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How to Identify Patterns of Citywide Dynamic Traffic at a Low Cost? An In-Depth Neural Network Approach with Digital Maps
2021
Complexity
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We propose a stacked convolutional autoencoder-based method to extract a low-dimension feature vector for each input. We compute and analyze the distances between vectors. ...
The distance captured by our method can represent the evolution of different traffic conditions during the morning and evening peak hours. ...
Parameters of the Stacked Convolutional Autoencoder. To achieve better training performance, we set the stacked convolutional autoencoder shown in Figure 3 . ...
doi:10.1155/2021/6648116
fatcat:eti5u5ljrfdqrajejouaqtaqfa
Evolutionary Hierarchical Sparse Extreme Learning Autoencoder Network for Object Recognition
2018
Symmetry
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In this paper, a novel sparse autoencoder derived from ELM and differential evolution is proposed and integrated into a hierarchical hybrid autoencoder network to accomplish the end-to-end learning with ...
When extended to the stacked autoencoder network, which is a typical symmetrical representation learning model architecture, ELM manages to realize hierarchical feature extraction and classification, which ...
However, differential evolution was only applied to the evolutionary sparse ELM autoencoder, which was stacked at higher layers of the whole network for high-level feature extraction. ...
doi:10.3390/sym10100474
fatcat:bgmt5gtjonerrkynpynn5akq7q
No-reference image quality assessment using shearlet transform and stacked autoencoders
2015
2015 IEEE International Symposium on Circuits and Systems (ISCAS)
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Then, stacked autoencoders are applied to exaggerate the discriminative parts of the primary features. ...
The resulting algorithm, which we name SESANIA (ShEarlet and Stacked Autoencoders based Noreference Image quality Assessment), is tested on several databases (LIVE, Multiply Distorted LIVE and TID2008) ...
A stacked autoencoder is a neural network consisting of multiple layers of sparse autoencoders in which the outputs of each layer are wired to the inputs of the successive layer. ...
doi:10.1109/iscas.2015.7168953
dblp:conf/iscas/LiPXFYCC15
fatcat:5l5swe4gmbcrfjgks7u4d7djuy
Detection of COVID-19 from Chest X-ray and CT Scan Images using Improved Stacked Sparse Autoencoder
2021
Pertanika journal of science & technology
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This radiology image is usually examined by the expert to detect the presence of COVID-19 symptom. In this study, the improved stacked sparse autoencoder is used to examine the radiology images. ...
COVID-19 can be potentially detected by using Chest X-ray and computed tomography (CT) images, as these images contain essential information of lung infection. ...
MATERIALS AND METHODS
Stacked Sparse Autoencoder Architecture The stacked sparse autoencoder is built by stacking the sparse autoencoder with many numbers. ...
doi:10.47836/pjst.29.3.14
fatcat:73zbmp2jqnevbn7r4trqkz2cze
No-reference image quality assessment with shearlet transform and deep neural networks
2015
Neurocomputing
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Then, stacked autoencoders are applied as 'evolution process' to 'amplify' the primary features and make them more discriminative. ...
The resulting algorithm, which we name SESANIA (ShEarlet and Stacked Autoencoders based Noreference Image quality Assessment) is tested on several database (LIVE, Multiply Distorted LIVE and TID2008) individually ...
Acknowledgement The work described in this paper was substantially supported by a grant from the City University of Hong Kong, Kowloon, Hong Kong with Project number of 7004058. ...
doi:10.1016/j.neucom.2014.12.015
fatcat:gqjtnaadw5fojgyd6zfxax3ram
Discriminative Feature Learning with Constraints of Category and Temporal for Action Recognition
[chapter]
2015
Lecture Notes in Computer Science
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Representations of depth maps are learned and reconstructed using a stacked denoising autoencoder. ...
Recently, with the availability of the depth cameras, a lot of studies of human action recognition have been conducted on the depth sequences. ...
We use the output of the last autoencoder as the output for the stacked architecture. Fig. 3 . Fine tuning of the stacking architecture. ...
doi:10.1007/978-3-319-21963-9_16
fatcat:odgieaepofe6lczfsrxygvg24y
Graphical classification of DNA sequences of HLA alleles by deep learning
2018
Human Cell
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Alleles of human leukocyte antigen (HLA)-A DNAs are classified and expressed graphically by using artificial intelligence "Deep Learning (Stacked autoencoder)". ...
The two-dimensional plot of HLA-A DNAs gives a clear outlook for characterizing the various alleles. ...
of Japan and by a research support of Osaka University. ...
doi:10.1007/s13577-017-0194-6
pmid:29327117
pmcid:PMC5852191
fatcat:r6logiap7bgpxgqihmy7dzh7kq
Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans
2016
Scientific Reports
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Technical explanation of SDAE method The stacked autoencoder (SDAE) architecture can be constructed by the basic building block of autoencoder. ...
An autoencoder is a two layered neural network that attempts to seek the representative patterns (mostly sparse), denoted as , of the original data , at the hidden (second) layer with the deterministic ...
Technical explanation of SDAE method The stacked autoencoder (SDAE) architecture can be constructed by the basic building block of autoencoder. ...
doi:10.1038/srep24454
pmid:27079888
pmcid:PMC4832199
fatcat:l7gevkmohrdkhlqabmwi6n4yyy
An evolutionary autoencoder for dynamic community detection
2020
Science China Information Sciences
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In this paper, we propose a semi-supervised algorithm (sE-Autoencoder) to overcome the effects of nonlinear property on the low-dimensional representation. ...
However, such reconstruction does not address the nonlinear characteristics of networks. ...
More specifically, three stacked autoencoders are applied because existing studies have proved that three stacked autoencoders can reach well effectiveness in the static community detection [25] . ...
doi:10.1007/s11432-020-2827-9
fatcat:io44rpasn5fypgqebitcmplmda
Exploring order parameters and dynamic processes in disordered systems via variational autoencoders
[article]
2021
arXiv
pre-print
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extension of the variational autoencoder applied to semantically segmented atom-resolved data seeking the most effective reduced representation for the system that still contains the maximum amount of ...
This approach allowed us to explore the dynamic evolution of electron beam-induced processes in a silicon-doped graphene system, but it can be also applied for a much broader range of atomic-scale and ...
An image stack was then acquired capturing the continued evolution of the four holes under the 100 kV e-beam. ...
arXiv:2006.10267v2
fatcat:skmrjsmhtvblvjxcclblrqbkma
Advanced texture analysis and classification methods for the automatic diagnosis of the Hepatocellular Carcinoma
2019
Applied Medical Informatics
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Deep learning techniques such as Stacked Denoising Autoencoders (SAE) and Convolutional Neural Networks (CNN) were also experimented [3]. ...
Thus, we perform the characterization and supervised recognition of HCC, as well as the unsupervised discovery of the HCC evolution phases. ...
Deep learning techniques such as Stacked Denoising Autoencoders (SAE) and Convolutional Neural Networks (CNN) were also experimented [3] . ...
doaj:e7cc07c1da5a47dfae317b52f80d14d2
fatcat:353qkfnuabcevor63mgdwufnli
Temporal Autoencoding Restricted Boltzmann Machine
[article]
2012
arXiv
pre-print
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A lot of this work has focused on the development of Gabor-like filters learned when enforcing sparsity constraints on a natural image dataset. ...
Here we investigate exactly this problem in established temporal deep learning algorithms as well as a new learning paradigm suggested here, the Temporal Autoencoding Restricted Boltzmann Machine (TARBM ...
Martin Nawrot of the Freie Universität Berlin for his enthusiastic support and invaluable feedback. ...
arXiv:1210.8353v1
fatcat:is5upswkrfcudaxy3ehv2ooqlq
Stacked Autoencoder based Feature Compression for Optimal Classification of Parkinson Disease from Vocal Feature Vectors using Immune Algorithms
2021
International Journal of Advanced Computer Science and Applications
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The selected features will further be extracted using the Stacked Autoencoder technique to improve and increase the accuracy rate and quality of classification with reduced run time. ...
Machine Learning is an integral part of artificial intelligence it takes huge data as input and train by making use of existing algorithms to understand the pattern of the data. ...
The results of AIRS -Stacked Autoencoder, AIRS 2 -Stacked Autoencoder, and the proposed AIRS Parallel -Stacked Autoencoder were presented as the Table V for comparison. ...
doi:10.14569/ijacsa.2021.0120558
fatcat:uny3wkrhtzgttmvouind73mn6m
Building Extraction Based on an Optimized Stacked Sparse Autoencoder of Structure and Training Samples Using LIDAR DSM and Optical Images
2017
Sensors
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In this paper, a building extraction method is proposed based on a stacked sparse autoencoder with an optimized structure and training samples. ...
Since stacked sparse autoencoder (SSAE) neural network can learn the essential characteristics of the data in depth, SSAE was employed to extract buildings from the combined DSM data and optical image. ...
Conflicts of Interest: The authors declare no conflicts of interest. ...
doi:10.3390/s17091957
pmid:28837118
pmcid:PMC5621110
fatcat:icft4yrtobfatfdp3f2sj5w6ru
Proactive Critical Energy Infrastructure Protection via Deep Feature Learning
2020
Energies
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Specifically, we propose the two challenging architectures based on learning deep features, namely—Long Short Term Memory-Stacked Autoencoders (LSTM-SAE), and Convolutional Neural Network (CNN-SAE), for ...
This paper focuses on the challenging application of wind turbine (WT) monitoring. ...
An extension of the aforementioned analysis is the so-called Stacked Sparse autoencoders (SSAE) scheme in which multiple shallow SAE architectures are stacked. ...
doi:10.3390/en13102622
fatcat:6m5znvk3hbgmdotlzqwkyqw2sy
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