Machine learning-based energy consumption models for rural housing envelope retrofits incorporating uncertainty: A case study in Jiaxian, China by Taoyuan Zhang, Zao Li, Zihuan Zhang, Yulu Chen, Xia Sun

“…These factors serve as inputs for ML models built with neural networks and random forests. The coverage ratio and evenness were used to assess residual distributions. …”
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Machine Learning Strategies for Forecasting Mannosylerythritol Lipid Production Through Fermentation: A Proof-of-Concept by Carolina A. Vares, Sofia P. Agostinho, Ana L. N. Fred, Nuno T. Faria, Carlos A. V. Rodrigues

“…Three ML models, neural networks (NNs), support vector machines (SVMs), and random forests (RFs), were used. An NN provided predictions with a mean squared error (MSE) of 0.69 for day 4 and 1.63 for day 7 and a mean absolute error (MAE) of 0.58 g/L and 1.1 g/L, respectively. …”
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Long-Term Retrospective Predicted Concentration of PM_2.5 in Upper Northern Thailand Using Machine Learning Models by Sawaeng Kawichai, Patumrat Sripan, Amaraporn Rerkasem, Kittipan Rerkasem, Worawut Srisukkham

“…The best ML prediction model was selected considering root mean square error (RMSE), mean prediction error (MPE), relative prediction error (RPE) (the lower, the better), and coefficient of determination (R²) (the bigger, the better). …”
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Evaluating expandable global positioning system collars for white‐tailed deer neonates by Tyler R. Obermoller, Samuel J. Overfors, Shauntel M. Stahlke, Raena A. Kemna, Eric S. Michel, Joseph K. Bump

“…., fix success rate, mean linear error) on wild neonatal deer in grassland and forested habitat types. …”
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Optimizing guest experience in smart hospitality: Integrated fuzzy-AHP and machine learning for centralized hotel operations with IoT by Sinan Andan Diwan

“…The results demonstrate improved precision and accuracy of the proposed RF model, evidenced by significantly lower Mean Square Error (MSE) and Root Mean Square Error (RMSE) as compared to the DNN. …”
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Land surface phenology from SPOT VEGETATION time series by A. Verger, I. Filella, F. Baret, J. Peñuelas

“…The accuracy of the derived phenological metrics, evaluated using available ground observations for birch forests in Europe, cherry in Asia and lilac shrubs in North America showed an overall root mean square error lower than 19 days for the start, end and length of season, and good agreement between the latitudinal gradients of VEGETATION LAI phenology and ground data.…”
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AI-Driven Insect Detection, Real-Time Monitoring, and Population Forecasting in Greenhouses by Dimitrios Kapetas, Panagiotis Christakakis, Sofia Faliagka, Nikolaos Katsoulas, Eleftheria Maria Pechlivani

“…For insect population prediction, random forests, gradient boosting, LSTM, and the ARIMA, ARIMAX, and SARIMAX models were evaluated. …”
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Tree volume modeling of different poplar clones in plantations in the Vojvodina region by Pantić Damjan, Borota Dragan, Janjatović Živan

“…Based on preliminary research, the samples were structured in such a way that their implementation would provide data for the development of models for calculating volume (and indirectly volume increment) within acceptable margins of error. However, as the data collection took place exclusively after the regular felling in the plantations of the mentioned clones without the possibility of a targeted selection of missing trees in certain treatment segments and due to time and spatial limitations in data collection after the storms of 2023, which caused significant damage to the forests in the region especially poplar plantations, as well as the need for their urgent rehabilitation, the planned sample was realized with 102.6% for I-214, 51.5% for M1 and 46.1% for Deltoid clones, with relatively poor distribution across river alluviums, soil types and diameter and height classes. …”
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Digital Twin Framework Using Real-Time Asset Tracking for Smart Flexible Manufacturing System by Asif Ullah, Muhammad Younas, Mohd Shahneel Saharudin

“…It achieved a mean coordinate error (MCE) between 1.2 and 2.8 m and a 100% building rate. …”
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Unified Deep Learning Model for Global Prediction of Aboveground Biomass, Canopy Height, and Cover from High-Resolution, Multi-Sensor Satellite Imagery by Manuel Weber, Carly Beneke, Clyde Wheeler

“…Regular measurement of carbon stock in the world’s forests is critical for carbon accounting and reporting under national and international climate initiatives and for scientific research but has been largely limited in scalability and temporal resolution due to a lack of ground-based assessments. …”
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Hourly surface nitrogen dioxide retrieval from GEMS tropospheric vertical column densities: benefit of using time-contiguous input features for machine learning models by J. Gödeke, A. Richter, K. Lange, P. Maaß, H. Hong, H. Lee, J. Park

“…We demonstrate that using these time-contiguous inputs leads to reliable improvements regarding all considered performance measures, such as Pearson correlation or mean square error. For random forests, the average performance gains are between 4.5 % and 7.5 %, depending on the performance measure. …”
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Spatial Prediction of Soil Total Phosphorus in a Karst Area: Comparing GWR and Residual-Centered Kriging by Laimou Lu, Penghui Li, Liang Zhong, Mingbao Luo, Liyuan Xing, Chunlai Zhang

“…The GWRK model exhibited superior accuracy (80.6%), with predicted concentration of TP closely aligning with observed TP values, effectively capturing fine spatial variations, and showing the lowest mean standardized error, average standard error, and mean absolute error. …”
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Evaluating Feature Impact Prior to Phylogenetic Analysis Using Machine Learning Techniques by Osama A. Salman, Gábor Hosszú

“…Applying machine learning models for Arabic and Aramaic scripts, such as deep neural networks (DNNs), support vector machines (SVMs), and random forests (RFs), each model was used to compare the phylogenies. …”
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Blood profile of proteins and steroid hormones predicts weight change after weight loss with interactions of dietary protein level and glycemic index. by Ping Wang, Claus Holst, Malene R Andersen, Arne Astrup, Freek G Bouwman, Sanne van Otterdijk, Will K W H Wodzig, Marleen A van Baak, Thomas M Larsen, Susan A Jebb, Anthony Kafatos, Andreas F H Pfeiffer, J Alfredo Martinez, Teodora Handjieva-Darlenska, Marie Kunesova, Wim H M Saris, Edwin C M Mariman

“…A predictor panel of 15 variables enabled an optimal classification by Random Forests with an error rate of 24±1%. A logistic regression model with independent variables from 9 blood analytes had a prediction accuracy of 92%.…”
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A Bio-Inspired Data-Driven Locomotion Optimization Framework for Adaptive Soft Inchworm Robots by Mahtab Behzadfar, Arsalan Karimpourfard, Yue Feng

“…The neural network achieves superior velocity prediction performance, with a coefficient of determination (R²) of 0.9362 and a root mean squared error (RMSE) of 0.3898, surpassing previously reported models, including linear regression, LASSO, decision trees, and random forests. …”
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An Automated Method for Detection and Enumeration of Olive Trees Through Remote Sensing by Muhammad Waleed, Tai-Won Um, Aftab Khan, Zubair Ahmad

“…Country olive forests are one of the major contributors with economic aspects. …”
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Global 30-m annual median vegetation height maps (2000–2022) based on ICESat-2 data and Machine Learning by Maria O. Hunter, Leandro Parente, Yu-feng Ho, Carmelo Bonannella, Laerte Guimarães Ferreira, Douglas Morton, Davide Consoli, Lindsey Sloat

“…Abstract Accurately measuring vegetation height is essential for understanding ecosystem structure, carbon storage, and biodiversity, yet global height models have overwhelmingly focused on forests, excluding ecosystems with shorter herbaceous vegetation or shrubs. …”
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The Use of Explainable Machine Learning for the Prediction of the Quality of Bulk-Tank Milk in Sheep and Goat Farms by Daphne T. Lianou, Yiannis Kiouvrekis, Charalambia K. Michael, Natalia G. C. Vasileiou, Ioannis Psomadakis, Antonis P. Politis, Angeliki I. Katsafadou, Eleni I. Katsarou, Maria V. Bourganou, Dimitra V. Liagka, Dimitrios C. Chatzopoulos, Nikolaos M. Solomakos, George C. Fthenakis

“…Tools found with the lowest mean absolute percentage error (MAPE) between the five tools used to test predictions for each target value were selected. …”
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Predictive identification of oral cancer using AI and machine learning by Saraswati Patel, Dheeraj Kumar

“…Using convolutional neural networks (CNNs), support vector machines (SVMs), and random forests, we compared the effectiveness of these techniques in improving diagnostic accuracy and mean squared error (MSE). …”
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Spectral purification improves monitoring accuracy of the comprehensive growth evaluation index for film-mulched winter wheat by Zhikai Cheng, Xiaobo Gu, Yadan Du, Zhihui Zhou, Wenlong Li, Xiaobo Zheng, Wenjing Cai, Tian Chang

“…The accuracy of remote-sensing inversion of the CGEI based on the ANN model was higher than for the individual agronomic parameters, with a coefficient of determination of 0.75, a root mean square error of 8.40, and a mean absolute value error of 6.53. …”
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