IEEE

Wrapper Feature Selection: Comparative Study of Binary Metaheuristic Algorithms

Abstract: Feature selection is one of the major challenges in machine learning problems. It reduces the number of features and improves performance by removing the irrelevant and unnecessary ones.
Tech Xplore

New federated learning algorithm enables private, robust, and fast AI development

Three heads are better than one. Versions of this proverb are found worldwide and throughout history. Yet in the race to ...

Optimizing RNA design with AI and an Ising machine: Encoding matters

RNA has emerged as one of the most promising molecules in modern medicine, enabling advances from mRNA vaccines and gene ...
IEEE

Hybrid Deep Machine Learning Feature Selection for High-Dimensional Cybersecurity Data

Abstract: The rapid increase in cyber threats has heightened the demand for Intrusion Detection Systems (IDS) that are both accurate and efficient. While deep learning models outperform traditional ...
The Lancet

Development and validation of a generalisable machine learning algorithm for identifying interstitial lung disease cohorts: a retrospective cohort study

aDepartment of Medicine, University of California San Francisco, San Francisco, CA, USA bDepartment of Medicine, University of California Los Angeles, Los Angeles, CA, USA cDepartment of Medicine, ...
JD Supra

The Generative AI Illusion: Why We Are Accidentally Reverting to TAR 1.0

Dr. Jeremy Pickens, Managing Director of Applied Science at Elevate and recipient of the 2025 Relativity AI Visionary Award, ...