Novel applications of intelligent computing paradigms for the analysis of nonlinear reactive transport model of the fluid in soft tissues and microvessels

Ahmad, Iftikhar; Ilyas, Hira; Urooj, Aysha; Aslam, Muhammad Saeed; Shoaib, Mohammed; Raja, Muhammad Asif Zahoor

doi:10.1007/s00521-019-04203-y

Cited by 119 publications

(43 citation statements)

References 71 publications

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“…In the future, one may explore, investigate, or exploit the stochastic numerical computing approaches based on the artificial intelligence paradigm [37][38][39][40][41][42][43][44] for alternate, accurate, robust, and stable solutions, not only for the given biological fluidic model involving nano-materials, but also for other stiff nonlinear systems, which are still a challenge for traditional/classical numerical methods.…”

Section: Discussionmentioning

confidence: 99%

Heat transfer analysis of biological nanofluid flow through ductus efferentes

et al. 2020

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A theoretical investigation on heat transfer of nanofluid transport in a ciliated channel is presented. A mathematical model for cilia oriented nanofluid transport through the human male reproductive tract is exhibited. Considering the structure of tubules, the Reynolds number is considered very small. The concept of a long wavelength approximation is incorporated to model the nanofluid flow problem. The exact analytical expressions for the x-component of velocity, temperature, nanoparticle concentration, stream function, volume flow rate, and pressure gradient are calculated. The solution for the transverse component of velocity, pressure rise per wavelength, and friction force on the wall of the channel is explored numerically, while variations in crucial flow parameters are examined graphically.

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Section: Discussionmentioning

confidence: 99%

Heat transfer analysis of biological nanofluid flow through ductus efferentes

et al. 2020

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“…(6) via Meyer wavelet neural networks (MWNN) optimized with global search efficacy of genetic algorithms (GAs) and sequential quadratic programming (SQP), i.e., MWNN-GASQP. The solvers based on meta-heuristic intelligent computing have been extensively applied for the analysis of linear/nonlinear, singular/non-singular systems using neural networks optimized with evolutionary/swarming-based computing schemes (Lodhi 2019;Raja et al 2017a;Bukhari 2020;Waseem 2020;Ahmad 2020Ahmad ,2019. Some recent applications of the evolutionary/swarming-based numerical computing are Painlevé equation-based models in random matrix theory (Raja et al 2018a), nonlinear prey-predator models (Umar 2019), Bagley-Torvik systems in fluid mechanics.…”

Section: Problem Statement and Related Workmentioning

confidence: 99%

FMNEICS: fractional Meyer neuro-evolution-based intelligent computing solver for doubly singular multi-fractional order Lane–Emden system

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In the present study, a novel fractional Meyer neuro-evolution-based intelligent computing solver (FMNEICS) is presented for numerical treatment of doubly singular multi-fractional Lane-Emden system (DSMF-LES) using combined heuristics of Meyer wavelet neural networks (MWNN) optimized with global search efficacy of genetic algorithms (GAs) and sequential quadratic programming (SQP), i.e., MWNN-GASQP. The design of novel FMNE-ICS for DSMF-LES is presented after derivation from standard Lane-Emden equation, and the singular points and shape factors along with fractional-order terms are analyzed. The MWNN modeling strength is used to represent the system model DSMF-LES in the meansquared error-based merit function and optimization of the networks is carried out with integrated optimization ability of GASQP. The verification, validation, and perfection of the FMNEICS for three different cases of DSMF-LES are established through comparative studies from reference solutions on convergence, robustness, accuracy, and stability measures. Moreover, the observations through the statistical analysis further authenticate the worth of proposed fractional MWNN-GASQP-based stochastic solver. Keywords Multi-fractional Lane-Emden model • Multi-singular systems • Artificial neural networks • Meyer wavelet neural networks • Sequential quadratic programming • Genetic algorithms Mathematics Subject Classification 34-XX • 34A08 • 34A34 • 82B31

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“…The strength of artificial intelligent (AI) based computing solvers has been exploited by the research community on large scale to obtain the approximated solutions of many problems arises in broad fields of applied science and technology. Some potential, recent reported studies having paramount significance including Van-der-Pol oscillatory systems, optics, electrically conducting solids, reactive transport system, nanofluidics, nanotechnology, fluid dynamics, astrophysics, circuit theory, plasma, atomic physics, bioinformatics, energy, power and functional mathematics see [24][25][26][27][28][29][30][31][32][33][34] and references cited therein. The said information is the motivational affinities to investigate in AI base numerical computing solver for the COVID-19 model.…”

Section: Problem Statement With Significancementioning

confidence: 99%

Intelligent computing with Levenberg–Marquardt artificial neural networks for nonlinear system of COVID-19 epidemic model for future generation disease control

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The aim of this work is to design an intelligent computing paradigm through Levenberg-Marquardt artificial neural networks (LMANNs) for solving the mathematical model of Corona virus disease 19 (COVID-19) propagation via human to human interaction. The model is represented with systems of nonlinear ordinary differential equations represented with susceptible, exposed, symptomatic and infectious, super spreaders, infection but asymptomatic, hospitalized, recovery and fatality classes, and reference dataset of the COVID-19 model is generated by exploiting the strength of explicit Runge-Kutta numerical method for metropolitans of China and Pakistan including Wuhan, Karachi, Lahore, Rawalpindi and Faisalabad. The created dataset is arbitrary used for training, validation and testing processes for each cyclic update in Levenberg-Marquardt backpropagation for numerical treatment of the dynamics of COVID-19 model. The effectiveness and reliable performance of the design LMANNs are endorsed on the basis of assessments of achieved accuracy in terms of mean squared error based merit functions, error histograms and regression studies.

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Novel applications of intelligent computing paradigms for the analysis of nonlinear reactive transport model of the fluid in soft tissues and microvessels

Cited by 119 publications

References 71 publications

Heat transfer analysis of biological nanofluid flow through ductus efferentes

Heat transfer analysis of biological nanofluid flow through ductus efferentes

FMNEICS: fractional Meyer neuro-evolution-based intelligent computing solver for doubly singular multi-fractional order Lane–Emden system

Intelligent computing with Levenberg–Marquardt artificial neural networks for nonlinear system of COVID-19 epidemic model for future generation disease control

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