Numerical Investigations through ANNs for Solving COVID-19 Model

Umar, Muhammad; Sabir, Zulqurnain; Raja, Muhammad Asif Zahoor; Javeed, Shumaila; Ahmad, Harith; Elagen, S.K.; Khames, Ahmed

doi:10.3390/ijerph182212192

Cited by 12 publications

(5 citation statements)

References 40 publications

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“…Sabir et al introduced a stochastic solver based on the Levenberg-Marquardt backpropagation neural networks (LMBNNs) for the nonlinear host-vector-predator model 9 . The COVID-19 spreading model is investigated using artificial neural networks with Levenberg-Marquardt backpropagation training by Umar et al 10 . Another work by Umar et al introduces a numerical computing technique using artificial neural networks optimized with particle swarm optimization and active-set algorithms to solve the nonlinear corneal shape model 11 .…”

Section: Literature Reviewmentioning

confidence: 99%

A novel neural network model with distributed evolutionary approach for big data classification

Haritha

Shailesh

Judy

et al. 2023

Sci Rep

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The considerable improvement of technology produced for various applications has resulted in a growth in data sizes, such as healthcare data, which is renowned for having a large number of variables and data samples. Artificial neural networks (ANN) have demonstrated adaptability and effectiveness in classification, regression, and function approximation tasks. ANN is used extensively in function approximation, prediction, and classification. Irrespective of the task, ANN learns from the data by adjusting the edge weights to minimize the error between the actual and predicted values. Back Propagation is the most frequent learning technique that is used to learn the weights of ANN. However, this approach is prone to the problem of sluggish convergence, which is especially problematic in the case of Big Data. In this paper, we propose a Distributed Genetic Algorithm based ANN Learning Algorithm for addressing challenges associated with ANN learning for Big data. Genetic Algorithm is one of the well-utilized bio-inspired combinatorial optimization methods. Also, it is possible to parallelize it at multiple stages, and this may be done in an extremely effective manner for the distributed learning process. The proposed model is tested with various datasets to evaluate its realizability and efficiency. The results obtained from the experiments show that after a specific volume of data, the proposed learning method outperformed the traditional methods in terms of convergence time and accuracy. The proposed model outperformed the traditional model by almost 80% improvement in computational time.

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Section: Literature Reviewmentioning

confidence: 99%

A novel neural network model with distributed evolutionary approach for big data classification

Haritha

Shailesh

Judy

et al. 2023

Sci Rep

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show abstract

“…Recently, ANN-based solvers have been exploited for the numerical treatment of the COVID system with its variants [30][31][32][33][34][35][36][37][38][39], HIV infection system [40][41][42], Dange fever [2,[43][44][45][46], hepatitis virus system [47], influenza virus [48][49][50], and HBV virus [7]. The majority of these ANN-based modeling uses log-sigmoid, tan-sigmoid, and radial basis functions as an activation function, however, there is a need to explore other activation functions like the Mexican hat wavelet which has theoretically good approximation capabilities.…”

Section: Introductionmentioning

confidence: 99%

Swarm-optimized numerical investigation of Dengue Fever Model

Riaz,

Ullah,

Alasiry

et al. 2024

Preprint

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The current study aims to present a swarm-optimized technique for the numerical treatment of dengue fever non-linear model. The model is composed of the coupled nonlinear system comprising the susceptible, infected, and recovered compartments. The system is transformed into an unsupervised single layer feed-forward artificial neural network with a Mexican hat wavelet activation function in the hidden layer. The unknowns of the neural network is optimized with particle swarm optimization as an efficient global search aided by the effective local search technique based on sequential quadratic programming. The presented results are compared with state of art Runge-Kutta method and other modern reported techniques on various performance indicators like absolute error, mean average deviation, global absolute error, global mean average deviation, convergence, and computational complexity. Comprehensive Monte Carlo simulations and their statistical analysis are presented to ensure accuracy, consistency in convergence, and computational complexity in terms of execution time. It is observed that the proposed scheme is accurate, reliable, convergent, and computationally viable in treating the nonlinear coupled system under consideration.

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“…For example, the influenza disease model is one of the models that have been solved using a novel design of the ANNs accompanied by the Levenberg-Marquardt backpropagation [21]. COVID-19 pandemic is one of the most important models that have been solved by Umar et al in [22]. In addition, Umar et al [23] proposed a computational framework applying the same algorithm for solving the SIR model, which provides better understanding of dengue fever.…”

Section: Introductionmentioning

confidence: 99%

An Intelligence Computational Approach for the Fractional 4D Chaotic Financial Model

Weera¹,

Botmart²,

Chantawat³

et al. 2023

Computers, Materials &Amp; Continua

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The main purpose of the study is to present a numerical approach to investigate the numerical performances of the fractional 4-D chaotic financial system using a stochastic procedure. The stochastic procedures mainly depend on the combination of the artificial neural network (ANNs) along with the Levenberg-Marquardt Backpropagation (LMB) i.e., ANNs-LMB technique. The fractional-order term is defined in the Caputo sense and three cases are solved using the proposed technique for different values of the fractional order α. The values of the fractional order derivatives to solve the fractional 4-D chaotic financial system are used between 0 and 1. The data proportion is applied as 73%, 15%, and 12% for training, testing, and certification to solve the chaotic fractional system. The acquired results are verified through the comparison of the reference solution, which indicates the proposed technique is efficient and robust. The 4-D chaotic model is numerically solved by using the ANNs-LMB technique to reduce the mean square error (MSE). To authenticate the exactness, and consistency of the technique, the obtained performances are plotted in the figures of correlation measures, error histograms, and regressions. From these figures, it can be witnessed that the provided technique is effective for solving such models to give some new insight into the physical behavior of the model.

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Numerical Investigations through ANNs for Solving COVID-19 Model

Cited by 12 publications

References 40 publications

A novel neural network model with distributed evolutionary approach for big data classification

A novel neural network model with distributed evolutionary approach for big data classification

Swarm-optimized numerical investigation of Dengue Fever Model

An Intelligence Computational Approach for the Fractional 4D Chaotic Financial Model

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