A Component Model with Verifiable Composition for the Construction of Emergency Management Systems

Rana, Tauseef; Imran, Muhammad Ali; Baz, Abdullah

doi:10.1007/s13369-020-04819-6

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…Examples of actual uses of this type of model include cassava mosaic disease [ 2 ], pine wilt disease [ 3 ], and potato late blight [ 4 ]. Later, new methods for studying nonlinear dynamics and numerical simulations helped solve complex ecological problems [ 5 , 6 ]. This accelerated the creation of more realistic and complicated plant disease models.…”

Section: Introductionmentioning

confidence: 99%

A Numerical Study of the Dynamics of Vector-Born Viral Plant Disorders Using a Hybrid Artificial Neural Network Approach

Alhakami

Umar

Sulaiman

et al. 2022

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Most plant viral infections are vector-borne. There is a latent period of disease inside the vector after obtaining the virus from the infected plant. Thus, after interacting with an infected vector, the plant demonstrates an incubation time before becoming diseased. This paper analyzes a mathematical model for persistent vector-borne viral plant disease dynamics. The backpropagated neural network based on the Levenberg–Marquardt algorithm (NN-BLMA) is used to study approximate solutions for fluctuations in natural plant mortality and vector mortality rates. A state-of-the-art numerical technique is utilized to generate reference data for obtaining surrogate solutions for multiple cases through NN-BLMA. Curve fitting, regression analysis, error histograms, and convergence analysis are used to assess accuracy of the calculated solutions. It is evident from our simulations that NN-BLMA is accurate and reliable.

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Section: Introductionmentioning

confidence: 99%

A Numerical Study of the Dynamics of Vector-Born Viral Plant Disorders Using a Hybrid Artificial Neural Network Approach

Alhakami

Umar

Sulaiman

et al. 2022

Entropy

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“…In this paper, numerical solutions for fully wetted longitudinal porous heat exchangers with different thermal conductivities are calculated based on the simple concept of artificial intelligence (AI), implemented through the application of neural networks and optimization procedures of meta-heuristic techniques [ 35 , 36 , 37 , 38 , 39 ]. Recently, artificial intelligence-based stochastic techniques have been successfully implemented for various problems in different domains of reaction kinematics [ 40 , 41 ], marine engineering [ 42 ], wireless communication [ 43 ], and fluid dynamics [ 44 , 45 , 46 ].…”

Section: Introductionmentioning

confidence: 99%

On the Computational Study of a Fully Wetted Longitudinal Porous Heat Exchanger Using a Machine Learning Approach

Alhakami

Khan

Sulaiman

et al. 2022

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The present study concerns the modeling of the thermal behavior of a porous longitudinal fin under fully wetted conditions with linear, quadratic, and exponential thermal conductivities surrounded by environments that are convective, conductive, and radiative. Porous fins are widely used in various engineering and everyday life applications. The Darcy model was used to formulate the governing non-linear singular differential equation for the heat transfer phenomenon in the fin. The universal approximation power of multilayer perceptron artificial neural networks (ANN) was applied to establish a model of approximate solutions for the singular non-linear boundary value problem. The optimization strategy of a sports-inspired meta-heuristic paradigm, the Tiki-Taka algorithm (TTA) with sequential quadratic programming (SQP), was utilized to determine the thermal performance and the effective use of fins for diverse values of physical parameters , such as parameter for the moist porous medium, dimensionless ambient temperature, radiation coefficient, power index, in-homogeneity index, convection coefficient, and dimensionless temperature. The results of the designed ANN-TTA-SQP algorithm were validated by comparison with state-of-the-art techniques, including the whale optimization algorithm (WOA), cuckoo search algorithm (CSA), grey wolf optimization (GWO) algorithm, particle swarm optimization (PSO) algorithm, and machine learning algorithms. The percentage of absolute errors and the mean square error in the solutions of the proposed technique were found to lie between 10−4 to 10−5 and 10−8 to 10−10, respectively. A comprehensive study of graphs, statistics of the solutions, and errors demonstrated that the proposed scheme’s results were accurate, stable, and reliable. It was concluded that the pace at which heat is transferred from the surface of the fin to the surrounding environment increases in proportion to the degree to which the wet porosity parameter is increased. At the same time, inverse behavior was observed for increase in the power index. The results obtained may support the structural design of thermally effective cooling methods for various electronic consumer devices.

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A Machine Learning Strategy for the Quantitative Analysis of the Global Warming Impact on Marine Ecosystems

et al. 2022

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It is generally observed that aquatic organisms have symmetric abilities to produce oxygen (O2) and fix carbon dioxide (CO2). A simulation model with time-dependent parameters was recently proposed to better understand the symmetric effects of accelerated climate change on coastal ecosystems. Changes in environmental elements and marine life are two examples of variables that are expected to change over time symmetrically. The sustainability of each equilibrium point is examined in addition to proving the existence and accuracy of the proposed model. To support the conclusions of this research compared to other studies, numerical simulations of the proposed model and a case study are investigated. This paper proposes an integrated bibliographical analysis of artificial neural networks (ANNs) using the Reverse-Propagation with Levenberg–Marquaradt Scheme (RP-LMS) to evaluate the main properties and applications of ANNs. The results obtained by RP-LMS show how to prevent global warming by improving the management of marine fish resources. The reference dataset for greenhouse gas emissions, environmental temperature, aquatic population, and fisheries population (GAPF) is obtained by varying parameters in the numerical Adam approach for different scenarios. The accuracy of the proposed RP-LMS neural network is demonstrated using mean square error (MSE), regression plots, and best-fit output. According to RP-LMS, the current scenario of rapid global warming will continue unabated over the next 50 years, damaging marine ecosystems, particularly fish stocks.

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A Component Model with Verifiable Composition for the Construction of Emergency Management Systems

Cited by 3 publications

References 37 publications

A Numerical Study of the Dynamics of Vector-Born Viral Plant Disorders Using a Hybrid Artificial Neural Network Approach

A Numerical Study of the Dynamics of Vector-Born Viral Plant Disorders Using a Hybrid Artificial Neural Network Approach

On the Computational Study of a Fully Wetted Longitudinal Porous Heat Exchanger Using a Machine Learning Approach

A Machine Learning Strategy for the Quantitative Analysis of the Global Warming Impact on Marine Ecosystems

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