Functional connectome fingerprinting: Identifying individuals and predicting cognitive functions via autoencoder
Sign Up to like & getrecommendations! Published in 2021 at "Human Brain Mapping"
DOI: 10.1002/hbm.25394
Abstract: Functional network connectivity has been widely acknowledged to characterize brain functions, which can be regarded as “brain fingerprinting” to identify an individual from a pool of subjects. Both common and unique information has been shown… read more here.
Keywords: fingerprinting identifying; autoencoder; connectome fingerprinting; functional connectome ... See more keywords
Attention augmented residual autoencoder for efficient polyp segmentation
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Imaging Systems and Technology"
DOI: 10.1002/ima.22814
Abstract: Colon cancer has been reported to be one of the frequently diagnosed cancers and the leading cause of cancer deaths. Early detection and removal of malicious polyps, which are precursors of colon cancer, can enormously… read more here.
Keywords: polyp segmentation; segmentation; autoencoder; attention augmented ... See more keywords
DSVD‐autoencoder: A scalable distributed privacy‐preserving method for one‐class classification
Sign Up to like & getrecommendations! Published in 2021 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22296
Abstract: One‐class classification has gained interest as a solution to certain kinds of problems typical in a wide variety of real environments like anomaly or novelty detection. Autoencoder is the type of neural network that has… read more here.
Keywords: dsvd autoencoder; autoencoder; privacy; one class ... See more keywords
Atypical Sample Regularizer Autoencoder for Cross-Domain Human Activity Recognition
Sign Up to like & getrecommendations! Published in 2021 at "Information Systems Frontiers"
DOI: 10.1007/s10796-020-09992-5
Abstract: The sensor-based human activity recognition (HAR) using machine learning requires a sufficiently large amount of annotated data to realize an accurate classification model. This requirement stimulates the advancement of the transfer learning research area that… read more here.
Keywords: autoencoder; domain; activity recognition; representation ... See more keywords
Latent representation learning based autoencoder for unsupervised feature selection in hyperspectral imagery
Sign Up to like & getrecommendations! Published in 2021 at "Multimedia Tools and Applications"
DOI: 10.1007/s11042-020-10474-8
Abstract: In hyperspectral image (HSI) analysis, high-dimensional data may contain noisy, irrelevant and redundant information. To mitigate the negative effect from these information, feature selection is one of the useful solutions. Unsupervised feature selection is a… read more here.
Keywords: autoencoder; feature; latent representation; feature selection ... See more keywords
A novel feature extraction method based on discriminative graph regularized autoencoder for fault diagnosis
Sign Up to like & getrecommendations! Published in 2019 at "IFAC-PapersOnLine"
DOI: 10.1016/j.ifacol.2019.12.420
Abstract: Abstract Autoencoder has been popularly used as an effective feature extraction method in fault diagnosis. However, the autoencoder algorithms neglect local structure and class information that is available in the training set. To address this… read more here.
Keywords: diagnosis; method; autoencoder; fault diagnosis ... See more keywords
Anomaly detection during milk processing by autoencoder neural network based on near-infrared spectroscopy
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Food Engineering"
DOI: 10.1016/j.jfoodeng.2021.110510
Abstract: Abstract Anomaly detection during milk processing (such as changes in fat or temperature, added water or cleaning solution) can assure a satisfactory final product quality, including compositional and hygienic characteristics, as well as adulteration with… read more here.
Keywords: spectroscopy; milk; milk processing; autoencoder ... See more keywords
An autoencoder based formulation for compressed sensing reconstruction.
Sign Up to like & getrecommendations! Published in 2018 at "Magnetic resonance imaging"
DOI: 10.1016/j.mri.2018.06.003
Abstract: This work proposes a new formulation for image reconstruction based on the autoencoder framework. The work follows the adaptive approach used in prior dictionary and transform learning based reconstruction techniques. Existing autoencoder based reconstructions are… read more here.
Keywords: reconstruction; autoencoder based; based formulation; autoencoder ... See more keywords
A Fast Deep AutoEncoder for high-dimensional and sparse matrices in recommender systems
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.06.109
Abstract: Abstract A latent factor analysis (LFA)-based model has outstanding performance in extracting desired patterns from High-dimensional and Sparse (HiDS) data for building a recommender systems. However, they mostly fail in acquiring non-linear features from an… read more here.
Keywords: dimensional sparse; high dimensional; autoencoder; recommender systems ... See more keywords
Deep-learned time-signal intensity pattern analysis using an autoencoder captures magnetic resonance perfusion heterogeneity for brain tumor differentiation
Sign Up to like & getrecommendations! Published in 2020 at "Scientific Reports"
DOI: 10.1038/s41598-020-78485-x
Abstract: Current image processing methods for dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) do not capture complex dynamic information of time-signal intensity curves. We investigated whether an autoencoder-based pattern analysis of DSC MRI captured representative… read more here.
Keywords: signal intensity; time signal; autoencoder; tumor ... See more keywords
Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data
Sign Up to like & getrecommendations! Published in 2020 at "Physics of Fluids"
DOI: 10.1063/5.0020721
Abstract: We propose a customized convolutional neural network based autoencoder called a hierarchical autoencoder, which allows us to extract nonlinear autoencoder modes of flow fields while preserving the contribution order of the latent vectors. As preliminary… read more here.
Keywords: order; field; autoencoder; convolutional neural ... See more keywords