Network Prediction Method Research Articles

Research on BP Neural Network Prediction of Financial Transformation and Upgrading Potential of Enterprises in the Digital and Intelligent Era

In order to reduce the operational risks of enterprises and help enterprises improve their core competitiveness, this paper studies the BP neural network prediction method for the financial transformation and upgrading potential of enterprises in the age of digital intelligence. Select the sample data of enterprises from Shanghai and Shenzhen Stock Exchanges, use the financial data of these enterprises to build an indicator of the potential for financial transformation and upgrading of enterprises, and use the nonparametric test method to eliminate the duplicate content in the indicator, and provide the tested indicators as input to the BP neural network model. Under the prediction of the model, obtain the potential for financial transformation and upgrading of different enterprises. The model test results show that the prediction method has a high fitting effect, which can divide the company’s financial transformation and upgrading potential into three states: poor, ordinary, and high, while maintaining accurate prediction ability.

A novel complex network prediction method based on multi-granularity contrastive learning

A novel complex network prediction method based on multi-granularity contrastive learning

Flight Trajectory Prediction Based on LSTM

Track prediction is a key technology to avoid potential navigation dangers and provide reliable scheduling. Due to the insufficient processing ability of traditional neural networks for long sequence data such as track data, the prediction error of aircraft trajectories is relatively large. In this paper, the Long Short-Term Memory network (LSTM) is used for 4D track prediction, and the excessive dependence between adjacent data is reduced through the sliding window method. The ADS-B track data is standardized, outliers are processed using the 3σ Principle, and the smoothness of the time series is ensured through cubic spline interpolation. Four-dimensional track prediction and two-dimensional prediction of each feature of Height, Speed, Speed, Longitude, and Latitude are carried out for the standard data. Performance is judged by Root Mean Squared Error (RMSE) and the average prediction error of each feature. Through comparison, the track prediction effect of the LSTM neural network is better than the existing BP neural network prediction method.

Research on neural networks in computer network security evaluation and prediction methods

Anomaly detection in networks to identify intrusions is a common and successful security measure used in many different types of network infrastructure. Network data traffic has increased due to the proliferation of viruses and other forms of cyber-attacks as network technology and applications have developed quickly. The limitations of classical intrusion detection, such as poor detection accuracy, high false negatives, and dependence on dimensionality reduction methods, become more apparent in the face of massive traffic volumes and characteristic information. That’s why IoT infrastructures often use Software-Defined Networking (SDN), allowing for better network adaptability and control. Hence, this paper’s convolutional neural network-based Security Evaluation Model (CNN-SEM) is proposed to secure the source SDN controller from traffic degradation and protect the source network from DDoS assaults. The proposed CNN-SEM system might defend against DDoS assaults once discovered by applying and testing a Convolutional Neural Network (CNN). The model can automatically extract the useful aspects of incursion samples, allowing for precise classification of such data. The detection and mitigation modules evaluate the proposed SDN security system’s performance, and the findings showed promise against next-generation DDoS assaults. The experimental results show the CNN-SEM achieves a high accuracy ratio of 96.6%, a detection ratio of 97.1%, precision ratio of 97.2%, a performance ratio of 95.1% and an enhanced security rate of 98.1% compared to other methods.

Construction of data base containing flow field information under different blade placement of pumping station in digital twin

Abstract The data base in digital twin technology includes various data such as the structure, performance, and operating status of the physical object. And the data base is the key point for the construction of the digital twin system. However, the data bases of different digital twin systems usually contain different information. In this paper, the flow state of the pumping station under different angles of four blades is selected as the research object. The research methods in this paper mainly include Latin hypercube sampling, numerical simulation and neural network prediction. The Latin hypercube sampling is used to select the typical Angle difference model. The numerical simulation is used to obtain flow field information. The neural network prediction method is used to construct a complete data base. The study incorporates an actual large-scale mixed-flow pump station to head, efficiency, and shaft power as the prediction targets. It utilizes the different angle difference data of ten sets of four blades as the training set, forty sets of remaining samples as the experiment set, and employs the backpropagation neural network method for constructing the prediction network. By changing the experiment set and test set, the method can efficiently complete the construction of data base including actual test and numerical simulation, which provides a solid foundation for the construction of digital twin platform.

Identification of two molecularly and prognostically distinct subtypes in acral melanoma using network prediction method.

Acral melanoma, characterized by its aggressiveness and poor prognosis compared to other melanoma subtypes, poses significant challenges in clinical management. However, the molecular underpinnings driving the biological and clinical features of this disease remain poorly understood. In this study, our aim was to elucidate the molecular landscape and the correlation between subtypes and clinical features of acral melanoma. We conducted comprehensive analyses to dissect the molecular characteristics of acral melanoma, employing a combination of multi-omics data analysis and network-based disease gene prediction algorithms. Single-cell RNA-Seq data were utilized to investigate the contribution of immunocytes to the molecular classification of acral melanoma. Additionally, we used clinical samples to validate the correlation between new subtypes and the prognosis of acral melanoma and the expression of subtype markers and verified the interaction between macrophages and acral melanoma cells at cellular level. Our study reveals the existence of two distinct subtypes of acral melanoma exhibiting marked differences in clinical behaviour, cellular and molecular mechanisms. We identified a robust biomarker panel (EREG, VSIG4, FCGR3A and RAB20) that accurately distinguishes these two subtypes with an impressive AUC of 0.946, validated using clinical samples. Subtype I, characterized by thinner Breslow thickness, demonstrates a favourable prognosis, whereas Subtype II represents a high-risk subtype with a propensity for dermal invasion. Notably, the signature gene EREG of Subtype I is enriched in FCN1+ macrophages, known for promoting inflammatory and immune responses. Conversely, signature genes VSIG4 and FCGR3A of Subtype II are enriched in SPP1+ macrophages, which exhibit significant crosstalk with tumour cells. Our findings significantly enhance the understanding of the molecular landscape of acral melanoma and offer novel insights into its clinical management by identifying distinct subtypes and potential therapeutic targets. The findings have to be confirmed in different cohorts in the future for full validation.

A deep neural network prediction method for diabetes based on Kendall's correlation coefficient and attention mechanism.

Diabetes is a chronic disease, which is characterized by abnormally high blood sugar levels. It may affect various organs and tissues, and even lead to life-threatening complications. Accurate prediction of diabetes can significantly reduce its incidence. However, the current prediction methods struggle to accurately capture the essential characteristics of nonlinear data, and the black-box nature of these methods hampers its clinical application. To address these challenges, we propose KCCAM_DNN, a diabetes prediction method that integrates Kendall's correlation coefficient and an attention mechanism within a deep neural network. In the KCCAM_DNN, Kendall's correlation coefficient is initially employed for feature selection, which effectively filters out key features influencing diabetes prediction. For missing values in the data, polynomial regression is utilized for imputation, ensuring data completeness. Subsequently, we construct a deep neural network (KCCAM_DNN) based on the self-attention mechanism, which assigns greater weight to crucial features affecting diabetes and enhances the model's predictive performance. Finally, we employ the SHAP model to analyze the impact of each feature on diabetes prediction, augmenting the model's interpretability. Experimental results show that KCCAM_DNN exhibits superior performance on both PIMA Indian and LMCH diabetes datasets, achieving test accuracies of 99.090% and 99.333%, respectively, approximately 2% higher than the best existing method. These results suggest that KCCAM_DNN is proficient in diabetes prediction, providing a foundation for informed decision-making in the diagnosis and prevention of diabetes.

Research on indoor and outdoor positioning switching algorithm based on improved PSO-BP

Aiming at the problems of poor positioning accuracy in the indoor and outdoor junction areas and the loss of positioning signals and discontinuous positioning results during the transition from the indoor area to the outdoor area, this paper proposes a machine learning method to solve the positioning problem in the indoor and outdoor junction areas and the switching problem of positioning methods. An indoor and outdoor positioning switching algorithm based on Particle Swarm Optimization-Back Propagation (PSO-BP) and BP neural network is designed. Through this algorithm, the position of the positioning tag can be judged independently and the coordinates of the positioning tag in the indoor and outdoor junction area can be predicted independently. The experimental results show that the accuracy of positioning area judgment based on PSO-BP neural network can reach 99.91%. The indoor and outdoor boundary area coordinates obtained by BP neural network prediction method are lower than the root mean square error of ultra wide band indoor positioning method and BDS positioning method. The algorithm model proposed in this paper effectively improves the positioning problem of the indoor and outdoor junction area and the positioning problem of the transition from the indoor area to the outdoor area, improves the positioning accuracy and positioning stability of the indoor and outdoor junction area, reduces the positioning cost, and has strong practicability.

Tension identification of bridge suspension rods based on multiple impedance parameters from self-inductive coils

ABSTRACT This study introduces a method for identifying tension in suspension rods based on impedance from self-inductive coils, grounded in electromagnetic induction and magnetic elasticity principles. The theoretical feasibility of this method was analysed through a high-frequency inductive coil model. Steel strand tensile tests, utilising self-inductive coil sensors at various frequencies, explored the relationship between resistance, inductance, and capacitance with tension. According to the experimental results, the sensitivity coefficient is used to determine the optimal prediction frequency of each impedance parameter. Within the optimal frequency range, a single impedance parameter regression prediction method based on the impedance difference ratio index was proposed. To improve the accuracy and stability of the predictions, a GA-BP neural network prediction model based on the fusion of multiple impedance parameters was proposed. The results indicate that the GA-BP neural network prediction method, which integrates multiple impedance parameters, achieves higher accuracy in identifying cable forces than the single impedance parameter regression prediction method. Specifically, at a frequency of 5 kHz, the relative error of the GA-BP neural network prediction method, which integrates multiple impedance parameters, was only 3.74%. This research offers a novel approach for future in-service tension identification in bridge suspension rods.

Prediction of dredged soil settlement based on improved BP neural network

Based on the measured settlement data from the Reclamation Project of Eastern Hengsha Shoal, two improved BP neural networks optimized by the particle swarm optimization (PSO) algorithm and genetic algorithm (GA) respectively are employed to predict the long-term consolidation settlement of the dredged soil in the reclamation area. By comparing the prediction results of the improved neural networks with those obtained from standard BP neural network and various curve fitting methods including hyperbola method, Hoshino method, and Asaoka method, as well as field-measured data, the improved neural network methods are validated to have a better predictive performance. The error analysis results indicate that compared with the standard BP neural network and curve-fitting prediction methods, the improved BP neural networks exhibit smaller prediction errors in terms of MAE, MSE, RMSE, MAPE, etc., and have a closer match between the predicted settlement values and the actual values. The selected GA-BP and PSO-BP neural network prediction methods in this study demonstrate higher prediction accuracy. Meanwhile, GA-BP demonstrates higher prediction accuracy and generalization ability than PSO-BP, but PSO-BP shows faster convergence in terms of prediction speed. The preferred methods presented in this paper exhibit good robustness and high reliability, which can achieve accurate prediction of dredged soil settlement. Additionally, unlike the traditional curve-fitting methods, the selected methods can effectively reduce the influence of subjective factors during the settlement prediction, and provide a new approach for settlement prediction, which can be effectively utilized as a substitute for long-term settlement monitoring and give reference for the design of settlement control of foundation of further construction projects.

Shear Wave Velocity Prediction Based on the Long Short-Term Memory Network with Attention Mechanism

Shear wave velocity (VS) is a vital prerequisite for rock geophysics. However, due to historical, cost, and technical reasons, the shear wave velocity of some wells is missing. To reduce the deviation of the description of underground oil and gas distribution, it is urgent to develop a high-precision neural network prediction method. In this paper, an attention module is designed to automatically calculate the weight of each part of the input value. Then, the weighted data are fed into the long short-term memory network to predict shear wave velocities. Numerical simulations demonstrate the efficacy of the proposed method, which achieves a significantly lower MAE of 38.89 compared to the LSTM network’s 45.35 in Well B. In addition, the relationship between network input length and prediction accuracy is further analyzed.

Corrigendum: Research on neural network prediction method for upgrading scale of natural gas reserves

Corrigendum: Research on neural network prediction method for upgrading scale of natural gas reserves

Deep learning model optimization of 110 kV line ice-melting technology without power failure

Abstract In order to achieve ice disaster warning for transmission lines, this article proposes an improved method based on particle swarm optimization (PSO) backpropagation (BP) neural network for ice thickness prediction. The study selects multiple factors such as temperature, humidity, and wind speed, introducing neural network prediction methods to explore the situation of ice thickness on transmission lines. The predictions from this model demonstrate closer alignment with actual values and achieve superior prediction accuracy compared to both the BP model and the PSO-BP model, and the average absolute percentage error of improved PSO (IPSO)-BP is 0.007. Using a 110 kV line as the experimental object, the study conducts ice melting without power outage by changing of the grid currents. Additionally, this predictive model method is employed for ice warnings, assessing ice coverage levels by computing the ice coverage ratio. This facilitates precise control over the activation and deactivation of ice melting devices. The method proposed in this study addresses the issue of low accuracy resulting from the singular data types used in traditional early warning models. Future efforts should focus on further validating the applicability of this method under varying climatic and environmental conditions to achieve real-time, precise control over line ice melting.

Supply Side Reform of Tourism in Ethnic Areas Empowers Common Wealth--Based on the Perspective of Tourism Human Resources

Abstract As part of the supply-side reform, tourism in ethnic areas is increasingly recognized as crucial for achieving shared prosperity. This paper introduces a model from the perspective of tourism human resources designed to predict the demand for such resources. The model aims to inform strategies that support supply-side reforms in tourism for ethnic areas. The GM(1,1) model is constructed with the gray algorithm model, and on the basis of the gray algorithm model, the gray prediction method is effectively combined with the BP neural network prediction method, and the prediction preferences are made according to the variance as well as the prediction method idea of the preferred combination, and the final prediction results are obtained. In the analysis of the tourism human resources profile and demand forecast in Xinjiang, the total number of tourism employment in 2022 accounts for 17.22% of the total number of jobs, and the total number of tourism students in school reaches 6,189. Only 11.66% of tourism human resources are highly educated talents with a bachelor’s degree or above, and the number of personnel with senior titles is only 8.42%, which is obviously low in high-education and high-level talents. Nearly 80% of the personnel in non-tourism-related categories are still there, and the specialization of tourism management personnel is low. The demand for tourism human resources in Xinjiang in 2027 is expected to reach 2.1179 million.

Network security situation assessment and prediction method based on multimodal transformation in edge computing

Network security play a critical role in ensuring the robustness and integrity of network systems. We propose a novel method based on multimodal transformation in edge computing for accurate and reliable data communication. The method leverages Graph Convolutional Networks (GCNs) to capture and analyze the complex relationships and dependencies among network entities, enabling enhanced prediction capabilities. By integrating multimodal transformation, diverse data sources are fused in the model. The assessments and comprehensive comparisons with established algorithms unequivocally establish the supremacy of our proposed approach, particularly with respect to metrics such as accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). Our method exhibits promising performance, enabling proactive threat detection and mitigation in network security by more then 90% accuracy. In the future work, we have included the different multimodal transformation techniques, addressing interoperability challenges, scaling the method for large-scale networks, and adapting the model to dynamic network environments.

Aircraft trajectory prediction and aviation safety in ADS-B failure conditions based on neural network

With the rapid expansion of transportation demand, the number of global flights has rapidly increased, which also poses challenges to air traffic management (ATM). Considering that the radar system in ATM can no longer meet the requirements of flight safety, a very promising next-generation air traffic control technology—Automatic Dependent Surveillance Broadcast (ADS-B) technology has been introduced. However, in the event of on-board equipment failure and local area signal interference, the ADS-B’s signal will disappear or be interrupted. This sudden situation can pose a danger to aviation safety. To solve this problem, this article proposes a bidirectional long short-term memory (Bi-LSTM) network prediction method combining historical ADS-B data to short-term predict the trajectory of aircraft, which can improve aviation safety in busy airspace. Firstly, the problem of frequent dynamic modeling of different types of aircraft was solved by utilizing historical ADS-B data as the data source. Secondly, the data cleansing method is proposed for ADS-B raw data. Furthermore, considering that the spatial trajectory of the aircraft is a complex time series with continuity and interactivity, a bidirectional LSTM based aircraft trajectory prediction framework is proposed to further improve prediction accuracy. Finally, a trajectory with frequent changes was selected for prediction, and compared with 7 prediction methods. The results showed that the proposed method had high prediction accuracy, thus also improving the aviation safety of the aircraft.

Application of Artificial Neural Network in Predicting Direct Economic Losses Due to Earthquake

Accurately predicting the direct economic losses caused by earthquakes is important for policy makers for disaster budgets. Before a disaster strikes, it is important to consider the public policy costsassociated with disaster relief and recovery. The aim of this study is to provide a risk assessment approach, which can benefit all parties involved. Artificial neural networks are widely used for time seriesforecasting, especially financial forecasting. Therefore, this study proposes a cutting-edge forecastingmethod such as backpropagation neural network (BPNN) and other prediction methods: neural networkautoregressive (NNAR) and ARIMA-GARCH to obtain the best prediction results. This paper appliesinterpolation data to increase the amount of data used. Two interpolations were applied to amplify theoriginal small sample with virtual points, namely cubic splines and further piecewise interpolation using. The results of this study are the cubic spline interpolation is the most effective way to solve thesmall sampling problem to predict direct economic losses due to the Indonesian earthquake and theBPNN method outperforms other traditional methods with an RMSE of 0.024 in the training period and0.174 in the testing period, significantly lower than other methods. The results of this research can beused as reference material for the government in estimating the level of earthquake losses and can beused to develop risk reduction strategies

MICA: a multi-omics method to predict gene regulatory networks in early human embryos.

Recent advances in single-cell omics have transformed characterisation of cell types in challenging-to-study biological contexts. In contexts with limited single-cell samples, such as the early human embryo inference of transcription factor-gene regulatory network (GRN) interactions is especially difficult. Here, we assessed application of different linear or non-linear GRN predictions to single-cell simulated and human embryo transcriptome datasets. We also compared how expression normalisation impacts on GRN predictions, finding that transcripts per million reads outperformed alternative methods. GRN inferences were more reproducible using a non-linear method based on mutual information (MI) applied to single-cell transcriptome datasets refined with chromatin accessibility (CA) (called MICA), compared with alternative network prediction methods tested. MICA captures complex non-monotonic dependencies and feedback loops. Using MICA, we generated the first GRN inferences in early human development. MICA predicted co-localisation of the AP-1 transcription factor subunit proto-oncogene JUND and the TFAP2C transcription factor AP-2γ in early human embryos. Overall, our comparative analysis of GRN prediction methods defines a pipeline that can be applied to single-cell multi-omics datasets in especially challenging contexts to infer interactions between transcription factor expression and target gene regulation.

Wind Power Analysis Using Machine Learning in Wind Turbines

Wind Power Analysis Using Machine Learning in Wind Turbines

Research on neural network prediction method for upgrading scale of natural gas reserves

With the gradual decline of natural gas production, reserve upgrading has become one of the important issues in natural gas exploration and development. However, the traditional reserve upgrade forecasting method is often based on experience and rules, which is subjective and unreliable. Therefore, a prediction method based on neural network is proposed in this paper to improve the accuracy and reliability of reserve upgrade prediction. In order to achieve this goal, by collecting the relevant data of natural gas exploration and development in Sichuan Basin, including geological parameters, production parameters and other indicators, and processing and analyzing the data, the relevant characteristics of reserves increase are extracted. Then, a neural network model based on multi-layer perceptron (MLP) is constructed and trained and optimized using backpropagation algorithm. The results show that the prediction accuracy of the constructed neural network model can reach more than 90% and can effectively predict the reserve upgrading. Experiments show that the model has high accuracy and reliability, and is significantly better than the traditional prediction methods. The method has good stability and reliability, and is suitable for a wider range of natural gas fields.