Prediction and evaluation of environmental quality for nursing sow buildings via multisource sensor information fusion by Chong Chen, Xingqiao Liu, Chaoji Liu, Chengyang Yu

“…The validation test results indicate that this model outperformed four other models, achieving a coefficient of determination (R2) of 0.9086, a Mean Absolute Error (MAE) of 0.0639, a Root Mean Squared Error (RMSE) of 0.1787, and a computational time of 7.5862 s. …”
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Comparative Study on Soil Infiltration Characteristics of Different Land Use Types in Horqin Sandy Land by Yin Jiawang, Ala Musa, Su Yuhang, Jiang Shaoyan

“…The initial infiltration rates ranged from 1.595 mm/min to 12.020 mm/min, and followed the order of bare sandy land>Caragana korshinskii plantation>corn field > Pinus sylvestris plantation>Caragana microphylla plantation>meadow grassland>abandoned grassland>sparse forest grassland. The infiltration rate at 15 min varied from 0.617 mm/min to 3.690 mm/min, and followed the order of bare sandy land>Caragana korshinskii plantation>Pinus sylvestris plantation>Caragana microphylla plantation>corn field>abandoned grassland>meadow grassland>sparse forest grassland. …”
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Accuracy Prediction of Compressive Strength of Concrete Incorporating Recycled Aggregate Using Ensemble Learning Algorithms: Multinational Dataset by Menghay Phoeuk, Minho Kwon

“…Results demonstrate that the proposed models are highly accurate and generalizable, with high coefficients of determination and low error predictions. The CatBoost model performed the best, exhibiting an R2 of 0.938 and low mean absolute error and root mean squared error values of 2.639 and 3.885, respectively, in the blind evaluation process. …”
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An Integrated Framework for Cryptocurrency Price Forecasting and Anomaly Detection Using Machine Learning by Hani Alnami, Muhammad Mohzary, Basem Assiri, Hussein Zangoti

“…Evaluation metrics, such as the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²), demonstrate the superior precision and reliability of the Random Forest and Gradient Boosting models. …”
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Asteroid Types, Albedos, and Diameters Catalog from Gaia DR3: Intelligent Inversion Results via Multisource Information Fusion by Jiayi Ge, Xiaoming Zhang, Juan Li, Huijuan Wang, Dawei Xu, Xiaojun Jiang

“…Cross validation and independent testing show that AadRF reduces the root mean square error for albedo and diameter predictions by 64.0 percentage points and 70.2 percentage points, respectively, compared to the traditional method, with corresponding mean absolute percentage errors of 28.9% and 17.5%. …”
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Impact of lightweight clay aggregate with slag and biomedical waste ash on self-compacting concrete using machine learning approach by Kennedy C. Onyelowe, Viroon Kamchoom, Shadi Hanandeh, Ahmed M. Ebid, Janneth Alejandra Viñan Villagran, Raúl Gregorio Martínez Pérez, Fausto Ulpiano Caicedo Benavides, Paul Awoyera, Siva Avudaiappan

“…Six advanced machine learning methods such as the Artificial Neural Network (ANN), Support Vector Regression (SVR), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGB), Random Forest (RF), and Adaptive Boosting (AdaBoost) were used to model the concrete behavior. …”
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A reconstruction of the ice thickness of the Antarctic Peninsula Ice Sheet north of 70° S by K. Shahateet, K. Shahateet, K. Shahateet, J. J. Fürst, F. Navarro, T. Seehaus, D. Farinotti, D. Farinotti, M. Braun

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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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Integration of Aerial Mapping using UAV and Low-cost Backpack LiDAR for Biomass and Carbon Stock Estimation Calculation by Q. P. A. N. Ila, M. N. Cahyadi, H. H. Handayani, A. B. Raharjo, R. Mardiyanto, I. W. Farid, D. Saptarini, E. E. Saratoga

“…The total forest area in Indonesia reaches 62.97% of Indonesia's land area or approximately 125.76 hectares, requiring effective and accurate inventory methods. …”
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Hyperspectral Remote Sensing Combined with Ground Vegetation Surveys for the Study of the Age of Rodent Mounds by Hao Qi, Xiaoni Liu, Tong Ji, Chenglong Ma, Yafei Shi, Guoxing He, Rong Huang, Yunjun Wang, Zhuoli Yang, Dong Lin

“…The rodent mound classification models established using vegetation characteristic indicators and vegetation indices through Random Forest could distinguish rodent mounds of different ages, with out-of-bag error rates of 36.96% and 21.74%, respectively. …”
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A Hierarchical-Based Learning Approach for Multi-Action Intent Recognition by David Hollinger, Ryan S. Pollard, Mark C. Schall, Howard Chen, Michael Zabala

“…Although the TCN and BiLSTM classifiers achieved classification accuracies of 89.87% and 89.30%, respectively, they did not surpass the performance of the action-generic random forest model when used in combination with an action-specific random forest model. …”
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Integration of Drone and Satellite Imagery Improves Agricultural Management Agility by Michael Gbenga Ogungbuyi, Caroline Mohammed, Andrew M. Fischer, Darren Turner, Jason Whitehead, Matthew Tom Harrison

“…The standard error of the mean (SEM) for the field biomass, derived from UAS-measured sward height changes, was 1240 kg DM/ha. …”
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Advances in Surveying Topographically Complex Ecosystems with UAVs: Manta Ray Foraging Algorithms by Shijie Yang, Jiateng Yuan, Zhibo Chen, Hanchao Zhang, Xiaohui Cui

“…This study introduces an innovative UAV cruise data collection path planning approach using the manta ray foraging optimization (MRFO) algorithm to enhance efficiency and energy utilization in forest ecosystem monitoring. Traditionally reliant on costly manual patrols, this method leverages UAVs and ground-based sensors for data collection. …”
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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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Machine learning models for predicting tibial intramedullary nail length by Sercan Capkin, Ali Ihsan Kilic, Hakan Cici, Mehmet Akdemir, Mert Kahraman Marasli

“…The performance of the models was evaluated using the mean squared error (MSE) and the R-squared values. Results The linear regression model demonstrated superior performance compared to the random forest, decision tree, and XGBoost models, with an R-squared value of 0.89, an MSE of 117.53, and a root mean squared error (RMSE) of 10.84 mm. …”
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A Soft Sensor Based Inference Engine for Water Quality Assessment and Prediction by Micheal A Ogundero, Theophilus A Fashanu, Foluso O Agunbiade, Kehinde Orolu, Ahmed A Yinusa, Usman A Daudu, Muhammed O H Amuda

“…Similarly, the Dissolved Oxygen was estimated by the Random Forest model with a mean squared error (MSE) of 1.0335 for training and 0.7150 for validation. …”
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Navigating cognitive boundaries: the impact of CognifyNet AI-powered educational analytics on student improvement by Mrim M. Alnfiai, Faiz Abdullah Alotaibi, Mona Mohammed Alnahari, Nouf Abdullah Alsudairy, Asma Ibrahim Alharbi, Saad Alzahrani

“…Evaluated through rigorous 5-fold cross-validation on a comprehensive dataset of 1200 anonymized student records and validated across multiple educational platforms, including UCI Student Performance and Open University Learning Analytics datasets, CognifyNet demonstrates superior performance over conventional approaches, achieving 10.5% reduction in mean squared error and 83% reduction in mean absolute error compared to baseline random forest models, with statistical significance confirmed through paired t-tests (p < 0.01). …”
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Comparison of Three Supervised Learning Methods for Digital Soil Mapping: Application to a Complex Terrain in the Ecuadorian Andes by Martin Hitziger, Mareike Ließ

“…The performance of three statistical learning methods is compared: linear regression, random forest, and stochastic gradient boosting of regression trees. …”
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Pressurized Water Reactor Transient Detection With Artificial Intelligence to Support Reactor Operators by Ceyhun Yavuz, Senem Şentürk Lüle

“…When accuracy, precision, recall, and F1-score are compared together, the random forest method showed the best performance.…”
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Assessing the impact of multi-source environmental variables on soil organic carbon in different land use types of China using an interpretable high-precision machine learning meth... by Feng Wang, Ruilin Liang, Shuyue Li, Meiyan Xiang, Weihao Yang, Miao Lu, Yingqiang Song

“…The results of descriptive statistics described the order of SOC content: forest land > grassland > cultivated land > unused land. …”
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