AI enables a who's who of brown bears in Alaska

A team of scientists from EPFL and Alaska Pacific University has developed an AI program that can recognize individual bears ...
Devdiscourse

AI-driven methods accelerate detection of pesticides in food systems

In a study titled Recent Applications of Machine Learning Algorithms for Pesticide Analysis in Food Samples, published in the ...
GitHub

rskworld/fraud-detection-ml

Advanced fraud detection system using machine learning to identify fraudulent transactions and activities. This project implements multiple machine learning algorithms including Random Forest, XGBoost ...
Frontiers

Supervised machine learning approaches for early detection of metabolic and udder health disorders in dairy cows using sensor-derived data

Large Animal Clinic, Veterinary Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania Therefore, it was hypothesized that by analyzing real-time physiological, behavioral, and milk ...
Scientific Research Publishing

Performance of Different Machine Learning Algorithms for Credit Risk Classification ()

1 Department of Information Technology and Computer Science, School of Computing and Mathematics, The Cooperative University of Kenya, Nairobi, Kenya. 2 Department of Computing and Informatics, School ...
Frontiers

Comparative evaluation of fast-learning classification algorithms for urban forest tree species identification using EO-1 hyperion hyperspectral imagery

Introduction: Accurate identification of forest tree species is essential for sustainable forest management, biodiversity assessment, and environmental monitoring. Urban forests, in particular, ...
Scientific Research Publishing

Breiman, L. (2001) Random Forests. Machine Learning, 45, 5-32.

ABSTRACT: Atrial fibrillation (AF) is a leading cardiac arrhythmia associated with elevated mortality risk, particularly in low-resource settings where early risk stratification remains challenging.
IEEE

A Novel Host Based Intrusion Detection System using Supervised Learning by Comparing SVM over Random Forest

Abstract: In order to foresee future developments in host-based intrusion detection systems, this research compares the efficacy of SVM and Random Forest, two supervised-learning-based models.