Search Results - Stacked sparse autoencoder data reconstruction*

A sparse stacked denoising autoencoder with optimized transfer learning applied to the fault diagnosis of rolling bearings by Sun, Meidi, Wang, Hui, Liu, Ping, Huang, Shoudao, Fan, Peng

“… As an unsupervised deep learning algorithm, a stacked autoencoder (SAE) can relieve the pressure of labelling data…”
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Electricity theft detection based on stacked sparse denoising autoencoder by Huang, Yifan, Xu, Qifeng

“…Inspired by the powerful feature extraction and the data reconstruction ability of autoencoder, a stacked sparse denoising autoencoder is developed for electricity theft detection in this paper…”
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A denoising method for ECG signals based on CEEMDAN-TSO and stacked sparse autoencoders by Li, Shun, Li, Juan, Mao, Jiandong, Hong, Wei, Sun, Ao

“…), Tuna Swarm Optimization (TSO), and Stacked Sparse Autoencoder (SSAE), named CEEMDAN-TSO-SSAE…”
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SDARE: A stacked denoising autoencoder method for game dynamics network structure reconstruction by Huang, Keke, Li, Shuo, Dai, Penglin, Wang, Zhen, Yu, Zhaofei

“… Often, the direct detection of the interaction relationship is costly. Thus, network structure reconstruction, the inverse problem in complex networked systems…”
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Transformer-Based Autoencoder Framework for Nonlinear Hyperspectral Anomaly Detection by Wu, Ziyu, Wang, Bin

“…Recently, the autoencoder (AE) has received significant attention in the hyperspectral anomaly detection task…”
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A novel stacked sparse denoising autoencoder for mammography restoration to visual interpretation of breast lesion by Ghosh, Swarup Kr, Biswas, Biswajit, Ghosh, Anupam

“… A deep stacked convolutional autoencoder is designed by combining the autoencoder and the deconvolution network which conjointly reduces noisy artifacts and improves image details in mammogram…”
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An extreme rainfall-induced landslide susceptibility assessment using autoencoder combined with random forest in Shimane Prefecture, Japan by Nam, Kounghoon, Wang, Fawu

“… A stacked autoencoder (StAE) and a sparse autoencoder (SpAE) both consist of an input layer for raw data, hidden layer for feature extraction, and output layer for classification and prediction…”
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Robust Cell Detection and Segmentation in Histopathological Images Using Sparse Reconstruction and Stacked Denoising Autoencoders by Su, Hai, Xing, Fuyong, Kong, Xiangfei, Xie, Yuanpu, Zhang, Shaoting, Yang, Lin

“… In this paper, we present a cell detection and segmentation algorithm using the sparse reconstruction with trivial templates and a stacked denoising autoencoder (sDAE…”
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Stacked Autoencoder Based Weak Supervision for Social Image Understanding by Xu, Chaoyang, Dai, Yuanfei, Lin, Renjie, Wang, Shiping

“… are frequently utilized for this purpose. Autoencoder models have been validated to be effective in learning latent low-dimensional representations in unsupervised learning…”
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Hyperband Tuned Deep Neural Network With Well Posed Stacked Sparse AutoEncoder for Detection of DDoS Attacks in Cloud by Bhardwaj, Aanshi, Mangat, Veenu, Vig, Renu

“… Some of them are inability to access data coming to and from cloud service, theft and misuse of data hosted, no control over sensitive data access, advance threats like malware injection attack…”
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The optimally designed autoencoder network for compressed sensing by Zhang, Zufan, Wu, Yunfeng, Gan, Chenquan, Zhu, Qingyi

“… To this end, a deep learning based stacked sparse denoising autoencoder compressed sensing (SSDAE_CS) model, which mainly consists of an encoder sub-network and a decoder sub-network, is proposed and analyzed in this paper…”
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Fault classification based on variable‐weighted dynamic sparse stacked autoencoder for industrial processes by Dong, Jie, Tu, Yanan, Peng, Kaixiang

“… In this paper, a fault classification method based on a variable‐weighted dynamic sparse stacked autoencoder is proposed…”
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An intrusion detection method based on stacked sparse autoencoder and improved gaussian mixture model by Zhang, Tianyue, Chen, Wei, Liu, Yuxiao, Wu, Lifa

“… This paper proposes an intrusion detection method based on stacked sparse autoencoder and improved Gaussian mixture model (SIGMOD…”
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Modified Stacked Autoencoder Using Adaptive Morlet Wavelet for Intelligent Fault Diagnosis of Rotating Machinery by Shao, Haidong, Xia, Min, Wan, Jiafu, de Silva, Clarence W.

“… In this article, a modified stacked autoencoder (MSAE) that uses adaptive Morlet wavelet is proposed to automatically diagnose various fault types and severities of rotating machinery…”
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Infrared and Visible Image Fusion Based on Deep Decomposition Network and Saliency Analysis by Jian, Lihua, Rayhana, Rakiba, Ma, Ling, Wu, Shaowu, Liu, Zheng, Jiang, Huiqin

“…Traditional image fusion focuses on selecting an effective decomposition approach to extract representative features from the source image and attempts to find…”
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Multimode system condition monitoring using sparsity reconstruction for quality control by Bougheloum, Wafa, Bekaik, Mounir, Gherbi, Sofiane

“…In this paper, we introduce an improved multivariate statistical monitoring method based on the stacked sparse autoencoder (SSAE…”
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Lithium exploration targeting through robust variable selection and deep anomaly detection: An integrated application of sparse principal component analysis and stacked autoencoders by Esmaeiloghli, Saeid, Lima, Alexandre, Sadeghi, Behnam

“… Robust sparse…”
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Temporal-Spatial Neighborhood Enhanced Sparse Autoencoder for Nonlinear Dynamic Process Monitoring by Li, Nanxi, Shi, Hongbo, Song, Bing, Tao, Yang

“… Traditional autoencoders, such as stacked denoising autoencoders (SDAEs), have excellent nonlinear feature extraction capabilities, but they ignore the dynamic correlation between sample data…”
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Classification of Hyperspectral Images by Gabor Filtering Based Deep Network by Kang, Xudong, Li, Chengchao, Li, Shutao, Lin, Hui

“… Afterwards, deep features are captured by training a stacked sparse autoencoder deep network with the fused features obtained above as inputs…”
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RETRACTED: Optimization using Internet of Agent based Stacked Sparse Autoencoder Model for Heart Disease Prediction by Baviskar, Vaishali, Verma, Madhushi, Chatterjee, Pradeep, Singal, Gaurav, Gadekallu, Thippa Reddy

“…Recently, machine learning methods have been successfully used for the prediction of cardiovascular disease. Early diagnosis and prediction is necessary for…”
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