Stacked Autoencoder Model Research Articles

A novel deep learning‐based technique for driver drowsiness detection

AbstractEvery year, many people lose their lives because of road accidents. It is evident from statistics that drowsiness is one of the main causes of a large number of car accidents. In our research, we wish to solve this major problem by measuring the drowsiness level of the human brain while driving. The study aims to develop a novel technique to detect different alertness levels (i.e., awake, moderately drowsy, and maximally drowsy) of a person while driving. A hybrid model using a stacked autoencoder and hyperbolic tangent Long Short‐Term Memory (TLSTM) network with attention mechanism is designed for this purpose. The designed model uses different biopotential signals, such as electroencephalography (EEG), facial electromyography (EMG), and different biomarkers, such as pulse rate, respiration rate galvanic skin response, and head movement to detect a person's alertness level. Here, the stacked autoencoder model is used for automated feature extraction. TLSTM is used to predict a person's alertness level using stacked autoencoder network‐extracted features. The proposed model can classify awake, moderately drowsy, and maximally drowsy states of a person with accuracies of 99%, 98.3%, and 98.6%, respectively. The novel contributions of the paper includes (i) incorporation of an attention mechanism into the TLSTM network of the proposed hybrid model to focus on the emphatic states to enhance classification accuracy, and (ii) utilization of EEG, facial EMG, pulse rate, respiration rate, galvanic skin reaction, and head movement pattern to assess a person's alertness level.

Metaheuristics based dimensionality reduction with deep learning driven false data injection attack detection for enhanced network security

Recent sensor, communication, and computing technological advancements facilitate smart grid use. The heavy reliance on developed data and communication technology increases the exposure of smart grids to cyberattacks. Existing mitigation in the electricity grid focuses on protecting primary or redundant measurements. These approaches make certain assumptions regarding false data injection (FDI) attacks, which are inadequate and restrictive to cope with cyberattacks. The reliance on communication technology has emphasized the exposure of power systems to FDI assaults that can bypass the current bad data detection (BDD) mechanism. The current study on unobservable FDI attacks (FDIA) reveals the severe threat of secured system operation because these attacks can avoid the BDD method. Thus, a Data-driven learning-based approach helps detect unobservable FDIAs in distribution systems to mitigate these risks. This study presents a new Hybrid Metaheuristics-based Dimensionality Reduction with Deep Learning for FDIA (HMDR-DLFDIA) Detection technique for Enhanced Network Security. The primary objective of the HMDR-DLFDIA technique is to recognize and classify FDIA attacks in the distribution systems. In the HMDR-DLFDIA technique, the min–max scalar is primarily used for the data normalization process. Besides, a hybrid Harris Hawks optimizer with a sine cosine algorithm (hybrid HHO‐SCA) is applied for feature selection. For FDIA detection, the HMDR-DLFDIA technique utilizes the stacked autoencoder (SAE) method. To improve the detection outcomes of the SAE model, the gazelle optimization algorithm (GOA) is exploited. A complete set of experiments was organized to highlight the supremacy of the HMDR-DLFDIA method. The comprehensive result analysis stated that the HMDR-DLFDIA technique performed better than existing DL models.

Enhancing active noise control through stacked autoencoders: Training strategies, comparative analysis, and evaluation with practical setup

This work is motivated by the imperative to overcome the limitations of conventional active noise control (ANC) techniques. These limitations are rooted in linear systems like the least mean square algorithm when faced with nonlinear distortions in the acoustic environment and system electronics, such as loudspeakers. This work addresses the nonlinear ANC problem by conceptualizing ANC as a deep learning problem. A novel stacked autoencoder (SAE) model is proposed, which is trained to estimate the noise that should be played at a loudspeaker so that the noise received at an error microphone may be attenuated. ANC experiments are initially set up in an anechoic room to study the fundamental performance characteristics of the architecture without extraneous considerations of room impulse response. This is followed by performance analysis in a normal reflective room. Actual noise, comprising broadband and tonal noise, is recorded in both an anechoic room and a normal reflective room. The importance of these experiments in the design process lies in their ability to account for nonlinear effects in actual setups, surpassing the limitations of relying solely on models. Experiment outcomes demonstrate that the proposed SAE model yields a noise reduction (NR) of up to 26.44 dB for wideband noise and up to 35.74 dB for tonal noise types. Additionally, there is an improvement in the extent of the quiet zone provided by the proposed SAE for tonal noise cases as compared to the traditional filtered-x least mean square (FxLMS) technique.

Heterogeneous transfer learning considering feature representation and environmental consistency for landslide spatial prediction

ABSTRACT The difficult, time-consuming, and imbalanced acquisition of landslide inventories in complex and heterogeneous large areas often results in limited predictive performance for most statistical landslide spatial prediction methods. Although partial transfer learning methods have produced reliable predictive results and successfully implemented knowledge transfer between data-rich and data-scarce areas, most of these methods generally only extract inadequate environmental features and lack the ability to interpret when, what, and how to effectively transfer knowledge to other regions with limited data. In this paper, a heterogeneous transfer learning method considering feature representations and environmental consistency, which features robustness, similarity, and transferability, is proposed for landslide spatial prediction. Specifically, we trained a stacked autoencoder (SAE) to extract more nonlinear features among environmental factors, and added an environmental similarity criterion to the transfer adaptation boosting (TrAdaBoost) algorithm to minimize feature dissimilarities and avoid negative transfer in different scenarios. To evaluate the robustness of the proposed method, we first selected two target areas (Lushan County and Luding County, China) and a source area (Wenchuan County, China) as case study areas. Then, we directly combined the source area and target area as an additional dataset without considering transfer learning to validate the significance and necessity of the proposed method. The area under the receiver operating characteristic curve (AUC) of the two target regions for the proposed method were 0.920 and 0.972, respectively, which were greater than those of the traditional TrAdaBoost (0.909 and 0.969, respectively), SAE (0.790 and 0.937, respectively), and random forest (0.915 and 0.966, respectively) methods. Furthermore, the AUC values of the SAE (0.851 and 0.900) and random forest (0.890 and 0.935) models based on the expanded datasets were also lower than those of the proposed method. Therefore, the experimental results show that the proposed method can be generalized well due to its efficient utilization and high adaptability. Moreover, the proposed method can not only be applied to emergency rescue and disaster prevention but can also offer a promising way to improve landslide predictions of models with incomplete landslide inventories.

Damage detection of jacket platforms through improved stacked autoencoder and softmax classifier

The jacket platform is a common structural form for offshore operations, and its health status determines the safety of the marine development process. In addressing the issue of damage detection in jacket platforms, a damage detection and prediction model based on stacked autoencoder (SAE) and softmax classifier is proposed. The model first constructs a SAE model using a layer-wise greedy training strategy to extract features from input signals. Subsequently, the extracted features are input into the softmax classifier for detecting damage types in the jacket platform. The model also utilizes a risk coefficient to forecast potential faults on the jacket platform. Experimental results on the jacket platform model demonstrate that the proposed method achieves an overall accuracy of 96.7% in damage detection in both isolated noise and marine environments, which is an improvement of approximately 5% compared to the autoencoder (AE) model. Additionally, in the damage prediction experiments, the model achieves a precision of 98.7% and a recall of 77.5% for potential damage.

Video Forgery Detection using an Improved BAT with Stacked Auto Encoder Model

There are various public and private places such as banks, roads, offices, and homes equipped with cameras for surveillance. The surveillance videos are consisting of a precious source of information related to critical application scopes. The main problem is to aid powerful and accessible software that changes the content present in the video for the forgery creation of a video. The forgery involves region duplication that has a common video tampering. The existing techniques are utilized to detect video tampering from the forged videos that showed complexity in the background. Thus, it is important to overcome the problem of forgery detection in the research. The Spatio-temporal averaging model is carried out for the collection of a video sequence for obtaining the background information. This can detect the moving objects effectively for forgery detection. Next, the ResNet 18 is used for extraction of the feature vectors, and the discriminative feature vectors were reduced and improved the training time and accuracy. The Single Auto Encoder (SAE) is not able to reduce the input features' dimensionality. Thus, the SAE has used 3 encoders stacked on the top for detecting the forgery. It is based on the sequence of videos. In comparison to the existing models, the proposed approach outperformed them with accuracy rates of 98.6%, sensitivity rates of 98.60%, specificity rates of 98.47%, MCC rates of 97.29%, and precision rates of 99.93%.

Lithium exploration targeting through robust variable selection and deep anomaly detection: An integrated application of sparse principal component analysis and stacked autoencoders

Lithium is a strategic metal for high-technology industries that plays a vital role in realizing electromobility and effective energy storage for smartphones and electric/hybrid vehicles, in addition to being important in accumulating energy from renewable sources. Motivated by this, the development of state-of-the-art workflows is imperative to make Li exploration more efficient and guarantee a sustainable supply to the demanding markets in the future. In this paper, a computational protocol was described to address two critical challenges raised in the Li exploration: (i) selection of mineralization-related pathfinder variables and (ii) recognition of multi-element geochemical anomalies related to ore-forming processes. Robust sparse principal component analysis (SPCA) was employed to reduce active (non-zero) loading coefficients and straightforward selection of Li pathfinder variables. The selected variables were then fed into the training network of stacked autoencoders (SAE) to learn deep representations of multivariate input signals and quantify reconstruction errors linked to Li-vectoring geochemical anomalies. As a representative example, the proposed workflow, i.e., SPCA + SAE, was implemented in the R programming language and applied to a real-case experiment with stream sediment geochemical data pertaining to the Moalleman district, NE Iran. Moreover, compositional robust principal component analysis (RPCA) and Mahalanobis distance (MD) technique were adopted to constitute two comparative models, RPCA + SAE and SPCA + MD, as benchmarks to judge the competence of the SPCA + SAE model in variable selection and anomaly detection, respectively. A performance appraisal by success-rate curves and relevant area under the curves (AUCs) indicated that the SPCA + SAE model brings, by calculating the highest AUC, spatial patterns that are more promising for vectoring towards Li-mineralized grounds. Moreover, AUCSPCA+MD was measured to be greater than AUCRPCA+SAE, suggesting robust variable selection is a more important paradigm than deep anomaly detection for Li exploration targeting. Student's t–statistic method was eventually applied to the anomaly map from the SPCA + SAE model to define relevant thresholds for narrowing down prospective areas for the next round of Li exploration within the study area.

Modified fuzzy rough set technique with stacked autoencoder model for magnetic resonance imaging based breast cancer detection

Breast cancer is the common cancer in women, where early detection reduces the mortality rate. The magnetic resonance imaging (MRI) images are efficient in analyzing breast cancer, but it is hard to identify the abnormalities. The manual breast cancer detection in MRI images is inefficient; therefore, a deep learning-based system is implemented in this manuscript. Initially, the visual quality improvement is done using region growing and adaptive histogram equalization (AHE), and then, the breast lesion is segmented by Otsu thresholding with morphological transform. Next, the features are extracted from the segmented lesion, and a modified fuzzy rough set technique is proposed to reduce the dimensions of the extracted features that decreases the system complexity and computational time. The active features are fed to the stacked autoencoder for classifying the benign and malignant classes. The results demonstrated that the proposed model attained 99% and 99.22% of classification accuracy on the benchmark datasets, which are higher related to the comparative classifiers: decision tree, naïve Bayes, random forest and k-nearest neighbor (KNN). The obtained results state that the proposed model superiorly screens and detects the breast lesions that assists clinicians in effective therapeutic intervention and timely treatment.

Employing Sequence-to-Sequence Stacked LSTM Autoencoder Architecture to Forecast Indian Weather

One of the buzz words in current research depends for a weather forecast is the atmospheric temperature, and this value may change at any time. Predicting air temperature exactly is necessary since it is particularly important for various communities and events. In today’s world of massive amounts of data and unforeseen data fluctuations, long-short term memory (LSTM), a sort of neural network method, is becoming more and more popular. The present work employed a sequence to sequence LSTM autoencoder to forecast Indian weather patterns. The prediction model was trained using historical data obtained from the Khordha district administration. The proposed method primarily emphasizes two phases of the procedure. The present study involves an examination of a proposed model, wherein a comparison is made between four distinct variants of LSTM techniques: vanilla LSTM, stacked LSTM, bidirectional LSTM, and convLSTM. The objective of this analysis is to predict temperature patterns. Additionally, this paper utilizes an LSTM autoencoder model to forecast the temperature for the subsequent two-year period in the Khordha district of Odisha, India. The experimental investigation demonstrates that the utilization of Stacking encoder-decoders with bidirectional LSTM cells leads to a notable enhancement in the accuracy of the model, resulting in maximum efficacy. A notably lower Mean Absolute Error (MAE) result suggests that the 9-layer stacked autoencoder model has superior performance in predicting both the minimum and maximum temperature.

Discrimination of Maturity Stages of Cabernet Sauvignon Wine Grapes Using Visible-Near-Infrared Spectroscopy.

Grape quality and ripeness play a crucial role in producing exceptional wines with high-value characteristics, which requires an effective assessment of grape ripeness. The primary purpose of this research is to explore the possible application of visible-near-infrared spectral (Vis-NIR) technology for classifying the maturity stages of wine grapes based on quality indicators. The reflection spectra of Cabernet Sauvignon grapes were recorded using a spectrometer in the spectral range of 400 nm to 1029 nm. After measuring the soluble solids content (SSC), total acids (TA), total phenols (TP), and tannins (TN), the grape samples were categorized into five maturity stages using a spectral clustering method. A traditional supervised classification method, a support vector machine (SVM), and two deep learning techniques, namely stacked autoencoders (SAE) and one-dimensional convolutional neural networks (1D-CNN), were employed to construct a discriminant model and investigate the association linking grape maturity stages and the spectral responses. The spectral data went through three commonly used preprocessing methods, and feature wavelengths were extracted using a competitive adaptive reweighting algorithm (CARS). The spectral data model preprocessed via multiplicative scattering correction (MSC) outperformed the other two preprocessing methods. After preprocessing, a comparison was made between the discriminant models established with full and effective spectral data. It was observed that the SAE model, utilizing the feature spectrum, demonstrated superior overall performance. The classification accuracies of the calibration and prediction sets were 100% and 94%, respectively. This study showcased the dependability of combining Vis-NIR spectroscopy with deep learning methods for rapidly and accurately distinguishing the ripeness stage of grapes. It has significant implications for future applications in wine production and the development of optoelectronic instruments tailored to the specific needs of the winemaking industry.

AI Driven False Data Injection Attack Recognition Approach for Cyber-Physical Systems in Smart Cities

Abstract Cyber-physical systems (CPS) combine the typical power grid with recent communication and control technologies, generating new features for attacks. False data injection attacks (FDIA) contain maliciously injecting fabricated data as to the system measurements, capable of due to improper decisions and disruptions in power distribution. Identifying these attacks is vital for preserving the reliability and integrity of the power grid. Researchers in this domain utilize modern approaches namely machine learning (ML) and deep learning (DL) for detecting anomalous forms in the data that signify the existence of such attacks. By emerging accurate and effective detection approaches, this research purposes to improve the resilience of CPS and make sure of a secure and continuous power supply to consumers. This article presents an Improved Equilibrium Optimizer with Deep Learning Enabled False Data Injection Attack Recognition (IEODL-FDIAR) technique in a CPS platform. The main purpose of the IEODL-FDIAR technique is to enable FDIA attack detection and accomplishes security in the CPSS environment. In the presented IEODL-FDIAR technique, the IEO algorithm is used for the feature subset selection process. Moreover, the IEODL-FDIAR technique applies a stacked autoencoder (SAE) model for FDIA attack detection. Furthermore, the pelican optimization algorithm (POA) can be utilized for the optimum hyperparameter chosen for the SAE algorithm which in turn boosts the detection outcomes of the SAE model. To portray the better outcome of the IEODL-FDIAR system, a wide range of simulation analyses are executed. A wide comparison analysis described the improved results of the IEODL-FDIAR technique with existing DL models.

Automated grape leaf nutrition deficiency disease detection and classification Equilibrium Optimizer with deep transfer learning model

ABSTRACT Our approach includes picture preprocessing, feature extraction utilizing the SqueezeNet model, hyperparameter optimisation utilising the Equilibrium Optimizer (EO) algorithm, and classification utilising a Stacked Autoencoder (SAE) model. Each of these processes is carried out in a series of separate steps. During the image preprocessing stage, contrast limited adaptive histogram equalisations (CLAHE) is utilized to improve the contrasts, and Adaptive Bilateral Filtering (ABF) to get rid of any noise that may be present. The SqueezeNet paradigm is utilized to obtain relevant characteristics from the pictures that have been preprocessed, and the EO technique is utilized to fine-tune the hyperparameters. Finally, the SAE model categorises the diseases that affect the grape leaf. The simulation analysis of the EODTL-GLDC technique tested New Plant Diseases Datasets and the results were inspected in many prospects. The results demonstrate that this model outperforms other deep learning techniques and methods that are more often related to machine learning. Specifically, this technique was able to attain a precision of 96.31% on the testing datasets and 96.88% on the training data set that was split 80:20. These results offer more proof that the suggested strategy is successful in automating the detection and categorization of grape leaf diseases.

MODELING OF OPTIMAL MULTI KEY HOMOMORPHIC ENCRYPTION WITH DEEP LEARNING BIOMETRIC BASED AUTHENTICATION SYSTEM FOR CLOUD COMPUTING

More recently, cloud computing (CC) has gained considerable attention among research communities and business people. Inspite of the advantages of CC, security, and privacy remains a challenging problem. Therefore, biometric authentication systems have been employed and fingerprint is considered as widely employed to attain security. In addition, image encryption techniques can be used to encrypt the fingerprint biometric image to add an extra level of security. Based on these motivation, this study designs an optimal multikey homomorphic encryption (OMHE) with stacked autoencoder (SAE) based biometric authentication system for CC environment. The proposed OMHE-SAE model aims to encrypt the biometrics using OMHE technique and then verification takes place using SAE model. In addition, the OMHE technique involves the optimal key generation process using sandpiper optimization (SPO) algorithm to effectively choose the keys for encryption and decryption. Furthermore, the verification of decrypted biometrics takes place by the use of SAE model. A wide range of simulation analyses take place on benchmark datasets and the experimental outcomes portrayed the betterment of the OMHE-SAE More than cutting edge technology.

Correlating the Ambient Conditions and Performance Indicators of the LoRaWAN via Surrogate Gaussian Process-Based Bidirectional LSTM Stacked Autoencoder

LoRa’s biggest advantage is its flexibility, which is the ability to increase or decrease data rate and range while decreasing or increasing sensitivity. Whenever propagation conditions change frequently, this function allows the spreading factor to be modified accordingly. Despite their efficiency and scalability, adaptive data rate algorithms ignore and fail to factor in the complex correlation between ambient weather parameters influencing the communication channel desig n. In this research, a Bayesian surrogate Gaussian pr ocess based bidirectional LSTM stacked autoencoder model (BSGP BLSTMSAE) is proposed to estimate the channel perfor-mance indicators such as received signal strength indicator (RSSI) and signal to noise ratio (SNR) and to determine the correlation between the ambient weather conditions and per-formance indicators for the LoRaWAN network. Bayesian op-timization algorithm has been used to optimize the hyper parameters of the developed model. A LoRaWAN experimental multivariate time series dataset has been used for the evalua-tion of the developed model, which upon testing and validation produces high accuracy in predicting the channel performance indicators and ambient conditions of the experimental Lo-RaWAN network. The mean absolu te error of the developed model was around 0.45. Thus, the proposed model can predict the link performance indicators and thereby assist in real time optimization of the transmission parameters to enhance the network performance in LoRaWAN based systems at different ambient conditions

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.

A White Shark Equilibrium Optimizer with a Hybrid Deep-Learning-Based Cybersecurity Solution for a Smart City Environment.

Smart grids (SGs) play a vital role in the smart city environment, which exploits digital technology, communication systems, and automation for effectively managing electricity generation, distribution, and consumption. SGs are a fundamental module of smart cities that purpose to leverage technology and data for enhancing the life quality for citizens and optimize resource consumption. The biggest challenge in dealing with SGs and smart cities is the potential for cyberattacks comprising Distributed Denial of Service (DDoS) attacks. DDoS attacks involve overwhelming a system with a huge volume of traffic, causing disruptions and potentially leading to service outages. Mitigating and detecting DDoS attacks in SGs is of great significance to ensuring their stability and reliability. Therefore, this study develops a new White Shark Equilibrium Optimizer with a Hybrid Deep-Learning-based Cybersecurity Solution (WSEO-HDLCS) technique for a Smart City Environment. The goal of the WSEO-HDLCS technique is to recognize the presence of DDoS attacks, in order to ensure cybersecurity. In the presented WSEO-HDLCS technique, the high-dimensionality data problem can be resolved by the use of WSEO-based feature selection (WSEO-FS) approach. In addition, the WSEO-HDLCS technique employs a stacked deep autoencoder (SDAE) model for DDoS attack detection. Moreover, the gravitational search algorithm (GSA) is utilized for the optimal selection of the hyperparameters related to the SDAE model. The simulation outcome of the WSEO-HDLCS system is validated on the CICIDS-2017 dataset. The widespread simulation values highlighted the promising outcome of the WSEO-HDLCS methodology over existing methods.

Two-stage multi-dimensional convolutional stacked autoencoder network model for hyperspectral images classification

Deep learning models have been widely used in hyperspectral images classification. However, the classification results are not satisfactory when the number of training samples is small. Focused on above-mentioned problem, a novel Two-stage Multi-dimensional Convolutional Stacked Autoencoder (TMC-SAE) model is proposed for hyperspectral images classification. The proposed model is composed of two sub-models SAE-1 and SAE-2. The SAE-1 is a 1D autoencoder with asymmetric structre based on full connection layers and 1D convolution layers to reduce spectral dimensionality. The SAE-2 is a hybrid autoencoder composed of 2D and 3D convolution operations to extract spectral-spatial features from the reduced dimensionality data by SAE-1. The SAE-1 is trained with raw data by unsupervised learning and the encoder of SAE-1 is employed to reduce spectral dimensionality of raw data. The data after dimension reduction is used to train the SAE-2 by unsupervised learning. The fine-tuning of SAE-2 encoder and the training of classifier are implemented simultaneously with small number of samples by supervised learning. Comparative experiments are performed on three widely used hyperspectral remote sensing data. The extensive comparative experiments demonstrate that the proposed architecture can effectively extract deep features and maintain high classification accuracy with small number of training samples.

A novel transformer-based ECG dimensionality reduction stacked auto-encoders for arrhythmia beat detection.

Electrocardiogram (ECG) is a powerful tool for studying cardiac activity and diagnosing various cardiovascular diseases, including arrhythmia. While machine learning and deep learning algorithms have been applied to ECG interpretation, there is still room for improvement. For instance, the commonly used Recurrent Neural Networks (RNNs), reply on its previous state to update and is therefore ineffective for parallel computing. RNN also struggles to efficiently address the issue of long-distancereliance. To reduce computational complexity by dimensionality reduction of ECG signals we constructed a Stacked Auto-encoders model using Transformer for ECG-based arrhythmia detection. And overcome the challenges of long-term dependencies and limited parallelizability in traditional RNNs when applied to ECG signalprocessing. In this paper, a Transformer-Based ECG Dimensionality Reduction Stacked Auto-encoders model is proposed for ECG-based arrhythmia detection. The transformer is used to encode ECG signals into a feature matrix, which is then dimensionally reduced using unsupervised greedy training through the four linear layers. This resulted in a low-dimensional representation of ECG features, which are subsequently classified using support vector machines (SVM) to minimizeoverfitting. The proposed method is benchmarked on the MIT-BIH Arrhythmia database. In the 10-fold cross validation of beat-based arrhythmia detection, the average accuracy, sensitivity, specificity and F1 score of the proposed method are 99.83%, 98.84%, 99.84% and 99.13%, respectively, for the record-based arrhythmia detection which refers to the approach where the training and testing sets use ECG data from independent recorded patients are 88.10%, 49.79%, 91.56% and 39.95%, respectively. Compared to other existing ECG-based arrhythmia detection methods, our proposed approach exhibits improved detection accuracy and stronger generalization for arrhythmia beats. Additionally, the use of the record-based data division method makes our approach more suitable for clinicalpractice.

Optimized feature fusion-based modified cascaded kernel extreme learning machine for heart disease prediction in E-healthcare

In recent years, medical technological innovators have focused on diverse clinical therapies to find innovative ways to overcome clinical challenges. But still, there emerge certain drawbacks like high computational cost, increased error, less training ability, the requirement of high storage space and degraded accuracy. To conquer these drawbacks, the proposed research article presents an innovative cascaded extreme learning machine for effective heart disease (HD) prediction. Missing data filtering and normalization methods are carried out for data pre-processing. From the pre-processed data, the features are extracted using the Framingham risk factor extraction module, whereas the extracted features are fused to generate a feature vector. The most significant features are selected using Rhino Satin Herd optimization algorithm. Using a linear weight assignment approach, the feature weighting process is undertaken by allocating higher weights to significant features and less weight to unwanted features. Finally, classification is performed through the Cascaded kernel soft plus extreme learning machine with a stacked autoencoder model. The performance is analyzed using PYTHON to evaluate the superiority of the proposed model. The proposed model obtained an overall accuracy of 90%, precision of 94%, recall of 91.3% and F1 measure of 92.6% in the Cleveland-Hungarian dataset, which is comparatively superior to the existing methods. An accuracy of 92.6% is attained for predicting HD in terms of the heart patient dataset. The proposed model attains better performance because of effective accuracy outcome, reduced overfitting issues, fewer error rates, better convergence and training ability.

Evidence-Theory-Based Reliability Analysis From the Perspective of Focal Element Classification Using Deep Learning Approach

Abstract Epistemic uncertainty is widespread in reliability analysis of practical engineering products. Evidence theory is regarded as a powerful model for quantifying and analyzing epistemic uncertainty. However, the heavy computational burden has severely hindered its application in practical engineering problems, which is essentially caused by the repeated extreme analysis of limit-state function (LSF). In order to address the issue, this paper proposes a novel method to solve the evidence-theory-based reliability analysis (ETRA). It transforms the conventional ETRA problem into the classification of three classes of joint focal elements (JFEs) and then solves the classification problem effectively through a deep learning approach. The core of solving an ETRA problem is to determine whether the joint focal element is located in the reliable region, failure region, or intersected with the LSF. A spatial position feature reduction and arrangement method is proposed to classify the JFEs, which can effectively reduce the feature dimension and take into account the integrity and correlation of features. The stacked autoencoders model is then constructed and updated by extracting the spatial position features of the sampled JFEs to achieve high-accuracy classification of the remaining JFEs, and the reliability interval is calculated efficiently according to the classification results. Finally, the effectiveness of the proposed method is demonstrated using several numerical examples.