Network Prediction Model Research Articles

Predictive value of a constructed artificial neural network model for microvascular invasion in hepatocellular carcinoma: A retrospective study

BACKGROUND Microvascular invasion (MVI) is a significant risk factor for recurrence and metastasis following hepatocellular carcinoma (HCC) surgery. Currently, there is a paucity of preoperative evaluation approaches for MVI. AIM To investigate the predictive value of texture features and radiological signs based on multiparametric magnetic resonance imaging in the non-invasive preoperative prediction of MVI in HCC. METHODS Clinical data from 97 HCC patients were retrospectively collected from January 2019 to July 2022 at our hospital. Patients were classified into two groups: MVI-positive (n = 57) and MVI-negative (n = 40), based on postoperative pathological results. The correlation between relevant radiological signs and MVI status was analyzed. MaZda4.6 software and the mutual information method were employed to identify the top 10 dominant texture features, which were combined with radiological signs to construct artificial neural network (ANN) models for MVI prediction. The predictive performance of the ANN models was evaluated using area under the curve, sensitivity, and specificity. ANN models with relatively high predictive performance were screened using the DeLong test, and the regression model of multilayer feedforward ANN with backpropagation and error backpropagation learning method was used to evaluate the models’ stability. RESULTS The absence of a pseudocapsule, an incomplete pseudocapsule, and the presence of tumor blood vessels were identified as independent predictors of HCC MVI. The ANN model constructed using the dominant features of the combined group (pseudocapsule status + tumor blood vessels + arterial phase + venous phase) demonstrated the best predictive performance for MVI status and was found to be automated, highly operable, and very stable. CONCLUSION The ANN model constructed using the dominant features of the combined group can be recommended as a non-invasive method for preoperative prediction of HCC MVI status.

Transfer learning-based deep neural network model for performance prediction of hydrogen-fueled solid oxide fuel cells

Transfer learning-based deep neural network model for performance prediction of hydrogen-fueled solid oxide fuel cells

An artificial neural network and Levenberg-Marquardt training algorithm-based mathematical model for performance prediction

This research seeks to propose an innovative mathematical approach for measuring students’ performance in engineering education. The numerical solutions to this mathematical model and a thorough analysis are included in this study. Four categories – average candidates, poor candidates, below average candidates, and good candidates – are used to build the mathematical model that was built. To solve the differential system based on the migration rate and average student rate moving to weak and above average, the Adams numerical scheme is applied to the numerical results of the designed nonlinear mathematical model. Moreover, an artificial neural network is also applied to get the stochastic results of ANNs-LMB, also known as the Levenberg-Marquardt training algorithm. The ANNs-LMB procedures have been implemented with three samples of data scales using the authentication, testing and training, which are chosen as 75%, 15% and 10%, respectively. According to the findings, when the rate of students leaving engineering studies increased, good students performed better, and when the rate of students below average moved, it was due to an increase in the rate of migration above average, the performance of the good students was only impacted in this way. This research material can be used in different designs and models to improve the students’ performance.

Research on Utility Analysis and Risk Prediction of Economic Management of Construction Enterprises in Digital Economy

This paper determines the evaluation index system of economic management utility and prediction of construction enterprises based on the theory of sustainable development of the construction industry, the theory of economics, and the principle of construction of evaluation index system. Information entropy and fuzzy evaluation are used to measure the economic management utility of construction enterprises in the digital economy. And the economic management risk aspect is analyzed by BP neural network model for prediction. After analyzing, the comprehensive evaluation results of the economic management utility of the construction enterprise [3.9848, 3.2449, 1.2547, 0.5276, 0.9895] indicate that the economic management utility of the construction enterprise is in the excellent. Based on the results of risk prediction of economic management of the construction enterprise ( R 2 = 0.9842), it was confirmed that the model has a good ability to predict the risk of economic management. The results of the study make the economic management of construction enterprises better guaranteed and stabilize the development of the regional basic economy to a certain extent.

Energy efficiency optimization analysis of a ground source heat pump system based on neural networks and genetic algorithms

This paper reports on the performance of a ground source heat pump (GSHP) system located in Shandong Province, China. The system operation data were monitored and collected by a data collection system. According to the analysis of the accumulated operational data, it was found that the GSHP system showed a relative higher COP in cooling season of 2023 than that of 2022 due to the change of supplying water temperature at ground-source side. Based on the analyzed data, a BP neural network model for energy consumption prediction was established. Furthermore, genetic algorithm (GA) was used to optimize the control strategy on the basis of the energy consumption prediction model. Comparison between the artificial experience control strategy and the one optimized by the genetic algorithm was conducted. The results show that the optimization strategy of the genetic algorithm is superior in terms of energy saving, particularly in the load rate higher than 50%, in which, the average energy-saving rate reaches 39.66%. Within the load rate range of 30–50%, the energy-saving rate could also reach 7.84%.

Artificial Neural Networks Model for Photosynthetic Rate Prediction of Leaf Vegetable Crops under Normal and Nutrient-Stressed in Greenhouse

Artificial Neural Networks Model for Photosynthetic Rate Prediction of Leaf Vegetable Crops under Normal and Nutrient-Stressed in Greenhouse

Graph Fuzzy Attention Network Model for Metastasis Prediction of Prostate Cancer Based on mRNA Expression Data

Graph Fuzzy Attention Network Model for Metastasis Prediction of Prostate Cancer Based on mRNA Expression Data

A Multiple Controlled Toffoli Driven Adaptive Quantum Neural Network Model for Dynamic Workload Prediction in Cloud Environments.

The key challenges in cloud computing encompass dynamic resource scaling, load balancing, and power consumption. Accurate workload prediction is identified as a crucial strategy to address these challenges. Despite numerous methods proposed to tackle this issue, existing approaches fall short of capturing the high-variance nature of volatile and dynamic cloud workloads. Consequently, this paper introduces a novel model aimed at addressing this limitation. This paper presents a novel Multiple Controlled Toffoli-driven Adaptive Quantum Neural Network (MCT-AQNN) model to establish an empirical solution to complex, elastic as well as challenging workload prediction problems by optimizing the exploration, adaption, and exploitation proficiencies through quantum learning. The computational adaptability of quantum computing is ingrained with machine learning algorithms to derive more precise correlations from dynamic and complex workloads. The furnished input data point and hatched neural weights are refitted in the form of qubits while the controlling effects of Multiple Controlled Toffoli (MCT) gates are operated at the hidden and output layers of Quantum Neural Network (QNN) for enhancing learning capabilities. Complimentarily, a Uniformly Adaptive Quantum Machine Learning (UAQL) algorithm has evolved to functionally and effectually train the QNN. The extensive experiments are conducted and the comparisons are performed with state-of-the-art methods using four real-world benchmark datasets. Experimental results evince that MCT-AQNN has up to 32%-96% higher accuracy than the existing approaches.

Soot temperature and volume fraction field predictions via line-of-sight soot integral radiation equation informed neural networks in laminar sooting flames

This paper originally proposes a physics informed neural networks (PINNs) model for the simultaneous prediction of soot temperature and volume fraction fields in laminar flames from experimental soot integral radiation. Contrasted with the previous data-driven models, the PINNs model incorporates the line-of-sight soot radiation integral equation into the model architecture. Doing so, the superiority of the physics informed neural networks model is displayed in terms of prediction accuracy under limited training data size and training efficiency. Due to the significant reduction of training experimental data dependence, such gray-box physics informed methodology sheds light on practical combustion devices combustion monitoring, i.e., limited optical window endoscope engines.

A Bayesian Network Approach to Lung Cancer Screening: Assessing the Impact of Data Quantity, Quality, and the Combination of Data from Danish Electronic Health Records.

Background/Objectives: Lung cancer (LC) is the leading cause of cancer mortality, making early diagnosis essential. While LC screening trials are underway globally, optimal prediction models and inclusion criteria are still lacking. This study aimed to develop and evaluate Bayesian Network (BN) models for LC risk prediction using a decade of data from Denmark. The primary goal was to assess BN performance on datasets varying in size and completeness, simulate real-world screening scenarios, and identify the most valuable data sources for LC screening. Methods: The study included 38,944 patients evaluated for LC, with 11,284 (29%) diagnosed. Data on comorbidities, medications, and general practice were available for the entire cohort, while laboratory results, smoking habits, and other variables were only available for subsets. The cohort was divided into four subsets based on data availability, and BNs were trained and validated across these subsets using cross-validation and external validation. To determine the optimal combination of variables, all possible data combinations were evaluated on the samples that contained all the variables (n = 5587). Results: A model trained on the small, complete dataset (AUC 0.78) performed similarly on a larger dataset with 21% missing data (AUC 0.78). Performance dropped when 39% of data were missing (AUC 0.67), resulting in informative variables missing completely in the dataset. Laboratory results and smoking data were the most informative, significantly outperforming models based only on age and smoking status (AUC 0.70). Conclusions: BN models demonstrated moderate to strong predictive performance, even with incomplete data, highlighting the potential value of incorporating laboratory results in LC screening programs.

Laser-Induced Breakdown Spectroscopy and a Convolutional Neural Network Model for Predicting Total Iron Content in Iron Ores.

Laser-induced breakdown spectroscopy (LIBS) is a rapid method for detecting total iron (TFe) content in iron ores. However, accuracy and measurement error of univariate regression analysis in LIBS are limited due to factors such as laser energy fluctuations and spectral interference. To address this, multiple regression analysis and feature selection/extraction are needed to reduce redundant information, decrease the correlation between variables, and quantify the TFe content of iron ores accurately. Overall, 339 batches of iron ore samples from five countries were obtained from the ports of China during the discharging, and 2034 representative spectra were collected. A convolutional neural network (CNN) model for total iron content prediction in iron ores is established. The performance of variable importance random forest (VI-RF), variable importance back propagation artificial neural network (VI-BP-ANN), and CNN-assisted LIBS in predicting the TFe content of iron ores was compared. Coefficient of determination (R2), root mean square error (RMSE), mean relative error (MRE), and modeling time were selected for model evaluation. The result shows that variable importance significantly enhances the quantitative accuracy and reduces modeling time compared to traditional BP-ANN and RF models. Moreover, the CNN model outperformed manual feature selection methods (VI-BP-ANN and VI-RF), exhibiting the shortest modeling time, highest R2, lowest RMSE, and MRE. CNN model's unique characteristics, such as weight sharing and local connection, make it well suited for analyzing high-dimensional LIBS data in multivariate regression analysis. Our approach demonstrates the effectiveness of machine learning and deep learning approaches in improving the accuracy of LIBS for TFe content prediction in iron ores. CNN-assisted LIBS method holds great potential for practical applications in the mining industry.

Two-stage dual-attention spatiotemporal joint network model for multi-energy load prediction of integrated energy system

Accurate prediction of multi-energy load is essential for the scheduling, safe operation, and stability of integrated energy systems. This paper proposes a novel two-stage multi-energy load prediction model with dual-attention spatiotemporal joint network for integrated energy system. In the first stage of multi-task prediction, the integrated self-attention long short-term memory (LSTM) network is built to forecast the multi-energy load initially, and an error correction model on basis of BP network is constructed to reduce the impact of error accumulation on the second stage prediction. In the second stage of single-task prediction, a parallel spatiotemporal joint network model is proposed to extract the static spatial feature and dynamic temporal feature between multi-energy loads and between loads and meteorological factors, which fully captures the complex interactions and dependencies between the multi-energy loads. The prediction model integrates self-attention and graph attention mechanisms in both stages to improve the feature extraction capabilities. The simulation results demonstrate that, compared to bidirectional LSTM, LSTM-gated recurrent unit, time convolutional networks-LSTM and variational mode decomposition-convolutional neural network-LSTM models, the proposed multi-energy load prediction model reduces the average absolute percentage error by 66.25%, 16.83%, 53.58% and 5.29%, respectively.

Logic-based modeling of biological networks with Netflux.

Molecular signaling networks drive a diverse range of cellular decisions, including whether to proliferate, how and when to die, and many processes in between. Such networks often connect hundreds of proteins, genes, and processes. Understanding these complex networks is aided by computational modeling, but these tools require extensive programming knowledge. In this article, we describe a user-friendly, programming-free network simulation tool called Netflux (https://github.com/saucermanlab/Netflux). Over the last decade, Netflux has been used to construct numerous predictive network models that have deepened our understanding of how complex biological networks make cell decisions. Here, we provide a Netflux tutorial that covers how to construct a network model and then simulate network responses to perturbations. Upon completion of this tutorial, you will be able to construct your own model in Netflux and simulate how perturbations to proteins and genes propagate through signaling and gene-regulatory networks.

Prediction of melting and solid phase transitions temperatures and enthalpies for triacylglycerols using artificial neural networks

Triacylglycerols (TAG) are the main components of vegetable oils and any attempt to simulate vegetable oils processes will demand knowledge of their properties. However, experimental values are scarce, considering that several TAG in their pure forms are unavailable or too expensive for experimental measurements. On the other hand, correlating physical properties with TAG molecular structure is not simple. TAG is a molecule composed of 3 fatty acids (FA) esterified to a glycerol (GL) backbone, making properties dependent on carbon number (CN) of each FA, number of unsaturations (UN) of each FA, and position of the FA in the GL backbone. Few models are available in literature for prediction of TAG melting properties, with a special attention to melting temperature (Tfus) and enthalpy (ΔHfus) and solid-solid transition properties of the TAG polymorphic forms. Wesdorp's, Moorthy's et al. and Zeberg-Mikkelsen and Stenby's works present models based on the Group-Contribution theory nowadays used, despite some flaws, particularly considering the polymorphic transitions. Therefore, this work was aimed at evaluating Artificial Neural Network (ANN) models for prediction of TAG's Tfus and ΔHfus (β-form) as well as temperature and enthalpy transitions of molecule polymorphic forms (α and β’). Database was composed of temperature and enthalpy experimental data from literature. For each TAG, 7 input data were provided: total CN, as well as CN and UN at sn-1, 2 and 3 TAG position. The Multilayer Perceptron Feed Forward (MPL) model was used, and the topology was evaluated for number of hidden layers (HL), number of neurons (NN) and activation function at each hidden layer, and convergence algorithm. Number of HL and NN was screened by using a Central Composite Rotatable Design (CCRD). Models were further evaluated by Explainable Artificial Intelligence (XAI) and feature evaluation strategies. Architectures showed a significant higher accuracy for calculation and prediction of TAG's melting properties of the 3 polymorphic forms, with R2 higher to 0.91 for all databases when compared to literatures’ models (excepted for the prediction of the melting temperature of the β form, where Wesdorp's model presented a better predictive ability, despite great similarity). Good results were probably related to the well-defined physicochemical relationship between input (molecular structure descriptors) and output (melting properties), that could be described by XAI evaluation. This is an important advantage considering the improvement of the performance of process and products design including TAG molecules.

Data-driven pressure prediction for self-pressurization liquid hydrogen tank using transfer learning method

Prediction of pressure evolution in liquid hydrogen (LH2) tanks utilizing data-driven technology has gradually garnered significant attention. However, training an accurate data-driven model for pressure prediction in LH2 tanks is a challenge due to inadequate experimental data. To end this, this paper presents a Backpropagation Neural Network model for pressure prediction in LH2 tanks based on transfer learning on pre-trained models, using experimental data and results from thermodynamics models. Pre-trained models are firstly optimized by Bayesian optimization algorithms with large sample datasets from the thermal multi-zone model program and then performed the fine-tuning method with small sample datasets from experiments. The effectiveness of the proposed approach is validated by experimental data and numerical results. Compared with baseline models trained only with experimental data, the results demonstrate better model performance, with a maximum of 10.51% accuracy improvement. The proposed approach demonstrates considerable potential in facilitating the application of advanced machine learning algorithms for LH2 tank pressure prediction, significantly reducing the dependence on experimental data collection.

SEGODE: a structure-enhanced graph neural ordinary differential equation network model for temporal link prediction

SEGODE: a structure-enhanced graph neural ordinary differential equation network model for temporal link prediction

Heat pump digital twin: An accurate neural network model for heat pump behaviour prediction

Heat pumps are key in reaching the carbon emission reduction goals and can also be used to provide energy flexibility services. In this context, accurate heat pump control is crucial and requires a detailed heat pump model. Such a model should enable to take into account the latest information of the system in order to determine the expected heat pump behaviour. Several works already proposed a variety of heat pump models, but mainly adopt time steps above fifteen minutes. Given these larger time steps, the effects of the internal heat pump control strategies such as the compressor ramping rate or variable speed pump control logics are neglected. Hence, energy management systems are not able to obtain accurate information on the expected heat pump response when applying a certain control signal. Therefore, this work investigates the role of neural networks in developing a digital twin of a water/water heat pump, which adopts a time step of one minute and uses experimental data from a hardware-in-the-loop set-up. An architecture with six inputs and six outputs is proposed. The inputs contain the measured flow rates and inlet temperatures at both the evaporator and condenser, but also the measured and requested condenser outlet temperatures. The outputs of the model are the thermal capacity and electricity consumption, accompanied by the flow rates and outlet temperatures at both the evaporator and condenser. Two different types of neural networks are investigated, i.e. (1) feedforward neural networks and (2) long short-term memory neural networks using several time horizons regarding the previous heat pump operation. The models are evaluated for both a single time step ahead (monitoring) and multiple time steps ahead (behaviour prediction). Regarding the latter aspect, a long short-term memory neural network using the latest 60 min of operational data achieved the following prediction errors: electrical power consumption within ±10 % for 79.70 % of the time, condenser flow rate within ±10 % for 89.81 % of the time and condenser outlet temperature within ±1 % for 83.89 % of the time. The ability of long short-term memory neural networks to take into account previous control decisions is seen key in achieving reliable multiple time step ahead predictions. Finally, the replicability of the model to be used for a different heat emission system is also evaluated. Results show a poorer performance as the model is trained and tested on a different heat emission system, but indicate the potential for replicability when enriching the training dataset.

Bayesian networks model for prediction of agricultural soil penetration resistance in interaction with different parameters

Bayesian networks model for prediction of agricultural soil penetration resistance in interaction with different parameters

An optimization driven deep belief network model for crop yield prediction in IoT based smart agriculture

An optimization driven deep belief network model for crop yield prediction in IoT based smart agriculture

FST-GNN: Feedback Space-Time Graph Neural Network Model for Networked Multi-Agent Formation Prediction With Noise Interference

FST-GNN: Feedback Space-Time Graph Neural Network Model for Networked Multi-Agent Formation Prediction With Noise Interference