Deep Auto Research Articles

Identifying faults in UHVDC transmission systems using DAE and ensemble learning

Abstract The current fault identification technique for the ultra high voltage direct current transmission system has a low success rate, the process of extracting feature qualities is complex, and it is difficult to identify high resistance grounding. This research provides a defect evaluation method for UHVDC transmission system using Deep Auto Encoder (DAE) and integrated learning. Firstly, the fault voltage signal is feature extracted using DAE, and then, a series of obtained features are feature screened by XGBoost algorithm and combined with an integrated classifier consisting of Multi-Layer Perceptron (MLP), K Nearest Neighbor (KNN), and Random Forest (RF) classifiers to identify the UHVDC faults. Finally, ±800kV UHVDC transmission line models are established in MATLAB/Simulink simulation software to simulate different types of faults. The study’s findings demonstrate that the fault diagnostic method introduced in this research may significantly enhance the poor detection success rate of current fault diagnostics methods and is more resilient to excessive resistance.

Detection of Intrusions in Internet of Things Based Deep Auto Encoder Using Deepnets

Detection of Intrusions in Internet of Things Based Deep Auto Encoder Using Deepnets

An Integrated Feature Extraction Based on Principal Components and Deep Auto Encoder with Extra Tree for Intrusion Detection Systems

With advances in computer network technology, the Internet has become an integral part of our daily lives. It goes without saying that providing security to network data is crucial. To this effect, the intrusion detection system (IDS) is vital for defending networks against cyberattacks. However, the high dimensionality of data is a significant issue that impacts categorisation accuracy. Therefore, we proposed a novel integrated feature extraction method called PC-UDAE that uses principal component analysis (PCA) and Unsupervised Deep Autoencoder (UDAE) to extract linear and nonlinear relationships. Also, to address the class imbalance, synthetic minority class samples are generated using the combination of Synthetic Minority Over Sampling Technique and Edited Nearest Neighbour (SMOTE-ENN). Finally, the extracted features are trained by using supervised machine learning models like Random Forest (RF), Extreme Gradient Boosting Machine (XGBM), Decision Tree (DT), Light Gradient Boosting Machine (LGBM), Extra Tree (ET), Support Vector Machine (SVM), AdaBoost (AB), and K-Nearest Neighbour (KNN) with the original imbalanced and balanced data. We analysed our suggested model using UNSW-NB 15, NSL-KDD, Ton-IoT data sets and obtained 98.85%, 99.59%, and 99.97% accuracy, respectively. Our experimental findings show that our proposed method outperformed all other competing methods.

Robust night flow analysis in water distribution networks: A BiLSTM deep autoencoder approach

Night flow analysis is predominantly used to identify incipient (gradual) leakages in real-life Water Distribution Networks (WDNs). However, due to extreme stochastic demand especially in residentially dominated district metering areas (DMAs) and the emergence of sleepless cities, traditional night flow analysis methods have become inefficient. This study proposes a semi-supervised sequence-to-sequence Bidirectional Long Short-Term Memory (BiLSTM) deep Auto Encoder (AE) method for night flow analysis in WDNs. To enhance the generalisation power of the deep AE, a vigorous data augmentation procedure based engineering domain knowledge and inherent properties of DMA is presented. The proposed method learns the underlying benign patterns in night flow (2:00 am to 4:00 am) and identifies significant deviations that represent the development of incipient leakages. The method was validated on residential, commercial, and industrial DMAs in a real-life WDN in the Ålesund Municipality of Norway. The results from the study showed that the proposed method is a robust and superior alternative to traditional night flow under extreme stochastic consumer demand. The proposed BiLSTM deep AE method was able to identify unreported leakages in the DMAs within 1 day or at most 4 days compared with at least 15 days identification time by traditional minimum night flow (MNF) and average night flow (ANF) analyses. Additionally, traditional minimum night flow analysis also produced more false positives compared with the proposed method. The results also revealed the usage of point estimates to represent night flow as done in MNF and ANF analyses fail to capture all salient information regarding night flow compared to the BiLSTM deep AE.

Intelligent IoT Enabled Pipeline Leakage Detection and Monitoring System Using Machine Learning Algorithm

Pipeline networks are the safest transportation of oil and gas-related products that may be susceptible to failures. These kinds of failures are due to the reason of manufacturing defects, degradation of the material, environmental defects, and interference of the third party. These issues are addressed through the Internet of Things (IoT) based solutions and give promising outcomes in predicting the failures and monitoring. The IoT can combine the sensor technologies to monitor the gas flow, pressure, temperature, condition of the compressor, concentration, and other related features inside the pipeline. The leaks in the pipe are measured as a pin-sized hole that is detected by the IoT sensor with advanced technologies such as deep learning. This project proposed deep learning with a meta- heuristic approach-based model to detect leaks in pipelines through IoT sensors. The Deep Learning model called Deep Auto Encoder Neural network (DAENN) is the unsupervised model that can accurately classify the leaking and non-leaking pipeline conditions. The detection accuracy is further enhanced with the Bat Optimization algorithm (BOA) which obtained improved accuracy while leaks occur in the pipeline inside the sensor monitoring area. This observation deploys the monitoring sensors to cover the mentioned monitoring area. The proposed model can increase the system’s leak detection reliability and reduces the false alarm rate.

Detection of Malware in Large Networks using Deep Auto Encoders

Data mining and machine learning have been heavily studied in recent years with the purpose of detecting sophisticated malware. The majority of these approaches rely on architectures that do not involve deeply enough into the learning process, despite the fact that they have yielded excellent results. This is because deep learning is finding increasing application in both business and academia thanks due to its skills in feature learning. In this paper, we develop a Deep Auto Encoder (DAE) based detection mechanism to detect the malwares crawling in the large scale networks. The DAE act as an unsupervised deep learning model that helps in detecting the malwares. The simulation is conducted on two different datasets to test the robustness of the model. The results show that the proposed method has higher rate of accuracy in detecting the attacks than other methods.

An Autonomic Workload Prediction and Resource Allocation Framework for Fog-Enabled Industrial IoT

The Internet of Things (IoT) has revolutionized the industrial field with numerous facilities and advancements. The industrial IoT system demands delay-aware workload execution with the aid of a fog computing platform, and precise resource allocation is required in fog nodes (FNs) to execute the fluctuating industrial IoT workloads with minimal cost and delay. In view of the issue mentioned above, we introduce an autonomic workload prediction and resource allocation framework that efficiently allocates resources among fog nodes. In the proposed framework, the workloads are predicted in the analysis phase with the guidance of the Deep Auto Encoder (DAE) model, and the fog nodes are scaled based on the demand of Industrial IoT workloads. The crow search algorithm (CSA) is integrated with the framework for optimal fog node selection to improve cost and delay objectives. The proposed scheme is evaluated and compared with the existing optimization models in terms of execution cost, request rejection ratio, throughput, and response time. The simulation results establish that the proposed scheme outperformed other optimization models. The method provided a suitable solution for the optimal fog node placement problems in efficiently executing dynamic industrial IoT workloads.

ADL-BSDF: A Deep Learning Framework for Brain Stroke Detection from MRI Scans towards an Automated Clinical Decision Support System

Deep learning has emerged to be efficient Artificial Intelligence (AI) phenomena to solve problems in healthcare industry. Particularly Convolutional Neural Network (CNN) models have attracted researchers due to their efficiency in medical image analysis. According to World Health Organization (WHO), rapidly developing cerebral malfunction, brain stroke, is the second leading cause of death across the globe. Brain MRI scans, when analysed quantitatively, play vital role in diagnosis and treatment of stroke. There are many existing methods built on deep learning for stroke diagnosis. However, an automatic, reliable and faster method that not only helps in stroke diagnosis but also demarcate affected regions as part of Clinical Decision Support System (CDSS) is much desired. Towards this objective, we proposed an Automated Deep Learning based Brain Stroke Detection Framework (ADL-BSDF). It does not rely on expertise of healthcare professional in diagnosis and know the extent of damage enabling physician to make quick decisions. The framework is realized by two algorithms proposed. The first algorithm known as CNN-based Deep Learning for Brain Stroke Detection (CNNDL-BSD) focuses on accurate detection of stroke. The second algorithm, Deep Auto encoder for Stroke Severity Detection (DA-SSD), focuses on revealing extent of damage or severity of the stroke. The framework is evaluated against state of the art deep learning models such as EfficientNet, ResNet50 and VGG16.

Hypervolume Sen Task Scheduilng and Multi Objective Deep Auto Encoder based Resource Allocation in Cloud

Cloud Computing (CC) environment has restructured the Information Age by empowering on demand dispensing of resources on a pay-per-use base. Resource Scheduling and allocation is an approach of ascertaining schedule on which tasks should be carried out. Owing to the heterogeneity nature of resources, scheduling of resources in CC environment is considered as an intricate task. Allocating best resource for a cloud request remains a complicated task and the issue of identifying the best resource – task pair according to user requirements is considered as an optimization issue. Therefore the main objective of the Cloud Server remains in scheduling the tasks and allocating the resources in an optimal manner. In this work an optimized task scheduled resource allocation model is designed to effectively address large numbers of task request arriving from cloud users, while maintaining enhanced Quality of Service (QoS). The cloud user task requests are mapped in an optimal manner to cloud resources. The optimization process is carried out using the proposed Multi-objective Auto-encoder Deep Neural Network-based (MA-DNN) method which is a combination of Sen’s Multi-objective functions and Auto-encoder Deep Neural Network model. First tasks scheduling is performed by applying Hypervolume-based Sen’s Multi-objective programming model. With this, multi-objective optimization (i.e., optimization of cost and time during the scheduling of tasks) is performed by means of Hypervolume-based Sen’s Multi-objective programming. Second, Auto-encoder Deep Neural Network-based Resource allocation is performed with the scheduled tasks that in turn allocate the resources by utilizing Jensen–Shannon divergence function. The Jensen–Shannon divergence function has the advantage of minimizing the energy consumption that only with higher divergence results, mapping is performed, therefore improving the energy consumption to a greater extent. Finally, mapping tasks with the corresponding resources using Kronecker Delta function improves the makespan significantly. To show the efficiency of Multi-objective Auto-encoder Deep Neural Network-based (MA-DNN) cloud time scheduling and optimization between tasks and resources in the CC environment, we also perform thorough experiments on the basis of realistic traces derived from Personal Cloud Datasets. The experimental results show that compared with RAA-PI-NSGAII and DRL, MA-DNN not only significantly accelerates the task scheduling efficiency, task scheduling time but also reduces the energy usage and makespan considerably.

Design and hardware in loop testing of an intelligent controller for power quality improvement in a complex micro grid

The wide-ranging use of renewable energy sources (RES) in the modern era leads to the association of distributed non-conventional energy resources in contrary to large-scale and localized conventional non-renewable sources. The prevalent use of distributed sources produces various Power-Quality issues. A thorough review of the literature provides numerous approaches for Power Quality (PQ) improvements; however, most techniques can improve only a few PQ problems. The new technical challenges like power loss, stability, reliability and power quality (PQ) issues are observed in present micro grid scenario. In this regard, this research work is an attempt to address PQ improvement in a three-phase realistic complex micro grid (integration of three simple micro grids to the point of common coupling. Improvement of various PQ factors for example sag/swell, unbalancing, power factor (Pf), total harmonic distortion (THD), communication delay and addition of impedance has been addressed in this research work. In this research article, a Kernel based Deep Auto Regressive Exogenous Output Neural Network (KDNARX) controller has been proposed and applied to improve power quality issues in a complex or realistic three phase complex micro grid (RTPCMG) in grid connected mode of operation. The RTPCMG is a combination of three simple grids with individual ratings of (4 kW, 4 kW and 6 kW). Photo voltaic cell, wind generator (WG), fuel cell (FC) and battery energy storage system (BESS) are the constituent distributed generators (DGs) for the proposed micro grid. The PQ improvement by application of proposed KDNARX controller to the DG inverter switching, has been overly tested in 12 different power quality issues under various operating conditions. The Kernel parameters are optimized by a new optimization technique called Cosine Chaotic PSO (CCPSO) algorithm. The results proved the strength of the new control technique ensuring PQ improvement and stability study of a three phase AC micro grid. Also, the potentiality of the new control scheme is compared with ANN and NARX and its superiority has been proved. Further, some case studies are validated through hardware in loop (HIL) environment thus justifying its real time implementation.

The Application Mode of Multi-Dimensional Time Series Data Based on a Multi-Stage Neural Network

How to use multi-dimensional time series data is a huge challenge for big data analysis. Multiple trajectories of medical use in electronic medical data are typical time series data. Although many artificial-intelligence techniques have been proposed to use the multiple trajectories of medical use in predicting the risk of concurrent medical use, most existing methods pay less attention to the temporal property of medical-use trajectory and the potential correlation between the different trajectories of medical use, resulting in limited concurrent multi-trajectory applications. To address the problem, we proposed a multi-stage neural network-based application mode of multi-dimensional time series data for feature learning of high-dimensional electronic medical data in adverse event prediction. We designed a synthetic factor for the multiple -trajectories of medical use with the combination of a Long Short Term Memory–Deep Auto Encoder neural network and bisecting k-means clustering method. Then, we used a deep neural network to produce two kinds of feature vectors for risk prediction and risk-related factor analysis, respectively. We conducted extensive experiments on a real-world dataset. The results showed that our proposed method increased the accuracy by 5%~10%, and reduced the false rate by 3%~5% in the risk prediction of concurrent medical use. Our proposed method contributes not only to clinical research, where it helps clinicians make effective decisions and establish appropriate therapy programs, but also to the application optimization of multi-dimensional time series data for big data analysis.

Multi-layer defense algorithm against deep reinforcement learning-based intruders in smart grids

The Internet of Energy envisions the next generation of smart grids as a highly interconnected network, including advanced metering infrastructures, distributed energy resources, and bidirectional communication systems. The open architecture of IoE-based smart grid results in manifold security concerns, especially the risk of False Data Injection Attacks. The attack may target the technical aspects of a system since fabricating the network's data misleads power scheduling and routing strategies and interrupts the healthy operation of the power system. Additionally, monetary motivation for the intruder sometimes is the main motivation. The conventional cyber defense strategies are unable to detect well-developed False Data Injection Attacks, particularly once the intruder takes advantage of a Deep Reinforcement Learning-based attack development framework that analyzes the dynamic nature of the smart grids. This paper primarily outlines various possible passive attacks using statistical methods. Then, a reinforcement learning-based intruder as an active attack generator is developed, initialized by modeled passive attacks. The attack generator algorithm can simulate the network environment and subsequently creates unclassified attacks. After creating a dynamic attacker, a multilayer defense framework is developed using Snapshot Ensemble Deep Neural Network and an adoptable Deep Auto Encoder network to detect known and unknown threats. Performance evaluations and a real-world simulation prove that the proposed framework can successfully detect both passive and active, where the accuracy and false positive detection rate of the developed framework are 98.82% and 97.42%, respectively.

Remora whale optimization-based hybrid deep learning for network intrusion detection using CNN features

Security remains as a key role in this internet world owing to the fast expansion of users on the internet. Numerous existing intrusion detection approaches were introduced by numerous researchers to recognize and identify intruders. Meanwhile, the existing systems failed to achieve satisfactory detection accuracy. Hence, this paper develops a robust intrusion detection model, named Remora Whale Optimization (RWO)-based Hybrid deep model for detecting intrusions. Here, the input data is pre-processed, and thereafter data transformation is done. With the transformed data, effective CNN features are extracted and feature conversion is performed to convert the features into vector form. Moreover, RV-coefficient is accomplished for performing feature selection process and finally, network intrusions are effectively detected using Hybrid deep model where the Deep Maxout Network and Deep Auto Encoder are used. On the other hand, the training procedure of the Hybrid deep model is carried out using the designed optimization algorithm, named RWO, which is the hybridization of the Remora Optimization Algorithm (ROA) and Whale Optimization Algorithm (WOA). Furthermore, the devised technique achieved superior performance using the evaluation metrics, such as testing accuracy, precision, recall, and F1-score with the higher values of 0.938, 0.920, 0.932, and 0.926, respectively.

Evolutionary Algorithm with Deep Auto Encoder Network Based Website Phishing Detection and Classification

Website phishing is a cyberattack that targets online users for stealing their sensitive data containing login credential and banking details. The phishing websites appear very similar to their equivalent legitimate websites for attracting a huge amount of Internet users. The attacker fools the user by offering the masked webpage as legitimate or reliable for retrieving its important information. Presently, anti-phishing approaches necessitate experts to extract phishing site features and utilize third-party services for phishing website detection. These techniques have some drawbacks, as the requirement of experts for extracting phishing features is time consuming. Many solutions for phishing websites attack have been presented, such as blacklist or whitelist, heuristics, and machine learning (ML) based approaches, which face difficulty in accomplishing effectual recognition performance due to the continual improvements of phishing technologies. Therefore, this study presents an optimal deep autoencoder network based website phishing detection and classification (ODAE-WPDC) model. The proposed ODAE-WPDC model applies input data pre-processing at the initial stage to get rid of missing values in the dataset. Then, feature extraction and artificial algae algorithm (AAA) based feature selection (FS) are utilized. The DAE model with the received features carried out the classification process, and the parameter tuning of the DAE technique was performed using the invasive weed optimization (IWO) algorithm to accomplish enhanced performance. The performance validation of the ODAE-WPDC technique was tested using the Phishing URL dataset from the Kaggle repository. The experimental findings confirm the better performance of the ODAE-WPDC model with maximum accuracy of 99.28%.

Deep Auto Encoder Based Extreme Learning System for Automatic Segmentation of Cervical Cells

Nowadays, cervical cancer has emerged as one of the major causes of death and incredibly difficult to identify. In this research, three novel methods have been developed for the automatic detection and classification of cervical cancer in Pap images. There are five steps in this framework which includes data collection, preprocessing, segmentation, feature extraction, and classification of images. The proposed method is implemented using Herlev Pap smear data set. The input image from the data set is pre-processed by using an Anisotropic Diffusion Filter with Unsharp Masking technique that removes the noise in the Pap data. In the next step, an enhanced image sequence is segmented automatically by the proposed Advance Map-Based Superpixel Segmentation (AMBSS) algorithm. Finally, cervix cancer images are procured by utilizing an AMBSS and classified by Support Vector Machine classifier. The accuracy obtained is 85.4%. In order to improve the accuracy AMBSS with quasi newton-based Feed Forward Neural Network classification is used and the accuracy of 96.0% is obtained. Furthermore, AMBSS with Deep auto encoder-based Extreme Learning Machine classification is performed and achieved the accuracy of 99.1%. The findings show that the precision of classification typically exceeds other intelligent methods previously applied.

Deep auto encoders to adaptive E-learning recommender system

Adaptive learning, supported by Information & Communication Technology (TIC), is an important research area for educational systems which aim to improve the outcomes of students. Thus, the investigation of what should be adapted and how much to adapt constitute a foundation to Adaptive E-learning Systems (AES). In this paper, we compared three classes of Deep Auto Encoders and the popularity model to address the problem of learning and predicting the preferences of student on AES: Collaborative Denoising Auto Encoders (CDAE), Deep Auto Encoders for Collaborative Filtering (DAE-CF), and Deep Auto Encoders for Collaborative Filtering using Content Information (DAE-CI). The results point out that the DAE-CF is more effective providing significant adaptability. Furthermore, we present the concept named as signature of preference to represent a more granular class of adaptability. Therefore, this model may be used in e-learning systems to provide adaptability and help to improve the outcomes of students.

Performance of deer hunting optimization based deep learning algorithm for speech emotion recognition

This paper proposes a speech emotion recognition technique based on Optimized Deep Neural Network. The speech signals are denoised by presenting a novel adaptive wavelet transform with a modified galactic swarm optimization algorithm (AWT_MGSO). From the noise removed speech signals, the spectral features like LPC (Linear Prediction Coefficients), MFCC (Mel frequency cepstral coefficients), PSD (power spectral density) and prosodic features like energy, entropy, formant frequencies and pitch are extracted and certain features are selected by ASFO (Adaptive Sunflower Optimization Algorithm). The optimized DNN-DHO (Deep Neural Network with Deer Hunting Optimization Algorithm) is proposed for emotion classification. An enhanced squirrel search algorithm is proposed to update the weight in the optimized DNN_DHO classifier. In this study, all the eight emotions of the speech from RAVDESS (Ryerson Audio-Visual Database of Emotional Speech and Song) and TESS (Toronto Emotional Speech Set) databases for English and IITKGP-SEHSC (Indian Institute of Technology Kharagpur Simulated Emotion Hindi Speech Corpus) database for Hindi are classified. The experimental results are obtained and compared with the classifiers such as DNN_DHO, DNN (Deep Neural Network) and DAE (Deep Auto Encoder). The experimental results show that the proposed algorithm obtains maximum accuracy as 97.85% by the TESS dataset, 97.14% by the RAVDESS dataset and 93.75% by the IITKGP-SEHSC dataset by the DNN-HHO classifier.

Intelligent and Effective Intrusion Detection System using Machine Learning Algorithm

Intrusion Detection System observes the network traffic and identifies the attack and also inform the admin to corrective action. Powerful Intrusion Detection system is required for detection to various modern attack. There is need of efficient Intrusion Detection system .The focus of IDS research is the application of machine Learning and Deep Learning techniques. Projected work is combination of Deep Learning Technique in which Non Symmetric Deep Auto Encoder and Machine Learning Algorithm, Support Vector Machine Classifier is used to develop the Model. Stack power of the Non symmetric Deep Auto Encoder and Quickness with exactness of the SVM makes the Model very efficient. This Model not only improves the accuracy value but also improve recall and precision. It also cause the reduction of training time .To evaluate the performance of the Model and do the analysis the special Data set which are used are KDD CUP and NSL KDD Dataset.

Wavelet-Based Deep Auto Encoder-Decoder (WDAED)-Based Image Compression

In this work, we propose a Wavelet-based Deep Auto Encoder-Decoder Network (WDAED) based image compression which takes care of the various frequency components present in an image. Specifically, we demonstrate improvements over prior approaches utilizing this framework by introducing: (a) wavelet transform pre-processing for decomposing image into different frequencies for their separate processing (b) a very deep super-resolution network as a decoder of the convolutional autoencoder in order to achieve a good quality decompressed image. The end-to-end learning is performed for four wavelet sub-bands in parallel, minimizing the computational time. The encoder compresses the image by generating the latent space representations, whereas the decoder transforms the latent space to image space. The algorithm has been tested on various standard datasets i.e., ImageNet, Set 5, Set 14, Live 1, Kodak, Classic 5, General 100 and CLIC 2019 dataset. The proposed algorithm clearly exhibited the compression performance improvement of approximately 5%, 5.5%, and 13% in terms of PSNR, PSNRB and SSIM respectively.

A Design of Intrusion Detection using Modified Bat Algorithm and Deep Autoencoder Network

In this world, Intrusion Detection is more popular for preparing the network security systems. In current trend of increasing security system, there is a demand for Intrusion Detection. With these clarifications need to find a huge Data measurement, high speed traffic’s and frequent forms of threats. In this work, Intrusion Detection is done by Deep Auto-Encoder network (DAEN) and Modified BAT algorithm (MBA). Our approach improves the Deep Auto Encoder (DAE) classifier by manipulating the benefits of an additional process encourage through the atmosphere of microbats (Bat Procedure). The core aim of this work is to select the features based on Modified Bat Algorithm. Towards examine the model, using the NSL-KDD data’s and the survey of Modified Bat Algorithm will be discussed. Moreover, these methods do well to improve DAEN classifier and get reliable performance in standing of accuracy (96.06%), attack detection rate (95.05%).