High-speed AFM imaging reveals how brain enzyme forms a dodecameric ring structure

Kanazawa University, have captured real-time images showing how a key brain enzyme organizes itself to help memory formation.
IEEE

Sparse Autoencoder-Based Deep Learning Approach for Enhancing End-to-End Communication System Performance

Deep Learning (DL) has emerged as a transformative approach in artificial intelligence, demonstrating remarkable capabilities in solving complex problems once considered unattainable. Its ability to ...
EurekAlert!

Label attention meets deep learning: a novel model for ICD-11 automatic classification of Chinese EMRs

BiLSTM, an ICD-11 automatic coding model using MC-BERT and label attention. Experiments on clinical records show 83.86% ...

No Code Autonomous AI Research Assistant for Deep Web Research

Create a no-code AI researcher with two research modes and verifiable links, so you get quick answers and deeper findings ...

Deep learning creates virtual multiplexed immunostaining to improve cancer diagnosis

Researchers at the University of California, Los Angeles (UCLA), in collaboration with pathologists from Hadassah Hebrew ...

System can diagnose infections in 20 minutes, aiding fight against drug resistance

A new technique which slashes the time taken to diagnose microbial infections from days to minutes could help save lives and ...
Frontiers

Multi-perspective hotel operation process anomaly prediction method based on graph transformer and autoencoder

Due to the complexity of hotel operation processes, abnormal situations are inevitable, making proactive anomaly prediction essential for ensuring operational stability. Although current deep learning ...
Scientific Research Publishing

Traffic Speed Prediction Based on Autoencoder and Deep Learning ()

Traffic prediction is the core of intelligent transportation system, and accurate traffic speed prediction is the key to optimize traffic management. Currently, the traffic speed prediction model ...
IEEE

Deep Variational Autoencoder Based Parameter Learning of Bayesian Network with Multiple Latent Variables

Abstract: Intelligent systems could be increasingly powerful by applying probabilistic inferences over the dependence relations among observed and latent variables, which could be represented by the ...