Results 71 to 80 of about 5,065 (216)

A Survey for Deep Reinforcement Learning Based Network Intrusion Detection

Applied AI Letters, Volume 7, Issue 2, June 2026.
This paper surveys deep reinforcement learning (DRL) for network intrusion detection, evaluating model efficiency, minority attack detection, and dataset imbalance. Findings show DRL achieves state‐of‐the‐art results on public datasets, sometimes surpassing traditional deep learning.
Wanrong Yang, Alberto Acuto, Yihang Zhou, Dominik Wojtczak   +3 more
wiley   +1 more source

A model‐driven robust deep learning wireless transceiver

IET Communications, 2021
Recently, deep learning (DL) has been successfully applied in computer vision and natural language processing. The communication physical layer based on deep learning has received widespread attention.
Sirui Duan, Jingyi Xiang, Xiang Yu
doaj   +1 more source

Fibroblast Transcriptomics in Molecular Diagnostics of a Comprehensive Dystonia Cohort

Annals of Neurology, Volume 99, Issue 6, Page 1363-1378, June 2026.
Objective Genomic sequencing leaves >50% of dystonia‐affected individuals without a diagnosis. Where DNA‐oriented approaches remain insufficient, integrating multiomics is essential to advance genome interpretation. Herein, we incorporated RNA sequencing (RNA‐seq) data from 167 patients with dystonia across a range of ages and presentations. Methods We
Alice Saparov, Ivana Dzinovic, Theresa Brunet, Vicente A. Yépez, Florian Hölzlwimmer, Elisabetta Indelicato, Birgit Assmann, Susann Badmann, Diana Ballhausen, Steffen Berweck, Felix Brechtmann, Melanie Brugger, Kevork Derderian, Felix Distelmaier, Philip Harrer, Denisa Harvanova, Petra Havrankova, Ann‐Kathrin Jaroszynski, Miriam Kolnikova, Robert Kopajtich, Anne Koy, Magdalena Krygier, Lukas Kunc, Katarina Kusikova, Oliver Maier, Maria Mazurkiewicz‐Bełdzińska, Christian Mertes, Ava Oberlack, Timo Roser, Alexandra Sitzberger, Ugo Sorrentino, Antonia M. Stehr, Katharina Vill, Matias Wagner, Holger Prokisch, Sylvia Boesch, Jan Necpal, Robert Jech, Juliane Winkelmann, Elisabeth Graf, Julien Gagneur, Matej Skorvanek, Michael Zech   +42 more
wiley   +1 more source

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

The autoencoder structure.

, 2023
Food safety problems are becoming increasingly severe in modern society, and establishing an accurate food safety risk warning and analysis model is of positive significance in avoiding food safety accidents.
Hua Li (46469), Cheng Chen (136164), Huiti Jiang (15361235), Jie Zhong (39213), Enguang Zuo (15361232), Xiaoyi Lv (8671755), Chen Chen (6544), Lei Sun (45752)   +7 more
core   +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

Diffusional magnetic resonance imaging anonymizing with variational autoencoder

Quantitative Biology, Volume 14, Issue 2, June 2026.
Abstract Anonymization is a crucial de‐identification technique that protects data privacy while ensuring its utility for model building. Current generative models such as generative adversarial networks and variational auto‐encoders (VAEs) have been applied to medical image anonymization but mainly focus on general image features, lacking specificity ...
Yunheng Shen, Ling Zheng, Ruohan Liu, Haoran Feng, Hairong Lv   +4 more
wiley   +1 more source

Out-of-Roundness Wheel Damage Identification in Railway Vehicles Using AutoEncoder Models

Applied Sciences
This study presents a comparative analysis of three AutoEncoder (AE) models—Variational AutoEncoder (VAE), Sparse AutoEncoder (SAE), and Convolutional AutoEncoder (CAE)—to detect and quantify structural anomalies in railway vehicle wheels, such as ...
Renato Melo, Rafaelle Finotti, António Guedes, Vítor Gonçalves, Andreia Meixedo, Diogo Ribeiro, Flávio Barbosa, Alexandre Cury   +7 more
doaj   +1 more source

Pediatric Risk Mapping From Co‐Exposure to Extreme Temperatures and Air Pollutants

GeoHealth, Volume 10, Issue 6, June 2026.
Abstract Exposure to extreme temperatures and fine particulate matter is hazardous to human health, especially among children. With growing evidence on the impact of these environmental hazards on child health—including increased risks of respiratory illnesses, heat‐related illnesses, and developmental challenges—there is a substantial need to identify
Jagadeesh Puvvula, Jonathan J. Szeto, Jabeen Taiba, Jennifer Ko, Jesse E. Bell, Wei‐Ting Hwang, Rebecca A. Simmons, Marilyn Howarth, Sameed Ahmed M. Khatana, Kyle Jackson, Aimin Chen   +10 more
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