Results 21 to 30 of about 5,065 (216)

Autoencoder-Based Representation Learning for Similar Patients Retrieval From Electronic Health Records: Comparative Study [PDF]

JMIR Medical Informatics
BackgroundBy analyzing electronic health record snapshots of similar patients, physicians can proactively predict disease onsets, customize treatment plans, and anticipate patient-specific trajectories.
Deyi Li, Aditi Shukla, Sravani Chandaka, Bradley Taylor, Jie Xu, Mei Liu   +5 more
doaj   +2 more sources

An Analysis of Image Classification Using Wavelet-Based Autoencoder Architecture and Extreme Learning Machine

Journal of Electrical and Computer Engineering
In 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, Salima Hassairi, Siwar Yahia, Ridha Ejbali, Mourad Zaied   +4 more
doaj   +2 more sources

A new hybrid model for improving outlier detection using combined autoencoder and variational autoencoder [PDF]

Scientific Reports
In this paper, we propose a new hybrid model, called AVE, that integrates the strengths of Autoencoder (AE) and Variational Autoencoder (VAE) to enhance outlier detection for numerous high-dimensional datasets.
Ahmed M. Daoud, Osama M. Elkomy, Walid I. Khedr, Khalid M. Hosny   +3 more
doaj   +2 more sources

DSFC-AE: A New Hyperspectral Unmixing Method Based on Deep Shared Fully Connected Autoencoder

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
The pervasive presence of mixed pixels in hyperspectral remote sensing imagery poses a substantial constraint on the quantitative progress of remote sensing technology. Hyperspectral unmixing (HU) techniques serve as effective means to address this issue.
Hao Chen 0192, Tao Chen 0004, Yuxiang Zhang 0001, Bo Du 0001, Antonio Plaza   +4 more
openaire   +3 more sources

SVD-AE: Simple Autoencoders for Collaborative Filtering

CoRR
Accepted by IJCAI ...
Seoyoung Hong 0001, Jeongwhan Choi 0002, Yeon-Chang Lee, Srijan Kumar, Noseong Park   +4 more
openaire   +4 more sources

Unsupervised TCN-AE-Based Outlier Detection for Time Series With Seasonality and Trend for Cellular Networks

, 2023
Timely identification of outliers occurring in key performance indicators (KPIs) of mobile cellular networks is crucial for prompt action to unexpected events.
Benjamin Premkumar Annamalai (10759098), Neelakantam V Venkatarayalu (10758618), Ronghong Mo (10759839), Simon Kok Kan Foo (17017866), Nathaniel Joseph Pereira (10760085), Sumei Sun (10759884), Yiyang Pei (10758864)   +6 more
core   +1 more source

CODE-AE Datasets

, 2021
This repo includes the datasets that are used to benchmark the experiment results of paper titled "CODE-AE: A Coherent De-confounding Autoencoder for Predicting Patient-Specific Drug Response From Cell Line ...
Di He
core   +1 more source

Multilayer Fisher extreme learning machine for classification

Complex & Intelligent Systems, 2022
As a special deep learning algorithm, the multilayer extreme learning machine (ML-ELM) has been extensively studied to solve practical problems in recent years.
Jie Lai, Xiaodan Wang, Qian Xiang, Jian Wang, Lei Lei   +4 more
doaj   +1 more source

Idea of AE separation from unpredicted source area during AE testing by autoencoder [PDF]

Proceedings of 1st International Electronic Conference on Applied Sciences, 2020
When conducting AE testing, there is an industrial need to separate AE from monitoring area to that from outside of the area in some cases. In this study, usefulness of autoencoder to solve this problem is discussed by simple experiment using an isotropic thin steel ruler.
openaire   +1 more source

AddAG-AE: Anomaly Detection in Dynamic Attributed Graph Based on Graph Attention Network and LSTM Autoencoder

, 2023
Recently, anomaly detection in dynamic networks has received increased attention due to massive network-structured data arising in many fields, such as network security, intelligent transportation systems, and computational biology.
Zhen Zhang, Gongxun Miao, Guohua Wu, Bing Lu, Yongjie Tong   +4 more
core   +1 more source