Suchergebnisse - Deep sparse autoencoder network

Predicting protein-protein interactions from protein sequences by a stacked sparse autoencoder deep neural network von Wang, Yan-Bin, You, Zhu-Hong, Li, Xiao, Jiang, Tong-Hai, Chen, Xing, Zhou, Xi, Wang, Lei

“… ). Then, these extracted features were reconstructed using the stacked autoencoder. Finally, a novel …”
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A Fuzzy Deep Neural Network With Sparse Autoencoder for Emotional Intention Understanding in Human-Robot Interaction von Chen, Luefeng, Su, Wanjuan, Wu, Min, Pedrycz, Witold, Hirota, Kaoru

“… A fuzzy deep neural network with sparse autoencoder (FDNNSA) is proposed for intention understanding based on human emotions and identification information (i.e …”
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Softmax regression based deep sparse autoencoder network for facial emotion recognition in human-robot interaction von Chen, Luefeng, Zhou, Mengtian, Su, Wanjuan, Wu, Min, She, Jinhua, Hirota, Kaoru

“… However, DNN suffers from problems of learning efficiency and computational complexity. To address these problems, deep sparse autoencoder network (DSAN …”
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Reliable Fault Diagnosis of Rotary Machine Bearings Using a Stacked Sparse Autoencoder-Based Deep Neural Network von Sohaib, Muhammad, Kim, Jong-Myon

“… In this study, using complex envelope spectra and stacked sparse autoencoder …”
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Robust approach for AMC in frequency selective fading scenarios using unsupervised sparse-autoencoder-based deep neural network von Shah, Maqsood Hussain, Dang, Xiaoyu

“… Application of deep learning in the area of automatic modulation classification (AMC) is still evolving. An unsupervised sparse-autoencoder-based deep neural network …”
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Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network von Chen, Zhuyun, Li, Weihua

“… First, time-domain and frequency-domain features are extracted from the different sensor signals, and then these features are input into multiple two-layer sparse autoencoder (SAE …”
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Stacked Sparse Autoencoder-Based Deep Network for Fault Diagnosis of Rotating Machinery von Qi, Yumei, Shen, Changqing, Wang, Dong, Shi, Juanjuan, Jiang, Xingxing, Zhu, Zhongkui

“… Thus, a stacked sparse autoencoder (SAE)-based machine fault diagnosis method is proposed in this paper …”
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EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder von 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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Deep network approach with stacked sparse autoencoders in detection of DDoS attacks on SDN-based VANET von Polat, Huseyin, Turkoglu, Muammer, Polat, Onur

“… In this study, the stacked sparse autoencoder (SSAE) + Softmax classifier deep network model is proposed to detect DDoS attacks targeting SDN-based VANETs …”
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Enhancing accuracy of prediction of critical heat flux in Circular channels by ensemble of deep sparse autoencoders and deep neural Networks von Zubair Khalid, Rehan, Ahmed, Ibrahim, Ullah, Atta, Zio, Enrico, Khan, Asifullah

“… •A novel ensemble of Deep Sparse AEs and DNN based CHF prediction method was proposed …”
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A hybrid Intrusion Detection System based on Sparse autoencoder and Deep Neural Network von Narayana Rao, K., Venkata Rao, K., P.V.G.D., Prasad Reddy

“… In the first stage, the unsupervised Sparse autoencoder (SAE) with smoothed l1 regularization …”
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Combustion stability monitoring through flame imaging and stacked sparse autoencoder based deep neural network von Han, Zhezhe, Hossain, Md. Moinul, Wang, Yuwei, Li, Jian, Xu, Chuanlong

“… •A novel deep learning model is established for predicting combustion stability.•Automatic generation of combustion stability label is achieved …”
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Predicting potential microbe-disease associations based on heterogeneous graph attention network and deep sparse autoencoder von 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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SAEROF: an ensemble approach for large-scale drug-disease association prediction by incorporating rotation forest and sparse autoencoder deep neural network von Jiang, Han-Jing, Huang, Yu-An, You, Zhu-Hong

“… In this work, we present a novel computational model combining sparse auto-encoder and rotation forest (SAEROF …”
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ASD-SAENet: A Sparse Autoencoder, and Deep-Neural Network Model for Detecting Autism Spectrum Disorder (ASD) Using fMRI Data von Almuqhim, Fahad, Saeed, Fahad

“… We designed and implemented a sparse autoencoder (SAE) which results in optimized extraction of features …”
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Breath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network von Aslam, Muhammad Aqeel, Xue, Cuili, Chen, Yunsheng, Zhang, Amin, Liu, Manhua, Wang, Kan, Cui, Daxiang

“… In this study, we proposed a new method for feature extraction using a stacked sparse autoencoder to extract the discriminative features from the unlabeled data of breath samples …”
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Open-circuit fault diagnosis of power rectifier using sparse autoencoder based deep neural network von Xu, Lin, Cao, Maoyong, Song, Baoye, Zhang, Jiansheng, Liu, Yurong, Alsaadi, Fuad E.

“… This paper is concerned with the open-circuit fault diagnosis of phase-controlled three-phase full-bridge rectifier by using a sparse autoencoder-based deep neural network (SAE-based DNN …”
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A method for fault detection in multi-component systems based on sparse autoencoder-based deep neural networks von Yang, Zhe, Baraldi, Piero, Zio, Enrico

“… •A single run-to-failure trajectory is enough to pre-train the deep neural network.•The computational burden of deep neural network hyperparameter setting is reduced …”
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PEMFC Residual Life Prediction Using Sparse Autoencoder-Based Deep Neural Network von Liu, Jiawei, Li, Qi, Han, Ying, Zhang, Guorui, Meng, Xiang, Yu, Jiaxi, Chen, Weirong

“… ) under dynamic operating conditions, this article proposes a PEMFC RUL forecast technique based on the sparse autoencoder (SAE …”
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Seven-layer deep neural network based on sparse autoencoder for voxelwise detection of cerebral microbleed von Zhang, Yu-Dong, Zhang, Yin, Hou, Xiao-Xia, Chen, Hong, Wang, Shui-Hua

“… we developed a seven-layer deep neural network (DNN), which includes one input layer, four sparse autoencoder layers, one softmax layer, and one output layer …”
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