Network Prediction Research Articles

Studies on Submersible Short-Circuit Blowing Based on Orthogonal Experiments, Back Propagation Neural Network Prediction, and Pearson Correlation Analysis

Short-circuit blowing is a crucial technical approach for ensuring the rapid surfacing of submersibles. In order to investigate the law, L18(37) orthogonal experiments based on a proportional short-circuit blowing model test bench were conducted. Subsequently, a Back Propagation Neural Network (BPNN) and Pearson correlation analysis were employed to train the experimental data; further examination of the correlation between individual factors and blowing served as an enhancement to the orthogonal experiments. It has been proved that both multi-factor combinations and personal factors, including blowing duration, sea tank back pressure, gas blowing pressure from the cylinder group, and sea valve flowing area, exert significant influence with Pearson correlation coefficients of 0.6535, 0.8105, 0.5569, and 0.5373, respectively. Notably, the F-ratio for blowing duration exceeds the critical value of 3.24. The statistical evaluation metrics for the BPNN ranged from 10−1 to 10−12, with relative errors below 3%, and achieving a prediction accuracy rate of 100%. Based on these findings, a robust predictive methodology for submersible short-circuit blowing has been established along with recommendations for engineering design and operational strategies that highlight its advantages as well as its initial condition settings.

MNPM: Research on Metabolic Neural Network Prediction Model for predicting carbon emission accuracy

MNPM: Research on Metabolic Neural Network Prediction Model for predicting carbon emission accuracy

Research on prediction and evaluation algorithm of sports athletes performance based on neural network.

The Ultimate Fighting Championship (UFC) stands as a prominent global platform for professional mixed martial arts, captivating audiences worldwide. With its continuous growth and globalization efforts, UFC events have garnered significant attention and achieved commendable results. However, as the scale of development expands, the operational demands on UFC events intensify. At its core, UFC thrives on the exceptional performances of its athletes, which serve as the primary allure for audiences. This study aims to enhance the allure of UFC matches and cultivate exceptional athletes by predicting athlete performance on the field. To achieve this, a recurrent neural network prediction model based on Bidirectional Long Short-Term Memory (BiLSTM) is proposed. The model seeks to leverage athlete portraits and characteristics for performance prediction. The proposed methodology involves constructing athlete portraits and analyzing athlete characteristics to develop the prediction model. The BiLSTM-based recurrent neural network is utilized for its ability to capture temporal dependencies in sequential data. The model's performance is assessed through experimental analysis. Experimental results demonstrate that the athlete performance prediction model achieved an overall accuracy of 0.7524. Comparative analysis reveals that the proposed BiLSTM model outperforms traditional methods such as Linear Regression and Multilayer Perceptron (MLP), showcasing superior prediction accuracy. This study introduces a novel approach to predicting athlete performance in UFC matches using a BiLSTM-based recurrent neural network. By leveraging athlete portraits and characteristics, the proposed model offers improved accuracy compared to classical methods. Enhancing the predictive capabilities in UFC not only enriches the viewing experience but also contributes to the development of exceptional athletes in the sport.

Evaluating gradient-based explanation methods for neural network ECG analysis using heatmaps.

Evaluate popular explanation methods using heatmap visualizations to explain the predictions of deep neural networks for electrocardiogram (ECG) analysis and provide recommendations for selection of explanations methods. A residual deep neural network was trained on ECGs to predict intervals and amplitudes. Nine commonly used explanation methods (Saliency, Deconvolution, Guided backpropagation, Gradient SHAP, SmoothGrad, Input × gradient, DeepLIFT, Integrated gradients, GradCAM) were qualitatively evaluated by medical experts and objectively evaluated using a perturbation-based method. No single explanation method consistently outperformed the other methods, but some methods were clearly inferior. We found considerable disagreement between the human expert evaluation and the objective evaluation by perturbation. The best explanation method depended on the ECG measure. To ensure that future explanations of deep neural networks for medical data analyses are useful to medical experts, data scientists developing new explanation methods should collaborate tightly with domain experts. Because there is no explanation method that performs best in all use cases, several methods should be applied. Several explanation methods should be used to determine the most suitable approach.

Data-driven multivariate time series prediction of in-vehicle equipment failure rates

Effectively predicting the failure rate of train-controlled on-board equipment is of great significance for rationally allocating equipment spares, drawing up maintenance plans, and reducing the occurrence of failures. In order to tackle the problem of the intricate attributes and limited predictive precision of the sample data pertaining to the failure rate of on-board equipment, in this paper, a multivariate time series prediction model for the failure rate of train-controlled on-board equipment is based on the combination of multivariate variational modal decomposition (MVMD) graph neural network (GNN) and transformer. First, the original failure rate time series, air temperature, humidity and sand and dust time series are modally decomposed using MVMD, and the intrinsic modal functions (IMFs) of each series are obtained; then, the dynamic graph of the GNN network is defined according to the IMFs, and Transformer’s self-attention mechanism is utilised to capture the dynamic graph’s temporal and spatial dependencies. Finally, the failure rate is output through the feed-forward neural network prediction value. Experiments using the historical fault data of CTCS3-300T train-controlled on-board equipment are carried out to confirm the efficacy of the suggested method, and it is contrasted with other conventional machine learning techniques. The outcomes of the experiment show that compared with other train-controlled on-board equipment failure prediction models, the proposed method has a very good superiority, as evidenced by its mean absolute error (MAE) of 0.0489 and root mean square error (RMSE) of 0.0510, which is of certain reference value for equipment operation and maintenance.

Pressure and Temperature Prediction of Oil Pipeline Networks Based on a Mechanism-Data Hybrid Driven Method

To ensure the operational safety of oil transportation stations, it is crucial to predict the impact of pressure and temperature before crude oil enters the pipeline network. Accurate predictions enable the assessment of the pipeline’s load-bearing capacity and the prevention of potential safety incidents. Most existing studies primarily focus on describing and modeling the mechanisms of the oil flow process. However, monitoring data can be skewed by factors such as instrument aging and pipeline friction, leading to inaccurate predictions when relying solely on mechanistic or data-driven approaches. To address these limitations, this paper proposes a Temporal-Spatial Three-stream Temporal Convolutional Network (TS-TTCN) model that integrates mechanistic knowledge with data-driven methods. Building upon Temporal Convolutional Networks (TCN), the TS-TTCN model synthesizes mechanistic insights into the oil transport process to establish a hybrid driving mechanism. In the temporal dimension, it incorporates real-time operating parameters and applies temporal convolution techniques to capture the time-series characteristics of the oil transportation pipeline network. In the spatial dimension, it constructs a directed topological map based on the pipeline network’s node structure to characterize spatial features. Data analysis and experimental results show that the Three-stream Temporal Convolutional Network (TTCN) model, which uses a Tanh activation function, achieves an error rate below 5%. By analyzing and validating real-time data from the Dongying oil transportation station, the proposed hybrid model proves to be more stable, reliable, and accurate under varying operating conditions.

Repairs and Breaks Prediction for Deep Neural Networks

With the increasing prevalence of software incorporating deep neural networks (DNNs), quality assurance for these software systems has become a crucial concern. To this end, various methods have been proposed to repair the misbehavior of DNNs by modifying their weights. However, these repair methods may not meet the developer's needs for a given dataset and model. In this study, we build prediction models for repair outcomes (i.e., repairs and breaks) to help determine whether the repair method is likely to work. By using our prediction models, developers and operators of DNNs can decide whether or not to apply a repair method, and if so, which method to use. Our prediction models utilize four metrics as explanatory metrics that represent the confidence or ambiguity in the DNN predictions. We experimented with four repair methods and 10 datasets. The experimental results demonstrate that our prediction models successfully select a repair method that meets developers’ needs in 16 out of 24 cases, resulting in an average time saving of 16.29% compared to the naive method. Based on these results, our prediction models can reduce costs for developers and operators when deciding whether to employ repair methods for real-world applications of DNNs.

Anisotropic induced polarization modeling with neural networks and effective medium theory

Interpreting three-dimensional induced polarization (IP) data requires rock physics models that reflect the omnipresent anisotropy of the Earth’s crust. The Generalized Effective Medium Theory of Induced Polarization (GEMTIP) can model the IP signatures of rocks with polarizable mineral inclusions. However, it is computationally demanding because it requires numerically solving the depolarization tensors of each inclusion. We aim to streamline GEMTIP simulations by (1) integrating anisotropic background conductivity and triaxial ellipsoidal inclusions in the model and (2) estimating the depolarization tensors with a neural network. We validate the neural network predictions against known solutions for spherical and spheroidal inclusions, and we test our method using data from an actual rock sample. The neural network shows a relative sensitivity of 56% to inclusion shape and 44% to host rock anisotropy and is up to 100,000 times faster than numerical integration. We release the pre-trained neural network implementation as an open-source Python package, thereby providing a new method to interpret the IP signatures of anisotropic rocks.

The essential oils from Asarum sieboldii Miq. alleviate allergic rhinitis by regulating tight junction and inflammation; Network analysis and preclinical validation

Ethnopharmacological relevanceEssential oils from herbs, including those from Asarum sieboldii Miq., are readily absorbed through mucous membranes, explaining their widespread use in inhalation formulations. Asarum sieboldii Miq. has a long history of traditional use for various medicinal purposes, attributed to its anti-inflammatory, antiallergic, and antioxidant properties. Despite research on Asarum sieboldii Miq. for allergic inflammation in respiratory diseases, detailed mechanistic studies are still lacking. Aim of the studyWe utilized bioinformatics and network pharmacology to identify the effectiveness of Asarum sieboldii Miq. in treating allergic rhinitis (AR). Our aim is to elucidate the potential therapeutic effects of essential oil derived from Asarum sieboldii Miq. (ASO), which is recognized for its diverse pharmacological properties, on AR. Materials and methodsCommon genes associated with the active compounds of ASO and AR were utilized to construct a related network and predict their mode of action. AR was induced in BALB/c mice by exposing them to ovalbumin (OVA) and particulate matter 10 (PM10; airborne particles <10 μm). With induction, aerosolized ASO (0.0002% and 0.02%) were administered via nebulizer for 5 minutes per day, three times a week for 7 weeks. Mice were examined for histopathological changes in the nasal tissue, nasal epithelial inflammation, the production of allergen-specific cytokine response in vitro and in vivo. ResultsThe common genes of ASO and AR were predicted to include 'Tight junction', 'Apoptosis', and 'TGF-beta signaling', which are the main pathways of pathogenesis in AR. Consistent with network prediction, nebulized ASO treatment effectively improved the expression of tight junction-related factors, zonula occludens-1, claudin-1, occludin and junction adhesion molecule A, in OVA+PM10 induced mice. Additionally, it reduced hyperplasia of nasal epithelial thickness, goblet cell counts, and inflammatory cell infiltration (eosinophils, neutrophils, macrophages, and lymphocytes) in nasal lavage fluid, while also alleviating allergic symptoms such as sneezing, rubbing, and serum IgE level when compared to the AR-induced group. The levels of mRNA and protein expression related to tight junctions were restored to normal levels by ASO, as confirmed in immunofluorescence analysis in nasal epithelial cells RPMI2650. Furthermore, treatment of ASO on PM10-treated nasal epithelial cells significantly reduced ROS production, recovered mitochondria membrane potential, and inhibition of the production of proinflammatory cytokines (TNF-α, IL-4, and IL-13) and inflammatory mediators linked to the MAPK/NF-κB signaling pathways. ConclusionIntranasal nebulization of ASO improves TJs and alleviates allergic nasal inflammation in AR. It supports the potential pharmaceutical application of ASO treatment for AR.

Prediction of moisture content ratio of emulsified asphalt chip seal based on machine learning and electrical parameters

The moisture content ratio (MCR) of the emulsified asphalt chip seal can determine its curing degree. However, the MCR of emulsified asphalt chip seal is difficult to measure on actual projects, and there is a lack of a method to assess its MCR. The objective of this study is to establish a prediction methodology for the MCR of emulsified asphalt chip seal based on machine learning and electrical parameters. Features such as electrical parameters and MCR of emulsified asphalt chip seal at different times were measured experimentally. The importance of the features was evaluated using a Random Forest (RF) model. A Back Propagation Neural Network (BPNN) prediction model was established using the important features. The weights and biases of the BPNN were optimized and initialized using the Improved Particle Swarm Optimization (IMPSO) algorithm. As a result, the RF-IMPSO-BPNN emulsified asphalt chip seal MCR prediction model was developed. This model was compared with five other models. The results show that compared to the RF-PSO-BPNN model, the improved RF-IMPSO-BPNN model can improve the ability of the neural network to find the global optimal solution. Compared to the other four machine learning models, the RF-IMPSO-BPNN model can achieve higher prediction accuracy while reducing the human and material resources of the various devices to collect part of the data. In addition, the emulsified asphalt chip seal cures at low MCR. The model predictions are more accurate at low MCR. Therefore, this study developed the RF-IMPSO-BPNN emulsified asphalt chip seal MCR prediction model, which can use fewer features to achieve higher accuracy and provide a rapid and non-destructive idea for judging its curing.

Analysis and Prediction of Path Loss for Mobile Communication Lines in Nasarawa State Using Propagation Models

The design of future-generation mobile communication systems depends critically on the path loss prediction methods and their suitability to various signal propagation regions. Even though 5G has been launched in Nigeria, its deployment still faces significant challenges especially in the suburban, and non-urban regions which still depends on 4G. Many operators are still in the process of upgrading their existing 4G networks to support network coverage and higher speed data throughput. Due to the unique mix of Nasarawa State with diverse terrains, in this study, path loss prediction for the key telecommunication networks in Nasarawa State was carried out using Signal Strength Info App. Data was collected for a period of eight weeks (August, 2nd to September, 4th 2023) focusing on 4G LTE networks operating within the 1800 MHz frequency band. Analysis of path loss using empirical models such as Hata, Egli, and COST-231 Hata models were compared with the measured path loss in different terrains of urban, sub-urban, and non-urban environments. The Root Mean Square Error (RMSE) analysis was carried out between empirical and measured path losses. Results shows that, the order of mean predicted path loss at 5km was free space model (BN&gt;CN&gt;DN.AN), Hata model (DN&gt;BN&gt;CN&gt;AN), Cos-231 Hata model (CN&gt;AN&gt;BN&gt;DN), and Egli model (AN&gt;BN&gt;DN&gt;CN). Hate model predicted the highest path loss values while Egli model predicted the lowest values. Overall, Egli model is found to be the most reliable and suitable path loss prediction model for entire Nasarawa State with RMSE value 5.99dBm, 2.90dBm and 3.14dBm for Nasarawa, Keffi and Akwanga LGA respectively. However, Cost-231 Hata model with RMSE value of 5.71dBm is suitable for path loss prediction in Karu due to its irregular terrain. The findings of this study are significant for practical applications in network planning, base station coverage, frequency allocation, and signal strength management, ensuring optimal mobile network performance across various terrains.

Pregestational neural network prediction of fetal growth restriction or small-for-gestational-age fetus with subsequent intensive care of the newborn

Pregestational neural network prediction of fetal growth restriction or small-for-gestational-age fetus with subsequent intensive care of the newborn

An Optimal Fuzzy Neural Network Prediction Model for Student Performance Prediction in Online Education

An Optimal Fuzzy Neural Network Prediction Model for Student Performance Prediction in Online Education

Identification of active ingredients in Naomaitai capsules using high-resolution mass spectrometry unite molecular network analysis and prediction of their action mechanisms.

Although Naomaitai capsule (NMC) is widely used in clinical practice and has a good curative effect for cerebral infarction, its material basis and mechanism of action remain unclear. In this study, ultra-high-performance liquid chromatography (UHPLC) coupled with quadrupole Orbitrap MS technology was used to analyse the in vivo and in vitro components of NMC, and the Global Natural Products Social Molecular Networking website was used to further analyse the components of NMC. Next, systems biology approaches were employed to investigate the mechanism of action of NMC. Finally, molecular docking technology was used to verify the network pharmacological results. In total, 177 compounds were identified in vitro, including 65 terpenoids, 62 flavonoids, 25 organic acids and 11 quinones. 64 compounds were identified in the blood of mice, and the main active components included ginkgolide C, ginkgolide A, ligustilide, tanshinone IIB, olmelin, emodin and puerarin. The main targets in vivo included TP53, SRC, STAT3, PIK3CA and PIK3R1. In conclusion, this study has revealed that NMC acts on multiple targets in the body through various active components, exerting synergistic effects in the treatment of CI. Its mechanism of action may involve inhibiting neuronal apoptosis, oxidative stress and inflammatory responses as well as reducing cerebral vascular permeability and promoting cerebral vascular regeneration.

Assessment of Socioeconomic Vulnerability in Selected Villages of Gosaba Block, Sundarban, India Using an Artificial Neural Network

The Indian Sundarban represent an endangered ecosystem with a distinct biogeographical composition. It is susceptible to natural disasters like storms, floods, and cyclones, hence jeopardizing its socioeconomic systems due to environmental stresses. This study aimed to evaluate the present socioeconomic vulnerability, identify key factors that exacerbate this vulnerability, and validate these factors using an Artificial Neural Network (ANN) prediction model. Researchers also wanted to validate this evaluation with ANN to promote substantive discussions between researchers, local authorities, and stakeholders. Comprehensive reviews and successful adaption plans will improve. The study conducted in selected villages with 160 households in the Gosaba Block, located on the periphery of the Sundarban. The present study employed an integrated vulnerability approach, calculating the exposure, adaptive capacity, and sensitivity index by weighting the initial eigenvalues of each indicator with a variance percentage through principal components analysis (PCA). Based on this criterion, 156 households (97.5%) exhibited an extremely high vulnerability score, while 4 households (2.5%) displayed a moderate vulnerability grade. The villages of Pakhiralay and Lahiripur exhibit significant vulnerability, with a markedly deficient adaptive capacity. The villages of Mathurakhand, Kumirmari, and Satjelia exhibit vulnerability alongside moderate adaptation capacity. All are priority villages, however, Villages with moderate adaptive capacity may become less vulnerable with adequate interventions Proximity to market strongly affects MLP neural networks' assessment of environmental, economic, and social variables' effects on exposure, sensitivity, and adaptive capacity. Respondent age, family size, social network, storm surges, supplementary sources of household income Dwelling years, temperature, embankment failure, and income greatly affect model performance. A policy initiative should prioritize the enhancement existing social and financial capital, facilitate access to local government, and promote community-oriented activities. National disaster, social welfare, and resource management strategies need separate action plans. The national government is also encouraged to decentralize governance to enable local governments to achieve their goals.

Predicting an opaque bubble layer during small-incision lenticule extraction surgery based on deep learning

AimThis study aimed to predict the formation of OBL during femtosecond laser SMILE surgery by employing deep learning technology.MethodsThis was a cross-sectional, retrospective study conducted at a university hospital. Surgical videos were randomly divided into a training (3,271 patches, 73.64%), validation (704 patches, 15.85%), and internal verification set (467 patches, 10.51%). An artificial intelligence (AI) model was developed using a SENet-based residual regression deep neural network. Model performance was assessed using the mean absolute error (EMA), Pearson’s correlation coefficient (r), and determination coefficient (R2).ResultsFour distinct types of deep neural network models were established. The modified deep residual neural network prediction model with channel attention built on the PyTorch framework demonstrated the best predictive performance. The predicted OBL area values correlated well with the Photoshop-based measurements (EMA = 0.253, r = 0.831, R2 = 0.676). The ResNet (EMA = 0.259, r = 0.798, R2 = 0.631) and Vgg19 models (EMA = 0.31, r = 0.758, R2 = 0.559) both displayed satisfactory predictive performance, while the U-net model (EMA = 0.605, r = 0.331, R2 = 0.171) performed poorest.ConclusionWe used a panoramic corneal image obtained before the SMILE laser scan to create a unique deep residual neural network prediction model to predict OBL formation during SMILE surgery. This model demonstrated exceptional predictive power, suggesting its clinical applicability across a broad field.

Assessing the Profit Impact of ARIMA and Neural Network Demand Forecasts in Retail Inventory Replenishment

This study investigates the integration of demand forecasting and inventory replenishment strategies to enhance retail profitability. A deterministic optimal replenishment model is utilized to analyze the predictive performance of various neural network architectures and ARIMA models using real sales data. The predictive accuracy and subsequent influence on optimal firm profits over a multi-period planning horizon is assessed. The Integer Programming model devised optimizes daily replenishment across multiple retail routes, taking into account sales revenue, supply costs, inventory holding, sales loss, and transportation expenses. The study is distinctive in its dual assessment: it evaluates both the accuracy of forecasting methods and their direct impact on profitability through systematic inventory decisions. Neural network architectures selected for minimizing error in product sales predictions have 6% lower mean squared error compared to Akaike Information Criterion minimizing ARIMA models. For longer horizon predictions necessary in performance gap grows larger, e.g., with %60 difference for predictions 15 days ahead. Predictions reflect as 1.6% higher profits on average, when neural network predictions and more efficient longer planning horizons of the optimization model are preferred. Planning 30 days ahead, optimizing with neural network predictions elicits 2.3% higher profits compared to profits attainable based on ARIMA predictions. Our findings illustrate how different forecasting methods can affect firm profitability by shaping inventory replenishment strategies. By merging mathematical optimization with time series forecasting, this research provides a comprehensive evaluation of how advanced predictive technologies can enhance retail inventory practices and improve profitability.

Trend Prediction of Vibration Signals for Pumped-Storage Units Based on BA-VMD and LSTM

Under “dual-carbon” goals and rapid renewable energy growth, increasing start-stop frequency poses new challenges to safe operations of pumped-storage power plant equipment. Ensuring equipment safety and predictive maintenance under complex conditions urgently requires vibration warnings and trend forecasting for pumped-storage units. In this study, the measured vibration-signal characteristics of pumped-storage units in a strong background-noise environment are obtained using a noise-reduction method that integrates BA-VMD and wavelet thresholding. We monitored the vibration-signal data of hydroelectric units over a long period of time, and the measured vibration-signal characteristics of pumped-storage units in a strong background-noise environment are accurately obtained using a noise-reduction method that integrates BA-VMD and wavelet thresholding. In this paper, a BP neural network prediction model, a support vector machine (SVM) prediction model, a convolutional neural network (CNN) prediction model, and a long short-term memory network (LSTM) prediction model are used to predict the trend of vibration signals of the pumped-storage unit under different operating conditions. The model prediction effect is analyzed by using the different error evaluation functions, and the prediction results are compared with the predicted results of the four different methods. By comparing the prediction effects of the four different methods, it is concluded that LSTM has higher prediction accuracy and can predict the vibration trends of hydropower units more accurately.

Neural Network-Based Optimization of LEO Transfers

This study investigates the application of neural networks to the evaluation of minimum-time low-thrust transfers in low Earth orbit. The findings demonstrate the effectiveness of utilizing costates to regularize the training loss, significantly enhancing the accuracy of the predictions of the neural networks, even when working with limited datasets. Remarkably precise estimates of transfer times are achieved by training the regularized networks on datasets comprising one million samples. The incorporation of a warm-started guess strategy, involving simpler neural networks to provide transfer time and costates predictions for new transfers, accelerates the data collection process, making this approach highly practical for real-world applications. Overall, the methodology employed in this research study holds significant promise for low-thrust space missions, particularly when the evaluation of multiple minimum-time transfers is necessary in mission planning. In fact, the trained neural networks significantly speed up convergence when solving optimal control problems with indirect optimization methods. Furthermore, the remarkable accuracy in estimating both minimum transfer times and costates provides the flexibility of relying entirely on neural networks for determining minimum time.

Generalization of neural network models for complex network dynamics

Differential equations are a ubiquitous tool to study dynamics, ranging from physical systems to complex systems, where a large number of agents interact through a graph. Data-driven approximations of differential equations present a promising alternative to traditional methods for uncovering a model of dynamical systems, especially in complex systems that lack explicit first principles. A recently employed machine learning tool for studying dynamics is neural networks, which can be used for solution finding or discovery of differential equations. However, deploying deep learning models in unfamiliar settings-such as predicting dynamics in unobserved state space regions or on novel graphs-can lead to spurious results. Focusing on complex systems whose dynamics are described with a system of first-order differential equations coupled through a graph, we study generalization of neural network predictions in settings where statistical properties of test data and training data are different. We find that neural networks can accurately predict dynamics beyond the immediate training setting within the domain of the training data. To identify when a model is unable to generalize to novel settings, we propose a statistical significance test.