Advancing Neurological Disease Prediction through Machine Learning Techniques

A study explores how AI and ML can improve early detection of neurological diseases, including Parkinson’s disease, ...
Tech Xplore

Self-driving cars: Near-miss driving data can expedite AV algorithm training

The safety performance of autonomous vehicle (AV) algorithms is boosted by 90% using simulation data that better incorporates ...

Google's TabFM skips per-dataset training and still predicts on tables it's never seen

Google's TabFM skips per-dataset training and still predicts on unseen tables, matching tuned baselines and cutting pipeline ...
NERDBOT

AI Model Selection: A Complete Guide to Choosing the Right AI Model for Your Business

Artificial Intelligence (AI) is transforming industries by automating processes, improving decision-making, and enhancing ...
RMD Open

Development and assessment of an assisted diagnosis model using machine learning for identifying adult-onset Still’s disease in fever of unknown origin: a retrospective …

Background Adult-onset Still’s disease (AOSD) is a systemic autoinflammatory disorder lacking a gold-standard diagnostic ...
IEEE

LASSO-mCGA: Machine Learning and Modified Compact Genetic Algorithm-Based Biomarker Selection for Breast Cancer Subtype Classification

Abstract: Breast cancer is the most common cancer type among females and is one of the leading causes of death worldwide. Being a heterogeneous disease, subtyping breast cancer plays a vital role in ...
bjo.bmj

Deep learning detection of diabetic retinopathy in Scotland’s diabetic eye screening programme

Background/Aims Support vector machine-based automated grading (known as iGradingM) has been shown to be safe, cost-effective and robust in the diabetic retinopathy (DR) screening (DES) programme in ...
Journal of Medical Internet Research

Mortality Risk Prediction in Patients With Antimelanoma Differentiation–Associated, Gene 5 Antibody–Positive, Dermatomyositis–Associated Interstitial Lung Disease ...

Objective: This study aimed to develop and validate a risk prediction model of 3-month mortality using machine learning (ML) in a large multicenter cohort of patients with anti-MDA5+DM-ILD in China.
IEEE

Learning the Optimal Discriminant SVM With Feature Extraction

Abstract: Subspace learning and Support Vector Machine (SVM) are two critical techniques in pattern recognition, playing pivotal roles in feature extraction and classification. However, how to learn ...