Forecasting Electricity Production in a Small Hydropower Plant (SHP) Using Artificial Intelligence (AI) by Dawid Maciejewski, Krzysztof Mudryk, Maciej Sporysz

“…Finally, after applying forecast quality measures of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R²), the most effective model was indicated. …”
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Analysis of Meshing Contact Characteristics of the Gear Transmission System Based on Data Mining Technology by Li Shengjia, Ma Yali, Zhao Yongsheng, Pu Dajun, Yan Shidang

“…The results show that the prediction error of the prediction model based on support vector machine is the smallest, and the average absolute percentage error is 3.87%, which is far less than the theoretical calculation error. …”
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Leveraging Artificial Intelligence for Smart Healthcare Management: Predicting and Reducing Patient Waiting Times with Machine Learning by Kristijan CINCAR, Todor IVAŞCU

“…Preliminary experiments contrasted different machine-learning strategies, showing that the ensemble methods Random Forest and XGBoost far surpassed the traditional approaches with a mean absolute error for waiting time prediction of fewer than ten minutes. …”
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Intelligent Feature Selection Ensemble Model for Price Prediction in Real Estate Markets by Daniel Cristóbal Andrade-Girón, William Joel Marin-Rodriguez, Marcelo Gumercindo Zuñiga-Rojas

“…The results indicate that the Stacking model achieved the best performance with an MAE (mean absolute error) of 14,090, MSE (mean squared error) of 5.338 × 10⁸, RMSE (root mean square error) of 23,100, R² of 0.924, and a Concordance Correlation Coefficient (CCC) of 0.960, also demonstrating notable computational efficiency with a time of 67.23 s. …”
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The Integration of Internet of Things and Machine Learning for Energy Prediction of Wind Turbines by Christos Emexidis, Panagiotis Gkonis

“…The models under comparison include Linear Regression, Random Forest, and Lasso Regression, which were evaluated using metrics such as coefficient of determination (R²), adjusted R², mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). …”
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Assessment of Machine Learning Methods for Concrete Compressive Strength Prediction by Oluwafemi Omotayo, Chinwuba Arum, Catherine Ikumapayi

“…The model performances were evaluated based on mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). …”
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Integrating Remote Photoplethysmography and Machine Learning on Multimodal Dataset for Noninvasive Heart Rate Monitoring by Rinaldi Anwar Buyung, Alhadi Bustamam, Muhammad Remzy Syah Ramazhan

“…We compare the performance of two machine learning models, support vector regression (SVR) and random forest regression on the multimodal dataset. The experimental results demonstrate that incorporating a multimodal approach enhances model performance, with the random forest model achieving superior results, yielding a mean absolute error (MAE) of 3.057 bpm, a root mean squared error (RMSE) of 10.532 bpm, and a mean absolute percentage error (MAPE) of 4.2% that outperforms the state-of-the-art rPPG methods. …”
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Machine Learning Modeling of Disease Treatment Default: A Comparative Analysis of Classification Models by Michael Owusu-Adjei, James Ben Hayfron-Acquah, Frimpong Twum, Gaddafi Abdul-Salaam

“…Logistic regression appears to have the highest negative-predicted value score of 75%, with the smallest error margin of 25% and the highest accuracy score of 0.90, and the random forest had the lowest negative predicted value score of 22.22%, registering the highest error margin of 77.78%. …”
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Bias in Discontinuous Elevational Transects for Tracking Species Range Shifts by Shixuan Li, Jiannan Yao, Yang Lin, Siyu Wu, Zhongjie Yang, Chao Jin, Yuhan Zhang, Zhen Wang, Jinliang Liu, Guochun Shen, Mingjian Yu

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Development of a Noninvasive Genotyping‐In‐Thousands (GTseq) Panel for Long‐Term Conservation of Western Great Lakes Gray Wolves (Canis lupus) by Samuel D. Hervey, Bridgett M. vonHoldt, Mark C. Romanski, Tyler J. Wheeldon, Brent R. Patterson, Kristin E. Brzeski

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Research on Support Vector Regression Short-Time Traffic Flow Prediction Model for Secondary Roads Based on Associated Road Analysis by Ganglong Duan, Yutong Du, Yanying Shang, Hongquan Xue, Ruochen Zhang

“…In comparison, other models tested in our study, such as LSTM, Random Forest, and Gradient Boosting Decision Tree (GBDT), had higher error values. …”
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A tree crown edge-aware clipping algorithm for airborne LiDAR point clouds by Shangshu Cai, Yong Pang

“…Dividing a forest point cloud dataset into tiles is a common practice in point cloud processing (e.g., individual tree segmentation), aimed at addressing memory constraints and optimizing processing efficiency. …”
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Refining satellite laser altimetry geolocation through full-waveform radiative transfer modeling and matching by Ameni Mkaouar, David Shean, Tiangyang Yin, Christopher S.R. Neigh, Rodrigo Vieira Leite, Paul M. Montesano, Abdelaziz Kallel, Jean-Phillipe Gastellu-Etchegorry

“…We evaluated this method across various sites with different forest canopy types, finding strong correlations between simulated and observed GEDI waveforms (r2∈[0.94,0.99]) and root mean square errors (RMSE) ∈[0.14,0.63]. …”
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Alpine Meadow Fractional Vegetation Cover Estimation Using UAV-Aided Sentinel-2 Imagery by Kai Du, Yi Shao, Naixin Yao, Hongyan Yu, Shaozhong Ma, Xufeng Mao, Litao Wang, Jianjun Wang

“…However, due to the low and sparse vegetation in alpine meadows, it is challenging to obtain pure vegetation pixels from Sentinel-2 imagery, resulting in errors in the FVC estimation using traditional pixel dichotomy models. …”
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Comparison of Three Supervised Learning Methods for Digital Soil Mapping: Application to a Complex Terrain in the Ecuadorian Andes by Martin Hitziger, Mareike Ließ

“…The median reduction of the root mean square error is around 5%. Elevation is the most important predictor. …”
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Triple-E Principle: Leveraging Occam’s Razor for Dance Energy Expenditure Estimation by Kuan Tao, Kun Meng, Bingcan Gao, Junchao Yang, Junqiang Qiu

“…A bidirectional stepwise regression model incorporating heart rate or triaxial motion sequences from accelerometers achieved an average goodness-of-fit of 0.73, identifying optimal accelerometer sites based on Efficiency principle. A random forest regression model minimized errors to 5% (MAPE) and 0.33 (RMSE) with data from all sites. …”
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Comparative assessment of standalone and hybrid deep neural networks for modeling daily pan evaporation in a semi-arid environment by Mohammed Achite, Manish Kumar, Nehal Elshaboury, Aman Srivastava, Ahmed Elbeltagi, Ali Salem

“…Model performances were compared using mean absolute error (MAE), root mean square error (RMSE), determination coefficient (R2), Nash–Sutcliffe efficiency (NSE) coefficient, and percentage bias (PBIAS). …”
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Comparative analysis of deep neural network architectures for renewable energy forecasting: enhancing accuracy with meteorological and time-based features by Sunawar Khan, Tehseen Mazhar, Muhammad Amir Khan, Tariq Shahzad, Wasim Ahmad, Afsha Bibi, Mamoon M. Saeed, Habib Hamam

“…In datasets abundant in spatial correlations, the CNN-LSTM hybrid demonstrated its utility by providing novel insights into spatial–temporal patterns; nonetheless, it lagged considerably in accuracy, with a mean squared error (MSE) of 0.020 and a R2 of 0.87. Conversely, traditional models demonstrated a reliable albeit less dynamic ability to elucidate the complexities of renewable energy data; for instance, Random Forest exhibited a mean squared error (MSE) of 0.025, while Support Vector Regression (SVR) recorded an MSE of 0.030. …”
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Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative Analysis by Fernando Pedro Silva Almeida, Mauro Castelli, Nadine Côrte-Real

“…This model achieved a Mean Squared Error of approximately 0.002-0.003, Mean Absolute Error of around 0.031-0.034, and Root Mean Squared Error of about 0.052-0.069. …”
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A Secure IIoT Environment That Integrates AI-Driven Real-Time Short-Term Active and Reactive Load Forecasting with Anomaly Detection: A Real-World Application by Md. Ibne Joha, Md Minhazur Rahman, Md Shahriar Nazim, Yeong Min Jang

“…The performance metrics for active load forecasting are 0.0183 Mean Squared Error (MSE), 0.1022 Mean Absolute Error (MAE), and 0.1354 Root Mean Squared Error (RMSE), while for reactive load forecasting, the metrics are 0.0202 (MSE), 0.1077 (MAE), and 0.1422 (RMSE). …”
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