Improving spatial resolution of Aqua MODIS and GCOM-C chlorophyll-a data for Cyprus coastal waters monitoring by Sofiia Drozd, Nataliia Kussul, Andrii Shelestov

“…Four images from spring-summer 2024 were selected for analysis, with Sentinel-3 spectral bands used as predictors and both multiple linear regression and random forest models applied. The results indicate that linear regression predicts higher coastal Chl-a values, while random forest smooths spatial gradients. …”
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High-throughput 3D phenotyping in northern quahogs Mercenaria mercenaria for dimensional trait measurement and weight prediction by Kai Shen, Xu Wang, Kai Gao, Jayme Yee, Paul McDonald, Huiping Yang

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Development of Ai-Based Crop Quality Grading Systems using Image Recognition by Dusi Prerna, Sharma Pooja

“…Often traditional crop grading methods are inconsistent of errors and tend to be inefficient and suboptimal grading results which cause costs. …”
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Estimating Maize Leaf Water Content Using Machine Learning with Diverse Multispectral Image Features by Yuchen Wang, Jianliang Wang, Jiayue Li, Jiacheng Wang, Hanzeyu Xu, Tao Liu, Juan Wang

“…The results indicate that the RFR model performs optimally during the seedling stage, with a root relative mean square error (RRMSE) of 2.99%, whereas estimation errors are larger during the tasseling stage, with an RRMSE of 4.13%. …”
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Real-Time Estimation of Near-Surface Air Temperature over Greece Using Machine Learning Methods and LSA SAF Satellite Products by Athanasios Karagiannidis, George Kyros, Konstantinos Lagouvardos, Vassiliki Kotroni

“…The mean absolute error (MAE) of the NSAT estimation model was 0.96 °C, while the mean biased error (MBE) was −0.01 °C and the R² was 0.976. …”
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Maximizing multi-source data integration and minimizing the parameters for greenhouse tomato crop water requirement prediction by Xinyue Lv, Youli Li, Lili Zhangzhong, Chaoyang Tong, Yibo Wei, Guangwei Li, Yingru Yang

“…The results show that the stacking model has the best prediction effect, and the error is lower than that of RandomForest, LightGBM, CatBoost, Average fusion model and Weighted fusion model. …”
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A precise estimation framework for individual tree AGB of Pinus kesiya var. Langbianensis utilizing point cloud registration Optimization by Zhibo Yu, Yong Wu, Ziyu Zhang, Chi Lu, Hong Wang, Zhi Liu, Xiaoli Zhang, Lei Bao, Jie Pan, Guanglong Ou, Hongbin Luo

“…Accurate estimation of individual tree above-ground biomass (AGB) is crucial for regional forest AGB measurement. In this study, 64 individual trees of Pinus kesiya var. langbianensis, exhibiting a range of diameters, were felled from natural forests in mountainous regions to develop region-specific allometric equations for AGB. …”
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Dementia Classification Based on Magnetic Resonance Scans Comparing Traditional and Modern Machine Learning Models’ Quintessence by Andreea POPOVICIU, Diogen BABUC, Todor IVAŞCU

“…The performance evaluation of each model was based on metrics such as accuracy, sensitivity, specificity, and statistical errors (determination coefficient, mean squared error, root mean squared error). …”
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Optimizing protein-ligand docking through machine learning: algorithm selection with AutoDock Vina by Ala’ Omar Hasan Zayed

“…The feature selection process was optimized using Gini importance metrics, with model performance evaluated through mean squared errors and mean absolute errors.…”
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Dynamic Classification: Leveraging Self-Supervised Classification to Enhance Prediction Performance by Ziyuan Zhong, Junyang Zhou

“…For each subarea, there will be the same type of model, such as linear or random forest model, to predict the results of that subareas. …”
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Predicting Type 2 diabetes onset age using machine learning: A case study in KSA. by Faten Al-Hussein, Laleh Tafakori, Mali Abdollahian, Khalid Al-Shali, Ahmed Al-Hejin

“…The MLR and RF models provided the best fit, achieving R2 values of 0.90 and 0.89, root mean square errors (RMSE) of 0.07 and 0.01, and mean absolute errors (MAE) of 0.05 and 0.13, respectively, using the logarithmic transformation of the onset age. …”
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Use of responsible artificial intelligence to predict health insurance claims in the USA using machine learning algorithms by Ashrafe Alam, Victor R. Prybutok

“…The algorithms examined include support vector machine (SVM), decision tree (DT), random forest (RF), linear regression (LR), extreme gradient boosting (XGBoost), and k-nearest neighbors (KNN). …”
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Using natural vegetation succession to evaluate how natural restoration proceeds under different climate in Yunnan, Southwest China. by Weifeng Gui, Qingzhong Wen, Wenyuan Dong, Xue Ran, Xiaosong Yang, Guangqi Zou, Dechang Kong

“…The times for natural succession to reach the forest stage vary from 5 to 19 years, which aligns with the order of indices. …”
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Individual Tree Segmentation Based on Region-Growing and Density-Guided Canopy 3-D Morphology Detection Using UAV LiDAR Data by Shihua Li, Shunda Zhao, Zhilin Tian, Hao Tang, Zhonghua Su

“…Forest tree information is crucial for monitoring forest resources and developing forestry management strategies. …”
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Pixel 5 Versus Pixel 9 Pro XL—Are Android Devices Evolving Towards Better GNSS Performance? by Julián Tomaštík, Jorge Hernández Olcina, Šimon Saloň, Daniel Tunák

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Inferring Parameters in a Complex Land Surface Model by Combining Data Assimilation and Machine Learning by L. T. Keetz, K. Aalstad, R. A. Fisher, C. Poppe Terán, B. Naz, N. Pirk, Y. A. Yilmaz, O. Skarpaas

“…Although errors were also consistently reduced with real data, comparing the emulator designs was less conclusive, which we mainly attribute to equifinality, structural uncertainty within CLM‐FATES, and/or unknown errors in the data that are not accounted for.…”
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Reconstructing Evapotranspiration in British Columbia Since 1850 Using Publicly Available Tree-Ring Plots and Climate Data by Hang Li, John Rex

“…ET satellite images from 1982 to 2010 formed our dataset to train models for each vegetated pixel. The random forest regression outperformed the other approaches with lower errors and better robustness (adjusted R² value = 0.69; root mean square error = 10.72 mm/month). …”
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Estimation of the vertical distribution of the fine canopy fuel in Pinus sylvestris stands using low density LiDAR data by L. A. Fidalgo-González, S. Arellano-Pérez, J. G. Álvarez-González, F. Castedo-Dorado, A. D. Ruiz-González, E. González-Ferreiro

“…The proposed models can be used to assess the effectiveness of different forest management alternatives for reducing crown fire hazard.…”
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Machine learning-based prediction of torsional behavior for ultra-high-performance concrete beams with variable cross-sectional shapes by Elhabyb Khaoula, Baina Amine, Bellafkih Mostafa, A. Deifalla, Amr El-Said, Mohamed Salama, Ahmed Awad

“…Three powerful algorithms, Random Forest, Gradient Boosting Regressor, and Long Short-Term Memory (LSTM), were trained and assessed on a dataset of 113 UHPC specimens. …”
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Simplifying drone-based aboveground carbon density measurements to support community forestry. by Ben Newport, Tristram C Hales, Joanna House, Benoit Goossens, Amaziasizamoria Jumail

“…Community-based forest restoration has the potential to sequester large amounts of atmospheric carbon, avoid forest degradation, and support sustainable development. …”
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