Results 61 to 70 of about 3,700 (193)
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 +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
FM-AE: Frequency-masked Multimodal Autoencoder for Zinc Electrolysis Plate Contact Abnormality Detection [PDF]
Comment: 2023 The 34th Chinese Process Control Conference (CPCC 2023)
Canzong Zhou +3 more
openalex +3 more sources
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 +4 more
wiley +1 more source
Weibull‐Neural Network Framework for Wind Turbine Lifetime Monitoring and Disturbance Identification
Wind Energy, Volume 29, Issue 4, April 2026.ABSTRACT Wind turbines are vital for sustainable energy, yet their reliability under diverse operational and environmental conditions remains a challenge, often leading to costly failures. This study presents a novel Weibull‐Neural Network Framework to enhance wind turbine lifetime monitoring by estimating reliability (R(t)) and mean residual life (MRL)
Fatemeh Kiadaliry +2 more
wiley +1 more source
Financial Time Series Uncertainty: A Review of Probabilistic AI Applications
Journal of Economic Surveys, Volume 40, Issue 2, Page 915-953, April 2026.ABSTRACT Probabilistic machine learning models offer a distinct advantage over traditional deterministic approaches by quantifying both epistemic uncertainty (stemming from limited data or model knowledge) and aleatoric uncertainty (due to inherent randomness in the data), along with full distributional forecasts.
Sivert Eggen +4 more
wiley +1 more source
Journal of Electrical and Computer EngineeringIn recent machine learning applications, promising outcomes have emerged through the integration of Deep Learning (DL) and Extreme Learning Machine (ELM) techniques with wavelet networks (WN), leading to high classification accuracy.
Salwa Said +4 more
doaj +1 more source
AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder and Regularization by Denoising [PDF]
, 2023 Min Zhao, Jie Chen, Nicolas Dobigeon
openalex +1 more source
An Overview of Deep Learning Techniques for Big Data IoT Applications
International Journal of Communication Systems, Volume 39, Issue 4, 10 March 2026.Reviews deep learning integration with cloud, fog, and edge computing in IoT architectures. Examines model suitability across IoT applications, key challenges, and emerging trends Provides a comparative analysis to guide future deep learning research in IoT environments.
Gagandeep Kaur +2 more
wiley +1 more source
Explaining anomalies through semi-supervised Autoencoders
ArrayThis work tackles the problem of designing explainable by design anomaly detectors, which provide intelligible explanations to abnormal behaviors in input data observations.
Fabrizio Angiulli +3 more
doaj +1 more source