IEEE

Leveraging Hybrid Ensemble Framework for Diabetes Prediction: Integrating Random Forest, ANN, ETC, and Stack-CNN

Abstract: Mellitus (DM), or diabetes, is a dominant universal medical affair dilemma harming a growing number of people around the world and putting them at severe risk. Diabetes is a long-lasting ...

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 ...

Pharmacy professor develops AI model to predict hospitalization of at-risk cardiac patients

When Arinze Nkemdirim Okere, PharmD, MBA, worked as the pharmacist for a hospital in Tallahassee, Florida, he noticed that ...
Oak Ridge National Lab

A machine learning approach to quantify degradation of nuclear fuels and the effects of fission products

Nuclear fuel performance is critically dependent on understanding the evolution of fuel properties under operational conditions, a complex challenge driven by chemical changes and substantial ...
IEEE

Improving Accuracy of Diabetes Prediction Using Machine Learning Techniques and Clinical Data

Abstract: Diabetes is a chronic condition affecting millions of people globally, requiring early and accurate prediction to mitigate associated health risks. This study employed a Kaggle dataset with ...
Frontiers

Classification of angioedema types using decision tree modeling

Introduction: All angioedema (AE) presents with transient, localized swelling; however, the underlying causes, prognosis, and treatments vary significantly. Consequently, identifying a specific AE ...
Frontiers

A robust ammonia metabolism gene signature identified by machine learning predicts prognosis and immunotherapy response in clear cell renal cell carcinoma

Background: Despite the widespread use of immune checkpoint inhibitors (ICIs) in advanced clear cell renal cell carcinoma (ccRCC), therapeutic resistance persists. The prognostic and immunomodulatory ...
GitHub

fares279/garment-productivity-prediction

A comprehensive machine learning solution for predicting productivity in garment manufacturing facilities using historical production data. This project enables factory managers to optimize resource ...