MICROBOCENOSIS OF THE RHIZOSPHERE OF SOFT WHEAT WHEN USING BIOLOGICAL PRODUCTS by Natalya N. Shuliko, Elena V. Tukmacheva, Irina A. Korchagina, Alina A. Kiseleva, Olga F. Khamova, Artem Yu. Timokhin, Yuri Yu. Parshutkin, Ekaterina V. Kubasova

“…The activity of hydrolytic enzymes in most variants of the experiment tended to increase relative to the control (up to 17%), however, the activity of the redox enzyme catalase decreased from the applied agricultural method within the experimental error (up to 4%). A statistically significant (p<0.05) positive (r=0.675) dependence of wheat yield on the amount of microflora in the rhizosphere was revealed.…”
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AI driven prediction of early age compressive strength in ultra high performance fiber reinforced concrete by Mohamed Abdellatief, Wafa Hamla, Hassan Hamouda

“…As part of evaluating model performance and conducting error analysis, this study investigated differences in prediction accuracy among five models across training and testing datasets. …”
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Improving fluoroprobe sensor performance through machine learning by D. Lafer, A. Sukenik, T. Zohary, O. Tal

“…The SVR model presented lower mean square error in predicting phytoplankton biomass, and extended FP capabilities to identify also dinoflagellates, an important taxonomic group in Lake Kinneret. …”
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Hyperspectral imaging combined with machine learning for high‐throughput phenotyping in winter wheat by Sehijpreet Kaur, Vijaya Gopal Kakani, Brett Carver, Diego Jarquin, Aditya Singh

“…Our results show that using complete hyperspectral variables results in superior r, R2, and lower root mean square error for both models. We conclude that relying solely on linear regression models with VIs may not always result in accurate predictions of plant traits in winter wheat. …”
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Retrieval of Dissolved Organic Carbon Storage in Plateau Lakes Based on Remote Sensing and Analysis of Driving Factors: A Case Study of Lake Dianchi by Yufeng Yang, Wei Gao, Yuan Zhang

“…The mixed-layer depth (2 m) was determined through error minimization analysis of 16 vertical profiles. …”
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Actual Truck Arrival Prediction at a Container Terminal with the Truck Appointment System Based on the Long Short-Term Memory and Transformer Model by Mengzhi Ma, Xianglong Li, Houming Fan, Li Qin, Liming Wei

“…The root mean square error (RMSE) values for the LSTM-Transformer model on two datasets are 0.0352 and 0.0379, and the average improvements are 23.40% and 18.43%, respectively. …”
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Analysis and Prediction of Coverage and Channel Rank for UAV Networks in Rural Scenarios With Foliage by Donggu Lee, Ozgur Ozdemir, Ram Asokan, Ismail Guvenc

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A convolutional neural network-based deep learning approach for predicting surface chloride concentration of concrete in marine tidal zones by Mohamed Abdellatief, Mahmoud E. Abd-Elmaboud, Mohamed Mortagi, Ahmed M. Saqr

“…The CNN’s performance was benchmarked against four machine learning (ML) models: stepwise linear regression (SLR), support vector machine (SVM), Gaussian process regression (GPR), and random forest (RF). Results demonstrated CNN’s superiority, achieving a coefficient of determination (R2) = 0.849 and a lower root mean square error (RMSE) = 0.18%, outperforming conventional models. …”
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Predictive modeling of rapid glaucoma progression based on systemic data from electronic medical records by Richul Oh, Hyunjoong Kim, Tae-Woo Kim, Eun Ji Lee

“…The predictive model was trained and tested using a random forest (RF) method and interpreted using Shapley additive explanation plots (SHAP). …”
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Modeling the Impact of Hydrogen Embrittlement on the Fracture Toughness of Low-Carbon Steel Using a Machine Learning Approach by Michael Gyaabeng, Ramadan Ahmed, Nayem Ahmed, Catalin Teodoriu, Deepak Devegowda

“…The chosen modeling techniques were k-nearest neighbors (KNN), random forest (RF), gradient boosting (GB), and decision tree regression (DT). …”
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Improved 30‐m Evapotranspiration Estimates Over 145 Eddy Covariance Sites in the Contiguous United States: The Role of ECOSTRESS, Harmonized Landsat Sentinel‐2 Imagery, Climate Rea... by Taufiq Rashid, Di Tian

“…The 30‐m ET estimation biases were significantly related to the biases in the upwelling longwave (RUL) and downwelling shortwave radiation (RDS) inputs, with ET estimates driven by MODIS radiation showing higher biases compared to those driven by ERA5‐Land radiation. The error diagnosis using random forest indicates that ET biases tend to be larger under higher ET estimates, and RUL and RDS were the primary contributors to the high bias at the higher ET ranges, with partial dependence plots revealing that the estimation biases tend to be higher under more humid environment, denser vegetation covers, and high net radiation conditions. …”
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Efficient Feature Selection and Hyperparameter Tuning for Improved Speech Signal-Based Parkinson’s Disease Diagnosis via Machine Learning Techniques by Deepak Painuli, Suyash Bhardwaj, Utku Kose

“…Machine learning (ML) techniques have shown promise in addressing these diagnostic challenges due to their higher efficiency and reduced error rates in analyzing complex, high-dimensional datasets, particularly those derived from speech signals. …”
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Validation of the NISAR Multi-Scale Soil Moisture Retrieval Algorithm across Various Spatial Resolutions and Landcovers Using the ALOS-2 SAR Data by Preet Lal, Gurjeet Singh, Narendra N. Das, Rowena B. Lohman

“…Validation using in situ measurements showed that the unbiased root mean square error was less than 0.06 [m3/m3] for most sites, matching NISAR’s accuracy requirements. …”
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Comparison of Neural Network, Ordinary Kriging, and Inverse Distance Weighting Algorithms for Seismic and Well-Derived Depth Data: A Case Study in the Bjelovar Subdepression, Croat... by Ana Brcković, Tomislav Malvić, Jasna Orešković, Josipa Kapuralić

“…The obtained maps were compared based on square error (using k-fold cross-validation) and the visual interpretation of isopaches. …”
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Determination of Pear Cultivars (Pyrus communis L.) Based on Colour Change Levels by Using Data Mining by Dilara Gerdan, Abdullah Beyaz, Mustafa Vatandaş

“…The predictions were done supervised machine learning algorithms (Decision Tree and Neural Networks with Meta-Learning Techniques; Majority Voting and Random Forest) by using KNIME Analytics software. The classifier performance (accuracy, error, F-Measure, Cohen's Kappa, recall, precision, true positive (TP), false positive (FP), true negative (TN), false negative (FN) values were given at the conclusion section of the research. …”
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A comparative ensemble approach to bedload prediction using metaheuristic machine learning by Ajaz Ahmad Mir, Mahesh Patel, Fahad Albalawi, Mohit Bajaj, Milkias Berhanu Tuka

“…The coefficient of determination (R 2) and root mean square error (RMSE) values vary between various models; however, XGB showed R 2 = 0.99 and RMSE = 0.11. …”
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Predicting fall risk in older adults: A machine learning comparison of accelerometric and non-accelerometric factors by Ana González-Castro, José Alberto Benítez-Andrades, Rubén González-González, Camino Prada-García, Raquel Leirós-Rodríguez

“…Performance was evaluated based on mean squared error (MSE) and coefficient of determination ( R 2 ), to assess how combining multiple data types influences prediction accuracy. …”
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Impact of PM_2.5 Pollution on Solar Photovoltaic Power Generation in Hebei Province, China by Ankun Hu, Zexia Duan, Yichi Zhang, Zifan Huang, Tianbo Ji, Xuanhua Yin

“…The optimal stacking configuration achieved superior performance (MAE = 0.479 MW, indicating an average prediction error of 479 kilowatts; R² = 0.967, reflecting that 96.7% of the variance in power output is explained by the model), demonstrating robust predictive capability under diverse atmospheric conditions. …”
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Investigating factors affecting the quality of water resources by multivariate analysis and soft computing approaches by Bing Cheng, Xinyu Liu, Keke Guo, Ahmad Rastegarnia

“…Modeling results based on error value, Wilmot agreement index, A20 index, determination coefficient, and violin diagrams showed that the SVM (R2 > 0.99, RMSE < 0.04, A20 = 1.00, WAI = 1.00) achieved better than the other models. …”
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The Role of Education in Building National Soft Power: An Empirical Analysis From a Global Perspective Using Deep Neural Networks by Yun Bai

“…The model also shows superior reliability with an MCC of 0.923 and an AUC of 0.978. Low error rates, including MAE (0.11), MSE (0.05), and RMSE (0.22), highlight its accuracy and predictive precision. …”
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