Frontiers

Early detection of brain pathologies through deep learning and advanced neuroimaging approaches

Recent advances in the field of medical imaging and computational neuroscience have transformed the landscape of brain pathology detection. The application ...
Nature

Deep learning-based high-speed railway communication systems

High-speed railway wireless communication systems are characterized by severe Doppler shifts and fast time-varying multipath, which challenge reliable connectivity in Long-Term Evolution for Railways ...
GitHub

graph-autoencoder

This project detects structural network anomalies using a GNN autoencoder. It contrasts this deep learning approach with the classic DBSCAN method. While DBSCAN only uses node features (CPU, RAM), the ...
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

Depression Detection Through EEG Signal Analysis: A Convolutional Autoencoder Deep Learning Model

Abstract: Depression is a debilitating and enervating mental health disorder that requires attention for necessitating accurate and efficient diagnostic techniques ...
The New York Times

This Kind of Sleep Is Essential for a Healthy Brain

Two particular phases in your nightly routine seem to play outsize roles in cognitive health. By Mohana Ravindranath A good night’s sleep isn’t just about the number of hours you log. Getting quality ...
Nature

Ensemble of feature augmented convolutional neural network and deep autoencoder for efficient detection of network attacks

An Intrusion Detection System (IDS) is a type of device that continuously observes system behaviour in promiscuous mode in order to collect network data for further analysis. The NIDS is an essential ...
LinkedIn

DeepSig Autoencoders And Meta-learning systems like DNDR (Deep Neural Decoder with Reinforcement): A Deep Dive

DeepSig employs deep learning-based autoencoders to revolutionize communication system design by optimizing both encoding and decoding processes in an end-to-end manner. This fundamentally departs ...
IEEE

TDAE: Tensored Deep Autoencoder for Classification of Hyperspectral Images

Abstract: Deep learning has achieved outstanding success in the hyperspectral image (HSI) classification task. Almost all the current deep learning methods are used to conduct classification ...
GitHub

Deep Convolutional Autoencoder for Dimensionality Reduction of 3D CAD Models

CAD-DR is a deep learning-based system for dimensionality reduction of 3D CAD models using a 3D convolutional autoencoder. The system supports full STL to voxel transformation, encoding, ...
LinkedIn

A Deep Dive into Quantum-Enhanced Variational Autoencoder for Synthetic Data Creation

In this article, we will explore the workings of a Quantum-Enhanced Variational Autoencoder (VAE) designed for synthetic data creation, specifically using MNIST data as an example. This guide will ...