An Implementation of Distributed Framework of Artificial Neural Network for Big Data Analysis

The process-induced deformation (PID) during the manufacturing of thermosetting composite materials can significantly compromise manufacturing precision. This paper introduces an innovative method that combines a finite element analysis (FEA), feature classification algorithms, and an Artificial Neural Network (ANN) framework to rapidly predict the PID of a typical L-shaped structure. Initially, a comprehensive range of parameters that influence PID are compiled in this research, followed by the generation of a dataset through FEA considering viscoelastic constitutive models, validated by experimental results. Influential parameters are classified using Random Forest and LASSO regression methods, with each parameter rated according to its impact on PID, delineating their varying degrees of importance. Subsequently, through a hyperparameter analysis, an ANN framework is developed to rapidly predict the PID, while also refining the assessment of the parameters’ significance. This innovative approach achieves a computational time reduction of 98% with less than a 5% loss in accuracy, and highlights that under limited computational conditions, considering only a subset or all of the parameters—the peak temperature, corner angle, coefficient of chemical shrinkage, coefficient of thermal expansion, curing pressure, and E1—minimizes accuracy loss. The study demonstrates that machine learning algorithms can effectively address the challenge of predicting composite material PID, providing valuable insights for practical manufacturing applications.

Monitoring heart conditions through electrocardiography (ECG) has been the cornerstone of identifying cardiac irregularities. Cardiologists often rely on a detailed analysis of ECG recordings to pinpoint deviations that are indicative of heart anomalies. This traditional method, while effective, demands significant expertise and is susceptible to inaccuracies due to its manual nature. In the realm of computational analysis, Artificial Neural Networks (ANNs) have gained prominence across various domains, which can be attributed to their superior analytical capabilities. Conversely, Spiking Neural Networks (SNNs), which mimic the neural activity of the brain more closely through impulse-based processing, have not seen widespread adoption. The challenge lies primarily in the complexity of their training methodologies. Despite this, SNNs offer a promising avenue for energy-efficient computational models capable of displaying a high-level performance. This paper introduces an innovative approach employing SNNs augmented with an attention mechanism to enhance feature recognition in ECG signals. By leveraging the inherent efficiency of SNNs, coupled with the precision of attention modules, this model aims to refine the analysis of cardiac signals. The novel aspect of our methodology involves adapting the learned parameters from ANNs to SNNs using leaky integrate-and-fire (LIF) neurons. This transfer learning strategy not only capitalizes on the strengths of both neural network models but also addresses the training challenges associated with SNNs. The proposed method is evaluated through extensive experiments on two publicly available benchmark ECG datasets. The results show that our model achieves an overall accuracy of 93.8% on the MIT-BIH Arrhythmia dataset and 85.8% on the 2017 PhysioNet Challenge dataset. This advancement underscores the potential of SNNs in the field of medical diagnostics, offering a path towards more accurate, efficient, and less resource-intensive analyses of heart diseases.

This work provides an advanced prototype for pipeline and wellbore tube inspection using electromagnetic (EM) resonance coupling, electromagnetic (EM) coupling, and machine learning. Utilizing only two transmitters and eight sensor coils, the described device can detect and characterize inner, outer, and total metal loss in a pipe's body. A defect in the pipe body alters the impedance of the transmitter and receiver coils and the mutual coupling between them, resulting in a relative change in one or more Rx outputs. A framework for artificial neural networks (ANN) is designed to assess the eight outputs and generate a two-dimensional map of the pipe cross-section. The ANN is trained using a finite-difference time-domain electromagnetic forward solver. The designed prototype is tested and validated through simulations and an experimental setup. The results demonstrate that the tool, with the assistance of the ANN, could not only detect single and double flaws with either complete or partial metal loss, but also define the defect's size, location, and depth.

Environmental sustainability in the city of Shanghai municipal solid waste treatment system: an integrated framework of artificial neural network (ANN) and LCA-emergy methodology

We review the structure of cerebral cortex to find out the number of neurons and synapses and its modular structure. The organization of these neurons is then studied and mapped onto the framework of an artificial neural network (ANN). The computational requirements to run this ANN model are then estimated. The conclusion is that it is possible to simulate the mouse cortex today on a cluster computer but not in real-time.

Protein interactions are essential in discovery of protein functions and fundamental biological processes. In this paper, we aim to create a reliable computational model for protein interaction prediction by integrating information from complementary data sources. An integrative Artificial Neural Network (ANN) framework is developed to predict protein-protein interactions (PPIs) from heterogeneous data in Human. Performance of our proposed framework is empirically investigated by combining protein domain data, molecular function and biological process annotations in Gene Ontology. Experimental results demonstrate that our approach can predict PPIs with high sensitivity of 82.43% and specificity of 78.67%. The results suggest that combining multiple data sources can result in a 7% increase in sensitivity compared to using only domain information. We are able to construct a protein interaction network with proteins around mitotic spindle checkpoint of the human interactome map. Novel predictions are made and some are supported by evidences in literature.KeywordsProtein-Protein InteractionInteraction Network PredictionHeterogeneous Data IntegrationIntegrative Neural Network

This article briefly summarises the author's research on automatic performance, started at CSC (Centro di Sonologia Computazionale, University of Padua) and continued at TMH‐KTH (Speech, Music Hearing Department at the Royal Institute of Technology, Stockholm). The focus is on the evolution of the architecture of an artificial neural networks (ANNs) framework, from the first simple model, able to learn the KTH performance rules, to the final one, that accurately simulates the style of a real pianist performer, including time and loudness deviations. The task was to analyse and synthesise the performance process of a professional pianist, playing on a Disklavier. An automatic analysis extracts all performance parameters of the pianist, starting from the KTH rule system. The system possesses good generalisation properties: applying the same ANN, it is possible to perform different scores in the performing style used for the training of the networks. Brief descriptions of the program Melodia and of the two Java applets Japer and Jalisper are given in the Appendix. In Melodia, developed at the CSC, the user can run either rules or ANNs, and study their different effects. Japer and Jalisper, developed at TMH, implement in real time on the web the performance rules developed at TMH plus new features achieved by using ANNs.

This study presents establishment of multiple input prediction model for automotive coil spring fatigue life estimation to shorten automotive suspension design process. Automotive suspension design is a lengthy work where any changes of the design lead to repetition of the entire process. It was hypothesised that the established model could be used to predict the spring design fatigue life without using any strain measurements. To initiate this model establishment, five sets of strain and acceleration measurement across different road conditions were collected and used for validations. To include spring stiffness as a parameter, a quarter car model was generated to obtain the force time histories from spring and vertical vibration of vehicle mass. In addition, artificial road profiles of road classes “A” to “D” were also generated for the quarter car simulation. Through adjusting the spring stiffness in the quarter car model, the spring and vehicle responses were varied. The simulated force time histories were used to predict springs’ fatigue life while acceleration time histories were used to calculate ISO 2631 ride-related vertical vibration. Subsequently, multiple linear regression approach was applied to determine the relationship between vehicle body frequency, ISO 2631 ride-related vertical vibration and spring fatigue life. The obtained regression had shown significance to the spring fatigue life with coefficient of determination of 0.8320. Reciprocally, multiple linear regression models were also used to predict the ISO 2631 ride-related vertical vibration with a coefficient of determination at 0.8810 and mean squared error values below 0.3430. To optimise the prediction results, artificial neural network was trained for the fatigue and vibration prediction purposes. The architectures of the artificial neural network were designed in terms of number of neurons and hidden layers to achieve a higher coefficient of determination of 0.9926 and lower mean squared error of 0.0824. For vibration prediction, the vehicle body frequency and spring fatigue life has shown a significant coefficient of determination to the ISO 2631 weighted vertical vibration, reaching 0.9579 with mean squared error of 0.0004. Based on the experimental strain and acceleration results, the predicted fatigue lives of multiple linear regression models were correlated well with the experimental results with coefficient of determination value of 0.9275. Meanwhile, the maximum difference of vibration prediction to experimental value using multiple linear regression models was only 18%. For artificial neural network predictions, the fatigue lives were mostly distributed within 1:2 or 2:1 life correlation and vibration prediction results were within 12%. For a good prediction, the target correlation value was above 0.80 to demonstrate a good fitted curve and the difference below 20%. The trained artificial neural network has shown outstanding capability in fatigue life or ride-related vertical vibration predictions. In this research, the main novelty was the trained artificial neural network for spring fatigue life or ride-related vertical vibration predictions which serve to reduce some procedures of automotive suspension design. The outcome of this study can be used to provide a new knowledge towards the field of fatigue research as well as vehicle ride dynamics. This research contributes to automotive industries especially in suspension spring design where the analysis of fatigue and ride-related vibration are provided.

An artificial neural network for mixed frequency data

Process safety consequence modeling using artificial neural networks for approximating heat exchanger overpressure severity

The objective of this work is the development of a highly effective Wiener-Physically-Informed-Neural-Network (W-PINN) modeling methodology for systems and processes with highly nonlinear dynamic and highly nonlinear static behavior. This approach has two basic stages. The first stage estimates all the unknown linear dynamic modeling coefficients. With these estimates fixed, the second stage estimates all the unknown static coefficients in an artificial neural network (ANN) framework. When the ANN is a nonlinear function, as in this work, the fitted model is a nonlinear dynamic and nonlinear static structure. In a previous one stage, 60-minute forecast modeling case, the dynamic outputs (vij, where i = the subject number and j = the sample number) were estimated in Excel® for eleven (11) type 1, freely-living, diabetes data sets (cases) of approximately two weeks of data, yielding an average input-only sensor glucose concentration (SGC) validation fit () of 0.68. With these vij’s fixed, this new approach obtains final, two-stage (TS) W-PINN, 60-minute forecast fitted models, for each of the 11 cases, in two ways. One two-stage approach uses the JMP® ANN toolbox for second stage modeling and achieves an average input-only SGC of 0.74 and maximum of 0.84. The second two-stage approach uses a novel ANN methodology coded in Python® and achieves an average input-only SGC of 0.82 and maximum of 0.93. Incorporating bias correction, using current and past SGC residuals, the Python® estimator improved the average from 0.82 to 0.87 with the maximum still 0.93.

It is important to correctly classify lung nodules in order to find lung cancer early and plan treatment. It is are very hard, though, because not all nodules are the same and some of their radiological features are the same across classes. Traditional binary classifiers don’t always do a good job of capturing the complexity of different types of nodules, like benign, malignant, inflammatory, and calcified. This makes the diagnosis less accurate and increases the number of false positives and negatives, which affects how well the treatment works. This study suggests a better multi-class Artificial Neural Network (ANN) framework to help with the classification of lung nodules. The model includes the best way to get features from CT scans of the patient by using descriptors based on shapes, textures, and histograms. To improve performance and reduce the number of dimensions, the Principal Component Analysis (PCA) method is used to choose features. We use a backpropagation algorithm to teach the artificial neural network (ANN) how to work with a set of labeled lung nodules. The suggested artificial neural network (ANN) was able to correctly sort 94.7% of the 1000 CT image samples in a test dataset. It had a kappa coefficient of 0.91, a recall of 93.5%, a precision of 92.1%, and an F1-score of 92.8%. The results of the experiment showed that these results were correct. Our method always does better than other hybrid models on a number of evaluation variables.

Predicting the entire pyrolysis process of representative charring material infiltrated with kerosene using an improved artificial neural network

Understanding the causal impacts among various parameters is essential for designing micro aerial vehicles (MAVs). The simulation of computational fluid dynamics (CFD) provides us with a technique to calculate aerodynamic forces precisely. However, even a single result regularly takes considerable computational time. Machine learning, due to the advance in computer hardware, shows another approach that can speed up the analysis process. In this study, we introduce an artificial neural network (ANN) framework to predict the transient aerodynamic forces and the corresponding energy consumption. Instead of considering the whole transient changes of each parameter as inputs, we utilised the technique of Fourier transform to simplify the ANN structure for minimising the computation cost. Furthermore, two typical activation functions, rectified linear unit (ReLU) and sigmoid, were attempted to build the network. The validity of the method was further examined by comparing it with CFD simulation. The result shows that both functions are able to provide highly accurate estimations that can be implemented for model construction under this framework. Consequently, this novel approach makes it possible to reduce the complexity of analysis, study the flapping wing aerodynamics and enable a more efficient way to optimise parameters.

Nowadays , nonlinear time series and artificial neural networks (ANN) models are used for forecasting in the field of business, agriculture and soon. Recent studies have shown, ANN have been successfully used for forecasting of financial and agriculture data series The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and outputs. ANN have more advantages that can approximate to model both linear and nonlinear structures in time series, they are not able to handling both structures equally well. The autoregressive integrated moving average (ARIMA) model and two ANN models namely, Radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) methods were applied to Hyderabad airport traffic data. The data obtained for 15 years from 2002–2003 to 2016–2017 about domestic and international passenger of International Airport of Hyderabad, India. In this research paper, we compared the performances of ARIMA, RBFNN and ERNN were based on three measures: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results showed that RBFNN obtained the smallest MAE, MAPE and RMSE in both the modeling and forecasting processes. The performances of the three models ranked in ascending order were: ARIMA, ERNN and the RBFNN model. Keywords: T ime series, forecasting, artificial neural networks, ARIMA models, radial basis function neural networks, and Elman recurrent neural networks Cite this Article R. Ramakrishna, Berhe Aregay, Tewodros Gebregergs. The Comparison in Time Series Forecasting of Air Traffic Data by Autoregressive Integrated Moving Average Model, Radial Basis Function and Elman Recurrent Neural Networks. Research & Reviews: Journal of Statistics . 2018; 7(3): 75–90p.