Suchergebnisse - Deep stack autoencoder

SDCA: a novel stack deep convolutional autoencoder – an application on retinal image denoising von Ghosh, Swarup Kr, Biswas, Biswajit, Ghosh, Anupam

“… This study represents a deep learning based approach to denoising images and restoring features using stack denoising convolutional autoencoder …”
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Application of deep stack autoencoder network in ship weight estimation von CHEN Jian,TANG Junyao,ZHU Shengguang,ZHOU Zhaozhao

“… the parameters.The deep stack autoencoder network is used to mine the deep data features and do analysis on the ShipWE self-built …”
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Epileptic Seizure Recognition Using Reduced Deep Convolutional Stack Autoencoder and Improved Kernel RVFLN From EEG Signals von Sahani, Mrutyunjaya, Rout, Susanta Kumar, Dash, Pradipta Kishor

“… In this paper, reduced deep convolutional stack autoencoder (RDCSAE) and improved kernel random vector functional link network (IKRVFLN …”
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Deep Convolutional Stack Autoencoder of Process Adaptive VMD Data With Robust Multikernel RVFLN for Power Quality Events Recognition von Sahani, Mrutyunjaya, Dash, Pradipta Kishore

“… ). A novel reduced deep convolutional neural network (RDCNN) embedded with stack autoencoder, that is, RDCSAE structure is introduced to extract the most discriminative …”
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Precise single step and multistep short-term photovoltaic parameters forecasting based on reduced deep convolutional stack autoencoder and minimum variance multikernel random vector functional network von Sahani, Mrutyunjaya, Choudhury, Sasmita, Siddique, Marif Daula, Parida, Tanmoy, Dash, Pradipta Kishore, Panda, Sanjib Kumar

“… To address this, we have developed a novel hybrid model: a reduced deep convolutional stack autoencoder with a minimum variance multikernel random vector functional link network (RDCSAE-MVMRVFLN …”
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Robust broad learning system with parametrized variational mode decomposition for schizophrenia diagnosis von Parija, Sebamai, Sahani, Mrutyunjaya, Rout, Susanta Kumar

“… ), which are selected using fuzzy dispersion entropy (FDE). The extracted BLIMFs are fed into deep stack autoencoder (DSAE …”
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Fuzzy Deep Stack of Autoencoders for Dealing with Data Uncertainty von Costa, Bruno, Jain, Jinesh

“… The recently introduced concepts of 'Autoencoders' and 'Deep Stacks of Autoencoders' (DSAE) and their use for dimensionality reduction and data compression problems have gained considerable attention and reached very promising results …”
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An Efficient Mutual Authentication and Fractional Lyrebird Optimization With Deep Learning–Based SIP‐Based DRDoS Attack Detection von Sreenivasulu, V., Ravikumar, C. V.

“… In this article, a deep learning model fractional lyrebird optimization algorithm–deep stack autoencoder (FLOA‐DSA …”
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Incorporating Heterogeneous Features into the Random Subspace Method for Bearing Fault Diagnosis von Chu, Yan, Ali, Syed Muhammad, Lu, Mingfeng, Zhang, Yanan

“… Primarily, via signal processing methods, statistical features are extracted, and via the deep stack autoencoder (DSAE …”
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Triple Stack Deep Variational Autoencoder For Improved Hand Gesture Recognition von Bahuguna, Arti, Bhaumik, Gopa, Govil, Mahesh Chandra

“… This paper proposes a novel approach for hand gesture recognition using a triple-stack deep variational autoencoder …”
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Microscopic segmentation and classification of COVID‐19 infection with ensemble convolutional neural network von Amin, Javeria, Anjum, Muhammad Almas, Sharif, Muhammad, Rehman, Amjad, Saba, Tanzila, Zahra, Rida

“… In Phase III, segmented images are passed to the stack sparse autoencoder (SSAE …”
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Screening of Potential Indonesia Herbal Compounds Based on Multi-Label Classification for 2019 Coronavirus Disease von Fadli, Aulia, Kusuma, Wisnu Ananta, Annisa, Batubara, Irmanida, Heryanto, Rudi

“… ), then predicting the new drug-target interactions. This study conducted multilabel DTI prediction using the stack autoencoder-deep neural network (SAE-DNN) algorithm …”
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Continuous estimation of wrist torques with stack-autoencoder based deep neural network: A preliminary study von Yu, Yang, Chen, Chen, Sheng, Xinjun, Zhu, Xiangyang

“… ) with a stack-autoencoder based deep neural network. With this kind of deep neural network, features used for regression could be extracted autonomously other than in hand-crafted manner …”
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SDAE-LFM: A Latent Factor Model for Recommendation Based on Stack Denoising AutoEncoder von Luo, Jianyan, Xing, Xing, Zheng, Hang, Xin, Mindong, Jia, Zhichun

“… ), applying Deep Learning technology for latent feature representation learning. A stack denoising autoencoder is applied to extracting feature about item from the label information …”
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Anomaly detection of industrial multi-sensor signals based on enhanced spatiotemporal features von Jiang, Lin, Xu, Hang, Liu, Jinhai, Shen, Xiangkai, Lu, Senxiang, Shi, Zhan

“… Then, a stack spatial–temporal autoencoder, which relies on the improved …”
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Effective wind power prediction using novel deep learning network: Stacked independently recurrent autoencoder von Wang, Lin, Tao, Rui, Hu, Huanling, Zeng, Yu-Rong

“… In this study, a novel deep learning network stacked by independent recurrent autoencoder (IRAE) is designed according to the characteristics of ultra-short-term wind power data, hereafter called SIRAE …”
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Psychosis speech recognition algorithm based on deep embedded sparse stacked autoencoder and manifold ensemble von Zhang, Yi, Qin, Xiaolin, Lin, Yuan, Li, Yongming, Wang, Pin, Zhang, Zuwei, Li, Xiaofei

“… To solve this problem, this paper proposes a deep embedded hybrid feature sparse stack autoencoder manifold ensemble algorithm …”
Weitere Angaben

Deep Adversarial Domain Adaptation Model for Bearing Fault Diagnosis von Liu, Zhao-Hua, Lu, Bi-Liang, Wei, Hua-Liang, Chen, Lei, Li, Xiao-Hua, Ratsch, Matthias

“… : the source domain and the target domain are inconsistent in their distribution. First, a deep stack autoencoder (DSAE …”
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Deep Feature Learning Architectures for Daily Reservoir Inflow Forecasting von Li, Chuan, Bai, Yun, Zeng, Bo

“… , deep restricted Boltzmann machine (DRBM) and stack Autoencoder (SAE), respectively, are introduced in this paper …”
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Damage characterization using CNN and SAE of broadband Lamb waves von Gao, Fei, Hua, Jiadong

“… ) and stack autoencoder (SAE) are promising to extract features from Lamb wave signals that can be linked with damage for subsequent localization and quantification …”
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