Improved Elman Neural Network Research Articles

Explainable Artificial Intelligence Enabled Intrusion Detection Technique for Secure Cyber-Physical Systems

A cyber-physical system (CPS) can be referred to as a network of cyber and physical components that communicate with each other in a feedback manner. A CPS is essential for daily activities and approves critical infrastructure as it provides the base for innovative smart devices. The recent advances in the field of explainable artificial intelligence have contributed to the development of robust intrusion detection modes for CPS environments. This study develops an Explainable Artificial Intelligence Enabled Intrusion Detection Technique for Secure Cyber-Physical Systems (XAIID-SCPS). The proposed XAIID-SCPS technique mainly concentrates on the detection and classification of intrusions in the CPS platform. In the XAIID-SCPS technique, a Hybrid Enhanced Glowworm Swarm Optimization (HEGSO) algorithm is applied for feature selection purposes. For intrusion detection, the Improved Elman Neural Network (IENN) model was utilized with an Enhanced Fruitfly Optimization (EFFO) algorithm for parameter optimization. Moreover, the XAIID-SCPS technique integrates the XAI approach LIME for better understanding and explainability of the black-box method for accurate classification of intrusions. The simulation values demonstrate the promising performance of the XAIID-SCPS technique over other approaches with maximum accuracy of 98.87%.

Retracted: Prediction Model of Rice Seedling Growth and Rhizosphere Fertility Based on the Improved Elman Neural Network.

[This retracts the article DOI: 10.1155/2022/2151682.].

ANAF-IoMT: A Novel Architectural Framework for IoMT-Enabled Smart Healthcare System by Enhancing Security Based on RECC-VC

The Internet of Medical Things (IoMT) is an arising trend that provides a significant amount of efficient and effective services for patients as well as healthcare professionals for the treatment of disparate diseases. The IoMT has numerous benefits; however, the security issue still persists as a challenge. The lack of security awareness among novice IoMT users and the risk of several intermediary attacks for accessing health information severely endanger the use of IoMT. In this article, rooted elliptic curve cryptography with Vigenère cipher (RECC-VC) centered security amelioration on the IoMT is proposed for enhancing security. First, this work utilizes the exponential K-anonymity algorithm for privacy preservation. Second, a new improved Elman neural network (IENN) is proposed for analyzing the sensitivity level of data. The Gaussian mutated chimp optimization is employed for weight updating in this IENN. Finally, a novel RECC-VC is proposed for securely uploading the data to the cloud server. Additionally, data are stored in the cloud server using blockchain technology. In experimental analysis, the proposed methodologies attain better results than the prevailing methods. The proposed IENN model achieves an accuracy of 96% and is validated against state-of-the-art methods. Also, the proposed RECC-VC attains 98% of the security level.

An improved deep learning approach based on exponential moving average algorithm for atrial fibrillation signals identification

Atrial fibrillation (AF) is one of the leading causes of heart diseases worldwide. An accurate AF detection method for timely treatment is attracting great attention by widespread scientific and clinical research in recent years. However, routine AF detection using the visual examination of electrocardiogram data is strenuous and relatively subjective. In this study, an improved Elman neural network (IENN) model is specially designed for automated AF signals identification. Inspired by the exponential moving average (EMA) algorithm, a weight strategy is installed into the existing ENN network that provides more effective and comprehensive discrimination ability to the model for ECG signals recognition. Besides, our IENN network is embedded into a convolutional network architecture and the existing multi-layer perceptron and ENN networks are considered as control subjects to evaluate the validity on two public databases. The results exhibit that the proposed model yields the accuracy, specificity and sensitivity of 97.9%, 97.8% and 98.0% on the MIT-BIH AF database and 97.1%, 97.1% and 97.2% on the MIT-BIH arrhythmia database, respectively. These superior performances enable the proposed IENN model to have considerable potential as an effective tool to aid cardiologists in detecting AF signals accurately.

Prediction Model of Rice Seedling Growth and Rhizosphere Fertility Based on the Improved Elman Neural Network.

Rice developing prognostication is a key part of precise agricultural management, and its advancement is an intricate course of events involving the interplay of breed and environmental element. The traditional research method is based on data analysis of rice growth prediction modeling, mining the concealed rapport between rice productivity and circumstance element, for instance, weather, sunlight, and water, and then predicting its yield and analyzing the complex rapport between the circumstance element and growth in every developing phase. In this dissertation, the improved ElmanNN is accustomed to establish a prediction model, and the ElmanNN is accustomed to determine the rapport between the circumstance element and growth in every developing phase simultaneously so as to avoid the arithmetic falling into local optimum easily. In this dissertation, the improved genetic arithmetic is accustomed to optimize the initial weight and threshold of Elman neural network, and the range of weight value multitudinous layers in the mould are obtained by training the network with samples that have been tested in the last few years. Finally, the rapport between growth and yield in six different periods is independently modeled, and the training samples are build up separately one by one based on physiological parameters and environmental indicators of rice at every level. The experiments show that the accuracy for the prediction model in the light of the improved ElmanNN has been beneficial.

Performance Evaluation of Enterprise Collaboration Based on an Improved Elman Neural Network and AHP-EW

In order to mitigate the influence of human subjectivity on indicator weights in the performance evaluation of enterprise collaboration, and explore the nonlinear relationship between the enterprise collaboration influencing factors and the evaluation results, this paper propose a combined performance evaluation model based on AHP-EW and an improved Elman neural network. Firstly, based on the characteristics of collaboration among manufacturing enterprises, the evaluation system for the collaborative performance of manufacturing enterprises is constructed from three dimensions. Moreover, this study combines subjective and objective weighting methods to obtain comprehensive weights that take into account both expert experience and objective information. Then, an improved Elman neural network is proposed and trained to predict and evaluate the collaborative performance indicator data, which greatly shortens the evaluation time and improves evaluation accuracy. The experimental results show that the proposed model has a faster convergence speed and higher accuracy, which will provide a valuable reference for decision making and the management of enterprise collaboration.

Numerical simulation of regional air pollution characteristics based on meteorological factors and improved Elman neural network algorithm

Numerical simulation of regional air pollution characteristics based on meteorological factors and improved Elman neural network algorithm

Software Defect Prediction Based on Elman Neural Network and Cuckoo Search Algorithm

In software engineering, defect prediction is significantly important and challenging. The main task is to predict the defect proneness of the modules. It helps developers find bugs effectively and prioritize their testing efforts. At present, a lot of valuable researches have been done on this topic. However, few studies take into account the impact of time factors on the prediction results. Therefore, in this paper, we propose an improved Elman neural network model to enhance the adaptability of the defect prediction model to the time-varying characteristics. Specifically, we optimized the initial weights and thresholds of the Elman neural network by incorporating adaptive step size in the Cuckoo Search (CS) algorithm. We evaluated the proposed model on 7 projects collected from public PROMISE repositories. The results suggest that the contribution of the improved CS algorithm to Elman neural network model is prominent, and the prediction performance of our method is better than that of 5 baselines in terms of F-measure and Cliff’s Delta values. The F-measure values are generally increased with a maximum growth rate of 49.5% for the POI project.

Gearbox fault diagnosis method based on the fusion of EEMD and improved Elman-NN

In connection with the complex operating conditions of gearbox, multiple vibration excitation sources, and difficulty in extracting vibration signal fault features, a novel method of gearbox fault diagnosis is proposed. based on the fusion of EEMD and improved Elman neural network (Elman-NN) is developed. The wavelet packet is utilized to denoise the collected vibration signals of four different types of gearboxes: broken teeth, cracks, wear, and normal, and then use the EEMD method to decompose the denoised vibration signals, and use the correlation coefficient criterion means to carry out the IMF pseudo component elimination, and then get a more effective signal. Calculate the energy feature of the effective signal and use it as the enter feature of the Elman-NN. Based on standard Elman-NN, a self-feedback factor β is added to construct a reformed Elman-NN. Experimental results indicate that compared with the standardized Elman-NN, the improved Elman-NN has higher diagnostic accuracy and diagnostic efficiency.

Price Prediction of Traditional Chinese Medicine Based on ARIMA and Improved Elman Neural Network

The price’s change of traditional Chinese medicine contains linear, non-linear and other miscellaneous factors. It is difficult for people to use a separate model such as neural network model to judge its price trend. Based on the background, a combined forecasting model is proposed in this paper, it consists of Autoregressive Integrated Moving Average model and Elman neural network which is improved by correlation analysis. The combined forecasting model can use its two algorithm model to deal with the linear and nonlinear factors. Meanwhile, the innovation of this paper is using correlation analysis to import extra additional parameters for the neural network, which can increase its accuracy. A large number of traditional Chinese medicine’s price data was collected to be training samples, the final results show that the combined forecasting model has advantages over stability and accuracy than ARIMA or Elman neural network.

Efficient heart disease prediction-based on optimal feature selection using DFCSS and classification by improved Elman-SFO.

Prediction of cardiovascular disease (CVD) is a critical challenge in the area of clinical data analysis. In this study, an efficient heart disease prediction is developed based on optimal feature selection. Initially, the data pre-processing process is performed using data cleaning, data transformation, missing values imputation, and data normalisation. Then the decision function-based chaotic salp swarm (DFCSS) algorithm is used to select the optimal features in the feature selection process. Then the chosen attributes are given to the improved Elman neural network (IENN) for data classification. Here, the sailfish optimisation (SFO) algorithm is used to compute the optimal weight value of IENN. The combination of DFCSS-IENN-based SFO (IESFO) algorithm effectively predicts heart disease. The proposed (DFCSS-IESFO) approach is implemented in the Python environment using two different datasets such as the University of California Irvine (UCI) Cleveland heart disease dataset and CVD dataset. The simulation results proved that the proposed scheme achieved a high-classification accuracy of 98.7% for the CVD dataset and 98% for the UCI dataset compared to other classifiers, such as support vector machine, K-nearest neighbour, Elman neural network, Gaussian Naive Bayes, logistic regression, random forest, and decision tree.

Power supply reliability evaluation based on big data analysis for distribution networks considering uncertain factors

The safety and reliability of urban power supply are critical to the sustainability of cities and society. Based on the analysis of big data, a power supply reliability evaluation method for urban distribution networks considering uncertain factors is proposed in this paper. The method has good adaptability and can support the analysis of safety improvement measures. By investigating historical data on distribution network topology and parameters, the main influencing factors affecting power supply reliability and the uncertainties of these factors are screened out. An improved Elman neural network (IENN) is used, and the main process of reliability evaluation is obtained for the complex urban distribution network. It can effectively simplify the calculation and includes multiple uncertain factors to improve evaluation accuracy. Case studies with actual urban distribution network data are used to verify the feasibility and effectiveness of the proposed method. Finally, some useful conclusions are given, including the problems of urban distribution network power supply, and the improvement measures for power supply to support the development of sustainable cities and society.

An Improved Elman Network for Stock Price Prediction Service

The rapid development of edge computing drives the rapid development of stock market prediction service in terminal equipment. However, the traditional prediction service algorithm is not applicable in terms of stability and efficiency. In view of this challenge, an improved Elman neural network is proposed in this paper. Elman neural network is a typical dynamic recurrent neural network that can be used to provide the stock price prediction service. First, the prediction model parameters and build process are analysed in detail. Then, the historical data of the closing price of Shanghai composite index and the opening price of Shenzhen composite index are collected for training and testing, so as to predict the prices of the next trading day. Finally, the experiment results validate that it is effective to predict the short-term future stock price by using the improved Elman neural network model.

Improved Elman neural network in turbine blade fault diagnosis

To remotely monitor and maintain large-scale complex equipment in real-time, it is required to create a comprehensive framework integrating remote data collection, transmission, storage, analysis and prediction. The framework is designed to provide manufacturers with proactive, systematic, integrated operation and maintenance service, where the data analysis and health forecasting are the most important part. This paper conducts health management for the turbine blades. An output-hidden feedback (OHF) Elman neural network is developed by adding a self-feedback factor in the context nodes. Thus, this improved method can increase the accuracy of the fault diagnosis for guide vane damage. Through the results, the applicability of this improved Elman neural network has been verified.

Application of BAS-Elman Neural Network in Prediction of Blasting Vibration Velocity

Abstract In the field of blasting Engineering, an important research topic is to get ac-curate prediction of blasting vibration, which is the prerequisite and guarantee of blasting vibration control. Traditional Sodev’s empirical formula and basic neural network prediction model do not have good performance on ac-curacy and convergence speed. To overcome these shortcomings, an improved Elman neural network prediction model that is based on Beetle Antennae search algorithm (BAS) is proposed. After setting up the Elman neural network model with considering the influential parameter of free surface area, the BAS is used to accelerate the network training by searching good initial parameters. The prediction effect of the BAS-Elman model is compared with those of BP algorithm model, Elman algorithm model and GA-Elman algorithm model. The results show that BAS-Elman neural network model has higher accuracy than other models and can be used to predict the blasting vibration velocity of tunnel engineering.

Breast cancer diagnosis based on a new improved Elman neural network optimized by meta‐heuristics

AbstractIn this article, a new optimized method for diagnosing and analyzing breast cancer from the mammography images is presented. In this regard, preprocessing is used to remove the Gaussian noises that are used to happen in the mammography images and also to remove the additional areas. Then, image segmentation is performed on the images to determine the areas where the contrast material is perceptible. Afterward, combined feature extraction based on a discrete wavelet transform and gray‐level co‐occurrence matrix is proposed for extracting the important information fromthe images. Finally, a new classification model based on an improved Elman neural network (ENN) has been proposed. The ENN is optimized by an improvedversion of the collective animal behavior algorithm. Simulation results areimplemented to the Mammographic Image Analysis Society database and the resultsare compared with three different methods.

Automated detection of atrial fibrillation and atrial flutter in ECG signals based on convolutional and improved Elman neural network

Atrial fibrillation (AF) and atrial flutter (AFL) are two common life-threatening arrhythmias. Both are not only easily transformed into each other, but also often cause misdiagnosis due to the similar clinical symptoms. The early efficient and accurate detection of AF and AFL is helpful to reduce the pain and injury of patients suffering from these diseases, and the traditional detection is often inefficient and laborious. Therefore, we propose a new model mechanism using an 11-layers network architecture to automatically classify AF and AFL signals. It is mainly constructed with the convolutional neural network (CNN) and the improved Elman neural network (IENN). Besides, we specifically design two relative models as control subjects to validate the classification performance of the proposed model. 10-fold cross-validation is also implemented on the MIT-BIH AF database (AFDB) and the MIT-BIH arrhythmia database (MITDB), respectively. The obtained results show that the model achieved the accuracy, specificity, and sensitivity of 98.8%, 98.6%, and 98.9% on the AFDB database and 99.4%, 99.1%, and 99.6% on the MITDB database, respectively. The model mechanism has been demonstrated to have more superior performance than two relative models and some advanced algorithms, which can be considered as a reliable and efficient identification system to aid physicians and save lives.

System identification of PEM fuel cells using an improved Elman neural network and a new hybrid optimization algorithm

Parameter identification of the proton exchange membrane fuel cell (PEMFC) is a good way of increasing their efficiency in the next designs. In this study, an optimized improved Elman neural network based on a new hybrid optimization algorithm is proposed for this purpose. The proposed algorithm is a hybrid algorithm based on a combination of two newly algorithms, the world cup optimization (WCO) and the fluid Search Optimization (FSO) algorithms. The proposed method is applied to improve the method efficiency for estimating the PEMFC model parameters. The method is then validated by four different operational conditions. The optimization algorithm efficiency is also analyzed by comparison with some popular algorithms. Simulation results showed that using the designed method gives higher accuracy forecast for the PEMFC model parameters.

Improved Elman Neural Network Short-Term Residents Load Forecasting Considering Human Comfort Index

The massive access of distributed power in distribution network increases the complexity of user’s power consumption mode. It puts higher requirements on the accuracy and stability of load forecasting. The forward neural network has limitation in dynamic performance, and the prediction accuracy needs to be improved. This paper considers the influence of daily feature correlation factors on residential load. Then a method for improved Elman neural network short-term residential load forecasting considering human comfort index is designed. Using the human comfort index overcomes the shortcomings of low accuracy in load prediction when meteorological factor as a direct input. The Elman neural network’s incentive function is improved. The softmax function serves as an incentive function for hidden layer. Short-term load forecasting model was established for the load of Nanchang, Jiangxi, China. In order to reduce the impact of residents’ load characteristics, the samples of load are divided into weekend load, seasonal load and typical weather type load. Experiments show that the improved Elman neural network has higher prediction accuracy under three load types, compared with Elman neural network and RBF neural network.

An improved Elman neural network with piecewise weighted gradient for time series prediction

Time series prediction is an important tool for system analysis. Traditional forecasting methods usually achieve prediction by establishing static model or only mining the information from the sequence itself, but without considering the dynamics of the system or its triggering factors. Elman neural network (ENN) is a dynamical model that can remember historical states. In order to better establish the real multivariate time series model and improve the single-step prediction accuracy, this study proposes an improved regularized ENN method based on piecewise time weighted gradient (PWRENN). Each time the parameters of PWRENN model are updated, the weighted gradient is calculated by weighing the current gradient and the previous gradients according to a monotonically increasing temporal function. By considering both the current gradient and previous gradients, PWRENN is able to simulate the time-lag effect in the time series prediction problem. Moreover, the system environment may change with time, and the historical data of the same slot at a different time should have different effects on the predicted data. This work uses a piecewise time function with different parameters at different time periods to more accurately model the real system. In PWRENN, L2 regularization is adopted to solve the overfitting problem and enhance the generalization performance. Furthermore, the effectiveness of the proposed method is verified in an artificial Mackey-Glass time series prediction and three landslide displacement predictions.