Tech Xplore

Mistaken correlations: Why it's critical to move beyond overly aggregated machine-learning metrics

MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data ...

Wikipedia volunteers spent years cataloging AI tells. Now there’s a plugin to avoid them.

The source material is a guide from WikiProject AI Cleanup, a group of Wikipedia editors who have been hunting AI-generated ...
InfoQ

MIT's Recursive Language Models Improve Performance on Long-Context Tasks

Researchers at MIT's CSAIL published a design for Recursive Language Models (RLM), a technique for improving LLM performance on long-context tasks. RLMs use a programming environment to recursively ...
Digital Trends

Perplexity AI turns into tutor with new language learning feature

What’s happened? Perplexity AI just dropped a new language learning feature built right into its platform. In a post shared on social media, the company announced a tool that helps users learn by ...
TheServerSide

Google Machine Learning Certification Sample Questions

Community driven content discussing all aspects of software development from DevOps to design patterns. The Google Cloud Professional Machine Learning Engineer certification validates your ability to ...
TheServerSide

Sample Questions for AWS' Machine Learning Associate Certification

Community driven content discussing all aspects of software development from DevOps to design patterns. The AWS Machine Learning Associate certification validates your ability to configure, build, and ...
ahajournals.org

Abstract 16388: Feature Selection Using Recursive Feature Elimination (RFE): Machine Learning Application in Predicting Permanent Pacemaker Implantation (PPMI) in Patients ...

Introduction: TAVR is increasingly being performed because of several recent large-scale clinical trials supporting its use across a range of patient sub-classes. Although it provides life-altering ...
BMJ

Advancing Alzheimer’s disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study

Objectives Alzheimer’s disease (AD) poses a significant challenge for individuals aged 65 and older, being the most prevalent form of dementia. Although existing AD risk prediction tools demonstrate ...
IEEE

Comparative Analysis of Feature Selection Techniques for Blood Sample-Based Multiple Disease Diagnosis Using Machine Learning

Abstract: Omitting feature selection leads to large increases in prediction error for disease diagnosis models from machine learning. The proposed study implies the ...
Frontiers

Ensemble learning techniques reveals multidimensional EEG feature alterations in pediatric schizophrenia

Schizophrenia (SCZ) is a severe mental disorder that impairs brain function and daily life, while its early and objective diagnosis remains a major clinical challenge due to the reliance on subjective ...