Search Results - unsupervised sparse‐autoencoder‐based deep neural network*

Robust approach for AMC in frequency selective fading scenarios using unsupervised sparse‐autoencoder‐based deep neural network

Authors: Maqsood Hussain Shah Xiaoyu Dang

Source: IET Communications. 13:423-432

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-com.2018.5688
https://ieeexplore.ieee.org/document/8648840
https://doi.org/10.1049/iet-com.2018.5688
https://dblp.uni-trier.de/db/journals/iet-com/iet-com13.html#ShahD19
https://digital-library.theiet.org/content/journals/10.1049/iet-com.2018.5688
https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-com.2018.5688

A Comparative Study of Lightweight, Sparse Autoencoder-Based Classifiers for Edge Network Devices: An Efficiency Analysis of Feed-Forward and Deep Neural Networks.

Authors: Jo, Mi Young Kim, Hyun Jung

Source: Sensors (14248220); Oct2025, Vol. 25 Issue 20, p6439, 22p

Subject Terms: EDGE computing, AUTOENCODERS, RESEARCH evaluation, ANOMALY detection (Computer security), FEEDFORWARD neural networks, ARTIFICIAL neural networks

Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties

Authors: Zhe Yang Dejan Gjorgjevikj Jianyu Long et al.

Source: Chinese Journal of Mechanical Engineering, Vol 34, Iss 1, Pp 1-12 (2021)

Subject Terms: Deep learning, Fault diagnostics, Novelty detection, Multi-head deep neural network, Sparse autoencoder, Ocean engineering, TC1501-1800, Mechanical engineering and machinery, TJ1-1570

File Description: electronic resource

Relation: https://doaj.org/toc/1000-9345; https://doaj.org/toc/2192-8258

Access URL: https://doaj.org/article/b89721cf6296483a8a2661e099c0789e

Robust approach for AMC in frequency selective fading scenarios using unsupervised sparse‐autoencoder‐based deep neural network.

Authors: Shah, Maqsood Hussain Dang, Xiaoyu

Source: IET Communications (Wiley-Blackwell); Mar2019, Vol. 13 Issue 4, p423-432, 10p

Subject Terms: ARTIFICIAL neural networks, ARTIFICIAL intelligence, DEEP learning, ORDER statistics, SIGNAL classification

Sparse Autoencoder-Based Multi-Head Deep Neural Networks for Machinery Fault Diagnostics With Novelty Detection

Authors: Zhe Yang Dejan Gjorgjevikj Jian-Yu Long et al.

Access URL: https://europepmc.org/article/PPR/PPR251499
https://www.researchsquare.com/article/rs-122416/v1.pdf
https://www.researchsquare.com/article/rs-122416/v1

Sparse Autoencoder Based Deep Neural Network for Voxelwise Detection of Cerebral Microbleed

Authors: Yi-Ding Lv Yudong Zhang Shuihua Wang et al.

Source: 2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS). :1229-1232

Subject Terms: 2. Zero hunger, 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 10. No inequality

Access URL: https://dblp.uni-trier.de/db/conf/icpads/icpads2016.html#ZhangHLCZW16
https://ieeexplore.ieee.org/document/7823881/
https://doi.org/10.1109/ICPADS.2016.0166
http://ieeexplore.ieee.org/document/7823881/

Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties.

Authors: Yang, Zhe Gjorgjevikj, Dejan Long, Jianyu et al.

Source: Chinese Journal of Mechanical Engineering; 6/4/2021, Vol. 34 Issue 1, p1-12, 12p

A comparative case study between shallow and deep neural networks in induction motor's fault diagnosis.

Authors: Gholaminejad, Azadeh Jorkesh, Saeid Poshtan, Javad

Source: IET Science, Measurement & Technology (Wiley-Blackwell); Jul2023, Vol. 17 Issue 5, p195-207, 13p

Subject Terms: ARTIFICIAL neural networks, INDUCTION motors, FAULT diagnosis, INDEPENDENT component analysis, SHORT circuits, POWER resources

Deep Neural Networks for Prediction of Cardiovascualr Diseases.

Authors: Aslam, Muhammad Aqeel Munir, Muhammad Asif Ahmad, Rauf et al.

Source: Nano Biomedicine & Engineering; 2022, Vol. 14 Issue 1, p81-89, 9p

Subject Terms: ARTIFICIAL neural networks, FORECASTING

Sparse Representation Convolutional Autoencoder for Feature Learning of Vibration Signals and its Applications in Machinery Fault Diagnosis.

Authors: Miao, Mengqi Sun, Yuanhang Yu, Jianbo

Source: IEEE Transactions on Industrial Electronics; Dec2022, Vol. 69 Issue 12, p13565-13575, 11p

Subject Terms: ROLLER bearings, FAULT diagnosis, ARTIFICIAL neural networks, SUPERVISED learning, DEEP learning, VIBRATION (Mechanics), MACHINERY

Improved DDoS Detection Utilizing Deep Neural Networks and Feedforward Neural Networks as Autoencoder.

Authors: Yaser, Ahmed Latif Mousa, Hamdy M. Hussein, Mahmoud

Source: Future Internet; Aug2022, Vol. 14 Issue 8, p240-N.PAG, 18p

Subject Terms: ARTIFICIAL neural networks, DEEP learning, FEEDFORWARD neural networks, DENIAL of service attacks, MACHINE learning, SOFTWARE-defined networking, COMPUTER network security

Reliable Fault Diagnosis of Rotary Machine Bearings Using a Stacked Sparse Autoencoder-Based Deep Neural Network.

Authors: Sohaib, Muhammad Kim, Jong-Myon

Source: Shock & Vibration; 5/7/2018, p1-11, 11p

Subject Terms: ROTARY combustion engines, FAULT diagnosis, NEURAL circuitry, COST effectiveness, ALGORITHMS

A Multistage Physics-Informed Neural Network for Fault Detection in Regulating Valves of Nuclear Power Plants.

Authors: Lai, Chenyang Ahmed, Ibrahim Zio, Enrico et al.

Source: Energies (19961073); Jun2024, Vol. 17 Issue 11, p2647, 23p

Subject Terms: NUCLEAR power plants, RELIEF valves, ARTIFICIAL neural networks, FLOW coefficient, VALVES, VECTOR data

Construction of Home Product Design System Based on Self-Encoder Depth Neural Network.

Authors: Lu, Guangpu

Source: Computational Intelligence & Neuroscience; 4/21/2022, p1-12, 12p

Subject Terms: DWELLING design & construction, DEEP learning, DOMESTIC architecture, FURNITURE design, COMPUTER vision, ALGORITHMS

Hand Gesture Recognition using Sparse Autoencoder-based Deep Neural Network Based on Electromyography Measurements.

Authors: Yucheng Wang Chunhui Wang Zhonghui Wang et al.

Source: Proceedings of SPIE; 1/26/2018, Vol. 10598, p1-7, 7p

Pre-trained Deep Convolution Neural Network Model With Attention for Speech Emotion Recognition.

Authors: Zhang, Hua Gou, Ruoyun Shang, Jili et al.

Source: Frontiers in Physiology; 3/2/2021, Vol. 11, pN.PAG-N.PAG, 13p

Subject Terms: CONVOLUTIONAL neural networks, ARTIFICIAL neural networks, EMOTION recognition, MACHINE learning, EMOTIONS

Deep Learning in Aircraft Design, Dynamics, and Control: Review and Prospects.

Authors: Dong, Yiqun Tao, Jun Zhang, Youmin et al.

Source: IEEE Transactions on Aerospace & Electronic Systems; Aug2021, Vol. 57 Issue 4, p2346-2368, 23p

Subject Terms: DEEP learning, OBJECT recognition (Computer vision), RECURRENT neural networks, FLIGHT control systems, CONVOLUTIONAL neural networks, PROBLEM solving

An enhanced deep learning‐based phishing detection mechanism to effectively identify malicious URLs using variational autoencoders.

Authors: Prabakaran, Manoj Kumar Meenakshi Sundaram, Parvathy Chandrasekar, Abinaya Devi

Source: IET Information Security (Wiley-Blackwell); May2023, Vol. 17 Issue 3, p423-440, 18p

Subject Terms: UNIFORM Resource Locators, ARTIFICIAL neural networks, PHISHING, VIRTUAL communities, CYBERCRIMINALS

Gear Fault Detection Method Based on Convex Hull Clustering of Autoencoder's Latent Space.

Authors: Batsch, Michał Kiczek, Bartłomiej

Source: Applied Sciences (2076-3417); Jun2024, Vol. 14 Issue 12, p5282, 21p

Subject Terms: ARTIFICIAL neural networks, GENERATIVE adversarial networks, SUPERVISED learning, PRINCIPAL components analysis, SIGNAL processing, MACHINE learning

Deep autoencoder based domain adaptation for transfer learning.

Authors: Dev, Krishna Ashraf, Zubair Muhuri, Pranab K. et al.

Source: Multimedia Tools & Applications; Jul2022, Vol. 81 Issue 16, p22379-22405, 27p

Subject Terms: SUPERVISED learning, TRANSFER of training, ARTIFICIAL neural networks, DEEP learning, PHYSIOLOGICAL adaptation, MACHINE learning, CONCEPT learning