Results 31 to 40 of about 234,412 (208)

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

Holographic-(V)AE: an end-to-end SO(3)-Equivariant (Variational) Autoencoder in Fourier Space

Physical Review Research, 2022
Group-equivariant neural networks have emerged as a data-efficient approach to solve classification and regression tasks, while respecting the relevant symmetries of the data. However, little work has been done to extend this paradigm to the unsupervised and generative domains. Here, we present Holographic
Gian Marco Visani, Michael N. Pun, Arman Angaji, Armita Nourmohammad   +3 more
openaire   +4 more sources

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

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   +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

Anomaly Detection for Agricultural Vehicles Using Autoencoders

Sensors, 2022
The safe in-field operation of autonomous agricultural vehicles requires detecting all objects that pose a risk of collision. Current vision-based algorithms for object detection and classification are unable to detect unknown classes of objects. In this
Esma Mujkic, Mark P. Philipsen, Thomas B. Moeslund, Martin P. Christiansen, Ole Ravn   +4 more
doaj   +1 more source

Orthogonal Matrix-Autoencoder-Based Encoding Method for Unordered Multi-Categorical Variables with Application to Neural Network Target Prediction Problems

Applied Sciences
Neural network models, such as BP, LSTM, etc., support only numerical inputs, so data preprocessing needs to be carried out on the categorical variables to convert them into numerical data.
Yiying Wang, Jinghua Li, Boxin Yang, Dening Song, Lei Zhou   +4 more
doaj   +1 more source

Missing-Insensitive Short-Term Load Forecasting Leveraging Autoencoder and LSTM

IEEE Access, 2020
In most deep learning-based load forecasting, an intact dataset is required. Since many real-world datasets contain missing values for various reasons, missing imputation using deep learning is actively studied.
Kyungnam Park, Jaeik Jeong, Dongjoo Kim, Hongseok Kim   +3 more
doaj   +1 more source

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

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

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

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