A decreasing failure rate model with a novel approach to enhance the artificial neural network's structure for engineering and disease data analysis

Sindhu, Tabassum Naz; Çolak, Andaç Batur; Lone, Showkat Ahmad; Shafiq, Anum; Abushal, Tahani A.

doi:10.1016/j.triboint.2023.109231

Cited by 18 publications

(3 citation statements)

References 39 publications

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“…Nowadays, deep neural networks are used in various fields and have been found to be as successful as general nonlinear models in medical diagnosis, prognosis and survival analysis [11]. They can also be used to examine key indicators characterizing the distribution of lifetime random variables in reliability and survival theory studies [12]. In studies of anaerobic digestion, Kwanho [13]propose a hybrid DL architecture, DA,LSTM,VSN, wherein a dual-stage-attention (DA)-based long short-term memory (LSTM) network is integrated with variable selection networks (VSNs) to enhance the model predictability.…”

Section: Introductionmentioning

confidence: 99%

Prediction of anaerobic digestion performance by quantum convolutional reconstruction gated recurrent neural network*

Hou,

Che,

et al. 2024

Phys. Scr.

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Methane as a renewable energy source has become a hot topic in recent years. Methane is a bioenergy source produced during the anaerobic digestion of organic waste, and the anaerobic digestion process must be monitored and controlled to produce the required amount of methane in a stable manner. Mathematical modeling is used to simulate digester operation to predict the biogas production from anaerobic digestion, to avoid reactor loading or performance degradation, and to ensure efficient operation of the system. In this paper, a Quantum Convolutional Reconstruction Gated Recurrent Neural Network is proposed. The original data features are extracted by convolutional neural network to reduce the dimensionality and retain the information, the parameterized quantum circuit is integrated in the gating recurrent unit, and the quantum reset gate and quantum update gate are constructed. The information extracted by the Convolution Neural networks is input into the quantum gated recurrent neural network, and the quantum storage unit integrates the information into the hidden layer state, thus processing the hidden layer state information more efficiently. The experimental results show that the prediction accuracy of the A Quantum Convolution Reconstructed Gated Recurrent Neural Network is improved from 81.95% to 88.21%, and the MAE value is reduced from 54.53% to 37.38%.

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Section: Introductionmentioning

confidence: 99%

Prediction of anaerobic digestion performance by quantum convolutional reconstruction gated recurrent neural network*

Hou,

Che,

et al. 2024

Phys. Scr.

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“…An artificial neural network is a basic network consisting of an input layer, an output layer, and one or more intermediate neuron layers [5]. The main advantage of this network is that it evaluates input patterns after training [6].…”

Section: Introductionmentioning

confidence: 99%

Validation Challenges in Data for Different Diesel Engine Performance Regimes Utilising HVO Fuel: A Study on the Application of Artificial Neural Networks for Emissions Prediction

Matijošius,

Rimkus,

Gruodis

2024

Machines

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Artificial neural networks (ANNs) provide supervised learning via input pattern assessment and effective resource management, thereby improving energy efficiency and predicting environmental fluctuations. The advanced technique of ANNs forecasts diesel engine emissions by collecting measurements during trial sessions. This study included experimental sessions to establish technical and ecological indicators for a diesel engine across several operational scenarios. VALLUM01, a novel tool, has been created with a user-friendly interface for data input/output, intended for the purposes of testing and prediction. There was a comprehensive collection of 12 input parameters and 10 output parameters that were identified as relevant and sufficient for the objectives of training, validation, and prediction. The proper value ranges for transforming into fuzzy sets for input/output to an ANN were found. Given that the ANN’s training session comprises 1,000,000 epochs and 1000 perceptrons within a single-hidden layer, its effectiveness can be considered high. Many statistical distributions, including Pearson, Spearman, and Kendall, validate the prediction accuracy. The accuracy ranges from 96% on average, and in some instances, it may go up to 99%.

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“…RNNs [32] can also be used to predict medication resistance in the human immunodeficiency virus (HIV). Sindhu TN [33] presented a novel method to improve the structure of artificial neural networks for engineering and disease data, which was a decreasing failure rate model. Anum Shafiq Anum [34] investigated breast cancer modeling and survival analyses with artificial neural networks, maximum likelihood estimation, and statistics and performed a comparison with artificial neural network techniques to investigate Darcy-Forchheimer's Tangent hyperbolic flow towards a cylindrical surface, with a focus on Parabolic Trough Solar Collectors [35] .…”

Section: Introductionmentioning

confidence: 99%

Recurrent neural network for the dynamics of Zika virus spreading

Nisar,

Anjum,

Raja

et al. 2024

AIMSPH

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<abstract> <p>Recurrent Neural Networks (RNNs), a type of machine learning technique, have recently drawn a lot of interest in numerous fields, including epidemiology. Implementing public health interventions in the field of epidemiology depends on efficient modeling and outbreak prediction. Because RNNs can capture sequential dependencies in data, they have become highly effective tools in this field. In this paper, the use of RNNs in epidemic modeling is examined, with a focus on the extent to which they can handle the inherent temporal dynamics in the spread of diseases. The mathematical representation of epidemics requires taking time-dependent variables into account, such as the rate at which infections spread and the long-term effects of interventions. The goal of this study is to use an intelligent computing solution based on RNNs to provide numerical performances and interpretations for the SEIR nonlinear system based on the propagation of the Zika virus (SEIRS-PZV) model. The four patient dynamics, namely susceptible patients S(y), exposed patients admitted in a hospital E(y), the fraction of infective individuals I(y), and recovered patients R(y), are represented by the epidemic version of the nonlinear system, or the SEIR model. SEIRS-PZV is represented by ordinary differential equations (ODEs), which are then solved by the Adams method using the Mathematica software to generate a dataset. The dataset was used as an output for the RNN to train the model and examine results such as regressions, correlations, error histograms, etc. For RNN, we used 100% to train the model with 15 hidden layers and a delay of 2 seconds. The input for the RNN is a time series sequence from 0 to 5, with a step size of 0.05. In the end, we compared the approximated solution with the exact solution by plotting them on the same graph and generating the absolute error plot for each of the 4 cases of SEIRS-PZV. Predictions made by the model appeared to be become more accurate when the mean squared error (MSE) decreased. An increased fit to the observed data was suggested by this decrease in the MSE, which suggested that the variance between the model's predicted values and the actual values was dropping. A minimal absolute error almost equal to zero was obtained, which further supports the usefulness of the suggested strategy. A small absolute error shows the degree to which the model's predictions matches the ground truth values, thus indicating the level of accuracy and precision for the model's output.</p> </abstract>

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A decreasing failure rate model with a novel approach to enhance the artificial neural network's structure for engineering and disease data analysis

Cited by 18 publications

References 39 publications

Prediction of anaerobic digestion performance by quantum convolutional reconstruction gated recurrent neural network*

Prediction of anaerobic digestion performance by quantum convolutional reconstruction gated recurrent neural network*

Validation Challenges in Data for Different Diesel Engine Performance Regimes Utilising HVO Fuel: A Study on the Application of Artificial Neural Networks for Emissions Prediction

Recurrent neural network for the dynamics of Zika virus spreading

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