Suchergebnisse - deep convolutional autoencoder network

Network traffic classification using deep convolutional recurrent autoencoder neural networks for spatial–temporal features extraction von D’Angelo, Gianni, Palmieri, Francesco

“… For this purpose, a novel autoencoder-based deep neural network architecture …”
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DOA estimation method for sparse arrays based on deep convolutional autoencoder and deep convolutional neural network von 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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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

“… Recent advancements in artificial intelligence (AI) have led to numerous medical discoveries. The field of computer vision (CV) for medical diagnosis has …”
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Segmentation of digital rock images using deep convolutional autoencoder networks von Karimpouli, Sadegh, Tahmasebi, Pejman

“… Recently, deep learning and machine learning algorithms have proposed several algorithms working with images, including Convolutional Neural Networks (CNN …”
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Radar Signal Intra-Pulse Modulation Recognition Based on Convolutional Denoising Autoencoder and Deep Convolutional Neural Network von Qu, Zhiyu, Wang, Wenyang, Hou, Changbo, Hou, Chenfan

“… ) and deep convolutional neural network (DCNN) is proposed in this paper. First, we use Cohen's time-frequency distribution to convert radar signals into time-frequency images (TFIs …”
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Rapid and Accurate Prediction of Soil Texture Using an Image-Based Deep Learning Autoencoder Convolutional Neural Network Random Forest (DLAC-CNN-RF) Algorithm von Zhao, Zhuan, Feng, Wenkang, Xiao, Jinrui, Liu, Xiaochu, Pan, Shusheng, Liang, Zhongwei

“… This study proposed a flexible smartphone-based machine vision system using a deep learning autoencoder convolutional neural network random forest (DLAC-CNN-RF …”
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Seismic random noise suppression using deep convolutional autoencoder neural network von Song, Hui, Gao, Yang, Chen, Wei, Xue, Ya-juan, Zhang, Hua, Zhang, Xiang

“… Contaminated seismic data seriously affect subsequent seismic data processing and imaging. In this paper, we propose a deep convolutional autoencoder neural network for denoising, which consists of encoding and decoding frameworks …”
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A stacked autoencoder‐based convolutional and recurrent deep neural network for detecting cyberattacks in interconnected power control systems von D'Angelo, Gianni, Palmieri, Francesco

“… It is based on stacked deep neural networks, which have proven to be capable to timely identify and classify attacks, by autonomously eliciting knowledge …”
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Inpainting non-anatomical objects in brain imaging using enhanced deep convolutional autoencoder network von Kumar, Puranam Revanth, Shilpa, B, Jha, Rajesh Kumar, Raju, B Deevena, Mohammed, Thayyaba Khatoon

“… In this paper, we proposed a deep convolutional autoencoder network with improved parameters as a robust method for inpainting non-anatomical objects in MRI and CT images …”
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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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Deep Convolutional Asymmetric Autoencoder-Based Spatial-Spectral Clustering Network for Hyperspectral Image von Liu, Baisen, Kong, Weili, Wang, Yan

“… In this paper, we propose a novel deep convolutional asymmetric autoencoder-based spatial-spectral clustering network (DCAAES2C-Net …”
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Ensemble of feature augmented convolutional neural network and deep autoencoder for efficient detection of network attacks von B, Selvakumar, M, Sivaanandh, K, Muneeswaran, B, Lakshmanan

“… ). The proposed work consists of three phases: (i) Feature Augmented Convolutional Neural Network (FA-CNN) (ii) Deep Autoencoder (iii …”
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Classification of Motor Imagery EEG Signals Based on Deep Autoencoder and Convolutional Neural Network Approach von Hwaidi, Jamal F., Chen, Thomas M.

“… Deep learning approaches, particularly the detection and analysis of motor imagery signals using convolutional neural network (CNN …”
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DCAVN: Cervical cancer prediction and classification using deep convolutional and variational autoencoder network von Khamparia, Aditya, Gupta, Deepak, Rodrigues, Joel J. P. C., de Albuquerque, Victor Hugo C.

“… We have adopted combination of convolutional network with variational autoencoder for data classification …”
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Convolutional Graph Autoencoder: A Generative Deep Neural Network for Probabilistic Spatio-Temporal Solar Irradiance Forecasting von Khodayar, Mahdi, Mohammadi, Saeed, Khodayar, Mohammad E., Wang, Jianhui, Liu, Guangyi

“… This probabilistic data generation model, i.e., convolutional graph autoencoder (CGAE), is devised based on the localized first-order approximation …”
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Few-shot traffic classification based on autoencoder and deep graph convolutional networks von Xu, Shengwei, Han, Jijie, Liu, Yilong, Liu, Haoran, Bai, Yijie

“… , and implement policy management. As graph convolutional networks (GCNs) take into account not only the features of the data itself, but also the relationships among sets of data during classification …”
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Particle Swarm Optimization for Efficiently Evolving Deep Convolutional Neural Networks Using an Autoencoder-Based Encoding Strategy von Yuan, Gonglin, Wang, Bin, Xue, Bing, Zhang, Mengjie

“… Deep convolutional neural networks (DCNNs) have achieved surpassing success in the field of computer vision, and a number of elaborately designed networks refresh the performance records in benchmark datasets …”
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Wavelet enabled convolutional autoencoder based deep neural network for hyperspectral image denoising von Paul, Arati, Kundu, Ahana, Chaki, Nabendu, Dutta, Dibyendu, Jha, C. S.

“… The proposed dual branch deep neural network based architecture works on wavelet transformed bands …”
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Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder von Zhao, Yu, Dong, Qinglin, Chen, Hanbo, Iraji, Armin, Li, Yujie, Makkie, Milad, Kou, Zhifeng, Liu, Tianming

“… •A new deep 3D convolutional autoencoder to model brain network maps.•Derived fine-granularity functional brain network atlases …”
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Broadband Communication Signal Detection Using Deep Convolutional Autoencoder and Spectrum Center Network von Wei, Bin

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