Neural Network Based Prediction Model Research Articles

Postoperative Karnofsky performance status prediction in patients with IDH wild-type glioblastoma: A multimodal approach integrating clinical and deep imaging features

Background and purpose Glioblastoma is a highly aggressive brain tumor with limited survival that poses challenges in predicting patient outcomes. The Karnofsky Performance Status (KPS) score is a valuable tool for assessing patient functionality and contributes to the stratification of patients with poor prognoses. This study aimed to develop a 6-month postoperative KPS prediction model by combining clinical data with deep learning-based image features from pre- and postoperative MRI scans, offering enhanced personalized care for glioblastoma patients. Materials and methods Using 1,476 MRI datasets from the Brain Tumor Segmentation Challenge 2020 public database, we pretrained two variational autoencoders (VAEs). Imaging features from the latent spaces of the VAEs were used for KPS prediction. Neural network-based KPS prediction models were developed to predict scores below 70 at 6 months postoperatively. In this retrospective single-center analysis, we incorporated clinical parameters and pre- and postoperative MRI images from 150 newly diagnosed IDH wild-type glioblastoma, divided into training (100 patients) and test (50 patients) sets. In training set, the performance of these models was evaluated using the area under the curve (AUC), calculated through fivefold cross-validation repeated 10 times. The final evaluation of the developed models assessed in the test set. Results Among the 150 patients, 61 had 6-month postoperative KPS scores below 70 and 89 scored 70 or higher. We developed three models: a clinical-based model, an MRI-based model, and a multimodal model that incorporated both clinical parameters and MRI features. In the training set, the mean AUC was 0.785±0.051 for the multimodal model, which was significantly higher than the AUCs of the clinical-based model (0.716±0.059, P = 0.038) using only clinical parameters and the MRI-based model (0.651±0.028, P<0.001) using only MRI features. In the test set, the multimodal model achieved an AUC of 0.810, outperforming the clinical-based (0.670) and MRI-based (0.650) models. Conclusion The integration of MRI features extracted from VAEs with clinical parameters in the multimodal model substantially enhanced KPS prediction performance. This approach has the potential to improve prognostic prediction, paving the way for more personalized and effective treatments for patients with glioblastoma.

Optimal design of neural network-based fuzzy predictive control model for recommending educational resources in the context of information technology

Abstract As the information technology develops, educational resource recommendation system has become an indispensable part of the education field. At the same time, people’s demand for personalised educational resources is also growing; therefore, how to accurately predict user demand and provide personalised recommendations has become an important issue. In this study, a fuzzy predictive control model on the grounds of neural network is proposed to optimise the design of educational resource recommendation in the context of information technology. After experimental testing, the model recommendation fit reached 95.16 and 92.91% on the two test datasets, which are significantly higher than the control model. The average F1 values of the proposed model also reached 95.21 and 88.77%, which are higher than the control model. In other control experiments, the proposed model of this study also has a better performance. The relevant outcomes showcase that the predictive performance and recommendation effect of the model can be further enhanced by improving the structure of the neural network and the parameter optimisation method. Meanwhile, the proposed model has high performance in information overload and personalised demand, which offers a useful reference for the optimal design of educational resource recommendation system.

Optimizing prediction accuracy in dynamic systems through neural network integration with Kalman and alpha-beta filters.

In the realm of dynamic system analysis, the Kalman filter and the alpha-beta filter are widely recognized for their tracking and prediction capabilities. However, their performance is often limited by static parameters that cannot adapt to changing conditions. Addressing this limitation, this paper introduces innovative neural network-based prediction models that enhance the adaptability and accuracy of these conventional filters. Our approach involves the integration of neural networks within the filtering algorithms, enabling the dynamic augmentation of parameters in response to performance feedback. We present two modified filters: a neural network-based Kalman filter and an alpha-beta filter, each augmented to incorporate neural network-driven parameter tuning. The alpha-beta filter is enhanced with neural network outputs for its α and β parameters, while the Kalman filter employs a neural network to optimize its internal parameter R and noise factor F. We assess these advanced models using the root mean square error (RMSE) metric, where our neural network-based alpha-beta filter demonstrates a significant 38.2% improvement in prediction accuracy, and the neural network-based Kalman filter achieves a 53.4% enhancement. Hence, our novel approach of integrating neural networks into filtering algorithms stands out as an effective strategy to significantly enhance their performance in dynamic environments.

Simplified Neural Network-Based Models for Oil Flow Rate Prediction

Available neural network-based models for predicting the oil flow rate (q<sub>o</sub>) in the Niger Delta are not simplified and are developed from limited data sources. The reproducibility of these models is not feasible as the models’ details are not published. This study developed simplified and reproducible three, five, and six-input variables neural-based models for estimating q<sub>o</sub> using 283 datasets from 21 wells across fields in the Niger Delta. The neural-based models were developed using maximum-minimum (max.-min.) normalized and clip-normalized datasets. The performances and the generalizability of the developed models with published datasets were determined using some statistical indices: coefficient of determination (R<sup>2</sup>), mean square error (MSE), root mean square error (RMSE), average relative error (ARE) and average absolute relative error (AARE). The results indicate that the 3-input-based neural models had overall R<sup>2</sup>, MSE, and RMSE values of 0.9689, 9.6185x10<sup>-4 </sup>and 0.0310, respectively, for the max.-min. normalizing method and R<sup>2</sup> of 0.9663, MSE of 5.7986x10<sup>-3</sup> and RMSE of 0.0762 for the clip scaling approach. The 5-input-based models resulted in R<sup>2</sup> of 0.9865, MSE of 5.7790×10<sup>-4</sup> and RMSE of 0.0240 for the max.-min. scaling method and R<sup>2</sup> of 0.9720, MSE of 3.7243x10<sup>-3</sup> and RMSE of 0.0610 for the clip scaling approach. Also, the 6-input-based models had R<sup>2</sup> of 0.9809, MSE of 8.7520x10<sup>-4</sup> and RMSE of 0.0296 for the max.-min. normalizing approach and R<sup>2</sup> of 0.9791, MSE of 3.8859 x 10<sup>-3</sup> and RMSE of 0.0623 for the clip scaling method. Furthermore, the generality performance of the simplified neural-based models resulted in R<sup>2</sup>, RMSE, ARE, and AAPRE of 0.9644, 205.78, 0.0248, and 0.1275, respectively, for the 3-input-based neural model and R<sup>2</sup> of 0.9264, RMSE of 2089.93, ARE of 0.1656 and AARE of 0.2267 for the 6-input-based neural model. The neural-based models predicted q<sub>o</sub> were more comparable to the test datasets than some existing correlations, as the predicted q<sub>o</sub> result was the lowest error indices. Besides, the overall relative importance of the neural-based models’ input variables on q<sub>o</sub> prediction is S>GLR>P<sub>wh</sub>>T/T<sub>sc</sub>>γ<sub>o</sub>>BS&W>γ<sub>g</sub>. The simplified neural-based models performed better than some empirical correlations from the assessment indicators. Therefore, the models should apply as tools for oil flow rate prediction in the Niger Delta fields, as the necessary details to implement the models are made visible.

Towards artificial intelligence-based disease prediction algorithms that comprehensively leverage and continuously learn from real-world clinical tabular data systems.

This manuscript presents a proof-of-concept for a generalizable strategy, the full algorithm, designed to estimate disease risk using real-world clinical tabular data systems, such as electronic health records (EHR) or claims databases. By integrating classic statistical methods and modern artificial intelligence techniques, this strategy automates the production of a disease prediction model that comprehensively reflects the dynamics contained within the underlying data system. Specifically, the full algorithm parses through every facet of the data (e.g., encounters, diagnoses, procedures, medications, labs, chief complaints, flowsheets, vital signs, demographics, etc.), selects which factors to retain as predictor variables by evaluating the data empirically against statistical criteria, structures and formats the retained data into time-series, trains a neural network-based prediction model, then subsequently applies this model to current patients to generate risk estimates. A distinguishing feature of the proposed strategy is that it produces a self-adaptive prediction system, capable of evolving the prediction mechanism in response to changes within the data: as newly collected data expand/modify the dataset organically, the prediction mechanism automatically evolves to reflect these changes. Moreover, the full algorithm operates without the need for a-priori data curation and aims to harness all informative risk and protective factors within the real-world data. This stands in contrast to traditional approaches, which often rely on highly curated datasets and domain expertise to build static prediction models based solely on well-known risk factors. As a proof-of-concept, we codified the full algorithm and tasked it with estimating 12-month risk of initial stroke or myocardial infarction using our hospital's real-world EHR. A 66-month pseudo-prospective validation was conducted using records from 558,105 patients spanning April 2015 to September 2023, totalling 3,424,060 patient-months. Area under the receiver operating characteristic curve (AUROC) values ranged from .830 to .909, with an improving trend over time. Odds ratios describing model precision for patients 1-100 and 101-200 (when ranked by estimated risk) ranged from 15.3 to 48.1 and 7.2 to 45.0, respectively, with both groups showing improving trends over time. Findings suggest the feasibility of developing high-performing disease risk calculators in the proposed manner.

Performance degradation evaluation of square steel tubes with local penetration damage

Steel tubular structures are susceptible to welding defects, cracking defects and environmental corrosion, which can negatively affect the stability and reliability of structure. This study tested the axial compression performance of twenty-six square steel tubular stubs with localized penetration damage simulated by artificial notch, considering the influence of notch length, orientation, and position. The test results showed that the failure mode of the specimens was dominated by the local buckling along the direction of the notch axis. The failure mode of the specimen with vertical notch showed notch opening, while those with horizontal notch showed notch closing, resulting in the secondary peak value in the load-displacement curve. The existence of local penetration damage reduced the ultimate strength and ductility of the tubular stubs. Compared to the ultimate strength of the specimens with middle notch, those with corner notch were weakened more, up to 27 %. Furthermore, the effects of notch dimension, position, orientation, and tube wall thickness on the axial bearing capacity of square steel tubular stubs are discussed by using a validated finite element model. Owing to the analysis, the ultimate strength of the stubs is reduced by increasing notch length, width and depth, whereas the reduction effect tends to be weakened as the notch orientation angle increases. The notch located in the corner is the most unfavorable for the axial compression performance of the square steel tubular stubs. A semi-empirical formula for evaluating the ultimate strength of square steel tubular stubs with local penetration damage is proposed by introducing the strength reduction factor. An artificial neural network-based ultimate strength prediction model is developed that can consider the effects of wide-range parameters, achieving high accuracy.

Analysis and compensation of kinematic errors in a five-axis machine tool based on an internal data-driven method

Abstract The increasing demand for highly efficient and precise machining in the aerospace industry is leading to increasingly stringent requirements for machine tool accuracy measurement and compensation technologies. However, existing methods can only measure the accuracy of machine tools after they have been shut down, using instruments that do not measure the kinematic errors during the machining process. To address this issue, a method for real-time prediction of kinematic errors in a five-axis machine tool has been proposed to enhance machining accuracy and efficiency. The method employs a neural network-based predictive model, which has been trained and validated through experimental testing and data collection using internal sensors of the machine tool. The study reveals that this predictive model can effectively forecast the kinematic errors, through real-time compensation, and can reduce errors by 60-86%, significantly enhancing machine performance.

A predictive model for classifying college students' academic performance based on visual-spatial skills.

As the application of visual-spatial skills in academic disciplines, vocational fields and daily life is becoming more and more prominent, it is of great theoretical and practical significance how to make use of big data and artificial intelligence technology to conduct research on the relationship between visual-spatial skills and students' grades. This paper explores and analyses from the perspective of artificial intelligence, combining students' visual-spatial skills and students' specific attribute characteristics to construct an expert system, which defines the prediction of academic performance as a classification problem corresponding to the five categories of excellent, good, moderate, passing, and weak, respectively, and based on which a deep neural network-based classification prediction model for students' performance is designed. The experimental results show that visual-spatial skills plays an important role in the professional learning of science and engineering students, while the classification model designed in this paper has high accuracy in the grade prediction process. This paper not only helps to fill the gaps in the current research field, but is also expected to provide scientific basis for educational practice and promote the development of the education field in a more intelligent and personalized direction.

Artificial Neural Network–based Prediction Model to Minimize Dust Emission in the Machining Process

BackgroundDust generated during various wood-related activities, such as cutting, sanding, or processing wood materials, can pose significant health and environmental risks due to its potential to cause respiratory problems and contribute to air pollution. Understanding the factors influencing dust emission is important for devising effective mitigation strategies, ensuring a safer working environment, and minimizing environmental impact. This study focuses on developing an artificial neural network (ANN) model to predict dust emission values in the machining of black poplar (Populus nigra L.), oriental beech (Fagus orientalis L.), and medium-density fiberboards. MethodsThe multilayer feed-forward ANN model is developed using a customized application built with MATLAB code. The inputs to the ANN model include material type, cutting width, number of blades, and cutting depth, whereas the output is the dust emission. Model performance is assessed through graphical and statistical comparisons. ResultsThe results reveal that the developed ANN model can provide adequate predictions for dust emission with an acceptable level of accuracy. Through the implementation of the ANN model, the study predicts intermediate dust emission values for different cutting widths and cutting depths, which are not considered in the experimental work. It is observed that dust emission tends to decrease with reductions in cutting width and cutting depth. ConclusionThis study introduces an alternative approach to optimize machining-process conditions for minimizing dust emissions. The findings of this research will assist industries in obtaining dust emission values without the need for additional experimental activities, thereby reducing experimental time and costs.

Corrigendum: Journal of Hydroinformatics 1 November 2023; 25 (6): 2660–2674; Development of a lightweight convolutional neural network-based visual model for sediment concentration prediction by incorporating the IoT concept, Cheng-Chia Huang, Che-Cheng Chang, Chiao-Ming Chang, Ming-Han Tsai. https://doi.org/10.2166/hydro.2023.215

Corrigendum: <i>Journal of Hydroinformatics</i> 1 November 2023; 25 (6): 2660–2674; Development of a lightweight convolutional neural network-based visual model for sediment concentration prediction by incorporating the IoT concept, Cheng-Chia Huang, Che-Cheng Chang, Chiao-Ming Chang, Ming-Han Tsai. https://doi.org/10.2166/hydro.2023.215

Development of convolutional neural network-based surrogate model for three-dimensional vacuum plume prediction via direct simulation Monte Carlo method

The vacuum plume phenomenon encountered during lunar exploration missions poses significant challenges, such as impingement forces, heat fluxes, and spacecraft contamination. Numerical simulation represents the predominant method for evaluating the impacts of vacuum plumes. However, the conventional direct simulation Monte Carlo (DSMC) method, despite being the standard, is notably time-consuming and impractical for real-time analysis. Addressing this limitation, our research explores deep learning, specifically convolutional neural networks (CNN), for the efficient prediction of vacuum plume dynamics. We introduce a novel CNN-based DSMC method (CNN-DSMC-3D), leveraging a dataset obtained from three-dimensional DSMC simulations. This approach translates the spacecraft's shape and boundary conditions into a signed distance function and an identifier matrix. The CNN-DSMC-3D method effectively predicts the vacuum plume field, aligning closely with DSMC results across various lunar surface conditions. Crucially, the CNN-DSMC-3D method achieves a speed increase in four to six orders of magnitude over the conventional DSMC method, demonstrating substantial potential for real-time aerospace applications and offering a paradigm shift in the simulation of lunar landing scenarios.

Experimental research and artificial neural network-based prediction model on compressive strength of hardened cement pastes containing fly ash and silica fume at high temperatures

Experimental research and artificial neural network-based prediction model on compressive strength of hardened cement pastes containing fly ash and silica fume at high temperatures

Neural Network-Based Predictive Models for Stock Market Index Forecasting

The stock market, characterised by its complexity and dynamic nature, presents significant challenges for predictive analytics. This research compares the effectiveness of neural network models in predicting the S&amp;P500 index, recognising that a critical component of financial decision making is market volatility. The research examines neural network models such as Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Artificial Neural Network (ANN), Recurrent Neural Network (RNN), and Gated Recurrent Unit (GRU), taking into account their individual characteristics of pattern recognition, sequential data processing, and handling of nonlinear relationships. These models are analysed using key performance indicators such as the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Directional Accuracy, a metric considered essential for prediction in both the training and testing phases of this research. The results show that although each model has its own advantages, the GRU and CNN models perform particularly well according to these metrics. GRU has the lowest error metrics, indicating its robustness in accurate prediction, while CNN has the highest directional accuracy in testing, indicating its efficiency in data processing. This study highlights the potential of combining metrics for neural network models for consideration when making decisions due to the changing dynamics of the stock market.

Exploratory study on experimental and prediction based stress-strain behaviour of corroded prestressing steel strands

Prestressing steel strands have been extensively used in recent years due to the increased popularity of prestressed concrete structures. However, the corrosion of strands has a detrimental effect on these structures. This necessitates a detailed investigation of the mechanical properties of the corroded strands and to understand their influence on the strength of these structures. Therefore, in this paper, a systematic attempt has been made to study the mechanical properties of corroded steel strands corroded using the accelerated corrosion method. Initially, the tensile test was conducted on these corroded steel strands to study the mechanical properties of the strands and X-ray diffraction analysis was done on the corrosion products of these corroded steel strands. Then, resilient backpropagation with backtracking neural network based prediction models were developed to predict the ultimate strength of the corroded strands. The experimental results showed that an increase in the corrosion level reduces the ultimate stress, yield stress, breaking stress and the corresponding strains of the strand. The proposed prediction models give optimum results of cost functions for ultimate stress and ultimate strain predictions. The comparison study of ultimate stress-strain results obtained from the proposed prediction models showed remarkable performance with the experimental results.

Unraveling Financial Markets: Deep Neural Network-Based Models for Stock Price Prediction

This paper delves into the potential and challenges of leveraging deep neural networks (DNNs) in stock price forecasting. Traditional econometric models often grapple with the complexities of financial time series data, leading to the exploration of DNNs, especially architectures like Long Short-Term Memory (LSTM) networks, to capture intricate patterns in such data. While these networks present promising results, challenges such as model interpretability, non-stationarity of data, overfitting, and computational demands remain. The financial sector's increasing digitization and influx of alternative data offer a unique opportunity for DNNs, emphasizing their capability for automatic feature extraction. However, the integration of DNNs necessitates a multi-disciplinary approach, involving financial experts, data scientists, and computational specialists. The paper concludes with an optimistic outlook for the synergy between deep learning and traditional financial theories, aiming for a harmonious blend of modern computational techniques with foundational financial principles.

Performance of the neural network-based prediction model in closed-loop adaptive optics.

Adaptive optics (AO) technology is an effective means to compensate for atmospheric turbulence, but the inherent delay error of an AO system will cause the compensation phase of the deformable mirror (DM) to lag behind the actual distortion, which limits the correction performance of the AO technology. Therefore, the feed-forward prediction of atmospheric turbulence has important research value and application significance to offset the inherent time delay and improve the correction bandwidth of the AO system. However, most prediction algorithms are limited to an open-loop system, and the deployment and the application in the actual AO system are rarely reported, so its correction performance improvement has not been verified in practice. We report, to our knowledge, the first successful test of a deep learning-based spatiotemporal prediction model in an actual 3 km laser atmospheric transport AO system and compare it with the traditional closed-loop control methods, demonstrating that the AO system with the prediction model has higher correction performance.

Novel robust Elman neural network-based predictive models for bubble point oil formation volume factor and solution gas–oil ratio using experimental data

Novel robust Elman neural network-based predictive models for bubble point oil formation volume factor and solution gas–oil ratio using experimental data

Traffic noise measurement, mapping, and modeling using soft computing techniques for mid-sized smart Indian city

The present study presents an investigation into the utilization of artificial neural networks (ANN) and multiple linear regression model (MLR) for the prediction of traffic noise levels in various locations of Dhanbad city at varying intervals. Traffic noise indices measurements were carried out using sound pressure level meter (SLM). The prediction of equivalent A-weighted sound level (LAeq) and the sound level exceeding 10 percent of the time (L10) is carried out using various influencing factors such as number of cars, number of 2 wheelers, number of 3 wheelers, number of heavy vehicle, number of medium commercial vehicles, and traffic speed. The findings demonstrate the ANN model's proficiency in comparison of MLR model for providing precise prediction accuracy of traffic noise indices with a R2 of 0.94 for LAeq and 0.91 for L10. Furthermore, the frequency spectrum analysis reveals that high peaks were observed at lower frequencies ranging from 31.5 Hz to 50 Hz, middle frequencies from 500 to 800 Hz and higher frequencies from 3.5 kHz to 5 kHz. The noise maps at varying intervals revealed that most of the locations are having higher noise levels due to increase in vehicular movement. The study emphasizes on the potential significance of the proposed neural network-based prediction model in collaboration with noise mapping as a vital tool for the anticipation of traffic noise levels and the formulation of noise mitigation strategies in context of the mid-sized smart city like Dhanbad, India.

Novel neural network-based metaheuristic models for the stability prediction of rectangular trapdoors in anisotropic and non-homogeneous clay

The problem of trapdoor stability is a crucial problem in geotechnical engineering. This study is the first to introduce novel neural network-based metaheuristic models for the stability prediction of 3D rectangular trapdoors in anisotropic and nonhomogeneous clays. However, no researcher has considered such trapdoor problems in the past. In this study, the dataset is obtained by using finite element limit analysis (FELA). The proposed hybrid machine learning models based on artificial neural networks (ANNs) and various types of optimization algorithms (OAs), namely, ant colony optimization (ACO), artificial lion optimization (ALO), the imperialist competition algorithm (ICA), and shuffled complex evolution (SCE), are also proposed in this paper by undergoing rigorous optimization to ensure accuracy and efficiency in capturing the intricate dynamics of the stability investigations from the models. The performance of the proposed ANN-based models is assessed using several performance metrics, regression plots, and rank analysis. The proposed ANN-SCE model outperforms the other proposed models in predicting trapdoor stability, where the ANN-SCE model achieved the highest rank, with a score of 58, followed by the ANN-ALO (47), ANN-ICA (33), and ANN-ACO (22) models. The proposed neural network-based metaheuristic models deliver precise and effective forecasts of trapdoor stability to make informed decisions concerning road design and mitigation tactics, ultimately improving the robustness of infrastructure facing geotechnical challenges.

Physics-constrained robust learning of open-form partial differential equations from limited and noisy data

Unveiling the underlying governing equations of nonlinear dynamic systems remains a significant challenge. Insufficient prior knowledge hinders the determination of an accurate candidate library, while noisy observations lead to imprecise evaluations, which in turn result in redundant function terms or erroneous equations. This study proposes a framework to robustly uncover open-form partial differential equations (PDEs) from limited and noisy data. The framework operates through two alternating update processes: discovering and embedding. The discovering phase employs symbolic representation and a novel reinforcement learning (RL)-guided hybrid PDE generator to efficiently produce diverse open-form PDEs with tree structures. A neural network-based predictive model fits the system response and serves as the reward evaluator for the generated PDEs. PDEs with higher rewards are utilized to iteratively optimize the generator via the RL strategy and the best-performing PDE is selected by a parameter-free stability metric. The embedding phase integrates the initially identified PDE from the discovering process as a physical constraint into the predictive model for robust training. The traversal of PDE trees automates the construction of the computational graph and the embedding process without human intervention. Numerical experiments demonstrate our framework's capability to uncover governing equations from nonlinear dynamic systems with limited and highly noisy data and outperform other physics-informed neural network-based discovery methods. This work opens new potential for exploring real-world systems with limited understanding.