Toward Real-Time Discharge Volume Predictions in Multisite Health Care Systems: Longitudinal Observational Study by Fernando Acosta-Perez, Justin Boutilier, Gabriel Zayas-Caban, Sabrina Adelaine, Frank Liao, Brian Patterson

“…In experiment 2, the mean absolute percentage error increased from 11% to 16% when predicting 4 hours in advance relative to zero hours in Hospital 1 and from 24% to 35% in Hospital 2. …”
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NDVI estimation using Sentinel-1 data over wheat fields in a semiarid Mediterranean region by Emna Ayari, Zeineb Kassouk, Zohra Lili-Chabaane, Nadia Ouaadi, Nicolas Baghdadi, Mehrez Zribi

“…Low root mean square error (RMSE) values characterize NDVI estimation during the first period. …”
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AI-enhanced automation of building energy optimization using a hybrid stacked model and genetic algorithms: Experiments with seven machine learning techniques and a deep neural net... by Mohammad H. Mehraban, Samad ME Sepasgozar, Alireza Ghomimoghadam, Behrouz Zafari

“…Seven machine learning (ML) models, including Linear Regression (LR), Decision Trees (DT), Random Forest Regressor (RFR), Gradient Boosting Machines (GBM), Support Vector Regressor (SVR), K-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGB), and a deep Feedforward Neural Network (FNN) are developed and assessed in predicting three key performance metrics: Energy Use Intensity (EUI), Predicted Percentage Dissatisfied (PPD), and Heating Load.A hybrid stacked model, combining FNN with XGB, using GBM meta learner, emerged as the top performer, achieving an impressive Coefficient of Determination (R²) of 0.99 and Mean Absolute Percentage Error (MAPE) of 0.02 across all targets. …”
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Effects of poultry by-product meal and complete replacement of fish oil with alternative oils on growth performance and gut health of rainbow trout (Oncorhynchus mykiss): a FEEDNET... by Imam Hasan, Simona Rimoldi, Biagina Chiofalo, Marianna Oteri, Micaela Antonini, Rosangela Armone, Violeta Kalemi, Laura Gasco, Genciana Terova

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UAV-Based Multispectral Winter Wheat Growth Monitoring with Adaptive Weight Allocation by Lulu Zhang, Xiaowen Wang, Huanhuan Zhang, Bo Zhang, Jin Zhang, Xinkang Hu, Xintong Du, Jianrong Cai, Weidong Jia, Chundu Wu

“…The growth inversion model was then constructed using machine learning methods, including linear regression (LR), random forest (RF), gradient boosting (GB), and support vector regression (SVR). …”
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Color-Sensitive Sensor Array Combined with Machine Learning for Non-Destructive Detection of AFB₁ in Corn Silage by Daqian Wan, Haiqing Tian, Lina Guo, Kai Zhao, Yang Yu, Xinglu Zheng, Haijun Li, Jianying Sun

“…The combined 1st D-PCA-KNN model showed optimal prediction performance, with determination coefficient (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mi>p</mi><mn>2</mn></msubsup></mrow></semantics></math></inline-formula> = 0.87), root mean square error (<i>RMSEP</i> = 0.057), and relative prediction deviation (<i>RPD</i> = 2.773). …”
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MHRA-MS-3D-ResNet-BiLSTM: A Multi-Head-Residual Attention-Based Multi-Stream Deep Learning Model for Soybean Yield Prediction in the U.S. Using Multi-Source Remote Sensing Data by Mahdiyeh Fathi, Reza Shah-Hosseini, Armin Moghimi, Hossein Arefi

“…The results demonstrated the model’s robustness and adaptability to unseen data, achieving an R² of 0.82 and a Mean Absolute Percentage Error (MAPE) of 9% in 2021, and an R² of 0.72 and MAPE of 12% in 2022. …”
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Prediction of Snacking Behavior Involving Snacks Having High Levels of Saturated Fats, Salt, or Sugar Using Only Information on Previous Instances of Snacking: Survey- and App-Base... by Shaima Dammas, Tillman Weyde, Katy Tapper, Gerasimos Spanakis, Anne Roefs, Emmanuel M Pothos

“…In all cases, the prediction problem was the time to the next HFSS snack from the current one, and the evaluation metric was the mean absolute error. ResultsThe ability of machine learning methods to predict the time of the next HFSS snack was assessed. …”
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Design and parameter optimization of portable tree transplanting machine by Zhang Jingping, Zhu Jianxi, Sun Teng

“…Seedling transplanting plays an important role in urban greening and forest plantation. However, this job is still relying on manual labor in China. …”
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Establishment of Hyperspectral Prediction Model of Water Content in Anshan-Type Magnetite by Xiaoxiao XIE, Yang BAI, Jiuling ZHANG, Yuna JIA

“…The prediction set determination coefficients (R2) of the models are 0.778 and 0.789, and the root mean square error (RMSE) were 5.45% and 5.41%, respectively. …”
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Transfer Learning Estimation and Transferability of LNC and LMA Across Different Datasets by Yingbo Wang, Mengzhu He, Lin Sun, Yong He, Zengwei Zheng

“…Compared to the RFR transfer model using spectra without updating, the root mean square error (<i>RMSE</i>) of the WDT-RFR transfer model with 5% samples transferred to estimate LMA and LNC increased by 7.9% and 4.8% on average, respectively. …”
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Enhancing model robustness to imbalanced species abundance distributions: Eliminating misclassified records via a model-agnostic approach, exemplified by tuna fisheries datasets by Zhexuan Li, Tianjiao Zhang, Liming Song

“…Anomalies in species abundance data can potentially cause classification errors in ecological forecasting models. Accurate estimation of anomalies locations can enhance the predictive capacity of models. …”
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Evaluating the Two-Source Energy Balance Model Using MODIS Data for Estimating Evapotranspiration Time Series on a Regional Scale by Mahsa Bozorgi, Jordi Cristóbal, Magí Pàmies-Sans

“…The overall mean results including clear-sky and all-sky observations as well as gap-filled data with the Aqua dataset showed the lowest errors with ET_RES, by a mean bias error (MBE) of 0.96 mm.day⁻¹, an average mean root square (RMSE) of 1.47 mm.day⁻¹, and a correlation (r) value of 0.51. …”
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Forecasting birth trends in Ethiopia using time-series and machine-learning models: a secondary data analysis of EDHS surveys (2000–2019) by Daniel Adane, Assefa Andargie Kassa, Berhanu Abebaw Mekonnen, Rahel Mulatie Anteneh, Chalachew Yenew, Meron Asmamaw Alemayehu, Tilahun Degu Tsega, Sintayehu Simie Tsega, Almaw Genet Yeshiwas, Abathun Temesegen, Habitamu Mekonen, Amare Genetu Ejigu, Abraham Teym, Gashaw Melkie Bayeh, Getaneh Atikilit, Tesfaneh Shimels, Wolde Melese Ayele, Ahmed Fentaw Ahmed, Birhanemaskal Malkamu, Wondimnew Desalegn Addis, Getasew Yirdaw, Chalachew Abiyu Ayalew, Kalaab Esubalew, Zeamanuel Anteneh Yigzaw

“…The models’ performance was evaluated using key metrics such as root mean square error (RMSE), mean absolute error (MAE) and R-squared value.Results GLMNET emerged as the best model, explaining 77% of the variance with an RMSE of 119.01. …”
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Epidemiological Manifestation of Combined Natural Foci of Tularemia, Leptospirosis and Hemorrhagic Fever with Renal Syndrome: Mixed Infections by T. N. Demidova, N. E. Sharapova, V. V. Gorshenko, T. V. Mikhailova, A. S. Semihin, A. E. Ivanova

“…Lack of experience in the diagnosis of natural focal infections of tularemia, leptospirosis and HFRS often leads to diagnostic errors, and the lack of alertness to their appearance makes it difficult to identify sporadic cases of diseases. …”
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Harnessing smartphone RGB imagery and LiDAR point cloud for enhanced leaf nitrogen and shoot biomass assessment - Chinese spinach as a case study by Aravind Harikumar, Aravind Harikumar, Itamar Shenhar, Itamar Shenhar, Itamar Shenhar, Miguel R. Pebes-Trujillo, Miguel R. Pebes-Trujillo, Lin Qin, Menachem Moshelion, Menachem Moshelion, Jie He, Jie He, Kee Woei Ng, Kee Woei Ng, Kee Woei Ng, Matan Gavish, Matan Gavish, Ittai Herrmann, Ittai Herrmann

“…The performance of crop parameter estimation was evaluated using three regression approaches: support vector regression, random forest regression, and lasso regression. The results demonstrate that combining smartphone RGB imagery with LiDAR data enables accurate estimation of leaf total reduced nitrogen concentration, leaf nitrate concentration, and shoot dry-weight biomass, achieving best-case relative root mean square errors as low as 0.06, 0.15, and 0.05, respectively. …”
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Enhancing soil organic carbon prediction by unraveling the role of crop residue coverage using interpretable machine learning by Yi Dong, Xinting Wang, Sheng Wang, Baoguo Li, Junming Liu, Jianxi Huang, Xuecao Li, Yelu Zeng, Wei Su

“…Furthermore, our results revealed that integrating crop residue coverage significantly improved the accuracy of SOC mapping as the Lin Concordance Correlation Coefficient (LCCC) increased from 0.75 to 0.83 and the root mean squared error (RMSE) decreased from 6.70 g kg−1 to 5.60 g kg−1. …”
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MRI Application in Quantification of Epiphyseal Development in the Wrist and Bone Age Estimation of Han Male Adolescents in East China by ZHOU Zhi-lu, ZHANG Dong-fei, CHEN Jie-min, WANG Ya-hui, HAO Hong-xia, LIU Tai-ang, HE Yu-heng, LONG Ding-nian, LIU Rui-jue, WAN Lei

“…Quantifying the maximum width of the epiphysis and corresponding metaphysis of bone and combining it with MRI image classification can effectively reduce the estimation error.…”
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Machine learning-based modelling and analysis of carbonation depth of recycled aggregate concrete by Xuyong Chen, Xuan Liu, Shukai Cheng, Xiaoya Bian, Xixuan Bai, Xin Zheng, Xiong Xu, Zhifeng Xu

“…The results show that the extreme gradient boosting (XGB) model exhibited the highest accuracy with an R² of 0.99 and a mean absolute percentage error (MAPE) of 6.632 on the training set, and an R² of 0.953 and a MAPE of 13.243 on the test set. …”
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Spatiotemporal monitoring of water storage in the North China Plain from 2002 to 2022 based on an improved GRACE downscaling method by Jinze Tian, Yu Chen, Shuai Wang, Xinlong Chen, Huibin Cheng, Xiaolong Tian, Xue Wang, Kun Tan

“…The enhanced downscaling model demonstrated improved performance, with a higher Nash-Sutcliffe efficiency (+0.05), an increased correlation coefficient (+0.03), and a reduced root-mean-square error (-0.32 cm). From 2014–2022, interannual water storage fluctuations intensified, with divergent trends for TWSA (0.30 cm/year), SWSA (1.47 cm/year), and GWSA (-0.97 cm/year). …”
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