Scientific Research Publishing

H-UQ-MFF: Hybrid Uncertainty-Aware Multi-Feature Fusion for Clinically-Translatable Glaucoma Detection with FDA-Compliant Validation ()

Although artificial intelligence (AI) has demonstrated potential in automating glaucoma screening, there is still a significant obstacle in transferring research datasets to actual clinical settings.
IEEE

Comparative Evaluation of Machine-Learning Classifiers for High-Dimensional Gene Expression Data

Abstract: High-dimensional gene expression data pose substantial challenges for machine-learning–based diagnostic modelling due to extreme dimensionality, noise, heterogeneous measurement conditions, ...
Purdue University

Courses & Faculty

Purdue’s innovative Master of Science in Data Science is an accessible, skills-focused master’s designed to meet the needs of professionals who have some background in data science and want to ...
GitHub

ECS7020P Principles of Machine Learning - Mini Project

This repository contains the mini project for the ECS7020P Principles of Machine Learning course at Queen Mary University of London. This project develops an automated song recognition system capable ...
marktechpost

Nested Learning: A New Machine Learning Approach for Continual Learning that Views Models as Nested Optimization Problems to Enhance Long Context Processing

How can we build AI systems that keep learning new information over time without forgetting what they learned before or retraining from scratch? Google Researchers has introduced Nested Learning, a ...
ahajournals.org

Claims‐Based Machine Learning Classifier of Modified Rankin Scale in Acute Ischemic Stroke

We developed a classifier to infer acute ischemic stroke severity from Medicare claims using the modified Rankin Scale at discharge. The classifier can be used to improve stroke outcomes research and ...
ahajournals.org

Abstract Wed136: Integration of Mechanistic Fontan Circulatory Models with Interpretable Machine Learning Classifiers

Abstract Wed136: Integration of Mechanistic Fontan Circulatory Models with Interpretable Machine Learning Classifiers Noah Schenk, BS, Alexander Egbe, MD, MPH, Brian Carlson, PhD, and Daniel Beard, ...
ascopubs.org

Bayesian Counterfactual Machine Learning Individualizes Radiation Modality Selection to Mitigate Immunosuppression

Tumor Site–Specific Radiation-Induced Lymphocyte Depletion Models After Fractionated Radiotherapy: Considerations of Model Structure From an Aggregate Data Meta-Analysis Lymphocytes play critical ...
Scientific Research Publishing

Molnar, C. (2022) Interpretable Machine Learning. A Guide for Making Black Box Models Explainable, Self-Published.

ABSTRACT: We consider various tasks of recognizing properties of DRSs (Decision Rule Systems) in this paper. As solution algorithms, DDTs (Deterministic Decision Trees) and NDTs (Nondeterministic ...