American Institute of Physics

Predicting stroke recovery motor progress using EEG data

A machine learning model trained on EEG data from patients recovering from strokes helps predict how new patients will regain ...
Visual Studio Magazine

Linear Regression with Pseudo-Inverse Training Using JavaScript

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression with pseudo-inverse training implemented using JavaScript. Compared to other training techniques, such as ...

AI model more accurately predicts cardiac event risk from PET scan data

Cardiovascular disease continues to be the leading cause of death worldwide. To save lives, constantly improving diagnostic ...
IEEE

A Wasserstein Distributionally Robust Multiclass Support Vector Machine for Industrial Fault Diagnosis

Abstract: Modern fault diagnosis models that rely on machine learning and AI tools have been beset by three key challenges: first, highly uncertain training data due to sensor noise and potentially ...
TweakTown

FSR 4 support comes to SteamOS and Proton, hinting at Steam Machine support

TL;DR: VKD3D-Proton 3.0 introduces AMD FSR 4 support and Anti-Lag for improved DirectX 12 game compatibility on Linux and SteamOS. Officially optimized for RDNA 4 GPUs, it includes fallback for older ...
The Cornell Daily Sun

BURZLAFF | The Basics (3): Learning with Machines — Four Principles for Using AI at Cornell

Every semester now begins with the same quiet contradiction. One syllabus declares, “No ChatGPT allowed.” Another encourages it as a research aid. And in between, most of us use it anyway. In a recent ...
InfoWorld

How to deploy machine learning models with AWS Lambda

AWS Lambda provides a simple, scalable, and cost-effective solution for deploying AI models that eliminates the need for expensive licensing and tools. In the rapidly evolving landscape of artificial ...
IEEE

Quantum Machine Learning for Image Classification: A Hybrid Model of Residual Network with Quantum Support Vector Machine

Abstract: Recently, there has been growing attention on combining quantum machine learning (QML) with classical deep learning approaches as computational techniques are key to improving the ...
Frontiers

Support vector machine-based classification of bulimia nervosa using diffusion tensor imaging

Background: Alterations in brain structure have been suggested to be associated with bulimia nervosa (BN). This study aimed to employ machine learning (ML) methods based on diffusion tensor imaging ...
Frontiers

Assessing the learning potential of freshmen in labor education courses using ordinal features and support vector machine

Introduction: Artificial intelligence (AI) marks a new wave of the information technology revolution and permeates various sectors as an indispensable tool. Despite its widespread adoption, its ...
Live Science

Scientists use quantum machine learning to create semiconductors for the first time – and it could transform how chips are made

Researchers have found a way to make the chip design and manufacturing process much easier — by tapping into a hybrid blend of artificial intelligence and quantum computing. When you purchase through ...
Wired

This AI Model Never Stops Learning

Modern large language models (LLMs) might write beautiful sonnets and elegant code, but they lack even a rudimentary ability to learn from experience. Researchers at Massachusetts Institute of ...