Suchergebnisse - Stacked sparse autoencoder data reconstruction

A sparse stacked denoising autoencoder with optimized transfer learning applied to the fault diagnosis of rolling bearings von 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 von 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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Hyperband Tuned Deep Neural Network With Well Posed Stacked Sparse AutoEncoder for Detection of DDoS Attacks in Cloud von 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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A denoising method for ECG signals based on CEEMDAN-TSO and stacked sparse autoencoders von 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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Fault classification based on variable‐weighted dynamic sparse stacked autoencoder for industrial processes von 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 von 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 von 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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Lithium exploration targeting through robust variable selection and deep anomaly detection: An integrated application of sparse principal component analysis and stacked autoencoders von Esmaeiloghli, Saeid, Lima, Alexandre, Sadeghi, Behnam

“… Robust sparse …”
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SDARE: A stacked denoising autoencoder method for game dynamics network structure reconstruction von 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 von 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 von 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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RETRACTED: Optimization using Internet of Agent based Stacked Sparse Autoencoder Model for Heart Disease Prediction von 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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Stacked sparse autoencoder networks and statistical shape models for automatic staging of distal femur trochlear dysplasia von Cerveri, Pietro, Belfatto, Antonella, Baroni, Guido, Manzotti, Alfonso

“… ), able to represent the individual morphology of the trochlea by means of a set of parameters and stacked sparse autoencoder (SSPA …”
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Building Extraction Based on an Optimized Stacked Sparse Autoencoder of Structure and Training Samples Using LIDAR DSM and Optical Images von Yan, Yiming, Tan, Zhichao, Su, Nan, Zhao, Chunhui

“… In this paper, a building extraction method is proposed based on a stacked sparse autoencoder with an optimized structure and training samples …”
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A Cylindrical Near-Field Acoustical Holography Method Based on Cylindrical Translation Window Expansion and an Autoencoder Stacked with 3D-CNN Layers von Wang, Jiaxuan, Zhang, Weihan, Zhang, Zhifu, Huang, Yizhe

“… Through a 3D convolution neural network (CNN) and stacked autoencoder framework (CSA), the data-driven CSA-NAH method can solve this problem by utilizing the information from data in each dimension …”
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Unsupervised Domain Adaptation via Stacked Convolutional Autoencoder von Zhu, Yi, Zhou, Xinke, Wu, Xindong

“… The second problem that arises is that redundancy of image data results in performance degradation …”
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An extreme rainfall-induced landslide susceptibility assessment using autoencoder combined with random forest in Shimane Prefecture, Japan von 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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Stacked Autoencoder Based Weak Supervision for Social Image Understanding von 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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The optimally designed autoencoder network for compressed sensing von 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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Enhanced fault detection in digital VLSI circuits using convolutional autoencoders von Savalam, Chandrasekhar, Medisetti, Sanjay, Korapati, Prasanti

“… ) techniques continues to grow. Conventional diagnostic methods often face limitations in identifying subtle stuck-at faults within complex and high-dimensional test data …”
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