Suchergebnisse - novel stack deep convolutional 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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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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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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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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BrainNet: a fusion assisted novel optimal framework of residual blocks and stacked autoencoders for multimodal brain tumor classification von Ullah, Muhammad Sami, Khan, Muhammad Attique, Almujally, Nouf Abdullah, Alhaisoni, Majed, Akram, Tallha, Shabaz, Mohammad

“… However, a few important challenges arise, such as (i) the selection of the most important deep learning architecture for classification (ii …”
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SDCAE: Stack Denoising Convolutional Autoencoder Model for Accident Risk Prediction Via Traffic Big Data von Chen, Chao, Fan, Xiaoliang, Zheng, Chuanpan, Xiao, Lujing, Cheng, Ming, Wang, Cheng

“… However, predicting the risk of citywide accidents remains an open issue. To address this problem, we propose SDCAE, a novel Stack Denoise Convolutional Auto-Encoder algorithm to predict the risk of traffic accident in the city-level …”
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Multilayer Fisher extreme learning machine for classification von Lai, Jie, Wang, Xiaodan, Xiang, Qian, Wang, Jian, Lei, Lei

“… To address this problem, a novel Fisher extreme learning machine autoencoder (FELM-AE) is proposed and is used as the component for the multilayer Fisher extreme leaning machine (ML-FELM …”
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Parametric Learning of Texture Filters by Stacked Fisher Autoencoders von Shahriari, Arash

“… The Fisher autoencoders are independently computed in stacks of variable depth based on the complexity of patterns under study and the ability of each individual filter to extract deep features …”
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Deep Decoupling Convolutional Neural Network for Intelligent Compound Fault Diagnosis von Huang, Ruyi, Liao, Yixiao, Zhang, Shaohui, Li, Weihua

“… To solve such a problem, a novel method called deep decoupling convolutional neural network is proposed for intelligent compound fault diagnosis …”
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Deep Neural Networks for In Situ Hybridization Grid Completion and Clustering von Li, Yujie, Huang, Heng, Chen, Hanbo, Liu, Tianming

“… As we stack multiple RBMs to form a deep belief network (DBN), we progressively …”
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Deep Learning-Based Assessment of ILD Designs in HRCT Pictures von A, Balamanikandan, M, Sukanya, M, Dhanalakshmi, M, Vimala, N, Arul, Yatm, Narayana Reddy

“… Several parts make up the model, including a fully convolutional network, a sparse stack autoencoder and decoder, an edge depth CNN, and a dilated convolution. The proposed model's performance is also contrasted with those of other models that are currently in use, including ResNet50, VGG16, and VGG19 …”
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Deep Learning Frameworks with Applications in Medical Signal and Image Classification von Jia, Guangyu

“… The main contributions of this research include: 1) proposing novel deep learning architectures which can improve the classification performance, generalisation ability …”
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