Results 61 to 70 of about 5,065 (216)

A tutorial on multi-view autoencoders using the multi-view-AE library

CoRR
There has been a growing interest in recent years in modelling multiple modalities (or views) of data to for example, understand the relationship between modalities or to generate missing data. Multi-view autoencoders have gained significant traction for their adaptability and versatility in modelling multi-modal data, demonstrating an ability to ...
Ana Lawry Aguila, André Altmann
openaire   +2 more sources

Machine learning‐driven advances in carbon‐based quantum dots: Opportunities accompanied by challenges

Responsive Materials, EarlyView.
Machine learning provides a unifying framework to connect structure, fluorescence properties, and applications of carbon‐based quantum dots. This review highlights how data‐driven strategies enable fluorescence regulation, reveal underlying mechanisms, and accelerate the rational design of functional carbon dots.
Liangfeng Chen, Siwei Yang, Xiangqian Cui, Ziheng Liu, Hang Wang, Yongqiang Li, Peng He, Miao Wang, Guqiao Ding   +8 more
wiley   +1 more source

Enhanced Autoencoder-Based Fraud Detection: A Novel Approach With Noise Factor Encoding and SMOTE

, 2023
Fraud detection is a critical task across various domains, requiring accurate identification of fraudulent activities within vast arrays of transactional data.
Çakır, Mert Yılmaz, Şirin, Yahya
core   +1 more source

Transformer-based autoencoder with ID constraint for unsupervised anomalous sound detection

EURASIP Journal on Audio, Speech, and Music Processing, 2023
Unsupervised anomalous sound detection (ASD) aims to detect unknown anomalous sounds of devices when only normal sound data is available. The autoencoder (AE) and self-supervised learning based methods are two mainstream methods.
Jian Guan, Youde Liu, Qiuqiang Kong, Feiyang Xiao, Qiaoxi Zhu, Jiantong Tian, Wenwu Wang   +6 more
doaj   +1 more source

Adversarial Example Detection and Restoration Defensive Framework for Signal Intelligent Recognition Networks

Applied Sciences, 2023
Deep learning-based automatic modulation recognition networks are susceptible to adversarial attacks, posing significant performance vulnerabilities. In response, we introduce a defense framework enriched by tailored autoencoder (AE) techniques.
Chao Han, Ruoxi Qin, Linyuan Wang, Weijia Cui, Dongyang Li, Bin Yan   +5 more
doaj   +1 more source

DQN‐Guided Subset‐Induced OCSVM Kernel Approximation for Imbalanced Anomaly Detection

IEEJ Transactions on Electrical and Electronic Engineering, EarlyView.
Anomaly detection under limited normal data remains a fundamental challenge due to severe class imbalance and scarcity of anomalies. We propose a novel framework that reformulates support vector selection in One‐Class SVM as a sequential decision‐making problem.
Wenqian Yu, Jiaying Wu, Jinglu Hu
wiley   +1 more source

Enterprise Network Security Using Few‐Shot Meta‐Learning

Artificial Intelligence for Engineering, EarlyView.
This paper involves a few‐shot learning study that uses model‐agnostic meta‐learning. A meta‐dataset was curated by combining six benchmark network intrusion detection datasets by parsing network traffic data from PCAP files. An MAML model performs meta‐training, validation and meta‐testing before and after fine‐tuning.
Sushant Jain, Sumukh Gupta, Amit Pundir, Sanjeev Singh, Geetika Jain Saxena   +4 more
wiley   +1 more source

Combining kernelised autoencoding and centroid prediction for dynamic multi‐objective optimisation

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Evolutionary algorithms face significant challenges when dealing with dynamic multi‐objective optimisation because Pareto optimal solutions and/or Pareto optimal fronts change. The authors propose a unified paradigm, which combines the kernelised autoncoding evolutionary search and the centroid‐based prediction (denoted by KAEP), for solving ...
Zhanglu Hou, Juan Zou, Gan Ruan, Yuan Liu, Yizhang Xia   +4 more
wiley   +1 more source

A Hybrid Wasserstein GAN and Autoencoder Model for Robust Intrusion Detection in IoT

The emergence of Generative Adversarial Network (GAN) techniques has garnered significant attention from the research community for the development of Intrusion Detection Systems (IDS).
Khattak, Aizaz Ahmad, Asiri, Fatima, Latif, Shahid, Ahmad, Jawad, Saidani, Oumaima, Alshehri, Mohammed S., Al Malwi, Wajdan   +6 more
core   +1 more source

Anomaly detection for wind turbine pitch bearings via autoencoder enhanced nonlinear autoregressive model [PDF]

, 2022
The pitch bearing, as a safety-critical unit in windturbines, is prone to damage. To prevent severe accidents,early anomaly detection for wind turbine pitch bearings ishighly desirable.
Zhang, Chao, Zhang, Long; id_orcid
core   +2 more sources