(NASDAQ: NXXT ), a pioneer in AI-driven energy innovation transforming how energy is produced, managed, and delivered, today ...

Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease, and if it is accurately predicted ...

Introduction Application of artificial intelligence (AI) tools in the healthcare setting gains importance especially in the domain of disease diagnosis. Numerous studies have tried to explore AI in ...

In this video, we will study Supervised Learning with Examples. We will also look at types of Supervised Learning and its ...

Based Detection, Linguistic Biomarkers, Machine Learning, Explainable AI, Cognitive Decline Monitoring Share and Cite: de Filippis, R. and Al Foysal, A. (2025) Early Alzheimer’s Disease Detection from ...

A hybrid model combining LM, GA, and BP neural networks improves TCM's diagnostic accuracy for IPF, achieving 81.22% ...

Researchers developed and validated a machine-learning algorithm for predicting nutritional risk in patients with nasopharyngeal carcinoma.

Discover the power of predictive modeling to forecast future outcomes using regression, neural networks, and more for improved business strategies and risk management.

Abstract: Offloading machine learning models for network classification on high-throughput programmable switches is a promising technology, enabling line-speed in-network classification. Existing ...

This project implements a complete end-to-end pipeline for analyzing RNA-Seq gene expression data to classify different cancer types. Using the PANCAN dataset from UCI Machine Learning Repository, we ...

The advent of the internet, as we all know, has brought about a significant change in human interaction and business operations around the world; yet, this evolution has also been marked by security ...