IEEE

Fourier Phase Preservation and Spectral Random Augmentation for Cross-Scene Hyperspectral Image Classification With Ensemble Networks

Abstract: Cross-scene hyperspectral image (HSI) classification faces significant challenges due to spectral–spatial domain shifts. Existing domain generalization (DG) methods typically generate ...
Visual Studio Magazine

Random Forest Regression Using C#

Random forest regression is a tree-based machine learning technique to predict a single numeric value. A random forest is a collection (ensemble) of simple regression decision trees that are trained ...
Frontiers

Advancing standardized cattle behavior classification with a random forest model evaluated across diverse datasets

The lack of precise, autonomous tools for monitoring and classifying cattle behavior limits farmers’ ability to make proactive and informed decisions regarding grazing and herd management. Currently, ...
IEEE

RandomViG: Random Vision Graph Neural Network for Image Classification

Abstract: Vision Graph Neural Network (ViG) is the first graph neural network model capable of directly processing image data. The community primarily focuses on the model structures to improve ViG's ...
AOL

“Random Stuff I Think Is Interesting”: 50 Fascinating Images And Facts From This Instagram Page

What do you like to get out of Instagram? Many users simply post photos of themselves and keep in touch with friends from college. Meanwhile, some use the platform to promote their business or grow ...
Scientific Research Publishing

Sun, J., & Ongsomwang, S. (2023). Optimal Parameters of Random Forest for Land Cover Classification with Suitable Data Type and Dataset on Google Earth Engine. Frontiers in ...

ABSTRACT: Arid and semiarid regions face challenges such as bushland encroachment and agricultural expansion, especially in Tiaty, Baringo, Kenya. These issues create mixed opportunities for pastoral ...
C&EN

Addressing Imbalanced Classification Problems in Drug Discovery and Development Using Random Forest, Support Vector Machine, AutoGluon-Tabular, and H2O AutoML

The classification models built on class imbalanced data sets tend to prioritize the accuracy of the majority class, and thus, the minority class generally has a higher misclassification rate.
Scientific Research Publishing

Machine Learning-Based Classification of Coronary Heart Disease: A Comparative Analysis of Logistic Regression, Random Forest, and Support Vector Machine Models ()

1 Department of Business Information System, Central Michigan University, Mount Pleasant, MI, USA. 2 Department of MPH, Central Michigan University, Mount Pleasant, MI, USA. 3 Department of ...