AHA/ASA Journals

Contrastive Machine Learning to Quantify Hypertensive Multiorgan Damage and Identify New Disease Phenotypes: A Multinational Multimodal Study

BACKGROUND: Hypertension induces structural and functional damage in multiple organs. Evidence of subclinical damage ...
IEEE

Fusion of Supervised Feature Selection and Unsupervised Clustering for Multinuclei Classification of MER Signals in DBS for Parkinson’s Disease

Abstract: In deep brain stimulation (DBS) surgery for Parkinson’s disease (PD), the accurate intraoperative identification of key nuclei—such as the subthalamic nucleus (STN)—is critical to ensuring ...
IEEE

Unsupervised Feature Selection-Driven Active Learning for Semi-Supervised Automatic ECG Analysis

Abstract: Automatic analysis methods of electrocardiograms (ECGs) usually required large-scale annotated training data, but the annotation process is extremely time-consuming. While semi-supervised ...
Nature

Supervised information gain-based feature selection for multimodal physiological signals in stress prediction

Feature selection plays a critical role in identifying the most informative signals across multiple modalities in physiological datasets. While correlation-based approaches have traditionally been ...
Nature

Collaborative representation and confidence-driven semi-supervised learning for hyperspectral image classification

Hyperspectral image (HSI) classification faces challenges in diverse scenarios due to spectral-spatial complexity and class imbalance. Existing methods lack generalizability. This paper presents a ...
The New York Times

Why the New Cannabis Classification Matters

Moving cannabis to a category of drugs that includes some common medicines will have implications for research, businesses and patients. By Jan Hoffman President Trump on Thursday ordered cannabis to ...
LinkedIn

Supervised Learning

Supervised learning is a fundamental machine learning paradigm in which an algorithm learns from labeled training data to make predictions or decisions. In supervised learning, the algorithm is ...
LinkedIn

Feature Selection in Supervised Machine Learning

Feature Selection is the process of reducing the number of input variables and choosing the best one to go while developing a predictive model. It is necessity to reduce the number of input variables ...