A comparative analysis of maximum likelihood estimation and artificial neural network modeling to assess electrical component reliability

Çolak, Andaç Batur; Sindhu, Tabassum Naz; Lone, Showkat Ahmad; Akhtar, Md. Tanwir; Shafiq, Anum

doi:10.1002/qre.3233

Cited by 10 publications

(4 citation statements)

References 53 publications

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“…Shafiq et al [26] presented the convective flow of MHD past a perpendicular surface. Cloak et al [27] investigated the relative inspection of maximum likelihood approximations and non-natural neural networks. Shafiq et al [28] investigated exponentiated Weibull spreading using IPL by numerical method.…”

Section: Introductionmentioning

confidence: 99%

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The influence of Marangoni convection in engine oil base nanofluid with the impact of variable viscosity and viscous dissipation on infinite disk

Rehman,

Khun,

Alotaibi

et al. 2024

Z Angew Math Mech

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In this research work, we will investigate the influence of MC (Marangoni convection) in engine oil as base fluid with viscous dissipation and the influence of variable viscosity on an infinite disk. The flow system takes into account MC with influence of variable viscosity, convective boundary conditions, and viscous dissipation. Furthermore, for the sake of the present research paper, the graphical results of the temperature and velocity profiles are presented. To convert a collection of PDEs to NODEs, we applied appropriate transformations. We utilize the Homotopy analysis method to solve this set of equations. The effects of temperature and velocities on numerical perceptions involving Casson, magnetic, Schmidt number, thermophoresis, and Brownian motion input factors are shown. A physical description is used to simulate and evaluate the structures of flow features such as velocity and temperature profiles in response to changes in developing factors. It is hoped that this issue would contribute positively to improving the heat transformation ratio in order to satisfy the demands of the industrial and engineering sectors.

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Section: Introductionmentioning

confidence: 99%

“…Cloak et al. [27] investigated the relative inspection of maximum likelihood approximations and non‐natural neural networks. Shafiq et al.…”

Section: Introductionmentioning

confidence: 99%

The influence of Marangoni convection in engine oil base nanofluid with the impact of variable viscosity and viscous dissipation on infinite disk

Rehman,

Khun,

Alotaibi

et al. 2024

Z Angew Math Mech

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“…Shafiq A [36] designed the numerical treatment of the Darcy-Forchheimer flow of Ree-Eyring fluids with a chemical reaction, examined [37] the significance of EMHD (Electromagnetohydrodynamic) graphene oxide (GO) water ethylene glycol nanofluid flow, examined the activation energy [38] and binary chemical reaction effects in the axisymmetric flow of third-grade nanofluid, and carried out a comparative analysis of the maximum likelihood estimation and artificial neural network modeling to evaluate the electrical component reliability [39] . Neural networks have been used in many different fields by Çolak AB and Shafiq A [40] – [45] .…”

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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“…Data-driven deep learning is another powerful tool for analyzing the dynamics of COVID-19 pandemic. It is a nonlinear mathematical tool with powerful learning ability, and is widely used in natural language processing [24], fault detections [25], image recognitions [26] and reliability analysis [27][28][29]. During COVID-19 pandemic, neural networks are used to construct various simulation frameworks to predict the development trend of the epidemic [30,31].…”

Section: Introductionmentioning

confidence: 99%

Combining the dynamic model and deep neural networks to identify the intensity of interventions during COVID-19 pandemic

He,

Tang,

Xiao

2023

PLoS Comput Biol

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During the COVID-19 pandemic, control measures, especially massive contact tracing following prompt quarantine and isolation, play an important role in mitigating the disease spread, and quantifying the dynamic contact rate and quarantine rate and estimate their impacts remain challenging. To precisely quantify the intensity of interventions, we develop the mechanism of physics-informed neural network (PINN) to propose the extended transmission-dynamics-informed neural network (TDINN) algorithm by combining scattered observational data with deep learning and epidemic models. The TDINN algorithm can not only avoid assuming the specific rate functions in advance but also make neural networks follow the rules of epidemic systems in the process of learning. We show that the proposed algorithm can fit the multi-source epidemic data in Xi’an, Guangzhou and Yangzhou cities well, and moreover reconstruct the epidemic development trend in Hainan and Xinjiang with incomplete reported data. We inferred the temporal evolution patterns of contact/quarantine rates, selected the best combination from the family of functions to accurately simulate the contact/quarantine time series learned by TDINN algorithm, and consequently reconstructed the epidemic process. The selected rate functions based on the time series inferred by deep learning have epidemiologically reasonable meanings. In addition, the proposed TDINN algorithm has also been verified by COVID-19 epidemic data with multiple waves in Liaoning province and shows good performance. We find the significant fluctuations in estimated contact/quarantine rates, and a feedback loop between the strengthening/relaxation of intervention strategies and the recurrence of the outbreaks. Moreover, the findings show that there is diversity in the shape of the temporal evolution curves of the inferred contact/quarantine rates in the considered regions, which indicates variation in the intensity of control strategies adopted in various regions.

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A comparative analysis of maximum likelihood estimation and artificial neural network modeling to assess electrical component reliability

Cited by 10 publications

References 53 publications

The influence of Marangoni convection in engine oil base nanofluid with the impact of variable viscosity and viscous dissipation on infinite disk

The influence of Marangoni convection in engine oil base nanofluid with the impact of variable viscosity and viscous dissipation on infinite disk

Recurrent neural network for the dynamics of Zika virus spreading

Combining the dynamic model and deep neural networks to identify the intensity of interventions during COVID-19 pandemic

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