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

A machine learning model incorporating functional assessments predicts one-year mortality in older patients with HF and improves risk stratification beyond established scores. Functional status at ...

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 ...

Schizophrenia is a severe and often highly debilitating psychiatric disorder characterized by distorted emotions, thinking patterns and altered perceptions of reality, as well as mental impairments.

These new AI assistants can analyze design constraints and create custom machine learning models as well as read, import and ...

In a study titled Recent Applications of Machine Learning Algorithms for Pesticide Analysis in Food Samples, published in the ...

Dengue and chikungunya, the two mosquito-borne diseases that frequently circulate at the same time, share the same Aedes ...

SCAN project aims to build European GNSS-based and AI-driven technologies to detect and assess roadway pavement problems.

What is the Role of Agentic AI in DevOps Security? How can organizations ensure the security of machine identities and secrets? A comprehensive security strategy, encompassing Non-Human Identities ...

Integrating deep learning in optical microscopy enhances image analysis, overcoming traditional limitations and improving classification and segmentation tasks.

Why Are Machine Identities Essential for Data Security in the Cloud? Where cloud environments have become the backbone of modern enterprises, securing data requires more than just human oversight.

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...