Search Results - deep conventional autoencoder network

Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities by Seyfioglu, Mehmet Saygin, Ozbayoglu, Ahmet Murat, Gurbuz, Sevgi Zubeyde

“… This architecture is shown to be more effective than other deep learning architectures, such as convolutional neural networks and autoencoders, as well as conventional classifiers…”
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Smartphone Motion Sensor-Based Complex Human Activity Identification Using Deep Stacked Autoencoder Algorithm for Enhanced Smart Healthcare System by Alo, Uzoma Rita, Nweke, Henry Friday, Teh, Ying Wah, Murtaza, Ghulam

“…Human motion analysis using a smartphone-embedded accelerometer sensor provided important context for the identification of static, dynamic, and complex…”
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A deep neural network approach to QRS detection using autoencoders by Belkadi, Mohamed Amine, Daamouche, Abdelhamid, Melgani, Farid

“…In this paper, a stacked autoencoder deep neural network is proposed to extract the QRS complex from raw ECG signals without any conventional feature extraction phase…”
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Automatic Modulation Classification Using Deep Learning Based on Sparse Autoencoders With Nonnegativity Constraints by Ali, Afan, Fan Yangyu

“…We demonstrate a novel method for the automatic modulation classification based on a deep learning autoencoder network, trained by a nonnegativity constraint algorithm…”
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Weak ultrasonic guided wave signal recognition based on one-dimensional convolutional neural network denoising autoencoder and its application to small defect detection in pipelines by Wu, Jing, Yang, Yingfeng, Lin, Zeyu, Lin, Yizhou, Wang, Yan, Zhang, Weiwei, Ma, Hongwei

“…•The denoising autoencoder integrated with convolutional neural network has good signal denoising ability…”
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Convolutional Autoencoder for Spectral-Spatial Hyperspectral Unmixing by Palsson, Burkni, Ulfarsson, Magnus O., Sveinsson, Johannes R.

“…). In this article, we present a new spectral-spatial linear mixture model and an associated estimation method based on a convolutional neural network autoencoder unmixing (CNNAEU…”
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A Wasserstein GAN Autoencoder for SCMA Networks by Miuccio, Luciano, Panno, Daniela, Riolo, Salvatore

“… In this letter, we design an end-to-end SCMA en/deconding structure based on the integration between a state-of-the-art autoencoder architecture and a novel Wasserstein Generative Adversarial Network (WGAN…”
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Deep Spectral Clustering Using Dual Autoencoder Network by Yang, Xu, Deng, Cheng, Zheng, Feng, Yan, Junchi, Liu, Wei

“… Deep clustering combines embedding and clustering together to obtain optimal embedding subspace for clustering, which can be more effective compared with conventional clustering methods…”
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Segmentation of digital rock images using deep convolutional autoencoder networks by 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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LSTM-based autoencoder models for real-time quality control of wastewater treatment sensor data by Seshan, Siddharth, Vries, Dirk, Immink, Jasper, van der Helm, Alex, Poinapen, Johann

“… long short-term memory (LSTM) autoencoder (AE) models, to reconcile faulty sensor signals in WWTPs as compared to autoregressive integrated moving average (ARIMA) models…”
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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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Heterogeneous Hypergraph Variational Autoencoder for Link Prediction by Fan, Haoyi, Zhang, Fengbin, Wei, Yuxuan, Li, Zuoyong, Zou, Changqing, Gao, Yue, Dai, Qionghai

“…) for link prediction in heterogeneous information networks (HINs). It first maps a conventional HIN to a heterogeneous hypergraph with a certain kind of semantics…”
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uDAS: An Untied Denoising Autoencoder With Sparsity for Spectral Unmixing by Qu, Ying, Qi, Hairong

“… . Conventional approaches use either geometrical- or statistical-based approaches. In this paper, we address the challenges of spectral unmixing with unsupervised deep…”
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A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction by Huang, Faming, Zhang, Jing, Zhou, Chuangbing, Wang, Yuhao, Huang, Jinsong, Zhu, Li

“… In this paper, a novel deep learning–based algorithm, the fully connected spare autoencoder (FC-SAE), is proposed for LSP…”
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Deep embedding clustering based on contractive autoencoder by Diallo, Bassoma, Hu, Jie, Li, Tianrui, Khan, Ghufran Ahmad, Liang, Xinyan, Zhao, Yimiao

“… To that end, we first introduce Contractive Autoencoders. Then we propose a deep embedding clustering framework based on contractive autoencoder (DECCA…”
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Lightweight Multi-Class Autoencoder Model for Malicious Traffic Detection in Private 5G Networks by Kim, Jinha, Kim, Hwankuk

“…This study proposes a lightweight autoencoder-based detection framework for the efficient detection of multi-class malicious traffic within a private 5G network slicing environment…”
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CyCU-Net: Cycle-Consistency Unmixing Network by Learning Cascaded Autoencoders by Gao, Lianru, Han, Zhu, Hong, Danfeng, Zhang, Bing, Chanussot, Jocelyn

“…) applications due to its powerful learning and data fitting ability. The autoencoder (AE) framework, as an unmixing baseline network, achieves good performance in HU by automatically learning low-dimensional embeddings and reconstructing data…”
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Gated Mixture Variational Autoencoders for Value Added Tax audit case selection by Kleanthous, Christos, Chatzis, Sotirios

“… To this end, we devise a novel Gated Mixture Variational Autoencoder deep network, that can be effectively trained with data from a limited number of audited taxpayers, combined with a large corpus of filed VAT returns…”
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A comprehensive survey on design and application of autoencoder in deep learning by Li, Pengzhi, Pei, Yan, Li, Jianqiang

“… With the development of deep learning technology, autoencoder has attracted the attention of many scholars…”
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A hybrid Intrusion Detection System based on Sparse autoencoder and Deep Neural Network by Narayana Rao, K., Venkata Rao, K., P.V.G.D., Prasad Reddy

“… In the second stage, the Deep Neural Network (DNN) was used to predict and classify attacks. The classifier classifies multi attack classification from the extracted…”
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