Deep Stacked Autoencoder Research Articles

Identification of multiple power quality disturbances in hybrid microgrid using deep stacked Auto-encoder based Bi-directional LSTM classifier

Identification of multiple power quality disturbances in hybrid microgrid using deep stacked Auto-encoder based Bi-directional LSTM classifier

Positional-attention based bidirectional deep stacked AutoEncoder for aspect based sentimental analysis

Positional-attention based bidirectional deep stacked AutoEncoder for aspect based sentimental analysis

M-Net based stacked autoencoder for ransomware detection using blockchain data

Ransomware is a kind of malevolent program software that encrypts the items on the hard disc and prevents the clients from accessing them until they are paid a ransom. Associations like monetary establishments and medical care areas (i.e., smart medical care) are mostly targeted by ransomware attacks. Ransomware assaults are crucial holes still in blockchain technology and prevent effective data communication in networks. This study aims to introduce an efficient system, named M-Net-based Stacked Autoencoder (M-Net_SA) for ransomware detection using blockchain data. Initially, the input data is taken from a dataset and then sent to the feature extraction process, which utilizes sequence-based statistical features. After that, data transformation is completed using the Yeo-Johnson transformation to transform the data into a usable format. After that, feature fusion is executed using a Deep Q-network (DQN) with Lorentzian similarity to enhance the representativeness of the target features. Finally, ransomware detection is accomplished by the proposed M-Net_SA, which is the integration of MobileNet and Deep Stacked Autoencoder (DSAE). The experimental validation of the proposed M-Net_SA is compared with other conventional techniques and the proposed model attained maximum accuracy, sensitivity, and specificity of 0.959, 0.967, and 0.957 respectively.

DRN-DSA: A hybrid deep learning network model for precipitation nowcasting using time series data

Precipitation nowcasting involves short-term weather forecasting, predicting rain or snow within the next two hours. By analyzing current atmospheric conditions, it aids meteorologists in identifying weather patterns and preparing for severe events such as flooding. These nowcasts are typically displayed on geographical maps by weather services. However, the rapidly changing climate conditions make precipitation nowcasting a formidable challenge, as accurate short-term forecasts are hindered by immediate weather fluctuations. Traditional nowcasting methods, like numerical models and radar extrapolation, have limitations in delivering highly detailed and timely precipitation nowcasts. To overcome this issue, an effective solution is framed for precipitation nowcasting using a hybrid network approach named Deep Residual Network-Deep Stacked Autoencoder (DRN-DSA). Initially, the input time series data is acquired from the dataset. Thereafter, the effective technical indicators are extracted at the feature extraction stage. Later on, precipitation-type nowcasting is carried out using the proposed hybrid DRN-DSA, which is developed by incorporating a Deep Stacked Autoencoder (DSA) and Deep Residual Network (DRN). Finally, Weather nowcasting is carried out using the same proposed hybrid DSA-DRN. Moreover, when compared to other traditional models, the proposed DRN-DSA has gained superior results with a Relative Absolute Error (RAE) of 0.295, Root Mean Square Error (RMSE) of 0.154, low Mean Square Error (MSE) of 0.0236, Mean Absolute Percentage Error (MAPE) of 0.295, and False Acceptance Rate (FAR) of 0.0118.

FDN-SA: Fuzzy deep neural-stacked autoencoder-based phishing attack detection in social engineering

Phishing attacks have emerged as a major social engineering threat that affects businesses, governments, and general internet users. This work proposes a social engineering phishing detection technique based on Deep Learning (DL). Initially, website data is taken from the dataset. Then, the features of Natural Language Processing (NLP) like bag of words, n-gram, hashtags, sentence length, Term Frequency- Inverse Document Frequency of records (TF-IDF), and all caps are extracted and then web feature extraction is carried out. Later, the feature fusion is done using the Neyman similarity with Deep Belief Network (DBN). Afterwards, oversampling is used for data augmentation to enhance the number of training samples. Lastly, the detection of phishing attacks is performed by employing the proposed Fuzzy Deep Neural-Stacked Autoencoder (FDN-SA). Here, the proposed FDN-SA is developed by combining a Deep Neural Network (DNN), and Deep Stacked Autoencoder (DSA). Further, the investigation of FDN-SA is accomplished based on the accuracy, True Positive Rate (TPR), and True Negative Rate (TNR) and is observed to compute values of 0.920, 0.925, and 0.921, respectively.

Advancing aquifer vulnerability mapping through integrated deep learning approaches

Groundwater vulnerability maps are crucial for safeguarding groundwater quality. A research gap exists in using advanced data fusion techniques to identify areas subject to seawater intrusion. To address this gap, this research enhances the GALDIT method and applies diverse deep learning models, combined with machine learning techniques, to improve the precision of aquifer vulnerability mapping. The new GALDITMW model incorporates the seawater mixing index and the parameters related to the production well density and aquifer porous medium. For the first time, supervised and unsupervised deep learning models, such as deep neural networks, deep belief networks, deep stacked autoencoders, and convolutional neural networks, are used for vulnerability mapping. In the second stage, the results of various machine learning models are fused to improve performance. The models' effectiveness is evaluated using a vulnerability index based on total dissolved solids (TDS) in an aquifer hydraulically connected with Salt Lake in central Iran, which faces groundwater depletion and salinization. The evaluation of the models based on performance metrics and the confusion matrix demonstrates that initial deep-learning models perform well. Significant improvements were observed in the second stage involving machine learning models, confirming their strong correlation (R2 > 0.985) with observed chloride values. The GPR model achieved an F1 score of 86.92%, an NSE of 0.911, and an RMSE reduction of 0.026 mg/L compared to the first-stage models. The proposed method offers a novel and accurate method for identifying vulnerable areas and provides helpful information for groundwater resource management.

A Survey of AI Methods for Detection of DDoS Attacks on Networks

This survey explores various artificial intelligence (AI) methods for detecting Distributed Denial of Service (DDoS) attacks on networks. It classifies these approaches into machine learning, deep learning, and other AI-based techniques, providing a comprehensive overview of current advancements in the field. Numerous research studies in the field of machine learning have evaluated DDoS attack detection performance using various datasets and techniques. Some noteworthy results are the supremacy of the J48 algorithm in SDN networks and the efficacy of the AdaBoost and Gradient Boost classifiers. In other investigations, Random Forest, Support Vector Machine, and Naive Bayes also showed excellent accuracy rates, up to 99.7%. To improve DDoS detection, deep learning techniques introduced autoencoders, hybrid models, and recurrent neural networks. These models achieved accuracy rates as high as 99.99%, frequently outperforming more conventional machine learning techniques. Enhanced detection rates were achieved by the utilization of a varied dataset in conjunction with deep-stacked autoencoders. Artificial intelligence methods such as Fuzzy Logic, Artificial Bee Colony, Ant Colony Optimization, and Whale Optimization Algorithm were used to identify DDoS assaults. These methods demonstrated high accuracy rates, efficient detection of various attack types, and improvements in reducing false positives; the integration of these techniques into intrusion detection systems offers a strong defense against dynamic DDoS threats. The overall survey highlights the effectiveness of AI techniques in DDoS attack detection across various methodologies.

A comprehensive bearing prognosis framework based on piecewise function stacking convolution auto-encoder and XGBoost algorithm

Abstract Accurately forecasting the remaining useful life (RUL) stands as a pivotal and formidable task within the realm of prognostics and health management. However, there is limited research that considers integrating early fault diagnosis during the bearing’s lifecycle with the prediction of its RUL. In this article, a comprehensive bearing prognosis framework based on piecewise function stacking convolution auto-encoder (AE) and XGBoost algorithm is proposed. To achieve this, an unsupervised piecewise function-based deep stacked convolutional AE was designed to construct the health indicator (HI) of the bearing for reducing the dependency on prior knowledge and furnishing a dynamic foundation for predicting RUL. The 4σ criterion based on HI’s increment was proposed for determining the fault occurrence time (FOT) of the bearing’s operational process. Subsequently, an XGBoost algorithm model was utilized to predict the RUL of faulty bearings. The efficacy of the bearing prognosis framework was validated by two real bearing test datasets. Results indicate the employed criterion of construct HI can flexibly adjust to various operational conditions and accurately pinpoint the bearing’s FOT. Furthermore, the findings demonstrate the proposed bearing prognosis framework achieves superior performance compared with several conventional anomaly detection methods.

Efficient Visual-Aware Fashion Recommendation Using Compressed Node Features and Graph-Based Learning

In fashion e-commerce, predicting item compatibility using visual features remains a significant challenge. Current recommendation systems often struggle to incorporate high-dimensional visual data into graph-based learning models effectively. This limitation presents a substantial opportunity to enhance the precision and effectiveness of fashion recommendations. In this paper, we present the Visual-aware Graph Convolutional Network (VAGCN). This novel framework helps improve how visual features can be incorporated into graph-based learning systems for fashion item compatibility predictions. The VAGCN framework employs a deep-stacked autoencoder to convert the input image’s high-dimensional raw CNN visual features into more manageable low-dimensional representations. In addition to improving feature representation, the GCN can also reason more intelligently about predictions, which would not be possible without this compression. The GCN encoder processes nodes in the graph to capture structural and feature correlation. Following the GCN encoder, the refined embeddings are input to a multi-layer perceptron (MLP) to calculate compatibility scores. The approach extends to using neighborhood information only during the testing phase to help with training efficiency and generalizability in practical scenarios, a key characteristic of our model. By leveraging its ability to capture latent visual features and neighborhood-based learning, VAGCN thoroughly investigates item compatibility across various categories. This method significantly improves predictive accuracy, consistently outperforming existing benchmarks. These contributions tackle significant scalability and computational efficiency challenges, showcasing the potential transformation of recommendation systems through enhanced feature representation, paving the way for further innovations in the fashion domain.

Hybrid optimization based deep stacked autoencoder for routing and intrusion detection

This research introduced the optimized Deep Stacked Autoencoder (DSA) for performing Intrusion Detection (ID) in the IoT. Firstly, IoT simulation is carried out and then, the information is routed by using the Chronological War Strategy Optimization (CWSO). Here, the CWSO is newly designed by incorporating the chronological concept with the WSO. After the routing, the ID is completed at the Base station (BS) by executing the following steps. Initially, data is obtained from a database, after that, feature normalization is done using min-max normalization. Meanwhile, Canberra distance is applied to execute the feature selection process. Finally, ID is performed using DSA, which is trained using the Competitive Swarm Henry War Strategy Optimization algorithm (CSHWO). The experimental result confirms that the invented scheme accomplished the superior outcome by the energy, f-score, precision, and recall values of 0.379, 0.913, 0.918 and 0.912, respectively.

Genetic fuzzy rules and hybrid QDCNN-F-DSAE for detecting attacker behavior with tuning of firewall

ABSTRACT The application of computer technology is increasing quickly as the field of information technology develops over time. Because of the growth of computer networks, there is a rise in network attacks. Moreover, the majority of transactions are conducted via the Internet, and then the web application security is becoming increasingly important. A network firewall shields the web servers from unauthorised and dangerous traffic. In this work, the hybrid Quantum Dilated Convolutional Neural Network fused Deep-Stacked Auto Encoder (QDCNN-F-DSAE) model with Genetic Fuzzy System (GFS) is developed for network protection. With the utilisation of genetic fuzzy system (GFS) and firewall tuning, the QDCNN-F-DSAE identified the attack, ransomware activity, and prompt decision-making. The data offered by the simulated environment is used to make decisions, and the decisions should be made quickly within a few seconds using the proposed QDCNN-F-DSAE with GFS. Furthermore, it identifies the attacks and alerts the users to store the data from the supplied source. The malicious function and protected private Local Area Network (LAN) are discovered and removed by the firewall after the formation of genetic fuzzy rules. Moreover, this model is estimated using the accuracy, sensitivity, and specificity measures that offered the finest values of 0.915, 0.908, and 0.920.

Transformer fault diagnosis based on adversarial generative networks and deep stacked autoencoder

Establishing a deep learning model for transformer fault diagnosis using transformer oil chromatogram data requires a large number of fault samples. The lack and imbalance of oil chromatogram data can lead to overfitting, lack of representativeness of the model, and unsatisfactory prediction results on test set data, making it difficult to accurately diagnose transformer faults. A conditional Wasserstein generative adversarial network with gradient penalty optimization (CWGAN-GP) is adopted in this paper, which based on gradient penalty optimization and expand the oil chromatography fault samples of 500 sets of transformer oil chromatography data with 5 types of faults. The proposed method is used to classify transformer faults using a deep autoencoder, and the sample quality of the neural network model proposed in this paper is compared with several other variants of generative adversarial neural network models. The research results show that after using the method proposed in this paper for sample expansion, the overall accuracy of fault diagnosis can reach 93.2 %, which is 4.98 % higher than the original imbalanced samples. Compared with other sample expansion methods, the accuracy of fault diagnosis of the algorithm in this paper is improved by 1.70 %–3.05 %.

Machine learning‐based data fusion method for wireless sensor networks

AbstractFor wireless sensor networks (WSNs), sensor nodes lose a certain amount of energy during the information collection and transmission process, and sensor nodes powered by non‐replaceable batteries have limited energy and need to be controlled for energy consumption. In the face of the energy consumption issue in WSN data transmission, research has been conducted to analyze data fusion methods in order to reduce energy consumption. Based on machine learning techniques, a Deep Stacked Auto‐Encoder (DSAE) model is constructed and trained using a layer‐wise greedy approach. By combining this model with WSN, an algorithm based on the DSAE model, called Deep Stacked Auto‐Encoder Data Fusion Algorithm (DSAEDFA), is obtained to do data fusion. The results show that compared to other algorithms, the proposed fusion algorithm has better fusion performance. When the number of iterations is set to 500, the DSAEDFA has 281 surviving nodes, which is 10 more than the Back‐Propagation Data Fusion Algorithm (BPDFA) and 144 more than the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm. When the number of failed nodes is 40, the DSAEDFA has a network survival time of 2562 rounds, which is 746 rounds longer than the LEACH algorithm. The research method effectively extends the lifespan of wireless sensor networks and reduces data transmission energy consumption. Compared to previous methods, the proposed method consider the factors of node residual energy and distance on the basis of traditional routing protocols, making the selection of cluster heads more reasonable. The proposed method can organically combine the DSAE model with the clustering model, optimize the data fusion method, and improve the performance of the algorithm. In addition, by combining the DSAE model, a machine learning technique with clustering models has been expanded in terms of the application scope.

Neighboring Envelope Embedded Stacked Autoencoder for Deep Learning on Hierarchically Structured Samples

A stacked autoencoder (SAE) is a widely used deep network. However, existing deep SAEs focus on original samples without considering the hierarchical structural information between samples. This limits the accuracy of the SAE. In recent years, state-of-the-art SAEs have suggested improvements in network structure, cost function, parameter optimization, and thereby the accuracy has been enhanced. However, the problem mentioned above is still not solved. Therefore, this paper is concerned with how to design a SAE that can conduct deep learning on hierarchically structured samples. This proposed SAE - neighboring envelope embedded stacked autoencoder (NE_ESAE) mainly consists of two parts. The first is the neighboring sample envelope learning mechanism (NSELM) that constructs sample-pairs by combining neighboring samples. In addition, the NSELM constructs multilayer sample spaces by multilayer iterative mean clustering, which considers similar samples and generates layers of envelope samples with hierarchical structural information. The second is an embedded stacked autoencoder (ESAE) to consider the original samples during training and in network structure, thereby finding the relationship of the samples with original features and deep features in a better manner. The experimental results show that our method has significantly better performance than some representative methods. Different from existing SAEs, the proposed NE_ESAE realizes deep learning on hierarchical structured samples, and makes SAE able to conduct cooperative deep sample and feature learning. The advantage that has been gained can be applied to other deep neural networks.

IDLTM-DMT: Intelligent Deep Learning based Trust Management with Decision Making Tool for Healthcare Internet of Things and Big Data Environment with Neutrosophic Set Analysis

Over the last few years development of Internet of Things (IoT) devices and communication technologies have resulted in the massive generation of health-related data. In the context of healthcare, IoT offers several advantages, including being able to observe patients very closely and using data for analytics. A major challenging issue that exists in the usage of IoT and big data in the medical field is security. As healthcare data is highly vulnerable and becomes a target for attacks, there are significant privacy issues related to the usage of big data analytics. Besides, implementing new data analysis tools and strategies for handling big data decision-making is a major issue. The capability to examine this amount of data is a significant aspect of big data in health care. For resolving these issues, this paper presents a new intelligent deep learning-based trust management with decision making tool (IDLTM-DMT) for IoT healthcare big data environments, incorporating Neutrosophic Set Analysis (NSA). The proposed IDLTM-DMT model enables IoT devices to gather healthcare data. The IDLTM-DMT model involves a DL based bidirectional long short-term memory (BiLSTM) model for vulnerability detection and thereby identifies the malicious traffic in the Network. Hadoop MapReduce is used for handling big data and a decision-making tool using Deep Stacked Auto Encoder (DSAE) is used for the classification of diseases that exist in big data. To optimize the DSAE model's hyperparameters and improve classification performance, the Sandpiper Optimization (SPO) Algorithm is employed. Neutrosophic Set Analysis is integrated to manage the indeterminacy and inconsistency of the data, enhancing the decision-making process. Extensive experimental analysis is conducted on the EEG Eye State Dataset, with results analyzed using various performance measures. The findings indicate that the proposed method achieves improved accuracy compared to existing methods, demonstrating the effectiveness of incorporating Neutrosophic Set Analysis in IoT healthcare big data environments.

LDSAE: LeNet deep stacked autoencoder for secure systems to mitigate the errors of jamming attacks in cognitive radio networks

LDSAE: LeNet deep stacked autoencoder for secure systems to mitigate the errors of jamming attacks in cognitive radio networks

LDSAE: LeNet Deep Stacked Autoencoder for Secure System to Mitigate the Errors of Jamming Attack in Cognitive Radio Network

LDSAE: LeNet Deep Stacked Autoencoder for Secure System to Mitigate the Errors of Jamming Attack in Cognitive Radio Network

Robust Gesture Recognition and Classification for Visually Impaired Persons Using Growth Optimizer with Deep Stacked Autoencoder

Visual impairment affects the major population of the world, and impaired vision people need assistance for their day-to-day activities. With the enormous growth and usage of new technologies, various devices were developed to help them with object identification in addition to navigation in the indoor and outdoor surroundings. Gesture detection and classification for blind people aims to develop technologies to assist those people to navigate their surroundings more easily. To achieve this goal, using machine learning and computer vision techniques is a better solution to classify and detect hand gestures. Such methods are utilized for finding the shape, position, and movement of the hands in real-time. With this motivation, this article presents a robust gesture recognition and classification using growth optimizer with deep stacked autoencoder (RGRC-GODSAE) model for visually impaired persons. The goal of the RGRC-GODSAE technique lies in the accurate recognition and classification of gestures to assist visually impaired persons. The RGRC-GODSAE technique follows the Gabor filter approach at the initial stage to remove noise. In addition, the RGRC-GODSAE technique uses the ShuffleNet model as a feature extractor and the GO algorithm as a hyperparameter optimizer. Finally, the deep stacked autoencoder model is exploited for the automated recognition and classification of gestures. The experimental validation of the RGRC-GODSAE technique is carried out on the benchmark dataset. The extensive comparison study showed better gesture recognition performance of the RGRC-GODSAE technique over other deep learning models.

Optimization Enabled Deep Learning-Based DDoS Attack Detection in Cloud Computing

Cloud computing is a vast revolution in information technology (IT) that inhibits scalable and virtualized sources to end users with low infrastructure cost and maintenance. They also have much flexibility and these resources are supervised by various management organizations and provided over the Internet by known standards, formats, and networking protocols. Legacy protocols and underlying technologies consist of vulnerabilities and bugs which open doors for intrusion by network attackers. Attacks as distributed denial of service (DDoS) are one of most frequent attacks, which impose heavy damage and affect performance of the cloud. In this research work, DDoS attack detection is easily identified in an optimized way through a novel algorithm, namely, the proposed gradient hybrid leader optimization (GHLBO) algorithm. This optimized algorithm is responsible to train a deep stacked autoencoder (DSA) that detects the attack in an efficient manner. Here, fusion of features is carried out by deep maxout network (DMN) with an overlap coefficient, and augmentation of data is carried out by the oversampling process. Furthermore, the proposed GHLBO is generated by integrating the gradient descent and hybrid leader-based optimization (HLBO) algorithm. Also, this proposed method is assessed by various performance metrics, such as the true positive rate (TPR), true negative rate (TNR), and testing accuracy with values attained as 0.909, 0.909, and 0.917, accordingly.

Automated damage diagnosis of concrete jack arch beam using optimized deep stacked autoencoders and multi-sensor fusion

A novel hybrid framework of optimized deep learning models combined with multi-sensor fusion is developed for condition diagnosis of concrete arch beam. The vibration responses of structure are first processed by principal component analysis for dimensionality reduction and noise elimination. Then, the deep network based on stacked autoencoders (SAE) is established at each sensor for initial condition diagnosis, where extracted principal components and corresponding condition categories are inputs and output, respectively. To enhance diagnostic accuracy of proposed deep SAE, an enhanced whale optimization algorithm is proposed to optimize network meta-parameters. Eventually, Dempster-Shafer fusion algorithm is employed to combine initial diagnosis results from each sensor to make a final diagnosis. A miniature structural component of Sydney Harbour Bridge with artificial multiple progressive damages is tested in laboratory. The results demonstrate that the proposed method can detect structural damage accurately, even under the condition of limited sensors and high levels of uncertainties.