Search Results - Multi-scale conventional autoencoder*

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

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

Index Terms: Physics - Fluid Dynamics, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2501.03070

Optimal Spectrum Sensing Framework for Cognitive Radio Networks Using Attention‐Based Autoencoder With Multi‐Scale Capsule Network.

Authors: Rao, R. Koteswara Sahaai, Madona B.

Source: International Journal of Communication Systems. 1/10/2026, Vol. 39 Issue 1, p1-19. 19p.

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

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

Source: APL Machine Learning, Vol 3, Iss 1, Pp 016112-016112-15 (2025)

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Physics, QC1-999, Electronic computers. Computer science, Fluid Dynamics (physics.flu-dyn), FOS: Physical sciences, QA75.5-76.95, Physics - Fluid Dynamics, Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2501.03070
https://doaj.org/article/236f8e1f2be64001a1d9c1d7b38ae1f2

Circular RNA-Drug Association Prediction Based on Multi-Scale Convolutional Neural Networks and Adversarial Autoencoders.

Authors: Wang, Yao Lei, Xiujuan Chen, Yuli et al.

Source: International Journal of Molecular Sciences. Feb2025, Vol. 26 Issue 4, p1509. 20p.

Subject Terms: *ARTIFICIAL neural networks, *CONVOLUTIONAL neural networks, *AUTOENCODERS, *CIRCULAR RNA, *DRUG target

Hierarchical-embedding autoencoder with a predictor (HEAP) as efficient architecture for learning long-term evolution of complex multi-scale physical systems

Authors: Khrabry, Alexander Startsev, Edward Powis, Andrew et al.

Subject Terms: Plasma Physics (physics.plasm-ph), FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, FOS: Physical sciences, Physics - Plasma Physics

Access URL: http://arxiv.org/abs/2505.18857

Multi-scale asynchronous correlation and 2D convolutional autoencoder for adolescent health risk prediction with limited fMRI data

Authors: Di Gao Guanghao Yang Jiarun Shen et al.

Source: Front Comput Neurosci
Frontiers in Computational Neuroscience, Vol 18 (2024)

Subject Terms: health risk assessment, deep learning, adolescence, Neurosciences. Biological psychiatry. Neuropsychiatry, behavioral health risks, functional magnetic resonance imaging, convolutional autoencoder, RC321-571, Neuroscience

Access URL: https://pubmed.ncbi.nlm.nih.gov/39473785
https://doaj.org/article/7e665edbb7b24faf972c7fe95a652ce0

DAR-MDE: Depth-Attention Refinement for Multi-Scale Monocular Depth Estimation.

Authors: Abdulwahab, Saddam Rashwan, Hatem A. El-Melegy, Moumen T. et al.

Source: Journal of Sensor & Actuator Networks; Oct2025, Vol. 14 Issue 5, p90, 28p

Subject Terms: AUTOENCODERS, DEPTH maps (Digital image processing), THREE-dimensional imaging, REAL-time computing

IoT-based prediction model for aquaponic fish pond water quality using multiscale feature fusion with convolutional autoencoder and GRU networks

Authors: Suma Christal Mary Sundararajan Yamini Bhavani Shankar Sinthia Panneer Selvam et al.

Source: Scientific Reports, Vol 15, Iss 1, Pp 1-21 (2025)

Subject Terms: Water quality prediction, Internet of things, Aquaponic fish ponds, Revamped fitness-based mother optimization algorithm, Multi-scale feature fusion-based convolutional autoencoder with gated recurrent unit, Medicine, Science

File Description: electronic resource

Relation: https://doaj.org/toc/2045-2322

Access URL: https://doaj.org/article/481247aba02e483987cf278ae390a214

A Variational Multi-Scale Error Compensation Network for Single-Pixel Imaging

Authors: Jian Lin Qiurong Yan Quan Zou et al.

Source: IEEE Photonics Journal, Vol 16, Iss 4, Pp 1-11 (2024)

Subject Terms: 0301 basic medicine, 0303 health sciences, 03 medical and health sciences, Single pixel imaging, deep learning, imaging reconstruction, variational autoencoder, Applied optics. Photonics, QC350-467, Optics. Light, TA1501-1820

Access URL: https://doaj.org/article/509f0c9832ec461ab53fa9d01fbfd372

Autoencoder-based Unsupervised Intrusion Detection using Multi-Scale Convolutional Recurrent Networks

Authors: Singh, Amardeep Jang-Jaccard, Julian

Index Terms: Computer Science - Cryptography and Security, text

URL: http://arxiv.org/abs/2204.03779

HVI-Based Spatial–Frequency-Domain Multi-Scale Fusion for Low-Light Image Enhancement.

Authors: Zhang, Yuhang Zheng, Huiying Xu, Xinya et al.

Source: Applied Sciences (2076-3417); Oct2025, Vol. 15 Issue 19, p10376, 16p

Subject Terms: FOURIER transforms, COLOR space, IMAGE processing, FEATURE extraction, NIGHT photography, AUTOENCODERS, FREQUENCY-domain analysis

Multi-scale Residual Segmentation Network for Histopathological Image.

Authors: BOZDAĞ KARAKEÇİ, Zehra TALU, Muhammed Fatih

Source: Dicle University Journal of Engineering / Dicle Üniversitesi Mühendislik Dergisi; eyl2024, Vol. 15 Issue 3, p623-632, 10p

Subject Terms: CONVOLUTIONAL neural networks, DEEP learning, IMAGE segmentation, OBJECT recognition (Computer vision), AUTOENCODERS, PATHOLOGY, ARTIFICIAL neural networks, IMAGE processing, ARTIFICIAL intelligence, MARKOV random fields, TRAINING needs

Spatial-Frequency-Scale Variational Autoencoder for Enhanced Flow Diagnostics of Schlieren Data.

Authors: Yang, Ronghua Wu, Hao Yang, Rongfei et al.

Source: Sensors (14248220); Oct2025, Vol. 25 Issue 19, p6233, 29p

Subject Terms: FLUID dynamics, AUTOENCODERS, FLOW visualization, FOURIER transforms, DEEP learning, FEATURE extraction, FORECASTING

A Multi-Scale Unsupervised Feature Extraction Network with Structured Layer-Wise Decomposition.

Authors: Günaydın, Yusuf Şevki Şen, Baha

Source: Applied Sciences (2076-3417); Jul2025, Vol. 15 Issue 13, p7194, 18p

Subject Terms: FEATURE extraction, MULTIPLE scale method, MACHINE learning, AUTOENCODERS, CALCULUS of variations, IMAGE segmentation

CResDAE: A Deep Autoencoder with Attention Mechanism for Hyperspectral Unmixing.

Authors: Zhao, Chong Wang, Jinlin Qiao, Qingqing et al.

Source: Remote Sensing; Nov2025, Vol. 17 Issue 21, p3622, 28p

Subject Terms: AUTOENCODERS, GEOLOGICAL surveys, HYPERSPECTRAL imaging systems, SURFACE properties, REMOTE-sensing images, PROSPECTING

IoT-based prediction model for aquaponic fish pond water quality using multiscale feature fusion with convolutional autoencoder and GRU networks.

Authors: Sundararajan, Suma Christal Mary Shankar, Yamini Bhavani Selvam, Sinthia Panneer et al.

Source: Scientific Reports; 1/14/2025, Vol. 15 Issue 1, p1-21, 21p

Subject Terms: OPTIMIZATION algorithms, WATER quality, FISH ponds, ARTIFICIAL intelligence, AUTOENCODERS

WOT-AE: Weighted Optimal Transport Autoencoder for Patterned Fabric Defect Detection.

Authors: Yang, Hui Kang, Linyan Yang, Tianjin

Source: Symmetry (20738994); Nov2025, Vol. 17 Issue 11, p1829, 18p

Subject Terms: TEXTILES, AUTOENCODERS, MATRIX decomposition, MACHINE learning, SYMMETRY, OUTLIER detection

Multi-scale variational autoencoder for imputation of missing values in untargeted metabolomics using whole-genome sequencing data

Authors: Chen Zhao Kuan-Jui Su Chong Wu et al.

Source: Computers in Biology and Medicine. 179:108813

Subject Terms: Whole Genome Sequencing, Humans, Metabolomics, Polymorphism, Single Nucleotide, Linkage Disequilibrium

Access URL: https://pubmed.ncbi.nlm.nih.gov/38955127

Unsupervised Convolutional Transformer Autoencoder for Robust Health Indicator Construction and RUL Prediction in Rotating Machinery.

Authors: Dahal, Amrit Huang, Hong-Zhong Huang, Cheng-Geng et al.

Source: Applied Sciences (2076-3417); Oct2025, Vol. 15 Issue 20, p10972, 22p

Subject Terms: REMAINING useful life, CONVOLUTIONAL neural networks, VIBRATION measurements, MACHINE learning, ENGINEERING systems, TRANSFORMER models, AUTOENCODERS

Multi-Channel Multi-Scale Convolution Attention Variational Autoencoder (MCA-VAE): An Interpretable Anomaly Detection Algorithm Based on Variational Autoencoder.

Authors: Liu, Jingwen Huang, Yuchen Wu, Dizhi et al.

Source: Sensors (14248220); Aug2024, Vol. 24 Issue 16, p5316, 24p

Subject Terms: CONVOLUTIONAL neural networks, ARTIFICIAL neural networks, DISTRIBUTION (Probability theory), INDUSTRIAL controls manufacturing, CONDITIONAL probability, AUTOENCODERS