Network Prediction Model Research Articles

Correction: Accuracy, Uncertainty and Explainability of Mixed Input Neural Network Models for Prediction of Missile Aerodynamic Loads

Correction: Accuracy, Uncertainty and Explainability of Mixed Input Neural Network Models for Prediction of Missile Aerodynamic Loads

Predicting unsteady incompressible fluid dynamics with finite volume informed neural network

The rapid development of deep learning has significant implications for the advancement of computational fluid dynamics. Currently, most pixel-grid-based deep learning methods for flow field prediction exhibit significantly reduced accuracy in predicting boundary layer flows and poor adaptability to geometric shapes. Although graph neural network models for unstructured grid-based unsteady flow prediction have better geometric adaptability, these models suffer from error accumulation in long-term predictions of unsteady flows. More importantly, fully data-driven models often require extensive training time, greatly limiting the rapid update and iteration speed of deep learning models when facing more complex unsteady flows. Therefore, this paper aims to balance the demands for training overhead and prediction accuracy by integrating physical constraints based on the finite volume method into the loss function of the graph neural network. Additionally, it incorporates a twice-message aggregation mechanism inspired by the extended stencil method to enhance the unsteady flow prediction accuracy and geometric shape generalization ability of the graph neural network model on unstructured grids. We focus particularly on the model's predictive accuracy within the boundary layer. Compared to fully data-driven methods, our model achieves better predictive accuracy and geometric shape generalization ability in a shorter training time.

Predicting churn with filter-based techniques and deep learning

Customer churn prediction is of utmost importance in the telecommunications industry. Retaining customers through effective churn prevention strategies proves to be more cost-efficient. In this study, attribute selection analysis and deep learning are integrated to develop a customer churn prediction model to improve performance while reducing feature dimensions. The study includes the analysis of customer data attributes, exploratory data analysis, and data preprocessing for data quality enhancement. Next, significant features are selected using two attribute selection techniques, which are chi-square and analysis of variance (ANOVA). The selected features are fed into an artificial neural network (ANN) model for analysis and prediction. To enhance prediction performance and stability, a learning rate scheduler is deployed. Implementing the learning rate scheduler in the model can help prevent overfitting and enhance convergence speed. By dynamically adjusting the learning rate during the training process, the scheduler ensures that the model optimally adapts to the data while avoiding overfitting. The proposed model is evaluated using the Cell2Cell telecom database, and the results demonstrate that the proposed model exhibits a promising performance, showcasing its potential as an effective churn prediction solution in the telecommunications industry.

Training Demand Prediction Models by Decision Error for Two-Stage Lot-Sizing Problems

Demand prediction to support appropriate production decisions is being actively studied. Many prediction models are designed to minimize the prediction error, which is measured by determining the difference between the predicted and ground-truth demand. However, these models ignore the effect of the prediction error on downstream production decisions. This prompted our study, which focuses on demand prediction models for two-stage uncapacitated lot-sizing problems. In this paper, we present a prediction model that minimizes the decision error, which is measured by the optimization objective of lot-sizing problems. Our model mitigates the impact of prediction errors by leveraging the structure of the lot-sizing problems. To enhance the ability to accommodate imperfect data, such as data based on inaccurate information, we subsequently extend the prediction models to distributionally robust versions. We consider the worst-case formulation in the feature space to enhance the robustness of the model to data imperfection. Numerical experiments demonstrate that the proposed prediction models are significantly superior to the traditional prediction methods when the model being trained is misspecified. In addition, the robust extension enables the models to train well on imperfect datasets while requiring less training data. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This study is motivated by the two-stage lot-sizing problem in manufacturing systems which involves the determination of the time at which (the setup decisions) to produce before the demand is revealed, and the production quantity decisions are adjusted during production processing. Predicting the demand properly while making the set-up decisions helps to decrease the production cost. This paper proposes a novel method that aims to minimize the total production cost to train demand prediction models. Practitioners can deploy the proposed method to train popular prediction models including linear prediction, decision tree, and neural network models. It is shown that the production cost associated with the decisions based on our models is lower than that associated with the traditional prediction models. Furthermore, our method enhances the ability of the model to handle model misspecification and imperfect data. Practitioners can apply our method to overcome the problems caused by imperfect data such as those containing contaminated and inaccurate measurements.

Application of an Artificial Neural Networks Model for Prediction of Instability Phenomena in Low Current Vacuum Arc by Cathode Spot Model

Application of an Artificial Neural Networks Model for Prediction of Instability Phenomena in Low Current Vacuum Arc by Cathode Spot Model

Deep Learning Approaches for Stream Flow and Peak Flow Prediction: A Comparative Study

Stream flow prediction is crucial for effective water resource management, flood prevention, and environmental planning. This study investigates the performance of various deep neural network architectures, including LSTM, biLSTM, GRU, and biGRU models, in stream flow and peak stream flow predictions. Traditional methods for stream flow forecasting have relied on hydrological models and statistical techniques, but recent advancements in machine learning and deep learning have shown promising results in improving prediction accuracy. The study compares the performance of the models using comprehensive evaluations with 1-6 input steps for both general stream flow and peak stream flow predictions. Additionally, a detailed analysis is conducted specifically for the biLSTM model, which demonstrated high performance results. The biLSTM model is evaluated for 1-4 ahead forecasting, providing insights into its specific strengths and capabilities in capturing the dynamics of stream flow. Results show that the biLSTM model outperforms other models in terms of prediction accuracy, especially for peak stream flow forecasting. Scatter plots illustrating the forecasting performances of the models further demonstrate the effectiveness of the biLSTM model in capturing temporal dependencies and nonlinear patterns in stream flow data. This study contributes to the literature by evaluating and comparing the performance of deep neural network models for general and peak stream flow prediction, highlighting the effectiveness of the biLSTM model in improving the accuracy and reliability of stream flow forecasts.

Geneticizing input selection based advanced neural network model for sediment prediction in different climate zone

The study focuses on developing an accurate prediction model for suspended sediment load (SSL) based on antecedent SSL and water discharge values. Two Artificial Intelligence (AI) models, Hybrid and Parallel, were employed to test on the Kelantan and Mississippi Rivers in different climate zones and river sizes. The parallel model showed better performance than the hybrid in most cases, with the best results based on Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) (432.06 and 782.15 respectively) for Kelantan and (31672.25 and 62356.60 respectively) for Mississippi. The multifunctional GA neural-network model results have proven its ability to predict SSL in tropical and semi-arid zones. In the Kelantan River, the 8-input combination set was the best prediction model, showing an improvement of more than 38% compared to traditional models. The proposed method has proven to be more accurate than traditional models, ensuring better water resource planning, agricultural management and reservoir operation.

Comparative Study of Neural Network Models for Prediction of Chaotic System

Comparative Study of Neural Network Models for Prediction of Chaotic System

Exploring the effectiveness of a multilayer neural network model for gold price prediction

Exploring the effectiveness of a multilayer neural network model for gold price prediction

Harnessing Attention-Based Graph Recurrent Neural Networks for Enhanced Conversational Flow Prediction via Conversational Graph Construction

Conversational flow refers to the progression of a conversation, encompassing the arrangement of topics discussed and how responses are delivered. A smooth flow involves participants taking turns to speak and respond naturally and intuitively. Conversely, a more disjointed flow may entail prolonged pauses or difficulties establishing common ground. Numerous factors influence conversation flow, including the personalities of those involved, their familiarity with each other, and the contextual setting. A conversational graph pattern outlines how a conversation typically unfolds or the underlying structure it adheres to. It involves combining different sentence types, the sequential order of topics discussed, and the roles played by different individuals. Predicting subsequent sentences relies on predefined patterns, the context derived from prior conversation flow in the data, and the trained system. The accuracy of sentence predictions varies based on the probability of identifying sentences that fit the subsequent pattern. We employ the Graph Recurrent Neural Network with Attention (GRNNA) model to generate conversational graphs and perform next-sentence prediction. This model constructs a conversational graph using an adjacency matrix, node features (sentences), and edge features (semantic similarity between the sentences). The proposed approach leverages attention mechanisms, recurrent updates, and information aggregation from neighbouring nodes to predict the next node (sentence). The model achieves enhanced predictive capabilities by updating node representations through multiple iterations of message passing and recurrent updates. Experimental results using the conversation dataset demonstrate that the GRNNA model surpasses the Graph Neural Network (GNN) model in next-sentence prediction, achieving an impressive accuracy of 98.89%.

State of health prediction of lithium-ion batteries based on SSA optimized hybrid neural network model

The accurate state of health (SOH) estimation of lithium-ion batteries (LIBs) is crucial for the operation and maintenance of new energy electric vehicles. To address this current problem, an improved hybrid neural network model for SOH prediction based on a sparrow search algorithm (SSA) optimized convolutional bi-directional long short-term memory neural network (CNN-Bi-LSTM) is proposed. Firstly, by analyzing the battery aging data, several feature indicators with highly correlated battery life degradation are constructed. Secondly, the CNN-Bi-LSTM model is used to extract the battery aging data features and the latent timing laws. Finally, the SSA optimizes the parameters to improve the model accuracy. Experimental results based on the NASA-Pcoe battery dataset show that the SSA-CNN-Bi-LSTM model outperforms other models, and the root-mean-square errors of the SOH prediction results are all less than 0.6 %. It indicates that the proposed SSA-CNN-Bi-LSTM model is capable of predicting SOH accurately and with high precision.

Application of Multivariate Regression and Artificial Neural Network Modelling for Prediction of Physicochemical Properties of Grape-Skin Compost.

The reusability of by-products in the food industry is consistent with sustainable and greener production; therefore, the aim of this paper was to evaluate the applicability of multiple linear regression (MLR), piecewise linear regression (PLR) and artificial neural network models (ANN) to the prediction of grape-skin compost's physicochemical properties (moisture, dry matter, organic matter, ash content, carbon content, nitrogen content, C/N ratio, total colour change of compost samples, pH, conductivity, total dissolved solids and total colour change of compost extract samples) during in-vessel composting based on the initial composting conditions (air-flow rate, moisture content and day of sampling). Based on the coefficient of determination for prediction, the adjusted coefficient of determination for calibration, the root-mean-square error of prediction (RMSEP), the standard error of prediction (SEP), the ratio of prediction to deviation (RPD) and the ratio of the error range (RER), it can be concluded that all developed MLR and PLR models are acceptable for process screening. Furthermore, the ANN model developed for predicting moisture and dry-matter content can be used for quality control (RER >11). The obtained results show the great potential of multivariate modelling for analysis of the physicochemical properties of compost during composting, confirming the high applicability of modelling in greener production processes.

Comparison of an interpretable extreme gradient boosting model and an artificial neural network model for prediction of severe acute pancreatitis.

Acute pancreatitis (AP) that progresses to persistent organ failure is referred to as severe acute pancreatitis (SAP). It is a condition associated with a relatively high mortality. A prediction model that would facilitate early recognition of patients at risk for SAP is crucial for improvement of patient prognosis. The aim of this study was to evaluate the accuracy of extreme gradient boosting (XGBoost) and artificial neural network (ANN) models for predicting SAP. A total of 648 patients with AP were enrolled. XGBoost and ANN models were developed and validated in the training (519 patients) and test sets (129 patients). The accuracy and predictive performance of the XGBoost and ANN models were evaluated using both the area under the receiver operating characteristic curves (AUCs) and the area under the precision‑recall curves (AUC‑PRs). A total of 15 variables were selected for model construction through a univariable analysis. The AUCs of the XGBoost and ANN models in 5‑fold cross‑validation of the training set were 0.92 (95% CI, 0.87-0.97) and 0.86 (95% CI, 0.78-0.92), respectively, whereas the AUCs for the test set were 0.93 (95% CI, 0.85-1) and 0.87 (95% CI, 0.79-0.96), respectively. The XGBoost model outperformed the ANN model in terms of both diagnostic accuracy and AUC‑PR. Individual predictions of the XGBoost model were explained using a local interpretable model‑agnostic explanation plot. An interpretable XGBoost model showed better discriminatory efficiency for predicting SAP than the ANN model, and could be used in clinical practice to identify patients at risk for SAP.

Integrating convolutional neural network and constitutive model for rapid prediction of stress-strain curves in fibre reinforced polymers: A generalisable approach

Despite recent advancements in using machine learning (ML) techniques to establish the microstructure-property linkage for composites’ representative volume elements (RVEs), challenges persist in effectively characterising the effect of microstructural randomness on material properties. This complexity arises from the difficulty of expressing randomness as definitive variables and its intertwined relations with other factors, such as material constituents. Such complexities result in limitations in generalising ML models across different material constituents. Conventional solutions to these challenges usually necessitate large datasets, which require considerable computational resources, for an accurate and generalisable ML models to be trained. This paper presents an innovative approach to tackling these challenges by integrating a high-accuracy convolutional neural network (CNN) with a novel microstructure-factored constitutive model (MCM). The MCM, rooted from classic empirical constitutive modelling, effectively segregates the microstructural and constituting material effects, extending the generalisability and thus significantly enhancing the efficacy of the CNN. This new approach enabled a CNN trained on the transverse stress-strain curves of one set of material constituents (CF/PEEK at 270 °C) to be generalised for the rapid prediction of various sets of material constituents at different temperatures, unseen by the CNN during training, with an average mean absolute percentage error around 3 %.

Colorectal cancer prediction via histopathology segmentation using DC-GAN and VAE-GAN

Colorectal cancer ranks as the third most common form of cancer in the United States. The Centres of Disease Control and Prevention report that males and individuals assigned male at birth (AMAB) have a slightly higher incidence of colon cancer than females and those assigned female at birth (AFAB) Black humans are more likely than other ethnic groups or races to develop colon cancer. Early detection of suspicious tissues can improve a person's life for 3-4 years. In this project, we use the EBHI-seg dataset. This study explores a technique called Generative Adversarial Networks (GAN) that can be utilized for data augmentation colorectal cancer histopathology Image Segmentation. Specifically, we compare the effectiveness of two GAN models, namely the deep convolutional GAN (DC-GAN) and the Variational autoencoder GAN (VAE-GAN), in generating realistic synthetic images for training a neural network model for cancer prediction. Our findings suggest that DC-GAN outperforms VAE-GAN in generating high-quality synthetic images and improving the neural network model. These results highlight the possibility of GAN-based data augmentation to enhance machine learning models’ performance in medical image analysis tasks. The result shows DC-GAN outperformed VAE-GAN.

A GCN-based adaptive generative adversarial network model for short-term wind speed scenario prediction

Wind prediction is of great significance for wind energy utilization due to the stochastic nature of wind. To effectively facilitate various downstream decision-making tasks such as wind turbine control, predictive wind Scenario Generation (SG), which is capable of providing a set of deterministic instantiated wind prediction results, plays a critical role. In this paper, a novel Graph neural networks-based Adaptive Predictive Generative Adversarial Network (GAPGAN) model is proposed for accurate prediction of short-term future scenarios of a wind field. In GAPGAN, the original multivariate time series data are first reconstructed into the form of a graph, and spatiotemporal features are then extracted using Graph Convolutional Networks (GCNs). Next, a predictive generative adversarial network (PGAN) framework is proposed, which could generate different outputs corresponding to given historical observations as conditions. Finally, an adaptive PGAN training mechanism is introduced to stabilize the training process, and the best SG model is selected based on the proposed comprehensive evaluation system. Based on wind speed data collected from 11 wind turbines, computational experiments validate that the GAPGAN outperforms five benchmarking models in terms of point prediction accuracy, shape similarity, uncertainty prediction quality, and prediction scenario diversity.

A Comparative Study of LSTM and Temporal Convolutional Network Models for Semisubmersible Platform Wave Runup Prediction

Abstract Wave runup prediction is necessary for offshore structure designs and early warnings. Data-driven methods based on machine learning have inspired reduced-order solutions for wave–structure interaction problems. This study provides the quantification of deep learning algorithms’ potential for wave runup prediction. Two prominent deep learning models were utilized to predict the wave runups along the fore column of semisubmersible under head seas. The long short-term memory (LSTM) and the temporal convolutional networks (TCNs) were comprehensively compared based on the datasets from a model test carried out in the deep ocean basin. The LSTM and TCN model structures were optimized to compare prediction accuracy and computational complexity reasonably. The results reveal that (1) both developed TCN and LSTM models had a satisfied prediction accuracy of over 90%. Their predictions were extended to 10 s into the future with accuracies over 80% and 45%, respectively. (2) With the noise-extended datasets, the LSTM model was robust to noises, while the TCN model showed better prediction performance on the extreme wave runup events. (3) The incident wave and dominant rotation provided the major information for wave runup prediction. TCN and LSTM models’ prediction accuracies were 91.5% and 89.3% based on the simplified input tensors composed of incident wave and pitch. The comparison showed the great potential of the TCN model to predict the nonlinear wave runup with less time and memory costs. The input tensors’ design and optimization based on physical understanding also play a significant role in the prediction performance.

Link prediction by continuous spatiotemporal representation via neural differential equations

With the continuous advancement of data science and machine learning, temporal link prediction has emerged as a crucial aspect of dynamic network analysis, providing significant research and application potential across various domains. While deep learning techniques have achieved remarkable results in temporal link prediction, most existing studies have focused on discrete model frameworks. These frameworks face limitations in capturing deep structural features and effectively aggregating temporal information. To address these limitations, we draw inspiration from neural differential equations to propose a Continuous Temporal Graph Neural Differential Equation (CTGNDE) network model for temporal link prediction. Specifically, we design a spatial graph Ordinary Differential Equation (ODE) to capture the spatial correlations inherent in complex spatiotemporal information. Then we employ Neural Controlled Differential Equation (Neural CDE) to learn complex evolution patterns and effectively aggregate temporal information. Finally, we characterize completely continuous and more accurate hidden state trajectories by coupling spatial and temporal messages. Experiments conducted on 10 real-world network datasets validated the superior performance of the CTGNDE model over the state-of-the-art baselines.

A Neural Network model for Indian Supreme Court legal judgment prediction

Industry experts are motivated to collect, collate, and analyse historical data in the legal sector in attempt to predict court case outcomes as the amount of historical data available in this field has increased over time. But using judicial data to predict and defend court judgements is no simple undertaking. Using Machine Learning (ML) models and traditional approaches for categorical feature encoding, previous research on predicting court outcomes using limited experimental datasets produced a number of unexpected predictions. The paper proposes an ensemble model combining Convolutional Neural Network (CNN), attention mechanism and eXtreme Gradient Boosting (XGB) algorithm. This model is primarily based on a self-attention network, which could simultaneously capture linguistic relationships over lengthy sequences like RNN (Recurrent Neural Network) and is nevertheless speedy to train like CNN. C-XGB can obtain accuracy that surpasses the state-of-art model on numerous classification/prediction tasks simultaneously as being twice as speedy to train. The proposed C-XGB model is designed to process the documents hierarchically and calculates the attention weights. Two convolutional layers are used to calculate the attention weights, one at the word level and another at the sentence level. And finally, at the last layer, the XGB algorithm predicts the input case file’s outcome. The experimental results shows that the proposed model outperforms the existing model with 4.67% improvement in accuracy value.

A Water Quality Prediction Method Based on Deep LSTM Network

Water quality parameters are key factors affecting marine ranching. The water quality parameters are not consistent, which are usually complex and variable. The traditional water quality prediction methods have problems such as low long-term prediction accuracy and weak generalization ability. In order to solve the above problems, this paper proposes a multivariate water quality prediction model based on WT-LSTM, which is compared with the BP neural network model for short-term prediction and long-term prediction of dissolved oxygen water quality parameters, and the experimental results show that the WT-LSTM model has good accuracy and generalization in both short-term and long-term prediction, and the short-term prediction accuracy can be up to 98.47% and the long-term prediction accuracy can be up to 98.28%.