Artificial intelligence systems that look nothing alike on the surface are starting to behave as if they share a common ...

Machine learning requires humans to manually label features while deep learning automatically learns features directly from raw data. ML uses traditional algorithms like decision tress, SVM, etc., ...

This repository contains the implementation of the paper of paper Deep Reinforcement Learning for Service Function Chain Placement with Graph Attention and Transformer Encoder. In this paper, we ...

Objectives: Sturge-Weber syndrome (SWS) is a congenital neurological disorder occurring in the early childhood. Timely diagnosis of SWS is essential for proper medical intervention that prevents the ...

This study proposes an automated tongue analysis system that combines deep learning with traditional Chinese medicine to enhance the accuracy and objectivity of tongue diagnosis. The system includes a ...

I am currently doing a small research for my study on Sparse Transfer Learning and SparseML library is a good approach for my work. My topic is about applying sparse transfer learning on different ...

Introduction: Artificial intelligence algorithms can help understand and predict the complex interactions between dietary intake and health outcomes, especially from large datasets. Precision ...

Abstract: In the medical field, identifying skin cancerous areas and characteristics in dermoscopy images enhances automatic skin cancer classification and diagnosis. However, due to an absence of ...