Search Results - sparse deep convolutional autoencoder*

Self‐Attention‐Based Deep Convolutional Sparse Dense Autoencoder Model With Chebyshev‐Based Osprey Algorithm for Cardiovascular Disease Detection by 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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SPAER: Sparse Deep Convolutional Autoencoder Model to Extract Low Dimensional Imaging Biomarkers for Early Detection of Breast Cancer Using Dynamic Thermography by 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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XAI-DSCSA: explainable-AI-based deep semi-supervised convolutional sparse autoencoder for facial expression recognition by Mohana, M., Subashini, P., Ghinea, George

“… as the Deep Semi-supervised Convolutional Sparse Autoencoder to address the aforementioned issues and enhance FER performance and prediction…”
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DOA estimation method for sparse arrays based on deep convolutional autoencoder and deep convolutional neural network by Guo, Shuhan, Zhang, Qin, Fu, Xiaolong, Zheng, Guimei, Zhou, Hao

“…This paper proposes a Direction-of-Arrival (DOA) estimation method based on Deep Convolutional Autoencoder (DCAE…”
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Online Public Opinion Situation Assessment by Deep Sparse Autoencoder and Time-Series Modeling Convolutional Neural Network by Sun, Wei, Xiao, Ran, Li, Xuanmo

“… This model employs Deep Sparse Autoencoder (DSAE) and Time-Series Modeling Convolutional Neural Networks (TBSMA-CNN…”
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TumorAwareNet: Deep representation learning with attention based sparse convolutional denoising autoencoder for brain tumor recognition by Bodapati, Jyostna Devi, Balaji, Bharadwaj Bagepalli

“…) images suitable for effective tumor recognition. The proposed model employs a Sparse Convolutional Denoising Autoencoder (SCDA…”
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Sparse Convolutional Denoising Autoencoders for Genotype Imputation by 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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EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder by Liu, Junxiu, Wu, Guopei, Luo, Yuling, Qiu, Senhui, Yang, Su, Li, Wei, Bi, Yifei

“…), Sparse Autoencoder (SAE), and Deep Neural Network (DNN) together. In the proposed network, the features extracted by the CNN are first sent to SAE for encoding and decoding…”
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Fully convolutional Deep Stacked Denoising Sparse Auto encoder network for partial face reconstruction by Dinesh, P.S., Manikandan, M.

“…•In this partial face detection (PFD) is used to overcome the challenges involved in face detection and reconstruction.•A novel PFD algorithm called Self-…”
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Predicting potential microbe-disease associations based on heterogeneous graph attention network and deep sparse autoencoder by Wang, Bo, Zhao, Wenlong, Du, Xiaoxin, Zhang, Jianfei, Zhang, Chunyu, Wang, Liping, He, Yang

“… We propose a computational framework called graph attention convolutional deep sparse autoencoder microbe-disease association (GCDSAEMDA…”
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Reciprocating compressor health monitoring based on BSInformer with deep convolutional AutoEncoder by Tian, Huixin, Ju, Bingtian, Feng, Shulin

“…•The reconstructed noisy data distribution is more similar to the normal data distribution. A composite model combining deep convolutional autoencoder and balanced sparse sampling Informer (MCA-BSInformer…”
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Multistage committees of deep feedforward convolutional sparse denoise autoencoder for object recognition by Shicao Luo, Yongsheng Ding, Kuangrong Hao

“… The network is trained layer-wise via denoise autoencoder (dA) with L-BFGS to optimize convolutional kernels and no backpropagation is used…”
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Classification of lung adenocarcinoma transcriptome subtypes from pathological images using deep convolutional networks by Antonio, Victor Andrew A., Ono, Naoaki, Saito, Akira, Sato, Tetsuo, Altaf-Ul-Amin, Md, Kanaya, Shigehiko

“… A sparse deep autoencoder which minimizes local information entropy on the encoding layer is then proposed…”
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Sparse Representation Convolutional Autoencoder for Feature Learning of Vibration Signals and its Applications in Machinery Fault Diagnosis by Miao, Mengqi, Sun, Yuanhang, Yu, Jianbo

“… Hence, in this article, a new deep neural network (DNN), sparse representation convolutional autoencoder (SRCAE…”
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Multi-frequency and multi-domain human activity recognition based on SFCW radar using deep learning by Jia, Yong, Guo, Yong, Wang, Gang, Song, Ruiyuan, Cui, Guolong, Zhong, Xiaoling

“…Deep learning and radar make it feasible to automatically recognize human activities in various lighting conditions, even occlusion case, which significantly promotes the application of activity…”
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SCAE—Stacked Convolutional Autoencoder for Fault Diagnosis of a Hydraulic Piston Pump with Limited Data Samples by Eraliev, Oybek, Lee, Kwang-Hee, Lee, Chul-Hee

“… In this study, we propose a novel DL model based on a stacked convolutional autoencoder (SCAE) to address the challenge of limited data…”
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A hybrid deep neural network based prediction of 300 MW coal-fired boiler combustion operation condition by Han, ZheZhe, Huang, YiZhi, Li, Jian, Zhang, Biao, Hossain, Md. Moinul, Xu, ChuanLong

“… To overcome these limitations, a novel prediction method for the combustion operation condition based on flame imaging and a hybrid deep neural network is proposed…”
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Hierarchical Individual Naturalistic Functional Brain Networks with Group Consistency Uncovered by a Two-Stage NAS-Volumetric Sparse DBN Framework by Xu, Shuhan, Ren, Yudan, Tao, Zeyang, Song, Limei, He, Xiaowei

“…) because of its dynamics and multimodal information. In recent years, various deep learning models, such as deep convolutional autoencoder (DCAE…”
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Optimizing MobileNetV2 for improved accuracy in early gastric cancer detection based on dynamic pelican optimizer by Zhou, Guoping, He, Qiyu, Liu, Xiaoli, Kai, Xinghua, Cao, Weikang, Ding, Junning, Zhuang, Bufeng, Xu, Shuhua, Thwin, Myo

“… The proposed approach utilizes a customized deep learning model called MobileNetV2, which is optimized using a Dynamic variant of the Pelican Optimization Algorithm (DPOA…”
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Attention-enhanced residual autoencoder for NIR spectral feature extraction and classification of grain varieties by Kabiso, Abel Chernet, Ko, Cheng-Hao

“… We propose SpecFuseNet, an attention-enhanced residual autoencoder, as a lightweight deep learning model for extracting NIR spectral features and classifying grain varieties…”
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