ISACA

Combating Bias in Learning Models

Machine learning (ML) models now influence decisions that were once reserved solely for human judgement: credit approvals, hiring outcomes, insurance pricing, and loan default predictions. When these ...
Frontiers

Advanced AI for missing modality learning and uncertainty‑aware medical image analysis

Modern medical imaging increasingly relies on artificial intelligence to support detection, diagnosis, and prognostic ...
AZoM

Berkeley Lab Uses Advanced Machine Learning to Optimize Chiral Perovskites

Chiral 2D metal halide perovskites (MHPs) are among the most promising materials for future technologies that exploit the ...
EurekAlert!

Bridging machine learning and COSMO-SAC for accurate prediction of infinite dilute activity coefficients of binary mixtures

Solvents play an indispensable role in numerous chemical processes, including gas absorption, extraction, and reactions, which makes solvent selection one of the critical decisions in early-stage ...
The New York Times

Teaching and Learning in the Age of A.I.

Re “Tech Giants Racing to Add A.I. to Schools Around the World” (Business, Jan. 5): With the proliferation of A.I. tools and the push for their adoption in schools, there has never been a greater need ...
Fox News

USPS worker found dead inside mailing machine in Michigan: 'Deeply saddened by the loss'

A U.S. Postal Service employee died after he became stuck inside a mail handling machine at a distribution center in Allen Park, Michigan, according to officials. Nicholas John Acker, 36, was stuck in ...
Visual Studio Magazine

Tsetlin Machine Binary Classification Using C#

The goal of a machine learning binary classification problem is to predict a variable that has exactly two possible values. For example, you might want to predict the sex of a company employee (male = ...
IEEE

Safe and Robust Binary Classification and Fault Detection Using Reinforcement Learning

Abstract: In this paper, we propose a learning-based method utilizing the Soft Actor-Critic (SAC) algorithm to train a binary Support Vector Machine (SVM) classifier. This classifier is designed to ...
GitHub

Efficient Binary Video Classification on a Local GPU-Enabled PC Using PyTorch

This repository provides an efficient binary video classification pipeline using PyTorch, optimized for local GPU-enabled PCs. It includes preprocessing and model inference tools for classifying ...
Frontiers

Migraine triggers, phases, and classification using machine learning models

In many countries, patients with headache disorders such as migraine remain under-recognized and under-diagnosed. Patients affected by these disorders are often unaware of the seriousness of their ...
IEEE

Advanced Receiver Operating Characteristic Curve Analysis to Identify Outliers in Binary Machine Learning Classifications for Precision Medicine

Abstract: The Receiver Operating Characteristic (ROC) curve is a critical tool for binary classification analysis in medicine, with the Area Under the ROC Curve (AUROC) serving as a widely accepted ...