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Crypto Machine Learning Algorithms Explained for Beginners

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...
JD Supra

Not A Categorical Ban: Federal Circuit Narrowed Spectrum of Patent Eligible Machine Learning Claims

Recentive Analytics, Inc. v. Fox Corp., No. 23-2437 (Fed. Cir. 2025) – On April 18, 2025, the Federal Circuit upheld the district court’s dismissal of the case on the ground that the patents were ...
The Manila Times

Mr.Chongwei Shi Enhances Genomic Analysis Through Signal Processing and Machine Learning Integration for Gene Identification

A signal-processing–based framework converts DNA sequences into numerical signals to identify protein-coding regions. By integrating spectral analysis and SVM classification, the approach improves ...
Redmondmag.com

Linking a Physical GPU to a Hyper-V Virtual Machine

This step-by-step guide explains how to use Discrete Device Assignment to attach a physical GPU directly to a Hyper-V virtual machine, enabling hardware-accelerated workloads such as AI while ...

Mind-Bending Sundance Doc ‘Ghost in the Machine’ Asks the Hard Questions About AI’s Origins and Potential Misuses

Documentary filmmaker Valerie Veatch joined IndieWire's Sundance Studio to discuss her artificial intelligence essay film, "Ghost in the Machine." ‘In the Blink of an Eye’ Review: Andrew Stanton’s ...

Deep learning model can predict cardiopulmonary disease in retinal images of premature infants

A deep learning model using retinal images obtained during retinopathy of prematurity (ROP) screening may be used to predict diagnosis of bronchopulmonary dysplasia (BPD) and pulmonary hypertension ...

Mapping cell development with mathematics-informed machine learning

The development of humans and other animals unfolds gradually over time, with cells taking on specific roles and functions ...
InfoWorld

16 open source projects transforming AI and machine learning

From fine-tuning open source models to building agentic frameworks on top of them, the open source world is ripe with ...
IEEE

Series Arc Fault Identification Based on a Novel Newton-Optimized Sparse Kernel Support Vector Machine

Abstract: Existing machine learning-based methods for series arc fault (SAF) identification still suffer from slow training speed when dealing with large-scale SAF datasets. For this reason, we ...
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 ...
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 ...