Kansas lawmakers are debating a competitive imbalance in high school sports, where private schools win a disproportionate share of championships.

Every regulatory framework must choose a primary unit of risk -- model, use case, or impact produced -- to determine the starting point of regulatory oversight.

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 = ...

In this study, we explore a novel method leveraging quantum generative adversarial networks (QGANs) for data augmentation in cancer image classification tasks. We focus on the application of QGANs to ...

The paper is devoted to the optimization of data structure in classification and clustering problems by mapping the original data onto a set of ordered feature vectors. When ordering, the elements of ...

ABSTRACT: Accurate prediction of malaria incidence is indispensable in helping policy makers and decision makers intervene before the onset of an outbreak and potentially save lives. Various ...

Middle East peace, climate change, Ukraine — if Sisyphus were assigned one of today’s global problems, he’d plead to be returned to rock rolling. So let’s focus for a moment on a global challenge that ...

As organizations evolve, traditional data classification—typically designed for regulatory, finance or customer data—is being stretched to accommodate employee data. While classification processes and ...

This repository contains two basic prediction models: Credit Card Fraud Detection and Titanic Survival Prediction. Both models demonstrate the use of machine learning for binary classification tasks.