Suchergebnisse - deep convolutional autoencoder model

Self‐Attention‐Based Deep Convolutional Sparse Dense Autoencoder Model With Chebyshev‐Based Osprey Algorithm for Cardiovascular Disease Detection von Divya, N. J., Kumar, N. Suresh, Devi, R. Kanniga

“… This paper presents a deep convolutional neural network (CNN) for categorizing five CVDs using conventional 12‐lead ECG data …”
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MoFA: Model-Based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction von Tewari, Ayush, Zollhofer, Michael, Hyeongwoo Kim, Garrido, Pablo, Bernard, Florian, Perez, Patrick, Theobalt, Christian

“… In this work we propose a novel model-based deep convolutional autoencoder that addresses the highly challenging problem of reconstructing a 3D human face from a single in-the-wild color image …”
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Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network von Wang, Wei, Zhao, Mengxue, Wang, Jigang

“… Android security incidents occurred frequently in recent years. To improve the accuracy and efficiency of large-scale Android malware detection, in this work, we propose a hybrid model based on deep autoencoder (DAE …”
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SPAER: Sparse Deep Convolutional Autoencoder Model to Extract Low Dimensional Imaging Biomarkers for Early Detection of Breast Cancer Using Dynamic Thermography von Yousefi, Bardia, Akbari, Hamed, Hershman, Michelle, Kawakita, Satoru, Fernandes, Henrique C., Ibarra-Castanedo, Clemente, Ahadian, Samad, Maldague, Xavier P. V.

“… ) prior to mammography. In this study, we propose a sparse deep convolutional autoencoder model named SPAER to extract low-dimensional deep thermomics to aid breast cancer diagnosis …”
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Deep convolutional autoencoder for urban land use classification using mobile device data von Sun, Zhihao, Peng, Zhenghong, Yu, Yang, Jiao, Hongzan

“… Considering the high dimensional characteristic of mobile-phone-call time series data, an improved land-use classification framework with deep convolutional autoencoder (DCAE …”
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Classification of Atypical White Blood Cells in Acute Myeloid Leukemia Using a Two-Stage Hybrid Model Based on Deep Convolutional Autoencoder and Deep Convolutional Neural Network von Elhassan, Tusneem A., Mohd Rahim, Mohd Shafry, Siti Zaiton, Mohd Hashim, Swee, Tan Tian, Alhaj, Taqwa Ahmed, Ali, Abdulalem, Aljurf, Mahmoud

“… ), as observed in peripheral blood (PB) smear images. The purpose of this work is to build a classification model for atypical AML WBCs based on their distinctive features …”
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Deep Convolutional Autoencoders for Robust Flow Model Calibration Under Uncertainty in Geologic Continuity von Jiang, Anyue, Jafarpour, Behnam

“… Subsurface flow model calibration is commonly performed by assuming that a known conceptual model of geologic continuity is available and can be used to constrain the solution search space …”
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Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders von Lee, Kookjin, Carlberg, Kevin T.

“… •Analysis including conditions under which the two methods are equivalent.•A novel convolutional autoencoder architecture to construct the nonlinear manifold …”
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A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders von Essien, Aniekan, Giannetti, Cinzia

“… The model comprises a deep convolutional …”
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Evaluation of CNN model by comparing with convolutional autoencoder and deep neural network for crop classification on hyperspectral imagery von Bhosle, Kavita, Musande, Vijaya

“… : Deep Neural Network (DNN) and Convolutional Autoencoder. According to the test results, the proposed optimized CNN model provided better results as compared to the other two methods. CNN has given 97 ± 0.58% overall accuracy for the Indian Pines standard dataset and 78 ± 2.43% for our study area dataset …”
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Notice of Removal: MoFA: Model-Based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction von Tewari, Ayush, Zollhofer, Michael, Kim, Hyeongwoo, Garrido, Pablo, Bernard, Florian, Perez, Patrick, Theobalt, Christian

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Carp-DCAE: Deep convolutional autoencoder for carp fish classification von Banerjee, Arnab, Das, Arijit, Behra, Samarendra, Bhattacharjee, Debotosh, Srinivasan, Nagesh Talagunda, Nasipuri, Mita, Das, Nibaran

“… •The latent features of Simple (SAE), Deep (DAE), and Convolutional deep (DCAE) autoencoder models are extracted and utilised to identify the major carp species …”
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An early fault online detection model of rolling bearing based on deep attention convolutional autoencoder and multi-decision fusion under variable operation conditions von Zhu, Wenchang, Miao, Qiuhua, Cao, Yudong, Huang, Peng, Fan, Hongwei

“… and variable operation conditions. Firstly, a novel attention mechanism is designed by combining the improved multi-head attention mechanism with rotary position embedding, and the Deep Attention Convolutional Autoencoder (DACAE …”
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An efficient compression of ECG signals using deep convolutional autoencoders von Yildirim, Ozal, Tan, Ru San, Acharya, U. Rajendra

“… •An efficient ECG compression method based on deep convolutional autoencoders (CAE).•A deep network structure of 27 layers consisting of encoder and decoder parts …”
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An Unsupervised Deep Feature Learning Model Based on Parallel Convolutional Autoencoder for Intelligent Fault Diagnosis of Main Reducer von Ye, Qing, Liu, Changhua

“… To tackle these problems, an unsupervised deep feature learning model based on parallel convolutional autoencoder (PCAE …”
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MR‐DCAE: Manifold regularization‐based deep convolutional autoencoder for unauthorized broadcasting identification von Zheng, Qinghe, Zhao, Penghui, Zhang, Deliang, Wang, Hongjun

“… ‐based deep convolutional autoencoder (MR‐DCAE) model for unauthorized broadcasting identification …”
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Depression Detection Through EEG Signal Analysis: A Convolutional Autoencoder Deep Learning Model von Firoz, Neda, Aksyonov, Sergey Vladimirovich, Berestneva, Olga Grigorievna, Savostyanov, Alexander

“… In this study, a novel approach is introduced that utilizes Convolutional Autoencoders for feature extraction from EEG signals, to enhance feature representation for classification of depression …”
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Sparse Convolutional Denoising Autoencoders for Genotype Imputation von Chen, Junjie, Shi, Xinghua

“… To explore the performance of deep learning for genotype imputation, in this study, we propose a deep model called a sparse convolutional denoising autoencoder (SCDA …”
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Bleaching Detection in Coral Reef using Deep Convolutional Autoencoder based Model von Gautam, Neeraj Kumar, Mishra, Mayank, Pati, Umesh C.

“… This work has proposed a Deep Convolution Autoencoder (DCAE) based model to detect the bleaching of the coral reefs …”
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Deep convolutional autoencoder augmented CFD thermal analysis of bearings with inter pad groove mixing von Yang, Jongin, Palazzolo, Alan

“… •Bearing groove model via a deep convolutional autoencoder training based on CFD data …”
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