Prediction of the Calorific Value and Moisture Content of <i>Caragana korshinskii</i> Fuel Using Hyperspectral Imaging Technology and Various Stoichiometric Methods by Xuehong De, Haoming Li, Jianchao Zhang, Nanding Li, Huimeng Wan, Yanhua Ma

“…For water content and calorific value, three quantitative detection models, partial least squares regression (PLSR), random forest regression (RFR), and extreme learning machine (ELM), respectively, were established, and Monte Carlo cross-validation (MCCV) was chosen to remove outliers from the raw spectral data to improve the model accuracy. …”
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Enhancing burned area monitoring with VIIRS dataset: A case study in Sub-Saharan Africa by Boris Ouattara, Michael Thiel, Barbara Sponholz, Heiko Paeth, Marta Yebra, Florent Mouillot, Patrick Kacic, Kwame Hackman

“…Based on a stratified random sampling, the validation results demonstrate varying levels of accuracy for the VIIRS-BA product across different confidence levels. The commission error (CE) ranges from 7.8% to 23.4%, while the omission error (OE) falls between 29.4% and 58.8%. …”
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A global daily seamless 9&thinsp;km vegetation optical depth (VOD) product from 2010 to 2021 by D. Hu, Y. Wang, H. Jing, L. Yue, Q. Zhang, L. Fan, Q. Yuan, Q. Yuan, Q. Yuan, H. Shen, L. Zhang

“…Our dataset can provide timely vegetation information during natural disasters (e.g., floods, droughts, and forest fires), supporting early disaster warning and real-time responses. …”
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Understanding the flowering process of litchi through machine learning predictive models by SU Zuanxian, NING Zhenchen, WANG Qing, CHEN Houbin

“…The algorithms (RF and STR) with the smallest Mean Absolute Error (MAE) and the highest residual error (RMSE) and the highest correlation coefficient (RP2) were selected for further parameter optimization and evaluation. …”
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Exploration of key genes associated with oxidative stress in polycystic ovary syndrome and experimental validation by Qinhua Li, Qinhua Li, Qinhua Li, Lei Liu, Yuhan Liu, Yuhan Liu, Yuhan Liu, Yuhan Liu, Tingting Zheng, Tingting Zheng, Tingting Zheng, Ningjing Chen, Ningjing Chen, Ningjing Chen, Peiyao Du, Peiyao Du, Peiyao Du, Hong Ye, Hong Ye, Hong Ye

“…Subsequently, the optimal machine model was obtained to identify key genes by comparing the performance of the random forest model (RF), support vector machine model (SVM), and generalized linear model (GLM). …”
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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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Estimating Leaf Nitrogen Accumulation Considering Vertical Heterogeneity Using Multiangular Unmanned Aerial Vehicle Remote Sensing in Wheat by Yuanyuan Pan, Jingyu Li, Jiayi Zhang, Jiaoyang He, Zhihao Zhang, Xia Yao, Tao Cheng, Yan Zhu, Weixing Cao, Yongchao Tian

“…The linear regression (LR) and random forest regression (RF) models were constructed to estimate the LNA for each individual leaf layer. …”
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Simulating water and salt changes in the root zone of salt–alkali fragrant pear and the selection of the optimal surface drip irrigation mode by Yanjie Li, Yanjie Li, Ping Gong, Ping Gong, Xinlin He, Xinlin He, Hongguang Liu, Hongguang Liu, Zhijie Li, Zhijie Li, Ling Li, Ling Li, Chunxia Wang, Chunxia Wang, Qiang Xu, Qiang Xu, Quan Chen, Jie Wei, Ping Lin, Xuyong Yu

“…Our study provides new insights into regulating soil water and salt environmental factors in the saline fragrant pear root zone and assessing the impact of soil water and salt management under precision irrigation strategies, and profoundly influences decision-making for irrigation of forest fruits in saline arid zones based on a production practice perspective.…”
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A machine-learning reconstruction of sea surface <i>p</i>CO₂ in the North American Atlantic Coastal Ocean Margin from 1993 to 2021 by Z. Wu, Z. Wu, W. Lu, A. Roobaert, L. Song, X.-H. Yan, W.-J. Cai

“…Compared with all the observation samples from SOCAT, the <span class="inline-formula"><i>p</i></span>CO<span class="inline-formula">₂</span> product yields a determination coefficient of 0.92, a root-mean-square error of 12.70 <span class="inline-formula">µ</span>atm, and an accumulative uncertainty of 23.25 <span class="inline-formula">µ</span>atm. …”
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Mapping football tactical behavior and collective dynamics with artificial intelligence: a systematic review by José E. Teixeira, José E. Teixeira, José E. Teixeira, José E. Teixeira, José E. Teixeira, José E. Teixeira, Eduardo Maio, Eduardo Maio, Eduardo Maio, Pedro Afonso, Pedro Afonso, Samuel Encarnação, Samuel Encarnação, Samuel Encarnação, Guilherme F. Machado, Guilherme F. Machado, Ryland Morgans, Tiago M. Barbosa, Tiago M. Barbosa, António M. Monteiro, António M. Monteiro, Pedro Forte, Pedro Forte, Pedro Forte, Ricardo Ferraz, Ricardo Ferraz, Luís Branquinho, Luís Branquinho, Luís Branquinho, Luís Branquinho

“…Concretely, the tactical behavior was expressed by spatiotemporal tracking data using convolutional neural networks, recurrent neural networks, variational recurrent neural networks, and variational autoencoders, Delaunay method, player rank, hierarchical clustering, logistic regression, XGBoost, random forest classifier, repeated incremental pruning produce error reduction, principal component analysis, and T-distributed stochastic neighbor embedding. …”
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Practical guidelines for reproducible N₂O flux chamber measurements in nutrient-poor ecosystems by N. Y. Triches, N. Y. Triches, J. Engel, A. Bolek, T. Vesala, M. E. Marushchak, A.-M. Virkkala, M. Heimann, M. Göckede

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Optimizing Energy Forecasting Using ANN and RF Models for HVAC and Heating Predictions by Khaled M. Salem, Javier M. Rey-Hernández, A. O. Elgharib, Francisco J. Rey-Martínez

“…The performances of both models are calculated using the Root Mean Square Percentage Error (RMSPE), Root Mean Square Relative Percentage Error (RMSRPE), Mean Absolute Percentage Error (MAPE), Mean Absolute Relative Percentage Error (MARPE), Kling–Gupta Efficiency (KGE), and also the coefficient of determination, R². …”
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How to use learning curves to evaluate the sample size for malaria prediction models developed using machine learning algorithms by Sophie G. Zaloumis, Megha Rajasekhar, Julie A. Simpson

“…Learning curves were produced for two machine learning algorithms, sparse Partial Least Squares-Discriminant Analysis plus Support Vector Machines (sPLSDA + SVMs) and random forests. Prediction error was measured using the balanced error rate (average of percentage of slow clearing infections incorrectly predicted as fast and percentage of fast clearing infections predicted as slow). …”
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Dynamic ensemble-based machine learning models for predicting pest populations by Ankit Kumar Singh, Md Yeasin, Ranjit Kumar Paul, A. K. Paul, Anita Sarkar

“…This study introduces a dynamic ensemble model with absolute log error (ALE) and logistic error functions using four machine learning models—artificial neural networks (ANNs), support vector regression (SVR), k-nearest neighbors (kNN), and random forests (RF). …”
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Machine learning assisted estimation of total solids content of drilling fluids by B.T. Gunel, Y.D. Pak, A.Ö. Herekeli, S. Gül, B. Kulga, E. Artun

“…Further optimization of the random forests model resulted in a mean absolute percentage error (MAPE) of 3.9% and 9.6% and R2 of 0.99 and 0.93 for the training and testing sets, respectively. …”
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Application of Artificial Intelligence Techniques for the Estimation of Basal Insulin in Patients with Type I Diabetes by Guillermo Edinson Guzman Gómez, Luis Eduardo Burbano Agredo, Veline Martínez, Oscar Fernando Bedoya Leiva

“…We used neural networks (NNs), Bayesian networks (BNs), support vector machines (SVMs), and random forests (RF). We then evaluated the agreement between predicted and actual values using several statistical error measurements: mean absolute error (MAE), mean square error (MSE), root-mean-square error (RMSE), Pearson’s correlation coefficient (R), and determination coefficient (R2). …”
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Developing a Framework for Building Condition Assessment of Schools in Osijek-Baranja County by Hana Begić Juričić, Hrvoje Krstić

“…Performance was evaluated using <i>R</i>², mean squared error (<i>MSE</i>), root mean squared error (<i>RMSE</i>), coefficient of variation of <i>RMSE</i> (<i>CVRMSE</i>), and mean absolute percentage error (<i>MAPE</i>). …”
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Forecasting loan, deferred rate and customer segmentation in banking industry: A computational intelligence approach by Mahtab Vasheghani, Ebrahim Nazari Farokhi, Behrooz Dolatshah

“…Specifically, the GA-PSO-MLP model achieves a 15 % higher classification accuracy than Logistic Regression, a 12 % improvement over Decision Trees, and an 8 % gain over Random Forests. Additionally, false positive rates are reduced by 20 %, and mean squared error (MSE) is lowered by 18 %. …”
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Embedded physical constraints in machine learning to enhance vegetation phenology prediction by Mengying Cao, Qihao Weng

“…This was followed by evergreen needle-leaved forests and mixed forests with RMSE of 12.32 and 13.28 days, respectively. …”
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Application of Hybrid ARIMA and Artificial Neural Network Modelling for Electromagnetic Propagation: An Alternative to the Least Squares Method and ITU Recommendation P.1546-5 for... by Ramz L. Fraiha Lopes, Simone G. C. Fraiha, Herminio S. Gomes, Vinicius D. Lima, Gervasio P. S. Cavalcante

“…This study sets out an empirical hybrid autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) model designed to estimate electromagnetic wave propagation in densely forested urban areas. Received signal power intensity data was acquired through measurement campaigns carried out in the Metropolitan Area of Belém (MAB), in the Brazilian Amazon. …”
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