Results 31 to 40 of about 5,065 (216)

Anomaly Detection of Metallurgical Energy Data Based on iForest-AE

Applied Sciences, 2022
With the proliferation of the Internet of Things, a large amount of data is generated constantly by industrial systems, corresponding in many cases to critical tasks.
Zhangming Xiong, Daofei Zhu, Dafang Liu, Shujing He, Luo Zhao   +4 more
doaj   +1 more source

An online deep extreme learning machine based on forgetting mechanism

Dianzi Jishu Yingyong, 2018
The development of deep learning promotes the development of deep online learning, and online learning tends to have strong effectiveness. Based on the principle of online extreme learning machine and the principle of autoencoder of deep extreme learning
Liu Buzhong
doaj   +1 more source

OF-AE: Oblique Forest AutoEncoders

, 2023
In the present work we propose an unsupervised ensemble method consisting of oblique trees that can address the task of auto-encoding, namely Oblique Forest AutoEncoders (briefly OF-AE). Our method is a natural extension of the eForest encoder introduced in [1].
openaire   +2 more sources

Integrated Autoencoder-Level Set Method Outperforms Autoencoder for Novelty Detection [PDF]

, 2022
Novelty detection (ND) has gained attention in many applications for its effectiveness in dealing with imbalanced data. Many ND algorithms have been proposed.
Coyle, Damien, Liu, Shuo
core   +1 more source

AE-MLP: A hybrid deep learning approach for DDoS detection and classification

, 2021
Distributed Denial-of-Service (DDoS) attacks are increasing as the demand for Internet connectivity massively grows in recent years. Conventional shallow machine learning-based techniques for DDoS attack classification tend to be ineffective when the ...
Yuanyuan Wei (233952), Seyit Camtepe (11854775), Wen Xu (292544), Julian Jang-Jaccard (9657695), Fariza Sabrina (9832118), Amardeep Singh (438785)   +5 more
core   +1 more source

An Unsupervised Machine Learning Approach for Monitoring Data Fusion and Health Indicator Construction

Sensors, 2023
The prediction of system degradation is very important as it serves as an important basis for the formulation of condition-based maintenance strategies.
Lin Huang, Xin Pan, Yajie Liu, Li Gong
doaj   +1 more source

MMiDaS-AE

Proceedings of the ACM Conference on Health, Inference, and Learning, 2020
Systematic review (SR) is an essential process to identify, evaluate, and summarize the findings of all relevant individual studies concerning health-related questions. However, conducting a SR is labor-intensive, as identifying relevant studies is a daunting process that entails multiple researchers screening thousands of articles for relevance.
Eric Wonhee Lee, Byron C. Wallace, Karla I. Galaviz, Joyce C. Ho   +3 more
openaire   +3 more sources

CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal Conversion

, 2023
Electroencephalography (EEG) is a prominent non-invasive neuroimaging technique providing insights into brain function. Unfortunately, EEG data exhibit a high degree of noise and variability across subjects hampering generalizable signal extraction ...
Nørskov, Anders Vestergaard, Zahid, Alexander Neergaard, Mørup, Morten   +2 more
core   +1 more source

Classes in ftgo-microservice and AE-C decomposition

, 2023
This table shows the original classes in the ftgo-microservice application and the decomposition predicted by the approach using an autoencoder with C-means (AE-C)
Anonymous
core   +1 more source

Stacked Denoising Extreme Learning Machine Autoencoder Based on Graph Embedding for Feature Representation

IEEE Access, 2019
Extreme learning machine is characterized by less training parameters, fast training speed, and strong generalization ability. It has been applied to obtain feature representations from the complex data in the tasks of data clustering or classification ...
Hongwei Ge, Weiting Sun, Mingde Zhao, Yao Yao   +3 more
doaj   +1 more source