Search Results - Multi-scale convolutional autoencoder*

An optimized multi-scale convolutional autoencoder for efficient abnormal event detection using rgb, depth and optical flow data

Authors: Abdullah Alqahtani

Source: Multimedia Tools and Applications. 84:34401-34435

Multi-scale convolutional adversarial autoencoder-based anomaly detector for lesion edge localization on diffusion MRI

Authors: Shuxin Cao Tenglong Wang Qingwei You et al.

Source: ISMRM Annual Meeting.

spaMGCN: a graph convolutional network with autoencoder for spatial domain identification using multi-scale adaptation

Authors: Tianjiao Zhang Hongfei Zhang Zhongqian Zhao et al.

Source: Genome Biol
Genome Biology, Vol 26, Iss 1, Pp 1-32 (2025)

Subject Terms: Multi-source feature fusion, Spatial multi-omics data, QH301-705.5, Discrete distribution spatial domain, Genetics, Methodology, Spatial domain identification, Biology (General), QH426-470

Access URL: https://doaj.org/article/b869d40174f1476ca8be9c4125e661e3

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

A multi-scale low rank convolutional autoencoder for process monitoring of nonlinear uncertain systems

Authors: Jiawei Yin Xuefeng Yan

Source: Process Safety and Environmental Protection. 188:53-63

Physics-assisted multi-scale convolutional autoencoder for turbulence reduced-order modeling

Authors: Mengxue Lu Zi Ye Longye Qiao et al.

Source: Physics of Fluids. 37

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

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

Stressed vegetation identification under natural gas micro-leakage from hyperspectral images using stacked autoencoder and multi-scale three-dimensional convolutional neural network

Authors: Kangni Xiong Jinbao Jiang Weiwei Ran et al.

Source: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. :1-13

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

Authors: Wang Y Lei X Chen Y et al.

Source: International journal of molecular sciences [Int J Mol Sci] 2025 Feb 11; Vol. 26 (4). Date of Electronic Publication: 2025 Feb 11.

Publication Type: Journal Article

Journal Info: Publisher: MDPI Country of Publication: Switzerland NLM ID: 101092791 Publication Model: Electronic Cited Medium: Internet ISSN: 1422-0067 (Electronic) Linking ISSN: 14220067 NLM ISO Abbreviation: Int J Mol Sci Subsets: MEDLINE

MeSH Terms: RNA, Circular*/genetics , RNA, Circular*/chemistry , RNA, Circular*/metabolism , Neural Networks, Computer* , Computational Biology*/methods, Humans ; Algorithms ; Pharmaceutical Preparations/chemistry ; Convolutional Neural Networks ; Autoencoder

Micro-motion Signal Enhancement via Convolutional Autoencoder Equipped with Multi-scale Feature Pyramid

Authors: Honglei Zhang Minglei Tang Wenpeng Zhang et al.

Source: 2023 8th International Conference on Signal and Image Processing (ICSIP). :758-762

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

Subject Terms: Internet of things, Aquaponic fish ponds, Science, Internet of Things, Fishes, Aquaculture, Article, Revamped fitness-based mother optimization algorithm, Water Quality, Medicine, Animals, Neural Networks, Computer, Water quality prediction, Ponds, Multi-scale feature fusion-based convolutional autoencoder with gated recurrent unit, Algorithms, Environmental Monitoring

Access URL: https://pubmed.ncbi.nlm.nih.gov/39809886
https://doaj.org/article/481247aba02e483987cf278ae390a214

An improved YOLOv8 model and mask convolutional autoencoder for multi-scale defect detection of ceramic tiles

Authors: Yuesheng Liu Weibin Qiu Kailong Fu et al.

Source: Measurement. 248:116847

Estimating method for power marketing data based on multi-scale convolutional variational autoencoder

Authors: Zesan Liu Jing Zhang Tian Tian et al.

Source: Fifth International Conference on Telecommunications, Optics, and Computer Science (TOCS 2024). :52

A Multi-scale Convolutional Autoencoder with Attention Mechanism for Fault Diagnosis of Rotating Machinery

Authors: Zihao Lei Hongguang Yun Feiyu Tian et al.

Source: Applications of Generative AI ISBN: 9783031462375

Colour‐patterned fabric defect detection based on an unsupervised multi‐scale U‐shaped denoising convolutional autoencoder model

Authors: Hongwei Zhang Shuting Liu Quanlu Tan et al.

Source: Coloration Technology. 138:522-537

Subject Terms: 01 natural sciences, 0104 chemical sciences

Dynamic Texture Feature Extraction Based on multi-scale Convolutional Autoencoder

Authors: Ziqi Zhu Yangwei Gu Huimin Yi

Source: 2021 4th International Conference on Intelligent Autonomous Systems (ICoIAS). :108-112

Access URL: https://www.computer.org/csdl/proceedings-article/icoias/2021/419500a108/1wG6cmFXGP6

Pansharpening based on convolutional autoencoder and multi-scale guided filter

Authors: Ahmad AL Smadi Shuyuan Yang Zhang Kai et al.

Source: EURASIP Journal on Image and Video Processing, Vol 2021, Iss 1, Pp 1-20 (2021)

Subject Terms: Pansharpening, Convolutional autoencoder, Guided image filtering, Adaptive intensity-hue-saturation AIHS, Electronics, TK7800-8360

File Description: electronic resource

Relation: https://doaj.org/toc/1687-5281

Access URL: https://doaj.org/article/98e86503a56348dda991a171974388e5

Convolutional variational autoencoder and multi-scale attention convolutional neural network based diagnostics on filament current sensors for mass spectrometers

Authors: Xinshuo Li Wenxing Zhou Jiancheng Yin et al.

Source: Engineering Applications of Artificial Intelligence. 138:109443

Additional file 1 of spaMGCN: a graph convolutional network with autoencoder for spatial domain identification using multi-scale adaptation

Authors: Zhang, Tianjiao Zhang, Hongfei Zhao, Zhongqian et al.