Stacked Denoising Sparse Autoencoder Research Articles

Internet of things with nanomaterials-based predictive model for wastewater treatment using stacked sparse denoising auto-encoder

Abstract Wastewater is a serious concern for the environment. There is a substantial amount of toxins that are discharged continuously from several pharmacological companies that lead to serious damage to public health and the ecosystem. Present wastewater treatment technologies include primary, tertiary, and secondary treatments that remove numerous contaminants; but pollutants in the nanoscale range were hard to remove with these steps. Some of these include inorganic and organic pollutants, pathogens, pharmaceuticals, and pollutants of developing concern. The utility of nanoparticles was a promising solution to this issue. Nanoparticles have exclusive properties permitting them to potentially eliminate residual pollutants but being eco-friendly and inexpensive. This study develops a new Archimedes optimization algorithm (AOA) with Stacked Sparse Denoising Auto-Encoder (SSDAE) model, named AOA-SSDAE for wastewater management in the IoT environment. The presented AOA-SSDAE technique aims to predict wastewater treatment depending on the influent indicators. In the presented AOA-SSDAE technique, the IoT devices are initially employed for the data collection process and then data normalization is performed to transform the collected data into a uniform format. For the predictive process, the SSDAE model is employed in this paper. To improve the SSDAE model's prediction capability, the AOA-based hyperparameter tuning process is involved.

Ship Shaft Frequency Extraction Based on Improved Stacked Sparse Denoising Auto-Encoder Network

The modulation spectrum of ship radiated noise contains information on shaft frequency, which is an important feature used to identify ships and a key parameter involved in calculating the number of propeller blades. To improve the shaft frequency extraction accuracy, a ship shaft frequency extraction method based on an improved stacked sparse denoising auto-encoder network (SSDAE) is proposed. Firstly, the mathematical model of the ship radiated noise modulation spectrum is built and data simulation is carried out based on this model, combined with the actual ship parameters. Secondly, we trained the SSDAE model using the simulation data and made slight adjustments to this model by using both simulation and measured data to improve it. Finally, the experimental ship modulation spectrum information was input to the SSDAE model for denoising, enhancement, and regression estimation. Accordingly, the shaft frequency was extracted. The simulation and experimental results show that the shaft frequency extraction method based on the improved SSDAE model has high accuracy and good robustness, especially under the conditions of both missing line spectra and noise interference.

Applications of Markov Decision Process Model and Deep Learning in Quantitative Portfolio Management during the COVID-19 Pandemic

Whether for institutional investors or individual investors, there is an urgent need to explore autonomous models that can adapt to the non-stationary, low-signal-to-noise markets. This research aims to explore the two unique challenges in quantitative portfolio management: (1) the difficulty of representation and (2) the complexity of environments. In this research, we suggest a Markov decision process model-based deep reinforcement learning model including deep learning methods to perform strategy optimization, called SwanTrader. To achieve better decisions of the portfolio-management process from two different perspectives, i.e., the temporal patterns analysis and robustness information capture based on market observations, we suggest an optimal deep learning network in our model that incorporates a stacked sparse denoising autoencoder (SSDAE) and a long–short-term-memory-based autoencoder (LSTM-AE). The findings in times of COVID-19 show that the suggested model using two deep learning models gives better results with an alluring performance profile in comparison with four standard machine learning models and two state-of-the-art reinforcement learning models in terms of Sharpe ratio, Calmar ratio, and beta and alpha values. Furthermore, we analyzed which deep learning models and reward functions were most effective in optimizing the agent’s management decisions. The results of our suggested model for investors can assist in reducing the risk of investment loss as well as help them to make sound decisions.

Modified metaheuristics with stacked sparse denoising autoencoder model for cervical cancer classification

Cervical cancer is the most commonly diagnosed cancer among women globally, with high mortality rate. For early diagnosis, automated and accurate cervical cancer classification approaches can be developed through effective classification of Pap smear cell images. The current study introduces a novel Modified Firefly Optimization Algorithm with Deep Learning-enabled cervical cancer classification (MFFOA-DL3) model for the classification of Pap Smear Images (PSI). The proposed MFFOA-DL3 model examines the PSI for the existence of cervical cancer cells. To accomplish this, the proposed MFFOA-DL3 model primarily applies Bilateral Filtering (BF)-based noise removal approach to get rid of the noise. Then, Kapur's entropy-based image segmentation technique is applied to determine the affected regions. Moreover, EfficientNet technique is also applied to generate the feature vectors. Finally, MFFOA with Stacked Sparse Denoising Autoencoder (SSDA) model is exploited to classify the PSI. In current study, MFFOA is utilized to appropriately modify the parameters related to SSDA model. The proposed MFFOA-DL3 model was experimentally validated using benchmark dataset. The results attained from extensive comparative analysis highlighted the better performance of MFFOA-DL3 model over other recent approaches.

A Novel Anti-Risk Method for Portfolio Trading Using Deep Reinforcement Learning

In the past decade, the application of deep reinforcement learning (DRL) in portfolio management has attracted extensive attention. However, most classical RL algorithms do not consider the exogenous and noise of financial time series data, which may lead to treacherous trading decisions. To address this issue, we propose a novel anti-risk portfolio trading method based on deep reinforcement learning (DRL). It consists of a stacked sparse denoising autoencoder (SSDAE) network and an actor–critic based reinforcement learning (RL) agent. SSDAE will carry out off-line training first, while the decoder will used for on-line feature extraction in each state. The SSDAE network is used for the noise resistance training of financial data. The actor–critic algorithm we use is advantage actor–critic (A2C) and consists of two networks: the actor network learns and implements an investment policy, which is then evaluated by the critic network to determine the best action plan by continuously redistributing various portfolio assets, taking Sharp ratio as the optimization function. Through extensive experiments, the results show that our proposed method is effective and superior to the Dow Jones Industrial Average index (DJIA), several variants of our proposed method, and a state-of-the-art (SOTA) method.

Machine Learning Applied to Problem-Solving in Medical Applications

Physical health plays an important role in overall well-being of the human beings. It is the most observed dimension of health among others such as social, intellectual, emotional, spiritual and environmental dimensions. Due to exponential increase in the development of wireless communication techniques, Internet of Things (IoT) has effectively penetrated different aspects of human lives. Healthcare is one of the dynamic domains with ever-growing demands which can be met by IoT applications. IoT can be leveraged through several health service offerings such as remote health and monitoring services, aided living, personalized treatment, and so on. In this scenario, Deep Learning (DL) models are employed in proficient disease diagnosis. The current research work presents a new IoT-based physical health monitoring and management method using optimal Stacked Sparse Denoising Autoencoder (SSDA) technique i.e., OSSDA. The proposed model utilizes a set of IoT devices to collect the data from patients. Imbalanced class problem poses serious challenges during disease diagnosis process. So, the OSSDA model includes Synthetic Minority Over-Sampling Technique (SMOTE) to generate artificial minority class instances to balance the class distribution. Further, the hyperparameter settings of the OSSDA model exhibit heavy influence upon the classification performance of SSDA technique. The number of hidden layers, sparsity, and noise count are determined by Sailfish Optimizer (SFO). In order to validate the effectiveness and performance of the proposed OSSDA technique, a set of experiments was conducted on diabetes and heart disease datasets. The simulation results portrayed a proficient diagnostic outcome from OSSDA technique over other methods. The proposed method achieved the highest accuracy values i.e., 0.9604 and 0.9548 on the applied heart disease and diabetes datasets respectively.

A Stacked Denoising Sparse Autoencoder Based Fault Early Warning Method for Feedwater Heater Performance Degradation

Power grid operation faces severe challenges with the increasing integration of intermittent renewable energies. Hence the steam turbine, which mainly undertakes the task of frequency regulation and peak shaving, always operates under off-design conditions to meet the accommodation demand. This would affect the operation economy and exacerbate the ullage of equipment. The feedwater heater (FWH) plays an important role in unit, whose timely fault early warning is significant in improving the operational reliability of unit. Therefore, this paper proposes a stacked denoising sparse autoencoder (SDSAE) based fault early warning method for FWH. Firstly, the concept of a frequent pattern model is proposed as an indicator of FWH performance evaluation. Then, an SDSAE- back-propagation (BP) based method is introduced to achieve self-adaptive feature reduction and depict nonlinear properties of frequent pattern modeling. By experimenting with actual data, the feasibility and validity of the proposed method are verified. Its detection accuracy reaches 99.58% and 100% for normal and fault data, respectively. Finally, competitive experiments prove the necessity of feature reduction and the superiority of SDSAE based feature reduction compared with traditional methods. This paper puts forward a precise and effective method to serve for FWH fault early warning and refines the key issues to inspire later researchers.

Power Grid Fault Diagnosis Method Based on Stacked Sparse Denoising Auto-Encoder and GRU Network

This research aims to solve the problems of feature extraction, data dimension reduction, gradient dissipation and low training efficiency in power grid fault classification. Aiming at PMU measurement data with high dimensionality and strong real-time characteristics, a fault diagnosis method based on Stacked Sparse Denoising Auto-Encoder and GRU network is proposed. The Stacked Sparse Denoising Auto-Encoder is used to reduce the sequence dimension to obtain the sparse feature expression of the data, and then the time-dependent features of the data extracted by the GRU are used to obtain the fault type. Simulations and experiments show that compared with the traditional neural network algorithm, the proposed method can effectively extract high-dimensional data features, reduce data dimensions, improve the efficiency of GRU network classification, accelerate the convergence speed and reduce the training time, and has better stability and higher accuracy.

A stacked convolutional sparse denoising autoencoder model for underwater heterogeneous information data

Deep learning denoising models can automatically extract underwater heterogeneous information data features to improve denoising performance through an internal network structure. In the present study, a novel stacked convolutional sparse denoising autoencoder (SCSDA) model was proposed in this paper to complete the blind denoising task of underwater heterogeneous information data. Specifically, for the first time, the stacked sparse denoising autoencoder (SSDA) was constructed by three sparse denoising autoencoders (SDA) to extract overcomplete sparse features. Then, the output of the last encoding layer of the SSDA was used as the input of the convolutional neural network (CNN) to further extract the deep features. Finally, in order to solve the lack of the pure underwater heterogenous information data during the acquisition and transmission process, the unrelated dataset was developed to simulate the underwater heterogeneous information data as the training set in proposed SCSDA model. Compared with the existing other algorithms, the experiment results demonstrate that the proposed SCSDA model combines the advantages of SSDA and CNN, which has great blind denoising ability. It can process faster and preserves more edge features of underwater heterogeneous information data. Also, it has a certain degree of robustness and effectiveness.

Transient stability evaluation model based on SSDAE with imbalanced correction

With the rapid development of machine learning technology, a new tool is provided for real-time stability evaluation in power systems. The training of a machine learning-based model is inseparable from a large number of training samples. However, compared with stable samples, unstable samples in power systems are infrequent. The results of the model evaluation are biased due to the imbalance of training samples. Faced with such a problem, a framework based on deep imbalanced learning is proposed. Firstly, for each sample, the samples nearby in the opposite class are applied to calculate its space information. Based on the space information of all samples, the spatial distribution characteristics of the training samples are obtained. And then, in order to obtain balanced training samples, unstable samples are generated according to their spatial distribution characteristics. Finally, stacked sparse denoising auto-encoder (SSDAE) based model, which has the ability of anti-noise, is established as the classifier. Simulation results in IEEE 39-bus system show the high performance of the proposed imbalanced correction scheme and evaluation scheme.

Fault Diagnosis of Rolling Bearing Based on Multiscale Intrinsic Mode Function Permutation Entropy and a Stacked Sparse Denoising Autoencoder

Effective intelligent fault diagnosis of bearings is important for improving safety and reliability of machine. Benefiting from the training advantages, deep learning method can automatically and adaptively learn more abstract and high-level features without much priori knowledge. To realize representative features mining and automatic recognition of bearing health condition, a diagnostic model of stacked sparse denoising autoencoder (SSDAE) which combines sparse autoencoder (SAE) and denoising autoencoder (DAE) is proposed in this paper. The sparse criterion in SAE, corrupting operation in DAE and reasonable designing of the stack order of autoencoders help to mine essential information of the input and improve fault pattern classification robustness. In order to provide better input features for the constructed network, the raw non-stationary and nonlinear vibration signals are processed with ensemble empirical mode decomposition (EEMD) and multiscale permutation entropy (MPE). MPE features which are extracted based on both the selected characteristic frequency-related intrinsic mode function components (IMFs) and the raw signal, are used as low-level feature for the input of the proposed diagnostic model for health condition recognition and classification. Two experiments based on the Case Western Reserve University (CWRU) dataset and the measurement dataset from laboratory were conducted, and results demonstrate the effectiveness of the proposed method and highlight its excellent performance relative to existing methods.

An adaptive image denoising method based on Deep Rectified Denoising Auto-Encoder

Stacked Sparse Denoising Auto-Encoder(SSDA) has been successfully applied to image denoising, which is superior to the most of the traditional image denoising algorithms. However, the algorithm has low training convergence speed and poor universality. To address these limitations, we present an adaptive image denoising method based on Deep Rectified Denoising Auto-Encoder. To reduce training difficulty and speed up convergence, the rectified linear units (ReLu) is used as activation function of the network, and batch normalization(BN) is used to normalizes the input data of the middle layer in network. Different from the previous image denoising model, the proposed model is trained to learn the mapping relationship between noisy image and noise by residual learning. To overcome the problem that the new model cannot effectively deal with noise not seen in training, we perform adaptive training of multiple channels on the improved model to obtain the optimal channel weights and jointly output the denoised image. The experimental results show that the proposed algorithm can not only outperform SSDA in the convergence speed, but also adaptively remove the noise that is not seen in training.

Deep Marginalized Sparse Denoising Auto-Encoder for Image Denoising

Stacked Sparse Denoising Auto-Encoder (SSDA) has been successfully applied to image denoising. As a deep network, the SSDA network with powerful data feature learning ability is superior to the traditional image denoising algorithms. However, the algorithm has high computational complexity and slow convergence rate in the training. To address this limitation, we present a method of image denoising based on Deep Marginalized Sparse Denoising Auto-Encoder (DMSDA). The loss function of Sparse Denoising Auto-Encoder is marginalized so that it satisfies both sparseness and marginality. The experimental results show that the proposed algorithm can not only outperform SSDA in the convergence speed and training time, but also has better denoising performance than the current excellent denoising algorithms, including both the subjective and objective evaluation of image denoising.

基于改进栈式稀疏去噪自编码器的自适应图像去噪

基于改进栈式稀疏去噪自编码器的自适应图像去噪

Infrared image enhancement with learned features

Due to the variation of imaging environment and limitations of infrared imaging sensors, infrared images usually have some drawbacks: low contrast, few details and indistinct edges. Hence, to promote the applications of infrared imaging technology, it is essential to improve the qualities of infrared images. To enhance image details and edges adaptively, we propose an infrared image enhancement method under the proposed image enhancement scheme. On the one hand, on the assumption of high-quality image taking more evident structure singularities than low-quality images, we propose an image enhancement scheme that depends on the extractions of structure features. On the other hand, different from the current image enhancement algorithms based on deep learning networks that try to train and build the end-to-end mappings on improving image quality, we analyze the significance of first layer in Stacked Sparse Denoising Auto-encoder and propose a novel feature extraction for the proposed image enhancement scheme. Experiment results prove that the novel feature extraction is free from some artifacts on the edges such as blocking artifacts, “gradient reversal”, and pseudo contours. Compared with other enhancement methods, the proposed method achieves the best performance in infrared image enhancement.

Face Verification via Learned Representation on Feature-Rich Video Frames

Abundance and availability of video capture devices, such as mobile phones and surveillance cameras, have instigated research in video face recognition, which is highly pertinent in law enforcement applications. While the current approaches have reported high accuracies at equal error rates, performance at lower false accept rates requires significant improvement. In this paper, we propose a novel face verification algorithm, which starts with selecting feature-rich frames from a video sequence using discrete wavelet transform and entropy computation. Frame selection is followed by representation learning-based feature extraction, where three contributions are presented: 1) deep learning architecture, which is a combination of stacked denoising sparse autoencoder (SDAE) and deep Boltzmann machine (DBM); 2) formulation for joint representation in an autoencoder; and 3) updating the loss function of DBM by including sparse and low rank regularization. Finally, a multilayer neural network is used as the classifier to obtain the verification decision. The results are demonstrated on two publicly available databases, YouTube Faces and Point and Shoot Challenge. Experimental analysis suggests that: 1) the proposed feature-richness-based frame selection offers noticeable and consistent performance improvement compared with frontal only frames, random frames, or frame selection using perceptual no-reference image quality measures and 2) joint feature learning in SDAE and sparse and low rank regularization in DBM helps in improving face verification performance. On the benchmark Point and Shoot Challenge database, the algorithm yields the verification accuracy of over 97% at 1% false accept rate whereas, on the YouTube Faces database, over 95% verification accuracy is observed at equal error rate.

Low-level structure feature extraction for image processing via stacked sparse denoising autoencoder

In this paper, we propose a novel low-level structure feature extraction for image processing based on deep neural network, stacked sparse denoising autoencoder (SSDA). The current image processing methods via deep learning are directly building and learning the end-to-end mappings between the input/output. Instead, we advocate the analysis of the first layer learning features from input data. With the learned low-level structure features, we improve two edge-preserving filters that are key to image processing tasks such as denoising, High Dynamic Range (HDR) compression and details enhancement. Due to the validity and superiority of the proposed feature extraction, the results computed by the two improved filters do not suffer from the drawbacks including halos, edge blurring, noise amplification and over-enhancement. More importantly, we demonstrate that the features trained from natural images are not specific and can extract the structure features of infrared images. Hence, it is feasible to handle tasks by using the trained features directly.

Deep Filter Banks for Land-Use Scene Classification

Land-use (LU) scene classification is one of the most challenging tasks in the field of remote sensing (RS) image processing due to its high intraclass variability and low interclass distance. Motivated by the challenge posed by this problem, we propose a novel hybrid architecture, deep filter banks, combining multicolumn stacked denoising sparse autoencoder (SDSAE) and Fisher vector (FV) to automatically learn the representative and discriminative features in a hierarchical manner for LU scene classification. SDSAE kernels describe local patches and a robust global feature of the RS image is built through the FV pooling layer. Unlike previous handcrafted features, we use machine-learning mechanisms to optimize our proposed feature extractor so that it can learn more suitable internal features from the RS data, boosting the final performance. Our approach achieves superior performance compared with the state-of-the-art methods, obtaining average classification accuracies of 92.7% and 90.4%, respectively, on the UC Merced and RSSCN7 data sets.