Open Heart

Artificial intelligence-based clustering to identify functional risk phenotypes in heart failure

Background Patients with heart failure (HF) frequently suffer from undetected declines in cardiorespiratory fitness (CRF), which significantly increases their risk of poor outcomes. However, current ...

Quantum algorithm beats classical tools on complement sampling tasks

Quantum computers—devices that process information using quantum mechanical effects—have long been expected to outperform classical systems on certain tasks. Over the past few decades, researchers ...
Tobacco Control

Pro-tobacco legislative representation and current smoking: an individual-level panel study of older adults in Japan

Using longitudinal data on more than 370 000 older Japanese adults, we found that living in constituencies represented by pro-tobacco legislators was associated with higher smoking prevalence. The ...
IEEE

Sample Denoising and Optimization Technique Based on Noise Filtering and Evolutionary Algorithms for Imbalanced Data Classification

Abstract: Imbalanced data remains a challenge in classification research and significantly influences classifier performance. The strategy that is widely used to address this issue is the data-level ...
IEEE

An Improved Sample Adaptation-Based Algorithm for Soil Prediction Driven by Environment Covariates

Abstract: Conventional machine learning approaches in precision agriculture improve soil prediction but fail to account for feature congruence between samples and prediction environments, resulting in ...
GitHub

Custom Clustering Algorithm without Machine Learning

This project focuses on developing a custom clustering algorithm to analyze wine data, providing an alternative to conventional machine learning techniques. The primary objective is to group wine ...
GitHub

A comprehensive implementation of self-supervised reinforcement learning algorithms including contrastive learning, predictive models, and representation learning for RL tasks.

Self-supervised reinforcement learning is a technique where agents learn useful representations and skills from the environment through self-generated tasks, such as predicting next states or learning ...