Results 111 to 120 of about 5,065 (216)

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HemaSphere, Volume 10, Issue S1, June 2026.
wiley   +1 more source

Domain Adaption Based on ELM Autoencoder

, 2017
We propose a new ELM Autoencoder (ELM-AE) based domain adaption algorithm which describes the subspaces of source and target domain by ELM-AE and then carries out subspace alignment to project different domains into a common new space.
Wan-Yu Deng, Qian Zhang, Yu-Tao Qu
core   +1 more source

Method for Detecting Disorder of a Nonlinear Dynamic Plant

Sensors
This paper proposes a new disorder detection method CCF-AE for a scalar dynamic plant based only on its input–output relation using a cross-correlation function and neural network autoencoder.
Xuechun Wang, Vladimir Eliseev
doaj   +1 more source

Out-of-roundness damage wheel idenficaon in railway vehicles using autoencoder models

The present study investigates the use of unsupervised Machine Learning (ML) techniques applied to the Structural Health Monitoring (SHM) of railway wheels with different stages of polygonalization in the running band.
Melo, Renato da Silva
core  

Investigation of the unsupervised artificial intelligence autoencoder models application for the object’s visual defects detection.

, 2022
Currently, artificial neural network algorithms based on supervised learning are used in the field of engineering and manufacturing. They demand long and attentive labor for labeling data.
Stakeliūnas, Vytautas,
core  

MGM-AE: Self-Supervised Learning on 3D Shape Using Mesh Graph Masked Autoencoders

2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
The challenges of applying self-supervised learning to 3D mesh data include difficulties in explicitly modeling and leveraging geometric topology information and designing appropriate pretext tasks and augmentation methods for irregular mesh topology. In this paper, we propose a novel approach for pre-training models on large-scale, unlabeled datasets ...
Zhangsihao Yang, Kaize Ding, Huan Liu 0001, Yalin Wang 0001   +3 more
openaire   +3 more sources

AE-MoSE: an AutoEncoder Mixture of Spatial Experts for Geodemographic Classification

This work introduces a novel approach to geodemographic classification that combines an AutoEncoder architecture with a Mixture of Experts framework, incorporating a Graph Neural Network into the gating component to create a Mixture of Spatial Experts approach (AE-MoSE).
De Sabbata, Stef, Liu, Pengyuan, Goodwin, Owen, Singleton, Alex   +3 more
openaire   +1 more source

Typical autoencoder structure.

Credit card fraud is a significant problem that costs billions of dollars annually. Detecting fraudulent transactions is challenging due to the imbalance in class distribution, where the majority of transactions are legitimate.
HaiChao Du (18114974), Hongliang Wang (440441), Li Lv (382638), An Guo (1451287)   +3 more
core   +1 more source

Image-derived input functions for [18F]LW223 and [18F]SynVesT-1 PET in the rodent determined using an autoencoder (IDIF-AE). [PDF]

Phys Med Biol
C Kutos J, von Kietzell A, McGowan I, Bertoglio D, G MacAskill M, As Tavares A, Wimberley C, S Clegg P.   +7 more
europepmc   +1 more source

Spatial-temporal graph neural network with autoencoder pretraining for intrusion detection in healthcare IoT ecosystems. [PDF]

Sci Rep
Tanvir MIM, Nadia NY, Rabby HR, Arif MH, Nur K, Ghose D.   +5 more
europepmc   +1 more source