Results 61 to 70 of about 3,896 (184)

AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder and Regularization by Denoising

IEEE Transactions on Geoscience and Remote Sensing
Spectral unmixing has been extensively studied with a variety of methods and used in many applications. Recently, data-driven techniques with deep learning methods have obtained great attention to spectral unmixing for its superior learning ability to automatically learn the structure information.
Min Zhao 0014, Jie Chen 0022, Nicolas Dobigeon   +2 more
openaire   +3 more sources

Explaining anomalies through semi-supervised Autoencoders

Array
This work tackles the problem of designing explainable by design anomaly detectors, which provide intelligible explanations to abnormal behaviors in input data observations.
Fabrizio Angiulli, Fabio Fassetti, Luca Ferragina, Simona Nisticò   +3 more
doaj   +1 more source

Deep Learning‐Based Multimodal Fusion of Whole‐Slide Images and RNA Sequencing Identifies Survival‐Relevant Glioblastoma Clusters

Cancer Medicine, Volume 15, Issue 8, August 2026.
ABSTRACT Glioblastoma is profoundly heterogeneous, and single‐modality analyses often miss prognostically relevant structure. We introduce a transparent, end‐to‐end workflow that fuses available whole‐slide histology and RNA‐seq to discover clinically meaningful glioblastoma subgroups using an unsupervised learning model after feature extraction ...
Amin Zadeh Shirazi, Guillermo A. Gomez
wiley   +1 more source

WOT-AE: Weighted Optimal Transport Autoencoder for Patterned Fabric Defect Detection

Symmetry
Patterned fabrics are characterized by strong periodic and symmetric structures, and defect detection in such materials is essentially the task of identifying local disruptions of global texture symmetry. Conventional low-rank decomposition methods separate defect-free regions as low-rank and defects as sparse components, yet singular value ...
Hui Yang, Linyan Kang, Tianjin Yang
openaire   +1 more source

Column-Wise Autoencoder Representation Learning for Intrusion Detection in Multi-MEC Edge Networks

Applied Sciences
Mobile Edge Computing (MEC) is a key enabler of 5G/6G services, but multi-base-station deployment enlarges the attack surface and motivates edge-native intrusion detection systems (IDSs).
Min-Gyu Kim, Jonghyun Kim
doaj   +1 more source

Conditional Generative Modeling for Enhanced Credit Risk Management in Supply Chain Finance

Naval Research Logistics (NRL), Volume 73, Issue 5, Page 719-742, August 2026.
ABSTRACT The rapid expansion of cross‐border e‐commerce (CBEC) has created significant opportunities for small‐ and medium‐sized sellers, yet financing remains a critical challenge due to their limited credit histories. Third‐party logistics (3PL)‐led supply chain finance (SCF) has emerged as a promising solution, leveraging in‐transit inventory as ...
Qingkai Zhang, L. Jeff Hong, Houmin Yan
wiley   +1 more source

An abnormal traffic detection method for chain information management system network based on convolutional neural network

Frontiers in Physics
Chain information management system is widely used, providing convenience for the operation and management of enterprises. However, the problem of abnormal network traffic becomes increasingly prominent currently.
Chao Liu, Chunxiang Liu, Changrong Liu
doaj   +1 more source

AI Based Velocity Calibration for Cassini's Cosmic Dust Analyzer (CDA)

Earth and Space Science, Volume 13, Issue 8, August 2026.
Abstract This paper presents a novel AI‐based velocity calibration method for the Cosmic Dust Analyzer (CDA) onboard the Cassini spacecraft, in particular for impacts on the large Impact Ionization Detector (IID). The legacy polynomial calibration function, derived from laboratory measurements at a dust accelerator facility, assigns a velocity ...
Thomas Albin, Jonas Simolka, Ralf Srama
wiley   +1 more source

AE‐Integrated: Real‐time network intrusion detection with Apache Kafka and autoencoder

Concurrency and Computation: Practice and Experience
SummaryUnknown cyber‐attack detection in network traffic streams is challenging but crucial to ensure network security. It is observed that new security threats occur on a daily basis and make cyberspace vulnerable. In the literature, machine learning and deep learning‐based network intrusion detection systems have gained a lot of success but still ...
Khushnaseeb Roshan, Aasim Zafar
openaire   +1 more source

Seismic Insights Into the Role of Rockfall in Rockslide Destruction Processes

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract The dynamic link between rockslide failure and rockfall activity remains elusive, primarily due to the challenge of detecting weak signals amidst high noise. We propose a seismic attribute guided deep learning framework that formulates rockfall detection as a time‐series segmentation task, facilitating the precise extraction of rockfall events
Yaojun Wang, Yuxi Chen, Tuo Zhang, Hai Huang, Qi Zhou   +4 more
wiley   +1 more source