical than grammatical errors (partial η2 = .17). …”
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