Multi-stage dual-domain progressive network enhances sparse-view CT reconstruction

A research team led by Professor Wang Hongzhi from the Hefei Institute of Physical Science of the Chinese Academy of Sciences has developed a multi-stage, dual-domain, progressive network with ...
Tech Xplore

Decoding the shadows: Vehicle recognition software uncovers unusual traffic behavior

Researchers at the Department of Energy's Oak Ridge National Laboratory have developed a deep learning algorithm that ...

AI-powered compressed imaging system developed for high-speed scenes

A research team from the Xi'an Institute of Optics and Precision Mechanics (XIOPM) of the Chinese Academy of Sciences, along with collaborators from the Institute National de la Recherche Scientifique ...
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 ...
GitHub

SARMAE: Masked Autoencoder for SAR Representation Learning

Synthetic Aperture Radar (SAR) imagery plays a critical role in all-weather, day-and-night remote sensing applications. However, existing SAR-oriented deep learning is constrained by data scarcity, ...
Beebom

What is Deep Learning in AI? Understanding How AI Really Works

Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter adjustments. It started with the ...
Scientific Research Publishing

Al-Qatf, M., Lasheng, Y., Al-Habib, M. and Al-Sabahi, K. (2018) Deep Learning Approach Combining Sparse Autoencoder with SVM for Network Intrusion Detection. IEEE Access, 6 ...

ABSTRACT: This work contributes to the development of intelligent data-driven approaches to improve intrusion management in smart IoT environments. The proposed model combines a hybrid ...
Scientific Research Publishing

Xu, J., Xiang, L., Liu, Q., Gilmore, H., Wu, J., Tang, J., et al. (2016) Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images. IEEE ...

ABSTRACT: Liver cancer is one of the most prevalent and lethal forms of cancer, making early detection crucial for effective treatment. This paper introduces a novel approach for automated liver tumor ...
IEEE

Online Public Opinion Situation Assessment by Deep Sparse Autoencoder and Time-Series Modeling Convolutional Neural Network

Abstract: We have developed an innovative framework for analyzing online public sentiment, referred to as the Online Public Opinion Situation Assessment (OPOSA) model. This model employs Deep Sparse ...
Frontiers

BANSMDA: a computational model for predicting potential microbe-disease associations based on bilinear attention networks and sparse autoencoders

Introduction: Predicting the relationship between diseases and microbes can significantly enhance disease diagnosis and treatment, while providing crucial scientific support for public health, ...
GitHub

Enhancing EV Cost Prediction Using AutoEncoder-Based Feature Learning

Electric Vehicle (EV) cost prediction involves analyzing complex, high-dimensional data that often contains noise, multicollinearity, and irrelevant features. Traditional regression models struggle to ...