Search Results - 2D-convolutional autoencoder (AE) network

DHCAE: Deep Hybrid Convolutional Autoencoder Approach for Robust Supervised Hyperspectral Unmixing

Authors: Fazal Hadi Jingxiang Yang Matee Ullah et al.

Source: Remote Sensing, Vol 14, Iss 4433, p 4433 (2022)

Subject Terms: hyperspectral unmixing (HSU), hybrid 3D, 2D-convolutional autoencoder (AE) network, supervised, spectral–spatial, remote sensing, Science

Relation: https://www.mdpi.com/2072-4292/14/18/4433; https://doaj.org/toc/2072-4292; https://doaj.org/article/69fbfd032a87448097b06b1e155cb7e2

Availability: https://doi.org/10.3390/rs14184433
https://doaj.org/article/69fbfd032a87448097b06b1e155cb7e2

Transfer Accent Identification Learning for Enhancing Speech Emotion Recognition.

Authors: Priya Dharshini, G. Sreenivasa Rao, K.

Source: Circuits, Systems & Signal Processing; Aug2024, Vol. 43 Issue 8, p5090-5120, 31p

Subject Terms: ARTIFICIAL neural networks, EMOTION recognition, FEATURE extraction, AUTOENCODERS, SYSTEM identification, AUTOMATIC speech recognition

Slim multi-scale convolutional autoencoder-based reduced-order models for interpretable features of a complex dynamical system.

Authors: Teutsch, Philipp Pfeffer, Philipp Sharifi Ghazijahani, Mohammad et al.

Source: APL Machine Learning; Mar2025, Vol. 3 Issue 1, p1-15, 15p

Subject Terms: PROPER orthogonal decomposition, REDUCED-order models, DYNAMICAL systems, FLUID flow, PRIOR learning, DEEP learning

A Combined Semi-Supervised Deep Learning Method for Oil Leak Detection in Pipelines Using IIoT at the Edge.

Authors: Spandonidis, Christos Theodoropoulos, Panayiotis Giannopoulos, Fotis

Source: Sensors (14248220); Jun2022, Vol. 22 Issue 11, p4105-4105, 20p

A Comprehensive Review of AI Integration for Fault Detection in Modern Power Systems: Data Processing, Modeling, and Optimization.

Authors: Liu, Youping Li, Pin Si, Yang et al.

Source: Energies (19961073); Sep2025, Vol. 18 Issue 18, p4983, 32p

Subject Terms: ARTIFICIAL intelligence, FAULT diagnosis, COMPUTER simulation, DEEP learning, RENEWABLE energy sources, ELECTRIC power systems, ELECTRONIC data processing, MATHEMATICAL optimization

Automated cardiac arrhythmia detection techniques: a comprehensive review for prospective approach.

Authors: Jha, Chandan Kumar

Source: Computer Methods in Biomechanics & Biomedical Engineering; Aug2025, Vol. 28 Issue 10, p1639-1654, 16p

Subject Terms: ARRHYTHMIA, MACHINE learning, CLASSIFICATION algorithms, DETECTION algorithms, ELECTROCARDIOGRAPHY, DEEP learning

Damage Localization and Severity Assessment in Composite Structures Using Deep Learning Based on Lamb Waves.

Authors: Azad, Muhammad Muzammil Munyaneza, Olivier Jung, Jaehyun et al.

Source: Sensors (14248220); Dec2024, Vol. 24 Issue 24, p8057, 15p

Subject Terms: ARTIFICIAL neural networks, CONVOLUTIONAL neural networks, LAMB waves, PIEZOELECTRIC transducers, DEEP learning, STRUCTURAL health monitoring

DHCAE: Deep Hybrid Convolutional Autoencoder Approach for Robust Supervised Hyperspectral Unmixing.

Authors: Hadi, Fazal Yang, Jingxiang Ullah, Matee et al.

Source: Remote Sensing; Sep2022, Vol. 14 Issue 18, pN.PAG-N.PAG, 20p

Subject Terms: HYPERSPECTRAL imaging systems, MULTISPECTRAL imaging, REMOTE sensing, SPECTRAL imaging, SIGNAL-to-noise ratio, PIXELS

Geographic Terms: WASHINGTON (D.C.)

Detection of particle contamination and lubrication outage in journal bearings in wind turbine gearboxes using surface acoustic wave measurements and machine learning.

Alternate Title: Erkennung von Partikelkontamination und Schmierungsausfall in Getriebegleitlagern von Windenergieanlagen mithilfe der Messung von akustischen Oberflächenwellen und maschinellem Lernen. (German)

Authors: Decker, Thomas Jacobs, Georg Raddatz, Malte et al.

Source: Forschung im Ingenieurwesen; 2/19/2025, Vol. 89 Issue 1, p1-13, 13p

One-Class Convolutional Neural Networks for Water-Level Anomaly Detection.

Authors: Nicholaus, Isack Thomas Lee, Jun-Seoung Kang, Dae-Ki

Source: Sensors (14248220); Nov2022, Vol. 22 Issue 22, p8764, 13p

Subject Terms: CONVOLUTIONAL neural networks, WATER levels, WATER utilities, SENSOR networks, FEATURE extraction

Evaluation of deep learning approaches for oil & gas pipeline leak detection using wireless sensor networks.

Authors: Spandonidis, Christos Theodoropoulos, Panayiotis Giannopoulos, Fotis et al.

Source: Engineering Applications of Artificial Intelligence. Aug2022, Vol. 113, pN.PAG-N.PAG. 1p.

Subject Terms: *PETROLEUM pipelines, *NATURAL gas pipelines, *WIRELESS sensor networks, *LEAK detection, *DEEP learning, *PETROLEUM products

Geographic Terms: GREECE

A review of deep learning-based stereo vision techniques for phenotype feature and behavioral analysis of fish in aquaculture.

Authors: Zhao, Yaxuan Qin, Hanxiang Xu, Ling et al.

Source: Artificial Intelligence Review; Jan2025, Vol. 58 Issue 1, p1-61, 61p

Accurate and explainable mortality forecasting with the LocalGLMnet.

Authors: Perla, Francesca Richman, Ronald Scognamiglio, Salvatore et al.

Source: Scandinavian Actuarial Journal; Sep2024, Vol. 2024 Issue 7, p739-761, 23p

Subject Terms: DEATH forecasting, MORTALITY, DEEP learning, DATABASES, DEATH rate

Machine Fault Diagnosis: Experiments with Different Attention Mechanisms Using a Lightweight SqueezeNet Architecture.

Authors: Zabin, Mahe Choi, Ho-Jin Kabir, Muhammad Kubayeeb et al.

Source: Electronics (2079-9292); Aug2024, Vol. 13 Issue 16, p3112, 17p

Subject Terms: HILBERT-Huang transform, FAULT diagnosis, ARTIFICIAL intelligence, TEXTURE mapping, COMPUTATIONAL complexity, DEEP learning

Early attention-deficit/hyperactivity disorder (ADHD) with NeuroDCT-ICA and rhinofish optimization (RFO) algorithm based optimized ADHD-AttentionNet.

Authors: Alhussen, Ahmed Alutaibi, Ahmed Ibrahim Sharma, Sunil Kumar et al.

Source: Scientific Reports; 2/26/2025, Vol. 15 Issue 1, p1-20, 20p

Subject Terms: PROCESS capability, ARTIFICIAL intelligence, IMAGE processing, BLENDED learning, ATTENTION-deficit hyperactivity disorder

Application of Deep Learning in Neuroradiology: Brain Haemorrhage Classification Using Transfer Learning.

Authors: Dawud, Awwal Muhammad Yurtkan, Kamil Oztoprak, Huseyin

Source: Computational Intelligence & Neuroscience; 6/3/2019, p1-12, 12p

Subject Terms: DEEP learning, NEURORADIOLOGY, ARTIFICIAL neural networks, SUPPORT vector machines, BRAIN

Exploring the efficacy of multi-flavored feature extraction with radiomics and deep features for prostate cancer grading on mpMRI.

Authors: Khanfari, Hasan Mehranfar, Saeed Cheki, Mohsen et al.

Source: BMC Medical Imaging; 11/22/2023, Vol. 23 Issue 1, p1-13, 13p

Subject Terms: FEATURE extraction, RADIOMICS, PROSTATE cancer, DISCRETE wavelet transforms, CURVELET transforms

Automated Detection of Sleep Stages Using Deep Learning Techniques: A Systematic Review of the Last Decade (2010–2020).

Authors: Loh, Hui Wen Ooi, Chui Ping Vicnesh, Jahmunah et al.

Source: Applied Sciences (2076-3417); Dec2020, Vol. 10 Issue 24, p8963, 24p

Subject Terms: SLEEP stages, DEEP learning, SLEEP interruptions, CONVOLUTIONAL neural networks, ARTIFICIAL intelligence, ELECTROENCEPHALOGRAPHY, MYOELECTRIC prosthesis

Monitoring the gas metal arc additive manufacturing process using unsupervised machine learning.

Authors: Mattera, Giulio Polden, Joseph Norrish, John

Source: Welding in the World; Nov2024, Vol. 68 Issue 11, p2853-2867, 15p

Accuracy of artificial intelligence for the detection of intracranial hemorrhage and chronic cerebral microbleeds: a systematic review and pooled analysis.

Authors: Matsoukas, Stavros Scaggiante, Jacopo Schuldt, Braxton R. et al.

Source: La Radiologia Medica; Oct2022, Vol. 127 Issue 10, p1106-1123, 18p