Recurrent Neural Network Algorithm Research Articles

Leveraging natural language processing for automated assessment and feedback production in virtual education settings

With the rapid development of online teaching, the traditional teacher face-to-face evaluation and feedback model in online teaching faces problems such as high labor costs, low efficiency, and insufficient personalization. The application of natural language processing technology in the field of education has become a trend. Based on this choice, this article applied natural language processing to achieve automated evaluation and personalized feedback of online teaching processes. This article chose to use recurrent neural network (RNN) algorithm for text generation, and adopted Seq2Seq-based (Sequence-to-Sequence) recurrent neural network framework for automatic text feedback generation. Then, a group of student performance data was collected and divided into a control group and an experimental group, and comparisons were made between manual feedback and automatically generated feedback, respectively. Finally, the effectiveness and impact of automatic evaluation and feedback generation were evaluated by comparing the performance data and analysis results of two groups of students. The average accuracy of the automatic evaluation system designed in this article was 95.6%, which was higher than the 84.5% of manual evaluation. The accuracy of the automatic evaluation system designed in this article was higher, and the time spent on manual evaluation and automatic evaluation was compared. The average time spent on manual evaluation was 62.2 seconds, while automatic evaluation shortened the time to 36.98 seconds. For the feedback results, the graphical curves of the manually generated and automatically generated feedback results in terms of feedback on student grades were basically consistent, which can fully prove that the automatically generated feedback results have high accuracy. The remarkable efficacy of natural language processing (NLP) in online education is vividly illustrated by a recent study, where an NLP model notched an impressive average ROUGE score of 93.8 points. This achievement outstrips the performance of three other algorithmic models in the realm of automatic assessment and feedback generation. Such comparative experiments underscore the transformative potential of NLP technology, significantly boosting teaching efficiency and enriching the learning experience by adeptly tackling the perennial challenges of evaluation and feedback in online education settings.

Intelligent video recommendation and interactive e-learning mode for English teaching courses based on machine learning

With the continuous deepening of intelligent education in China, English education is receiving increasing attention, and educational reform is urgent. However, English education in China is still in the early stages of intelligence, and the current educational resources lack uniformity, teaching methods are single, and the standards for measuring educational quality also need to be improved. Therefore, this article studies the intelligent recommendation system for English teaching course videos based on machine learning. Firstly, the relevant theories of machine learning were analyzed, and the multiple linear regression, recurrent neural network algorithms, and logistic regression models in machine learning technology were analyzed. At the same time, Markov decision processes are also used to reinforce machine learning algorithms. Secondly, an English teaching video recommendation platform was established to analyze the regional preference and style preference in the field of video recommendation. Finally, an English course recommendation system was developed through the video recommendation technology of machine learning and the current situation of English teaching courses, which can promote the innovation and application of English teaching models.

Prediction of Nursing Need Proxies Using Vital Signs and Biomarkers Data: Application of Deep Learning Models.

To develop deep learning models to predict nursing need proxies among hospitalised patients and compare their predictive efficacy to that of a traditional regression model. This methodological study employed a cross-sectional secondary data analysis. This study used de-identified electronic health records data from 20,855 adult patients aged 20 years or older, admitted to the general wards at a tertiary hospital. The models utilised patient information covering the preceding 2 days, comprising vital signs, biomarkers and demographic data. To create nursing need proxies, we identified the six highest-workload nursing tasks. We structured the collected data sequentially to facilitate processing via recurrent neural network (RNN) and long short-term memory (LSTM) algorithms. The STROBE checklist for cross-sectional studies was used for reporting. Both the RNN and LSTM predicted nursing need proxies more effectively than the traditional regression model. However, upon testing the models using a sample case dataset, we observed a notable reduction in prediction accuracy during periods marked by rapid change. The RNN and LSTM, which enhanced predictive performance for nursing needs, were developed using iterative learning processes. The RNN and LSTM demonstrated predictive capabilities superior to the traditional multiple regression model for nursing need proxies. Applying these predictive models in clinical settings where medical care complexity and diversity are increasing could substantially mitigate the uncertainties inherent in decision-making processes. We used de-identified electronic health record data of 20,855 adult patients about vital signs, biomarkers and nursing activities. The authors state that they have adhered to relevant EQUATOR guidelines: STROBE statement for cross-sectional studies. Despite widespread adoption of deep learning algorithms in various industries, their application in nursing administration for workload distribution and staffing adequacy remains limited. This study amalgamated deep learning technology to develop a predictive model to proactively forecast nursing need proxies. Our study demonstrates that both the RNN and LSTM models outperform a traditional regression model in predicting nursing need proxies. The proactive application of deep learning methods for nursing need prediction could help facilitate timely detection of changes in patient nursing demands, enabling the effective and safe nursing services.

Speech Emotional Recognition of Telephone Conversation by Using Deep Learning

In this research, we have compare two clustering algorithms, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) to support the SER model for voice communication. We proposed an approach to speech emotion recognition in telephone conversations using a combination of Mel-frequency cepstral coefficients (MFCC) for audio feature extraction. Only the zeroth coefficient (energy) of MFCC will be used, as energy can provide a good representation for sound. The extracted results are then classified using CNN and RNN. The CNN algorithm consistently achieved a higher accuracy than RNN with the values of 0.93 at epoch=50, 0.93 at epoch=100, 0.90 at epoch=150, and 0.93 at epoch=200. But the RNN algorithm has a faster training times than CNN. For optimizer test, Adam optimizer performs well for both models with respective accuracy values of 0.93 and 0.94, outperforming other optimizers for accuracy.

Surrogate model of turbulent transport in fusion plasmas using machine learning

Abstract The advent of machine learning (ML) has revolutionized the research of plasma confinement, offering new avenues for exploration. It enables the construction of models that effectively streamline the simulation process. While previous first-principles simulations have provided physics-based transport information, they have been inadequate fast for real-time applications or plasma control. In order to address this challenge, we introduce SExFC, a surrogate model based on the Gyro-Landau Extended Fluid Code (ExFC). An approach of physics-based database construction is detailed, as well the validity is illustrated. Through harnessing the power of ML, SExFC offers the capability to deliver rapid and precise predictions, facilitating real-time applications and enhancing plasma control. The proposed model integrates the recurrent neural network (RNN) algorithm, specifically leveraging the Gated Recurrent Unit (GRU) for iterative prediction of flux evolutions based on radial profiles. Therefore, the SExFC model has the potential to enable rapid and physics-based predictions that can be seamlessly integrated into future real-time plasma control systems.

Performance Comparison of Deep Learning Algorithm for Autism Spectrum Disorder Prediction

Autism Spectrum Disorder is a complex neurodevelopmental condition that significantly impacts social interaction and conduct in individuals. Early diagnosis and timely intervention are crucial for effectively managing the condition and enhancing overall outcomes. This study concentrates on creating predictive models through deep learning algorithms to assist in the early identification of ASD. The study employs an extensive dataset that covers various features linked to ASD traits, such as speech and language development, learning disorders, genetic factors, and behavioral characteristics. The investigation encompasses the assessment of three recurrent neural network (RNN) architectures-standard RNN, Long Short Term Memory(LSTM), and Gated Recurrent Unit(GRU) –for their effectiveness in predicting ASD. The dataset is split into 80% for training and 20% for testing. This study compares RNN, LSTM, and GRU algorithms. The classification results show that the LSTM and GRU models exhibited similar accuracy, with values of 71.03% and 70.78% respectively.

A Secured Keystroke-Based Model for Preventing Social Engineering Attacks using Recurrent Neural Network

Among several authentication problems, preventing social engineering attacks using behavioural biometric approach has not received the required attention especially with focus on keystroke dynamics. This study aims to leverage the power of deep learning for more accurate and robust continuous authentication based on typing patterns. The proposed framework for this study utilized deep learning algorithm for behavioural biometrics authentication using Keystroke dynamics. The deep learning model was developed using Recurrent Neural Network (RNN) algorithm and was optimized was to obtain a better performance with Bayesian optimization which, eventually enhanced the model's accuracy. The dataset was split into training and testing in the model design phase and some hyperparameters such as dense, activation, batch size, sigmoid, filament, input size and epoch were used and optimized for building the deep learning algorithm. The RNN model is used to generate the evaluation metrics such as log loss, accuracy, precision and recall. The result presented the accuracy, precision, recall, and loss function as 100%, 100%, 100%, and 36% respectively for optimized model. The cost metrics yielded 0.0032, 0.0032, and 0.0006 MAE, MSE, and RMSE respectively. The developed KBB shows high level of social engineering attacks mitigation in comparison with the existing solution from the performance measure results.

A machine-learning algorithm to grade heart murmurs and stage preclinical myxomatous mitral valve disease in dogs.

The presence and intensity of heart murmurs are sensitive indicators of several cardiac diseases in dogs, particularly myxomatous mitral valve disease (MMVD), but accurate interpretation requires substantial clinical expertise. Assess if a machine-learning algorithm can be trained to accurately detect and grade heart murmurs in dogs and detect cardiac disease in electronic stethoscope recordings. Dogs (n = 756) with and without cardiac disease attending referral centers in the United Kingdom. All dogs received full physical and echocardiographic examinations by a cardiologist to grade any murmurs and identify cardiac disease. A recurrent neural network algorithm, originally trained for heart murmur detection in humans, was fine-tuned on a subset of the dog data to predict the cardiologist's murmur grade from the audio recordings. The algorithm detected murmurs of any grade with a sensitivity of 87.9% (95% confidence interval [CI], 83.8%-92.1%) and a specificity of 81.7% (95% CI, 72.8%-89.0%). The predicted grade exactly matched the cardiologist's grade in 57.0% of recordings (95% CI, 52.8%-61.0%). The algorithm's prediction of loud or thrilling murmurs effectively differentiated between stage B1 and B2 preclinical MMVD (area under the curve [AUC], 0.861; 95% CI, 0.791-0.922), with a sensitivity of 81.4% (95% CI, 68.3%-93.3%) and a specificity of 73.9% (95% CI, 61.5%-84.9%). A machine-learning algorithm trained on humans can be successfully adapted to grade heart murmurs in dogs caused by common cardiac diseases, and assist in differentiating preclinical MMVD. The model is a promising tool to enable accurate, low-cost screening in primary care.

Intelligent Reflecting Surface-Assisted Wireless Communication Using RNNs: Comprehensive Insights

By adjusting the propagation environment using reconfigurable reflecting elements, intelligent reflecting surfaces (IRSs) have become potential techniques used to improve the efficiency of wireless communication networks. In IRS-assisted communication systems, accurate channel estimation is crucial for optimizing signal transmission and achieving high spectral efficiency. As mobile data traffic continues to surge and the demand for high-capacity and low-latency wireless connectivity grows, IRSs are becoming pivotal technologies in the development of next-generation communication networks. IRSs offer the potential to revolutionize wireless propagation environments, improving network capacity and coverage, particularly in high-frequency wave scenarios where traditional signals encounter obstacles. Amidst this evolving landscape, machine learning (ML) emerges as a powerful tool to harness the full potential of IRS-assisted communication systems, particularly given the escalating computational complexity associated with deploying and operating IRSs in dynamic environments. This paper presents an overview of preliminary results for IRS-assisted communication using recurrent neural networks (RNNs). We first implement single- and double-layer LSTM, BiLSTM, and GRU techniques for an IRS-based communication system. In the next phase, we explore a hybrid approach, combining different RNN techniques, including LSTM-BiLSTM, LSTM-GRU, and BiLSTM-GRU, as well as their reverse configurations. These RNN algorithms were evaluated with respect to bit error rate (BER) and symbol error rate (SER) for IRS-enhanced communication. According to the experimental results, the BiLSTM double-layer model and the BiLSTM-GRU combination demonstrated the highest BER and SER accuracy compared to other approaches.

A Robust Recurrent Neural Networks-Based Surrogate Model for Thermal History and Melt Pool Characteristics in Directed Energy Deposition.

In directed energy deposition (DED), accurately controlling and predicting melt pool characteristics is essential for ensuring desired material qualities and geometric accuracies. This paper introduces a robust surrogate model based on recurrent neural network (RNN) architectures-Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU). Leveraging a time series dataset from multi-physics simulations and a three-factor, three-level experimental design, the model accurately predicts melt pool peak temperatures, lengths, widths, and depths under varying conditions. RNN algorithms, particularly Bi-LSTM, demonstrate high predictive accuracy, with an R-square of 0.983 for melt pool peak temperatures. For melt pool geometry, the GRU-based model excels, achieving R-square values above 0.88 and reducing computation time by at least 29%, showcasing its accuracy and efficiency. The RNN-based surrogate model built in this research enhances understanding of melt pool dynamics and supports precise DED system setups.

PERAMALAN INDEKS HARGA SAHAM GABUNGAN (IHSG) MENGGUNAKAN METODE BACKPROPAGATION DAN RECURRENT NEURAL NETWORK (RNN)

Forecasting is an objective calculation and uses past data to predict something in the future. The economic activity of buying and selling shares is a common activity in this digital era. Investment in the capital market is currently popular among the general public. Forecasting in the digitalization era has undergone many developments, as well as its application not only in agriculture, but also in economics, politics and other fields. The development of forecasting in the economic field is growing rapidly with the use of more complex mathematical models and the application of information technology. Forecasting is an objective calculation and uses past data to predict something in the future. Utilization of artificial neural network (ANN) is one of the solutions for forecasting with more accurate results. One of the algorithms in ANN is the backpropagation algorithm. Apart from using other backpropagation forecasting methods that can be used is the deep learning (DL) method with a recurrent neural network algorithm. The backpropagation method produces an optimal MAPE of 13.28%. with an architectural model of seven input layer neurons, seven hidden layer neurons, and using the sigmoid activation function. The recurrent neural network method produces an optimal MAPE of 1.12%. with an architectural model of seven input layer neurons, ten hidden layer neurons, and using the tanh activation function. If the two optimal MAPEs of each method are compared, the Recurrent Neural Network Method produces more optimal forecasting compared to the backpropagation method.

Adjusted SpikeProp algorithm for recurrent spiking neural networks with LIF neurons

A problem related to the development of a supervised learning method for recurrent spiking neural networks is addressed in the paper. The widely used Leaky-Integrate-and-Fire model has been adopted as a spike neuron model. The proposed method is based on a known SpikeProp algorithm. In detail, the developed method enables gradient descent learning of recurrent or multi-layer feedforward spiking neural networks. The research included an extended verification study for the classical XOR classification problem. In addition, the developed learning method has been used to provide a spiking neural black-box model of fast processes occurring in a pressurised water nuclear reactor. The obtained simulation results demonstrate satisfactory effectiveness of the proposed approach.

Assessing current and future available resources to supply urban water demands using a high-resolution SWAT model coupled with recurrent neural networks and validated through the SIMPA model in karstic Mediterranean environments

Hydrological simulation in karstic areas is a hard task due to the intrinsic intricacy of these environments and the common lack of data related to their geometry. Hydrological dynamics of karstic sites in Mediterranean semiarid regions are difficult to be modelled mathematically owing to the existence of short wet episodes and long dry periods. In this paper, the suitability of an open-source SWAT method was checked to estimate the comportment of a karstic catchment in a Mediterranean semiarid domain (southeast of Spain), which wet and dry periods were evaluated using box-whisker plots and self-developed wavelet test. A novel expression of the Nash–Sutcliffe index for arid areas (ANSE) was considered through the calibration and validation of SWAT. Both steps were completed with 20- and 10-year discharge records of stream (1996–2015 to calibrate the model as this period depicts minimum gaps and 1985–1995 to validate it). Further, SWAT assessments were made with records of groundwater discharge and relating SWAT outputs with the SIMPA method, the Spain’s national hydrological tool. These methods, along with recurrent neural network algorithms, were utilised to examine current and predicted water resources available to supply urban demands considering also groundwater abstractions from aquifers and the related exploitation index. According to the results, SWAT achieved a “very good” statistical performance (with ANSE of 0.96 and 0.78 in calibration and validation). Spatial distributions of the main hydrological processes, as surface runoff, evapotranspiration and aquifer recharge, were studied with SWAT and SIMPA obtaining similar results over the period with registers (1980–2016). During this period, the decreasing trend of rainfalls, characterised by short wet periods and long dry periods, has generated a progressive reduction of groundwater recharge. According to algorithms prediction (until 2050), this declining trend will continue reducing groundwater available to meet urban demands and increasing the exploitation index of aquifers. These results offer valuable information to authorities for assessing water accessibility and to provide water demands in karstic areas.

A novel design of recurrent neural network to investigate the heat transmission of radiative Casson nanofluid flow consisting of carbon nanotubes (CNTs) across a curved stretchable surface

AbstractThis study aims to develop a supervised learning artificial recurrent neural network algorithm supported by Bayesian regularization called (ARNN‐BR) to analyze the impact of physical parameters, including radius of curvature (), Casson parameter (), heat generation parameter () and radiation parameter () on velocity fʹ(η), and temperature profiles θ(η) in Casson nanofluid consisting of carbon nanotubes (CNTs‐CNF) model for single and multiwalled CNTs across a curved stretched surface. The numerical dataset of the proposed model has been constructed by varying various parameters for five scenarios that are used in a Bayesian regularization‐based intelligent computing method to build networks for approximating the numerical solutions of CNTs‐CNF model. It is observed that increment in the dimensionless radius of curvature () causes to rise an increase in the velocity profile fʹ(η) for both SWCNTs and MWCNTs. However, a contrasting trend is observed when the Casson parameter () is increased to higher values. The temperature θ(η) of fluid increases as the heat generation parameter () and radiation parameter () increase. However, an opposite behavior is noticed when the dimensionless radius of curvature () varies. The effectiveness and significance of designed Bayesian regularization based artificial recurrent neural networks (ARNN‐BR) is demonstrated through regression index measurements, error histogram studies, auto‐correlation analysis and convergence curves showing a minimal level of mean square error (E‐11 to E‐04) for the comprehensive simulations of CNTs‐CNF model. The designed ARNN‐BR algorithm is employed in many domains such as voice recognition, machine translation, identification of neurological brain illnesses as well as for automated translation of texts across different languages.

Volleyball training video classification description using the BiLSTM fusion attention mechanism

This study aims to explore methods for classifying and describing volleyball training videos using deep learning techniques. By developing an innovative model that integrates Bi-directional Long Short-Term Memory (BiLSTM) and attention mechanisms, referred to BiLSTM-Multimodal Attention Fusion Temporal Classification (BiLSTM-MAFTC), the study enhances the accuracy and efficiency of volleyball video content analysis. Initially, the model encodes features from various modalities into feature vectors, capturing different types of information such as positional and modal data. The BiLSTM network is then used to model multi-modal temporal information, while spatial and channel attention mechanisms are incorporated to form a dual-attention module. This module establishes correlations between different modality features, extracting valuable information from each modality and uncovering complementary information across modalities. Extensive experiments validate the method's effectiveness and state-of-the-art performance. Compared to conventional recurrent neural network algorithms, the model achieves recognition accuracies exceeding 95 % under Top-1 and Top-5 metrics for action recognition, with a recognition speed of 0.04 s per video. The study demonstrates that the model can effectively process and analyze multimodal temporal information, including athlete movements, positional relationships on the court, and ball trajectories. Consequently, precise classification and description of volleyball training videos are achieved. This advancement significantly enhances the efficiency of coaches and athletes in volleyball training and provides valuable insights for broader sports video analysis research.

Lithium-Ion Battery Capacity Prediction with GA-Optimized CNN, RNN, and BP

Over the last 20 years, lithium-ion batteries have become widely used in many fields due to their advantages such as ease of use and low cost. However, there are concerns about the lifetime and reliability of these batteries. These concerns can be addressed by obtaining accurate capacity and health information. This paper proposes a method to predict the capacity of lithium-ion batteries with high accuracy. Four key features were extracted from current and voltage data obtained during charge and discharge cycles. To enhance prediction accuracy, the Pearson correlation coefficient between these features and battery capacities was analyzed and eliminations were made for some batteries. Using a genetic algorithm (GA), the parameter optimization of Convolutional Neural Network (CNN), Backpropagation (BP), and Recurrent Neural Network (RNN) algorithms was performed. The parameters that provide the best performance were determined in a shorter time using GA, which includes natural selection and genetic processes instead of a trial-and-error method. The study employed five metrics—Mean Square Error (MSE), Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), Mean Absolute Error (MAE), and Squared Correlation (R2)—to evaluate prediction accuracy. Predictions based on NASA experimental data were compared with the existing literature, demonstrating superior accuracy. Using 100 training data, 68 data predictions were made with a Root Mean Square Error (RMSE) of 0.1176%. This error rate represents an accuracy level 2.5 times higher than similarly accurate studies in the literature.

Prediksi Harga Bitcoin Menggunakan Algoritma Long Short Term Memory (LSTM)

Cryptocurrency is a digital currency made from sequence code or called blockchain, one of the cryptocurrencies is bitcoin. Prediction is a process that projects or imagines what might happen in the future based on data in the past or factors that influence the current situation. Bitcoin price prediction uses a deep learning approach with the Long Short Term Memory method. Long Short-Term Memory is a type of model from the Recurrent Neural Network (RNN) algorithm, a method designed to process data and can overcome the problem of price movements that have long-term dependencies that cannot be handled by traditional RNN models, the LSTM model has the ability “Remembers” information over long periods of time, so as to recognize patterns and trends. In this study, the prediction period used a dataset from 12 December 2020 to 14 April 2024. The evaluation results for the RMSE method for train data were 17318.40 and for test data were 27921.84 and for the MAPE method for train data it was 3.24% and for test data it was 3.24%. 5.36%. This shows that the RMSE and MAPE values in the data train are relatively small because they are vulnerable to the bitcoin price being too wide.

Deep learning restores speech intelligibility in multi-talker interference for cochlear implant users

Cochlear implants (CIs) do not offer the same level of effectiveness in noisy environments as in quiet settings. Current single-microphone noise reduction algorithms in hearing aids and CIs only remove predictable, stationary noise, and are ineffective against realistic, non-stationary noise such as multi-talker interference. Recent developments in deep neural network (DNN) algorithms have achieved noteworthy performance in speech enhancement and separation, especially in removing speech noise. However, more work is needed to investigate the potential of DNN algorithms in removing speech noise when tested with listeners fitted with CIs. Here, we implemented two DNN algorithms that are well suited for applications in speech audio processing: (1) recurrent neural network (RNN) and (2) SepFormer. The algorithms were trained with a customized dataset (∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document} 30 h), and then tested with thirteen CI listeners. Both RNN and SepFormer algorithms significantly improved CI listener’s speech intelligibility in noise without compromising the perceived quality of speech overall. These algorithms not only increased the intelligibility in stationary non-speech noise, but also introduced a substantial improvement in non-stationary noise, where conventional signal processing strategies fall short with little benefits. These results show the promise of using DNN algorithms as a solution for listening challenges in multi-talker noise interference.

Healthcare security in cloud‐based wireless sensor networks: Botnet attack detection via autoencoder‐aided goal‐based artificial intelligent agent

SummaryThis paper presents a new method in the field of healthcare security that specifically targets cloud‐based wireless sensor networks (WSNs). The suggested method integrates a goal‐based artificial intelligent agent (GAIA) with an autoencoder (AE) architecture, yielding an autoencoder‐based agent (AE‐A). The main goal of this integrated system is to improve the efficiency of identifying botnet assaults, with a specific emphasis on the evolving security threats related to cloud computing. Our concept is around creating a meticulously calibrated, goal‐driven AI agent tailored explicitly for healthcare applications. The agent meticulously analyses network data and proficiently integrates autoencoder‐enhanced anomaly detection techniques to uncover intricate patterns indicative of botnet activities. The adaptability of the goal‐based AI agent is improved by ongoing real‐time learning, guaranteeing that its responses are in line with the primary goal of neutralizing threats. The autoencoder serves a vital role in the system by functioning as a tool for extracting features. This approach enables the AI Agent to navigate complex information and derive significant insights efficiently. Cloud computing resources greatly enhance the functionalities of a system, enabling scalability, real‐time analysis, and improved responsiveness. Utilizing goal‐driven AI and autoencoder together proves to be a successful strategy in safeguarding healthcare‐oriented WSNs against botnet attacks. This technique takes a proactive stance in ensuring the security of sensitive medical data. The suggested model is evaluated against various models, including the bidirectional long short‐term memory (BLSTM) method, the hybrid BLSTM with recurrent neural network (BLSTM‐RNN) algorithm, and the Random Forest algorithm. The models are evaluated using metrics such as Matthews correlation coefficient (MCC), prediction rate, accuracy, recall, precision, and F1 score analysis. The investigation demonstrates that the suggested model achieved the most significant values of 93% MCC, 94% prediction rate, 91% accuracy, 98% recall, 98% precision, and 98% F1 score, respectively when compared to the existing models.

Enterprise financial sharing and risk identification model combining recurrent neural networks with transformer model supported by blockchain

The objective of this study is to investigate methodologies concerning enterprise financial sharing and risk identification to mitigate concerns associated with the sharing and safeguarding of financial data. Initially, the analysis examines security vulnerabilities inherent in conventional financial information sharing practices. Subsequently, blockchain technology is introduced to transition various entity nodes within centralized enterprise financial networks into a decentralized blockchain framework, culminating in the formulation of a blockchain-based model for enterprise financial data sharing. Concurrently, the study integrates the Bi-directional Long Short-Term Memory (BiLSTM) algorithm with the transformer model, presenting an enterprise financial risk identification model referred to as the BiLSTM-fused transformer model. This model amalgamates multimodal sequence modeling with comprehensive understanding of both textual and visual data. It stratifies financial values into levels 1 to 5, where level 1 signifies the most favorable financial condition, followed by relatively good (level 2), average (level 3), high risk (level 4), and severe risk (level 5). Subsequent to model construction, experimental analysis is conducted, revealing that, in comparison to the Byzantine Fault Tolerance (BFT) algorithm mechanism, the proposed model achieves a throughput exceeding 80 with a node count of 146. Both data message leakage and average packet loss rates remain below 10 %. Moreover, when juxtaposed with the recurrent neural networks (RNNs) algorithm, this model demonstrates a risk identification accuracy surpassing 94 %, an AUC value exceeding 0.95, and a reduction in the time required for risk identification by approximately 10 s. Consequently, this study facilitates the more precise and efficient identification of potential risks, thereby furnishing crucial support for enterprise risk management and strategic decision-making endeavors.