AI helps find trees in a forest: Researchers achieve 3D forest reconstruction from remote sensing data

Existing algorithms can partially reconstruct the shape of a single tree from a clean point-cloud dataset acquired by ...

AI model detects prediabetes using ECG data without need for blood tests

DiaCardia, a novel artificial intelligence model that can accurately identify individuals with prediabetes using either ...
Frontiers

Multidimensional dietary assessment and interpretable machine learning models predict the risk of prediabetes/diabetes and osteoporosis comorbidity in older adults

Background: The health burden of diabetes mellitus and osteoporosis (DM-OP) comorbidity in the aging population is increasing, and dietary factors are modifiable risk determinants. This study ...
Frontiers

Development and validation of machine learning-based diagnostic models using blood transcriptomics for early childhood diabetes prediction

Background: Early identification of Type 1 Diabetes Mellitus (T1DM) in pediatric populations is crucial for implementing timely interventions and improving long-term outcomes. Peripheral blood ...
Scientific Research Publishing

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

ABSTRACT: Accurate canopy height estimation is critical for forest management and carbon monitoring in Zambia’s ecologically diverse landscapes. This study developed a high-resolution canopy height ...
IEEE

Diabetes Prediction Using Machine Learning Algorithm: A Comparative Analysis

Abstract: Diabetes prediction is an essential task in healthcare that could be achieved through Machine Learning models. Several factors contribute to diabetes such as overweight, high cholesterol ...
GitHub

random-forest-algorithm

Design and Implementation of Random Forest algorithm from scratch to execute Pacman strategies and actions in a deterministic, fully observable Pacman Environment. This is a Machine Learning model ...