## What is/are Stacked Denoising?

Stacked Denoising - Given that deep neural networks have been a success in computer vision in the past few years, a data contrast enhancement approach with stacked denoising autoencoder (DAE) is proposed in this paper to enhance the abnormal regions in the thermal frames gathered by pulsed thermography.^{[1]}To address this problem, this work proposes a fusion data preprocessing method based on a stacked denoising auto-encoders network (SDAE) and generative adversarial networks (GANs).

^{[2]}First, Stacked Denoising Auto Encoder is used to extract user features, and Gated Recurrent Unit with auxiliary information is used to extract items’ latent vectors, respectively.

^{[3]}Aiming at the characteristics of large capacity and diversity of rolling bearing fault data, an intelligent rolling bearing fault diagnosis method based on stacked denoising auto encoders was proposed.

^{[4]}Through a stacked denoising autoencoder (SDAE), a total of five high-level features could be extracted from the original signals.

^{[5]}In this paper, we propose a two-step deep learning-based method combining stacked denoising autoencoders (SDAE) with SVM-RFE to accomplish the task of feature selection.

^{[6]}In this paper, we address this challenge for the TM speech recordings using a transfer learning approach based on the stacked denoising auto-encoders (SDA).

^{[7]}As an extension of stacked denoising autoencoders (SDAE), SCDAE replaces fully-connected layers with one-dimensional convolutional layers as middle structures.

^{[8]}To validate the proposed scheme, we have simulated two different architectures: 1) deep belief network (DBN) for classification and 2) stacked denoising autoencoder for the reconstruction of handwritten digits from the MNIST data set.

^{[9]}This paper presents a method for denoising and feature extraction of weld seam profiles with strong welding noise in gas metal arc welding (GMAW) process by using stacked denoising autoencoder (SDAE).

^{[10]}Based on stacked denoising auto-encoders (SDAE) and combined with the Monte-Carlo simulation (MCS) method, this paper proposes a hybrid algorithm (SDAE-Based-PPF, SPPF) for online calculation of probabilistic power flow (PPF) with tie-line power transfer.

^{[11]}In order to predict the subareas’ data in several future sensing cycles, we further propose a nonlinear autoregressive neural network, and a stacked denoising autoencoder to obtain the temporal–spatial correlation between the data from different cycles or subareas.

^{[12]}In order to solve above problems, by combining Auxiliary Classifier Generative Adversarial Network (ACGAN) and Stacked Denoising Auto Encoder (SDAE), a novel method is proposed for fault diagnosis.

^{[13]}The symmetric SDAE model is composed of two stacked denoising automatic codes, one is used to train item information to obtain the item feature matrix, and the other is used to train user information to obtain the user feature matrix.

^{[14]}The experimental results prove that the method used in this article has better performance than the current excellent S-Transform (S-T) algorithm, Wavelet Transform (WT) algorithm, Stacked Denoising Autoencoder (S-DAE) algorithm, and Improved Denoising Autoencoder (I-DAE) algorithm.

^{[15]}Aiming at the problem that the complex working conditions affect the effect of manual feature extraction in bearing fault diagnosis of metro traction motor, a fault diagnosis method of metro traction motor bearing based on improved stacked denoising autoencoder (SDAE) is proposed.

^{[16]}In this study, we combined a stacked denoising autoencoder and long short term memory technique with a rule-based refinement method applying a rule-based hidden Markov model (HMM) to perform robust and valid semantic trajectory extraction.

^{[17]}A stacked denoising autoencoder (SDAE) model is proposed to detect, identify, and reconcile faulty data based on data from a real WWTP in South Korea.

^{[18]}This paper presents a novel neural network based model, called gated relational probabilistic stacked denoising autoencoder with localized author (GRSLA) embedding, for global citation recommendation task.

^{[19]}We investigated thirty-three classification strategies based on eleven dimensionality reduction methods, namely, the least absolute shrinkage and selection operator (LASSO), genetic algorithm-support vector machine (GA-SVM), Pearson correlation coefficient (PCC), mutual information (MI), maximal relevance minimal redundancy Pearson correlation (mRMRP), maximal relevance minimal redundancy mutual information (mRMRMI), statistical features (SF), principal component analysis (PCA), kernel principal component analysis (KPCA), locally linear embedding (LLE), and stacked denoising autoencoder (SDAE), and three machine learning models, namely, artificial neural network (ANN), linear support vector machine (L-SVM), and Gaussian Naïve Bayes (GNB).

^{[20]}In the reconstruction sub-network, a cascade network combining stacked denoising autoencoder (SDA) and convolutional neural network (CNN) is designed to reconstruct signals.

^{[21]}In addition, an alternative optimization strategy is introduced, making the features learned by bi - temporal stacked denoising auto-encoder (SDAEs) have more consistent representations, as well as making the change detection map more accurate.

^{[22]}Additionally, its stacked denoising version seems to more robustly be able to recover them for new unseen data.

^{[23]}In this study, we developed a novel neural network ensemble, SE-SDAE, based on stacked denoising autoencoders (SDAEs) which identify different levels of cognitive load by electroencephalography (EEG) signals.

^{[24]}We use stacked denoising auto-encoders to extract features from clinical and genetic data, and use 3D-convolutional neural networks (CNNs) for imaging data.

^{[25]}Results In this study, we propose an ensemble deep learning model to predict plant lncRNA-protein interactions using stacked denoising autoencoder and convolutional neural network based on sequence and structural information, named PRPI-SC.

^{[26]}This paper proposes a method of fault feature extraction for motor based on the principle of stacked denoising autoencoder (SDAE) combined with the support vector machine (SVM) classifier.

^{[27]}Based on this scenario, in this article, we propose a cooperative link prediction model (CLPM) using a stacked denoising autoencoder (SDAE) to predict links of the IIoT-based MDs at the next moment through historical link information.

^{[28]}However, supervised deep learning models such as convolutional neural networks require a large number of labeled image data, which is intractable in applications, while unsupervised deep learning models like stacked denoising auto-encoder cannot employ label information.

^{[29]}In this paper, an adaptive feature extraction model based on stacked denoising autoencoder has been proposed.

^{[30]}Complete modified stacked denoising auto-encoder (CMSDAE) machines constitute a version of stacked auto-encoders in which a target estimate is included at the input, and are trained layer-by-layer by minimizing a convex combination of the errors corresponding to the input sample and the target.

^{[31]}In this paper, a new recognition method based on contour lines of ambiguity function and stacked denoising auto-encoders is proposed.

^{[32]}In this research, a new method named stacked denoising autoencoder (SDAE) with online sequential extreme learning machine (OS-ELM) is put forward for intelligent recognition of tool wear states.

^{[33]}This paper proposes a new DNN model, knowledge-based deep stacked denoising auto-encoders (KBSDAE), which inserts the knowledge (i.

^{[34]}The load signal image disaggregation is achieved through the powerful stacked denoising autoencoder noise extraction network.

^{[35]}NeuMF++ is proposed by incorporating effective latent representation into NeuMF via Stacked Denoising Autoencoders (SDAE).

^{[36]}Secondly, the stacked denoising autoencoders are used to extract the robust graph features at different scales with different corruption rates.

^{[37]}To solve these problems, we put forward a novel method to accurately detect HFOs based on the stacked denoising autoencoder (SDAE) and the ensemble classifier with sample weight adjusting factors.

^{[38]}A stacked denoising convolutional autoencoder (SDCA) model was proposed to classify X-ray images into three classes: normal, pneumonia, and COVID-19.

^{[39]}First, a DMLF model is designed by combining a Convolution Neural Network (CNN) and Stacked Denoising Autoencoder (SDAE) together to capture more comprehensive fault knowledge and extract features from different modal data.

^{[40]}Then, the representability of the physical features is improved by connecting stacked denoising autoencoders and squeeze-and-excitation networks.

^{[41]}In this study, a deep learning-based stacked denoising autoencoder (SDAE) method is proposed to directly predict battery life by extracting various battery features.

^{[42]}To address the uncertainties of renewable energy and loads in transient stability assessment with credible contingencies, this letter proposes a stacked denoising autoencoder (SDAE)-based probabilistic prediction method.

^{[43]}To address these problems, this letter proposes a novel RGB-D image classification framework based on reduced biquaternion stacked denoising convolutional autoencoder (RQ-SDCAE).

^{[44]}A continuous beam example is used for testing, and the test results show that the identification network based on the bridge load frequency changes and the Stacked Denoising Auto-Encoder(SDAE) network can effectively locate the damage of bridge, which enables the frequency changes data to overcome the nonunique limitation of the symmetrical structure, enhances the expression ability of the parameters and improves the effectiveness and the anti-noise property of bridge damage location identification.

^{[45]}Aiming at the characteristics of traditional intrusion detection, such as loss of features, low detection efficiency and poor adaptability, an intrusion detection method based on stacked denoising convolutional autoencoders is proposed, which combine convolutional neural network and denoising autoencoder to strengthen feature recognition ability, using Dropout and regularization methods to prevent overfitting, and using Adam algorithm to obtain optimal parameters.

^{[46]}The aim of this study was to investigate the capability of two unsupervised deep-learning algorithms (deep-belief networks, DBN) and stacked denoising autoencoders (SDA) to define transition rules in order to obtain more accurate simulated results.

^{[47]}In view of the low accuracy of traditional diagnosis algorithm, Stacked Denoising Auto-Encoder (SDAE) model as the first network is used to extract the basic and shallow feature of the fault signal; in order to acquire more robust and deep feature representation, Deep Belief Network (DBN) is configured as the second network.

^{[48]}Therefore, this paper proposes a novel unsupervised construction method of turbine engine health indicator based on stacked denoising autoencoders (SDAE).

^{[49]}The research on stacked denoising autoencoder (SDA) has demonstrated that noise-robust features could be learned through training a model to reduce the man-made (simulated) noises.

^{[50]}

## convolutional neural network

In the reconstruction sub-network, a cascade network combining stacked denoising autoencoder (SDA) and convolutional neural network (CNN) is designed to reconstruct signals.^{[1]}We use stacked denoising auto-encoders to extract features from clinical and genetic data, and use 3D-convolutional neural networks (CNNs) for imaging data.

^{[2]}Results In this study, we propose an ensemble deep learning model to predict plant lncRNA-protein interactions using stacked denoising autoencoder and convolutional neural network based on sequence and structural information, named PRPI-SC.

^{[3]}However, supervised deep learning models such as convolutional neural networks require a large number of labeled image data, which is intractable in applications, while unsupervised deep learning models like stacked denoising auto-encoder cannot employ label information.

^{[4]}Aiming at the characteristics of traditional intrusion detection, such as loss of features, low detection efficiency and poor adaptability, an intrusion detection method based on stacked denoising convolutional autoencoders is proposed, which combine convolutional neural network and denoising autoencoder to strengthen feature recognition ability, using Dropout and regularization methods to prevent overfitting, and using Adam algorithm to obtain optimal parameters.

^{[5]}, stacked denoising autoencoder, and convolutional neural network (CNN) significantly.

^{[6]}To overcome the data sparsity problem, we present a hybrid model named robust attention convolutional matrix factorization (RACMF) model, which is composed of attention convolutional neural network (ACNN) and additional stacked denoising autoencoder (aSDAE); ACNN and aSDAE are used to extract the items’ and users’ latent factors, respectively.

^{[7]}Different from the regular stacked denoising auto-encoder (SDAE) and convolutional neural network (CNN), SCSDAE integrates CNN and SDAE to learn effective features and accumulate the robustness layer by layer, which adopts SDAE as the feature extractor and stacks well-designed fully connected SDAE in a convolutional way to obtain much robust feature representations.

^{[8]}As an alternative to relying on previously established optimization based algorithms, we implement stacked denoising autoencoders and convolutional neural networks to perform signal reconstructions.

^{[9]}This algorithm is implemented in a rotor fault diagnosis experiment, and the results are compared with those obtained by using an ELM algorithm, a normal DELM algorithm, a stacked denoising auto encoder (SAE) algorithm and a convolutional neural network (CNN) algorithm.

^{[10]}This paper proposes an intrusion detection method based on hierarchical feature learning, which first learns the byte-level features of network traffic through deep convolutional neural networks and then learns session-level features using Stacked Denoising Autoencoder.

^{[11]}Three different deep learning architectures were mentioned amongst the included studies: convolutional neural network (CNN), massive training artificial neural network (MTANN), and deep stacked denoising autoencoder extreme learning machine (SDAE-ELM).

^{[12]}A fault diagnosis model of combining stacked denoising autoencoder (SDAE) and convolutional neural network (CNN) is proposed to solve the problem of difficult classification under strong noise environment.

^{[13]}

## deep learning method

Recently, deep learning methods that employ stacked denoising auto-encoders (SDAs) to learn new representations for both domains have been successfully applied in domain adaptation.^{[1]}We developed an unsupervised deep learning method, stacked denoising autoencoders (SdA), by integrating multi-platform genomic data for subtyping ccRCC with the goal of assisting diagnosis, personalized treatments and prognosis.

^{[2]}This paper proposes an effective deep learning method, enhanced stacked denoising autoencoder (ESDAE) with manifold regularization for wafer map pattern recognition (WMPR) in manufacturing processes.

^{[3]}Regarding the deep learning methods, we focus on Stacked Denoising Autoencoders (SAE), so we study its impact upon the performance of HCC computerized diagnosis.

^{[4]}The deep-learning methods called auto-encoder and stacked denoising autoencoders are applied to the collected data to extract the features of users and content, respectively.

^{[5]}This paper presents an effective and reliable deep learning method known as stacked denoising autoencoder (SDAE) for PPR in manufacturing processes.

^{[6]}

## deep neural network

In this paper, a novel deep neural network called stacked denoising and contractive auto-encoder (SDCAE) is designed for millimeter wave radar HRRP recognition.^{[1]}We utilize a deep neural network for both feature extraction and then classification based on unsupervised pre-training using stacked denoising autoencoder method and supervised fine-tuning using logistic regression on top.

^{[2]}Aiming at this problem, we use the stacked denoising auto-encoder (SDAE) to superimpose into deep neural network.

^{[3]}In this paper, the proposed face recognition system namely Deep Stacked Denoising Sparse Autoencoders (DSDSA) combines the deep neural network technology, sparse autoencoders and denoising task.

^{[4]}The proposed deep neural network (DNN) is based on the stacked denoising autoencoder (SDAE).

^{[5]}

## support vector machine

We investigated thirty-three classification strategies based on eleven dimensionality reduction methods, namely, the least absolute shrinkage and selection operator (LASSO), genetic algorithm-support vector machine (GA-SVM), Pearson correlation coefficient (PCC), mutual information (MI), maximal relevance minimal redundancy Pearson correlation (mRMRP), maximal relevance minimal redundancy mutual information (mRMRMI), statistical features (SF), principal component analysis (PCA), kernel principal component analysis (KPCA), locally linear embedding (LLE), and stacked denoising autoencoder (SDAE), and three machine learning models, namely, artificial neural network (ANN), linear support vector machine (L-SVM), and Gaussian Naïve Bayes (GNB).^{[1]}This paper proposes a method of fault feature extraction for motor based on the principle of stacked denoising autoencoder (SDAE) combined with the support vector machine (SVM) classifier.

^{[2]}

## Combining Stacked Denoising

In this paper, we propose a two-step deep learning-based method combining stacked denoising autoencoders (SDAE) with SVM-RFE to accomplish the task of feature selection.^{[1]}In the reconstruction sub-network, a cascade network combining stacked denoising autoencoder (SDA) and convolutional neural network (CNN) is designed to reconstruct signals.

^{[2]}To address this problem, an unsupervised outlier detection approach combining stacked denoising autoencoder (SDAE) and density-grid-based clustering method is proposed.

^{[3]}This paper uses a novel hybrid coding architecture, Deep Filter Banks (DFB), combining stacked denoising sparse autoencoder (SDSAE) and Fisher Vector (FV) for visual terrain classification.

^{[4]}We exhibit two DTF instantiations by combining stacked denoising autoencoder (SDAE) and CANDECOMP/PARAFAC (CP) tensor factorization.

^{[5]}A fault diagnosis model of combining stacked denoising autoencoder (SDAE) and convolutional neural network (CNN) is proposed to solve the problem of difficult classification under strong noise environment.

^{[6]}

## Marginalized Stacked Denoising

To obtain robust and informative feature representations, we first incorporate a sample-affinity matrix into the marginalized Stacked Denoising Autoencoder to obtain shared features that are then combined with the private features.^{[1]}Marginalized stacked denoising autoencoder model (mSDA) has proved to be effective to address this problem.

^{[2]}Marginalized stacked denoising autoencoder (mSDA), has recently emerged with demonstrated effectiveness in domain adaptation.

^{[3]}

## Sparse Stacked Denoising

The proposed method is based on sparse stacked denoising autoencoders (SSDAEs).^{[1]}Considering the data reconstruction ability and robustness, a sparse stacked denoising autoencoder (SSDAE) is proposed for feature extraction, which can indirectly improve the diagnostic accuracy in the target domain.

^{[2]}Experimental results show that our proposed method performs better than dictionary learning (K-singular value decomposition), transform learning, sparse stacked denoising autoencoder, and the gold standard BM3D algorithm.

^{[3]}

## Deep Stacked Denoising

This paper proposes a new DNN model, knowledge-based deep stacked denoising auto-encoders (KBSDAE), which inserts the knowledge (i.^{[1]}In this paper, the proposed face recognition system namely Deep Stacked Denoising Sparse Autoencoders (DSDSA) combines the deep neural network technology, sparse autoencoders and denoising task.

^{[2]}Three different deep learning architectures were mentioned amongst the included studies: convolutional neural network (CNN), massive training artificial neural network (MTANN), and deep stacked denoising autoencoder extreme learning machine (SDAE-ELM).

^{[3]}

## Modified Stacked Denoising

Complete modified stacked denoising auto-encoder (CMSDAE) machines constitute a version of stacked auto-encoders in which a target estimate is included at the input, and are trained layer-by-layer by minimizing a convex combination of the errors corresponding to the input sample and the target.^{[1]}We integrate CCRF with NN by introducing a Long Short-Term Memory (LSTM) component to learn the non-linear mapping from inputs to outputs of each region, and a modified Stacked Denoising AutoEncoder (SDAE) component for pairwise interactions modeling between regions.

^{[2]}

## New Stacked Denoising

This paper proposes a new stacked denoising autoencoders (SDAE), called manifold regularized SDAE (MRSDAE) based on particle swarm optimization (PSO), where manifold regularization and feature selection are embedded in the deep network.^{[1]}This paper proposes a new stacked denoising autoencoders (SDAE) algorithm, called manifold regularized SDAE (MRSDAE) based on particle swarm optimization (PSO), where manifold regularization and feature selection are embedded in the deep network smoothly.

^{[2]}

## Use Stacked Denoising

We use stacked denoising auto-encoders to extract features from clinical and genetic data, and use 3D-convolutional neural networks (CNNs) for imaging data.^{[1]}The proposed method uses stacked denoising autoencoder (SDAE), which takes the raw training data as the input of network and can directly obtain fault locations.

^{[2]}

## stacked denoising autoencoder

Given that deep neural networks have been a success in computer vision in the past few years, a data contrast enhancement approach with stacked denoising autoencoder (DAE) is proposed in this paper to enhance the abnormal regions in the thermal frames gathered by pulsed thermography.^{[1]}Through a stacked denoising autoencoder (SDAE), a total of five high-level features could be extracted from the original signals.

^{[2]}In this paper, we propose a two-step deep learning-based method combining stacked denoising autoencoders (SDAE) with SVM-RFE to accomplish the task of feature selection.

^{[3]}As an extension of stacked denoising autoencoders (SDAE), SCDAE replaces fully-connected layers with one-dimensional convolutional layers as middle structures.

^{[4]}To validate the proposed scheme, we have simulated two different architectures: 1) deep belief network (DBN) for classification and 2) stacked denoising autoencoder for the reconstruction of handwritten digits from the MNIST data set.

^{[5]}This paper presents a method for denoising and feature extraction of weld seam profiles with strong welding noise in gas metal arc welding (GMAW) process by using stacked denoising autoencoder (SDAE).

^{[6]}In order to predict the subareas’ data in several future sensing cycles, we further propose a nonlinear autoregressive neural network, and a stacked denoising autoencoder to obtain the temporal–spatial correlation between the data from different cycles or subareas.

^{[7]}The experimental results prove that the method used in this article has better performance than the current excellent S-Transform (S-T) algorithm, Wavelet Transform (WT) algorithm, Stacked Denoising Autoencoder (S-DAE) algorithm, and Improved Denoising Autoencoder (I-DAE) algorithm.

^{[8]}Aiming at the problem that the complex working conditions affect the effect of manual feature extraction in bearing fault diagnosis of metro traction motor, a fault diagnosis method of metro traction motor bearing based on improved stacked denoising autoencoder (SDAE) is proposed.

^{[9]}In this study, we combined a stacked denoising autoencoder and long short term memory technique with a rule-based refinement method applying a rule-based hidden Markov model (HMM) to perform robust and valid semantic trajectory extraction.

^{[10]}A stacked denoising autoencoder (SDAE) model is proposed to detect, identify, and reconcile faulty data based on data from a real WWTP in South Korea.

^{[11]}This paper presents a novel neural network based model, called gated relational probabilistic stacked denoising autoencoder with localized author (GRSLA) embedding, for global citation recommendation task.

^{[12]}We investigated thirty-three classification strategies based on eleven dimensionality reduction methods, namely, the least absolute shrinkage and selection operator (LASSO), genetic algorithm-support vector machine (GA-SVM), Pearson correlation coefficient (PCC), mutual information (MI), maximal relevance minimal redundancy Pearson correlation (mRMRP), maximal relevance minimal redundancy mutual information (mRMRMI), statistical features (SF), principal component analysis (PCA), kernel principal component analysis (KPCA), locally linear embedding (LLE), and stacked denoising autoencoder (SDAE), and three machine learning models, namely, artificial neural network (ANN), linear support vector machine (L-SVM), and Gaussian Naïve Bayes (GNB).

^{[13]}In the reconstruction sub-network, a cascade network combining stacked denoising autoencoder (SDA) and convolutional neural network (CNN) is designed to reconstruct signals.

^{[14]}In this study, we developed a novel neural network ensemble, SE-SDAE, based on stacked denoising autoencoders (SDAEs) which identify different levels of cognitive load by electroencephalography (EEG) signals.

^{[15]}Results In this study, we propose an ensemble deep learning model to predict plant lncRNA-protein interactions using stacked denoising autoencoder and convolutional neural network based on sequence and structural information, named PRPI-SC.

^{[16]}This paper proposes a method of fault feature extraction for motor based on the principle of stacked denoising autoencoder (SDAE) combined with the support vector machine (SVM) classifier.

^{[17]}Based on this scenario, in this article, we propose a cooperative link prediction model (CLPM) using a stacked denoising autoencoder (SDAE) to predict links of the IIoT-based MDs at the next moment through historical link information.

^{[18]}In this paper, an adaptive feature extraction model based on stacked denoising autoencoder has been proposed.

^{[19]}In this research, a new method named stacked denoising autoencoder (SDAE) with online sequential extreme learning machine (OS-ELM) is put forward for intelligent recognition of tool wear states.

^{[20]}The load signal image disaggregation is achieved through the powerful stacked denoising autoencoder noise extraction network.

^{[21]}NeuMF++ is proposed by incorporating effective latent representation into NeuMF via Stacked Denoising Autoencoders (SDAE).

^{[22]}Secondly, the stacked denoising autoencoders are used to extract the robust graph features at different scales with different corruption rates.

^{[23]}To solve these problems, we put forward a novel method to accurately detect HFOs based on the stacked denoising autoencoder (SDAE) and the ensemble classifier with sample weight adjusting factors.

^{[24]}First, a DMLF model is designed by combining a Convolution Neural Network (CNN) and Stacked Denoising Autoencoder (SDAE) together to capture more comprehensive fault knowledge and extract features from different modal data.

^{[25]}Then, the representability of the physical features is improved by connecting stacked denoising autoencoders and squeeze-and-excitation networks.

^{[26]}In this study, a deep learning-based stacked denoising autoencoder (SDAE) method is proposed to directly predict battery life by extracting various battery features.

^{[27]}To address the uncertainties of renewable energy and loads in transient stability assessment with credible contingencies, this letter proposes a stacked denoising autoencoder (SDAE)-based probabilistic prediction method.

^{[28]}The aim of this study was to investigate the capability of two unsupervised deep-learning algorithms (deep-belief networks, DBN) and stacked denoising autoencoders (SDA) to define transition rules in order to obtain more accurate simulated results.

^{[29]}Therefore, this paper proposes a novel unsupervised construction method of turbine engine health indicator based on stacked denoising autoencoders (SDAE).

^{[30]}The research on stacked denoising autoencoder (SDA) has demonstrated that noise-robust features could be learned through training a model to reduce the man-made (simulated) noises.

^{[31]}We integrate CCRF with NN by introducing a Long Short-Term Memory (LSTM) component to learn the non-linear mapping from inputs to outputs of each region, and a modified Stacked Denoising AutoEncoder (SDAE) component for pairwise interactions modeling between regions.

^{[32]}A reliability assessment and prediction model based on stacked denoising autoencoder and hierarchical bayesian is proposed in this paper.

^{[33]}In this paper, a novel change detection technique is proposed based on multiscale superpixel segmentation and stacked denoising autoencoders (SDAE).

^{[34]}To obtain robust and informative feature representations, we first incorporate a sample-affinity matrix into the marginalized Stacked Denoising Autoencoder to obtain shared features that are then combined with the private features.

^{[35]}, stacked denoising autoencoder, and convolutional neural network (CNN) significantly.

^{[36]}In particular, the neural network decomposed by TT format is a stacked denoising autoencoder (SDA) network, which called TT-SDA.

^{[37]}Secondly, based on the data preprocessed from multiple views, the stacked denoising autoencoders are used to automatically extract user features with different corruption rates.

^{[38]}In this paper, a multiperiod-ahead WSF model based on the analysis of variance, stacked denoising autoencoder (SDAE), and ensemble learning is proposed.

^{[39]}To overcome the data sparsity problem, we present a hybrid model named robust attention convolutional matrix factorization (RACMF) model, which is composed of attention convolutional neural network (ACNN) and additional stacked denoising autoencoder (aSDAE); ACNN and aSDAE are used to extract the items’ and users’ latent factors, respectively.

^{[40]}In addition, by comparing with stacked autoencoders, stacked denoising autoencoders, LeNet-5, speeded-up robust features, and pretrained deep learning model ImageNet-VGG-F algorithms, we find that our approach achieves satisfactory image categorization results on two benchmark datasets.

^{[41]}This task allows application of machine learning techniques, such as Stacked Denoising Autoencoders (DSAE).

^{[42]}The proposed method is based on sparse stacked denoising autoencoders (SSDAEs).

^{[43]}This paper proposes the use of Sum Rule and Xgboost to combine the outputs related to various Stacked Denoising Autoencoders (SDAEs) in order to detect abnormal HTTP queries.

^{[44]}To determine personalized properties in high dimensional EEG indicators, we introduce a feature mapping layer in stacked denoising autoencoder (SDAE) that is capable of preserving the local information in EEG dynamics.

^{[45]}We developed an unsupervised deep learning method, stacked denoising autoencoders (SdA), by integrating multi-platform genomic data for subtyping ccRCC with the goal of assisting diagnosis, personalized treatments and prognosis.

^{[46]}To address this problem, an unsupervised outlier detection approach combining stacked denoising autoencoder (SDAE) and density-grid-based clustering method is proposed.

^{[47]}Marginalized stacked denoising autoencoder model (mSDA) has proved to be effective to address this problem.

^{[48]}Secondly, a hard division method is proposed based on clustering which combines stacked denoising autoencoder and floating process knowledge.

^{[49]}To handle the dynamic fluctuation with time and sparsity of Wi-Fi signals, we propose an SDAE (Stacked Denoising Autoencoder)-based feature extraction method, which can obtain a robust and time-independent Wi-Fi fingerprint by learning the reconstruction distribution from a raw Wi-Fi signal and an artificial-noise-added Wi-Fi signal.

^{[50]}

## stacked denoising auto

To address this problem, this work proposes a fusion data preprocessing method based on a stacked denoising auto-encoders network (SDAE) and generative adversarial networks (GANs).^{[1]}First, Stacked Denoising Auto Encoder is used to extract user features, and Gated Recurrent Unit with auxiliary information is used to extract items’ latent vectors, respectively.

^{[2]}Aiming at the characteristics of large capacity and diversity of rolling bearing fault data, an intelligent rolling bearing fault diagnosis method based on stacked denoising auto encoders was proposed.

^{[3]}In this paper, we address this challenge for the TM speech recordings using a transfer learning approach based on the stacked denoising auto-encoders (SDA).

^{[4]}Based on stacked denoising auto-encoders (SDAE) and combined with the Monte-Carlo simulation (MCS) method, this paper proposes a hybrid algorithm (SDAE-Based-PPF, SPPF) for online calculation of probabilistic power flow (PPF) with tie-line power transfer.

^{[5]}In order to solve above problems, by combining Auxiliary Classifier Generative Adversarial Network (ACGAN) and Stacked Denoising Auto Encoder (SDAE), a novel method is proposed for fault diagnosis.

^{[6]}In addition, an alternative optimization strategy is introduced, making the features learned by bi - temporal stacked denoising auto-encoder (SDAEs) have more consistent representations, as well as making the change detection map more accurate.

^{[7]}We use stacked denoising auto-encoders to extract features from clinical and genetic data, and use 3D-convolutional neural networks (CNNs) for imaging data.

^{[8]}However, supervised deep learning models such as convolutional neural networks require a large number of labeled image data, which is intractable in applications, while unsupervised deep learning models like stacked denoising auto-encoder cannot employ label information.

^{[9]}Complete modified stacked denoising auto-encoder (CMSDAE) machines constitute a version of stacked auto-encoders in which a target estimate is included at the input, and are trained layer-by-layer by minimizing a convex combination of the errors corresponding to the input sample and the target.

^{[10]}In this paper, a new recognition method based on contour lines of ambiguity function and stacked denoising auto-encoders is proposed.

^{[11]}This paper proposes a new DNN model, knowledge-based deep stacked denoising auto-encoders (KBSDAE), which inserts the knowledge (i.

^{[12]}A continuous beam example is used for testing, and the test results show that the identification network based on the bridge load frequency changes and the Stacked Denoising Auto-Encoder(SDAE) network can effectively locate the damage of bridge, which enables the frequency changes data to overcome the nonunique limitation of the symmetrical structure, enhances the expression ability of the parameters and improves the effectiveness and the anti-noise property of bridge damage location identification.

^{[13]}In view of the low accuracy of traditional diagnosis algorithm, Stacked Denoising Auto-Encoder (SDAE) model as the first network is used to extract the basic and shallow feature of the fault signal; in order to acquire more robust and deep feature representation, Deep Belief Network (DBN) is configured as the second network.

^{[14]}Experimental results show that when combined with our method the stacked denoising auto-encoders achieves significantly improved performance on three challenging datasets.

^{[15]}Recently, deep learning methods that employ stacked denoising auto-encoders (SDAs) to learn new representations for both domains have been successfully applied in domain adaptation.

^{[16]}The first is to apply to CNN units the same improvement techniques that we have successfully used with Stacked Denoising Auto-Encoder classifiers.

^{[17]}Taking advantage of the deep structure and reconstructive strategy of stacked denoising auto encoders (SDAE), a SDAE-based optimal power flow (OPF) is developed to extract the high-level nonlinear correlations between the system operating condition and the OPF solution.

^{[18]}Different from the regular stacked denoising auto-encoder (SDAE) and convolutional neural network (CNN), SCSDAE integrates CNN and SDAE to learn effective features and accumulate the robustness layer by layer, which adopts SDAE as the feature extractor and stacks well-designed fully connected SDAE in a convolutional way to obtain much robust feature representations.

^{[19]}Finally, by integrating stacked denoising auto-encoder with optimized parameters and support vector regression, a dust transmittance model is established to determine dust transmittance based on the extracted features.

^{[20]}Aiming at this problem, we use the stacked denoising auto-encoder (SDAE) to superimpose into deep neural network.

^{[21]}The experimental results on four typical microarray datasets show that the DDAF outperforms the Dual Autoencoder Features (DAF) and the Cost-sensitive Oversampling Stacked Denoising Auto-Encoder (CO-SDAE), rendering the robust ability for dimensionality reduction and imbalanced classification.

^{[22]}This work presents a novel initial fault diagnosis framework based on sliding window stacked denoising auto-encoder (SDAE) and long short-term memory (LSTM) model.

^{[23]}In this paper, we characterize the main building blocks and numerically verify the classification accuracy and energy performance of SmartFog, a distributed and virtualized networked Fog technological platform for the support for Stacked Denoising Auto-Encoder (SDAE)-based anomaly detection in data flows generated by Smart-Meters (SMs).

^{[24]}It consists of offline optimization of LFC strategies with DRL and continuous action search, and online control with policy network where features are extracted by stacked denoising auto-encoders.

^{[25]}This algorithm is implemented in a rotor fault diagnosis experiment, and the results are compared with those obtained by using an ELM algorithm, a normal DELM algorithm, a stacked denoising auto encoder (SAE) algorithm and a convolutional neural network (CNN) algorithm.

^{[26]}(ii) The authors are the first to adopt under-complete stacked denoising auto-encoder (SDA) to construct pose prior by mapping canonical hand pose to latent representation.

^{[27]}Finally, it puts the synthetic samples into the training set and builds a stacked denoising auto encoder model for fault diagnosis.

^{[28]}Experimental results show that when combined with our method the stacked denoising auto-encoders achieve significantly improved performance on three challenging datasets.

^{[29]}In this paper, we propose to provide more information to Stacked Denoising Auto-Encoding classifiers in order to increase their performance.

^{[30]}In this work, the great representation capability of the stacked denoising auto-encoders is used to obtain a new method of imputating missing values based on two ideas: deletion and compensation.

^{[31]}In order to overcome the shortcomings of the artificial design algorithm to extract insufficient features, this paper proposes a graph-regularization stacked denoising auto-encoder (G-SDAE) network that achieves high detection accuracy and improve reliability.

^{[32]}Two hybrid deep learning architectures SDAE-DBN-PSVM (a four-layer Stacked Denoising Auto-encoder with three-layer Deep Belief Nets and Plane-based one class SVM) are implemented for these two channels to learn the high-level feature representation automatically and produce two anomaly scores.

^{[33]}In this paper, the Stacked Denoising Auto-Encoder (SDAE) is adopted for short-term load forecasting using four factors: historical loads, somatosensory temperature, relative humidity, and daily average loads.

^{[34]}Since stacked denoising auto-encoder can be filtered, noise of large numbers of mechanical vibration signals was used for deep learning structure to extract the characteristics of the noise.

^{[35]}The LNC-SDAE framework contains a preliminary label noise cleansing part and a stacked denoising auto-encoder.

^{[36]}

## stacked denoising sparse

Compared with the recently stacked denoising sparse autoencoder, the recognition accuracy is improved by 1%, not only the noise factor is not selected but also the training speed is significantly increased.^{[1]}To this end, we suggest a novel approach by combining the generative adversarial learning and the stacked denoising sparse autoencoder to determine the location of the faulty elements in antennas.

^{[2]}In this paper, we propose a novel semi -supervised distributed approach based on stacked denoising sparse autoencoder and SVM for large-scale intrusion detection systems.

^{[3]}In this paper, the proposed face recognition system namely Deep Stacked Denoising Sparse Autoencoders (DSDSA) combines the deep neural network technology, sparse autoencoders and denoising task.

^{[4]}This paper uses a novel hybrid coding architecture, Deep Filter Banks (DFB), combining stacked denoising sparse autoencoder (SDSAE) and Fisher Vector (FV) for visual terrain classification.

^{[5]}

## stacked denoising automatic

The symmetric SDAE model is composed of two stacked denoising automatic codes, one is used to train item information to obtain the item feature matrix, and the other is used to train user information to obtain the user feature matrix.^{[1]}[3] extracted depth functional features from high-dimensional gene expression profile by stacked denoising automatic encoder and identified a set of highly interacting genes for cancer biomarkers detection.

^{[2]}Secondly, stacked denoising automatic encoder (SDAE) is adopted to explore deep features from frequency spectrum of each individual sensor.

^{[3]}

## stacked denoising convolutional

A stacked denoising convolutional autoencoder (SDCA) model was proposed to classify X-ray images into three classes: normal, pneumonia, and COVID-19.^{[1]}To address these problems, this letter proposes a novel RGB-D image classification framework based on reduced biquaternion stacked denoising convolutional autoencoder (RQ-SDCAE).

^{[2]}Aiming at the characteristics of traditional intrusion detection, such as loss of features, low detection efficiency and poor adaptability, an intrusion detection method based on stacked denoising convolutional autoencoders is proposed, which combine convolutional neural network and denoising autoencoder to strengthen feature recognition ability, using Dropout and regularization methods to prevent overfitting, and using Adam algorithm to obtain optimal parameters.

^{[3]}