The Lancet

Effective sample size for individual risk predictions: quantifying uncertainty in machine learning models

Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance ...
Scientific Research Publishing

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

ABSTRACT: Despite its significant impacts on climate, environment, and public health, air pollution monitoring in Africa is still sparse. This study developed a machine learning framework of four ...
IEEE

Diabetic Prediction with Significant Factors by Random Forest-A Machine Learning Approach

Abstract: Diabetes is a major disease that is prevalent in an individual throughout one's life if once occurred. Unhealthy lifestyle and food habits are the main reasons, among others, to be affected ...
IEEE

Using Random Forests for Efficient Identification of Decoys Under Link Flooding Attacks in SDNs

Abstract: Software-defined networks (SDNs) face significant challenges from link flooding attacks (LFAs), where malicious bots flood towards a limited number of hidden hosts, known as decoys, at a low ...