Search Results - carl AND random tree algorithm~

Machine Learning Approaches for the Prediction of Displaced Abomasum in Dairy Cows Using a Highly Imbalanced Dataset.

Authors: Asgari, Zeinab Sadeghi-Sefidmazgi, Ali Pakdel, Abbas et al.

Source: Animals (2076-2615); Jul2025, Vol. 15 Issue 13, p1833, 15p

Subject Terms: MACHINE learning, RANDOM forest algorithms, ANIMAL welfare, BOOSTING algorithms, DIGESTIVE system diseases, DAIRY cattle, PREDICTION models

Classification Algorithms Used in Predicting Glaucoma Progression.

Authors: Tarcoveanu, Filip Leon, Florin Curteanu, Silvia et al.

Source: Healthcare (2227-9032); Oct2022, Vol. 10 Issue 10, pN.PAG-N.PAG, 20p

Subject Terms: RANDOM forest algorithms, PREDICTION models, GLAUCOMA, DIABETIC retinopathy, INTRAOCULAR lenses, STATISTICAL sampling, RETROSPECTIVE studies, EXPERIMENTAL design, SUPPORT vector machines, COMPARATIVE studies, DECISION trees, MACHINE learning, DISEASE progression

Stochastic Particle Approximations for Generalized Boltzmann Models and Convergence Estimates

Authors: Graham, Carl Meleard, Sylvie

Source: The Annals of Probability, 1997 Jan 01. 25(1), 115-132.

Access URL: https://www.jstor.org/stable/2959532

Prediction and Uncertainty Quantification of Flow Rate Through Rectangular Top-Hinged Gate Using Hybrid Gradient Boosting Models.

Authors: Nejatipour, Pourya Oliveto, Giuseppe Sapaev, Ibrokhim et al.

Source: Water (20734441); Dec2025, Vol. 17 Issue 24, p3470, 36p

Subject Terms: BOOSTING algorithms, METAHEURISTIC algorithms, HYDRAULIC models, FLOW velocity, WATER-supply engineering

Assessing data mining algorithms to predict the quality of groundwater resources for determining irrigation hazard.

Authors: Masoudi R Mousavi SR Rahimabadi PD et al.

Source: Environmental monitoring and assessment [Environ Monit Assess] 2023 Jan 23; Vol. 195 (2), pp. 319. Date of Electronic Publication: 2023 Jan 23.

Publication Type: Journal Article

Journal Info: Publisher: Springer Country of Publication: Netherlands NLM ID: 8508350 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-2959 (Electronic) Linking ISSN: 01676369 NLM ISO Abbreviation: Environ Monit Assess Subsets: MEDLINE

MeSH Terms: Groundwater*/analysis , Water Pollutants, Chemical*/analysis, Environmental Monitoring/methods ; Algorithms ; Random Forest ; Data Mining ; Water Quality

An Innovative Approach for Extraction of Smoking Addiction Levels Using Physiological Parameters Based on Machine Learning: Proof of Concept.

Authors: Bascil, Muhammet Serdar Iscanli, Irem Nur

Source: Diagnostics (2075-4418); Nov2025, Vol. 17 Issue 22, p2839, 22p

Subject Terms: NICOTINE addiction, MACHINE learning, PATIENT monitoring, DECISION trees, SUPPORT vector machines, SCIENTIFIC observation, K-nearest neighbor classification

Non-Glycemic Clinical Data for Type 2 Diabetes Detection in Mexican Adults: A Comparative Analysis of Atherogenic Indices, Statistical Transformations, and Machine Learning Algorithms.

Authors: Guerrero-Flores, Martin Hazael Maeda-Gutiérrez, Valeria Galván-Tejada, Carlos E. et al.

Source: Diagnostics (2075-4418); Jan2026, Vol. 16 Issue 1, p53, 26p

Streamflow Prediction with Time-Lag-Informed Random Forest and Its Performance Compared to SWAT in Diverse Catchments.

Authors: Moges, Desalew Meseret Virro, Holger Kmoch, Alexander et al.

Source: Water (20734441); Oct2024, Vol. 16 Issue 19, p2805, 21p

Subject Terms: MACHINE learning, RANDOM forest algorithms, HYDROLOGIC models, STREAMFLOW, ARTIFICIAL intelligence

Does machine learning outperform logistic regression in predicting individual tree mortality?

Authors: Vázquez-Veloso, Aitor Toraño Caicoya, Astor Bravo, Felipe et al.

Source: Ecological Informatics. Sep2025, Vol. 88, pN.PAG-N.PAG. 1p.

Subject Terms: Tree mortality, Machine learning, Artificial intelligence, Forest dynamics, Random forest algorithms

Drivers of global irrigation expansion: the role of discrete global grid choice.

Authors: Wagner, Sophie Stenzel, Fabian Krueger, Tobias et al.

Source: Hydrology & Earth System Sciences Discussions; 12/20/2023, p1-34, 34p

Subject Terms: IRRIGATION, HYDROLOGIC cycle, GEOSPATIAL data, CROP yields, RANDOM forest algorithms

Predictor Importance for Hydrological Fluxes of Global Hydrological and Land Surface Models.

Authors: Brêda, João Paulo L. F. Melsen, Lieke A. Athanasiadis, Ioannis et al.

Source: Water Resources Research; Sep2024, Vol. 60 Issue 9, p1-15, 15p

Subject Terms: SOIL texture, RANDOM forest algorithms, HYDROLOGIC models, SOIL mapping, RUNOFF

Usage of the XGBoost and MARS algorithms for predicting body weight in Kajli sheep breed.

Authors: Faraz, Asim Tırınk, Cem Önder, Hasan et al.

Source: Tropical Animal Health & Production. Aug2023, Vol. 55 Issue 4, p1-10. 10p.

Artificial intelligence-assisted mitotic counts improve efficiency, accuracy, and inter-pathologist agreement: a method toward canine soft tissue sarcoma grade standardization.

Authors: Borst, Luke Bacmeister, Cindy Yau, Wilson et al.

Source: Veterinary Oncology (3004-9814). 8/14/2025, Vol. 2 Issue 1, p1-19. 19p.

A Hybrid Decision Tree and Deep Learning Approach Combining Medical Imaging and Electronic Medical Records to Predict Intubation Among Hospitalized Patients With COVID-19: Algorithm Development and Validation.

Authors: Nguyen, Kim-Anh-Nhi Tandon, Pranai Ghanavati, Sahar et al.

Source: JMIR Formative Research; 2023, Vol. 7 Issue 1, p1-15, 15p

Machine learning based diagnostics of veterinary cancer on ultrasound and optical imaging data.

Authors: Maciulevičius, Martynas Rupšytė, Greta Raišutis, Renaldas et al.

Source: Veterinary Quarterly. Dec2025, Vol. 45 Issue 1, p1-19. 19p.

Algorithms, AI, Big Data, and Big Tech: IPS Scholarship on Digital Technologies.

Authors: Böhm, Madeleine

Source: International Political Sociology; Sep2025, Vol. 19 Issue 3, p1-25, 25p

Subject Terms: ALGORITHMS, ARTIFICIAL intelligence, DISCOURSE analysis, POLITICAL sociology, DIGITAL technology, BIG data, INTERDISCIPLINARY research, HIGH technology

Integrating UAV-LiDAR and Field Experiments to Survey Soil Erosion Drivers in Citrus Orchards Using an Exploratory Machine Learning Approach.

Authors: Rodrigo-Comino, Jesús Cambronero-Ruiz, Laura Moreno-Cuenca, Lucía et al.

Source: Water (20734441); Dec2025, Vol. 17 Issue 24, p3541, 22p

Subject Terms: SOIL erosion, HYDRAULIC conductivity, ORCHARDS, SOILS, REMOTE sensing, SUSPENDED sediments, MACHINE learning, FIELD research

Geographic Terms: SOUTHERN Europe, SPAIN

Hybrid Deep Learning Models for Analyzing Histological Images of the Zebrafish Intestine Under Oxidative Stress.

Authors: Pavel, Cristian Dan Moldovanu, Simona Pavel, Irina Andreea et al.

Source: Diagnostics (2075-4418); Nov2025, Vol. 17 Issue 22, p2859, 16p

Subject Terms: OXIDATIVE stress, CONVOLUTIONAL neural networks, ZEBRA danio, DEEP learning, IMAGE enhancement (Imaging systems), ARTIFICIAL intelligence, HISTOPATHOLOGY, CLASSIFICATION

A tale of scale: Plot but not neighbourhood tree diversity increases leaf litter ant diversity.

Authors: Skarbek, Carl J. Noack, Merle Bruelheide, Helge et al.

Source: Journal of Animal Ecology. Feb2020, Vol. 89 Issue 2, p299-308. 10p.

Investigation of the Wear Behavior of AA6063/Zirconium Oxide Nanocomposites Using Hybrid Machine Learning Algorithms.

Authors: Reena Roy, R. Shanmugam, Leninisha Vinothini, A. et al.

Source: Journal of Chemistry; 5/18/2023, p1-16, 16p

Subject Terms: MACHINE learning, DECISION trees, NANOCOMPOSITE materials, DECISION making, ACQUISITION of data