Design of Computer Methods for the Solution of Cervical Cancer Epidemic Model

Nonlinear modelling has a significant role in different disciplines of sciences such as behavioral,social,physical and biological sciences.The structural properties are also needed for such types of disciplines,as dynamical consistency,positivity and boundedness are the major requirements of the models in these fields.One more thing,this type of nonlinear model has no explicit solutions.For the sake of comparison its computation will be done by using different computational techniques.Regrettably,the aforementioned structural properties have not been restored in the existing computational techniques in literature.Therefore,the construction of structural preserving computational techniques are needed.The nonlinearmodel for cervical cancer is constructed by parametric perturbation technique.Well-known computer methods are considered for the computation of cervical cancer dynamics.The well-known existing methods in literature are Euler Maruyama,Euler and Runge Kutta.Nonstandard finite difference method or Implicitly driven explicit method is first time considered for aforesaid model under the assumptions given byMickens in a stochastic way.Unfortunately,the aforementioned existing methods did not reinstate structural properties of cervical cancer dynamics in the human population.Our plannedmethod is structural preserving and a powerful tool for all nonlinear models of biomedical engineering problems.We have verified that existing computational methods do not preserve dynamical properties.But,the implicitly driven explicit method is a good device for dynamical properties.In the support of assertions,convergence analysis of implicitly driven explicit method is presented.

BHGSO:Binary Hunger Games Search Optimization Algorithm for Feature Selection Problem

In machine learning and data mining,feature selection(FS)is a traditional and complicated optimization problem.Since the run time increases exponentially,FS is treated as an NP-hard problem.The researcher’s effort to build a new FS solution was inspired by the ongoing need for an efficient FS framework and the success rates of swarming outcomes in different optimization scenarios.This paper presents two binary variants of a Hunger Games Search Optimization(HGSO)algorithm based on V-and S-shaped transfer functions within a wrapper FS model for choosing the best features from a large dataset.The proposed technique transforms the continuous HGSO into a binary variant using V-and S-shaped transfer functions(BHGSO-V and BHGSO-S).To validate the accuracy,16 famous UCI datasets are considered and compared with different state-of-the-art metaheuristic binary algorithms.The findings demonstrate that BHGSO-V achieves better performance in terms of the selected number of features,classification accuracy,run time,and fitness values than other state-of-the-art algorithms.The results demonstrate that the BHGSO-V algorithm can reduce dimensionality and choose the most helpful features for classification problems.The proposed BHGSO-V achieves 95%average classification accuracy for most of the datasets,and run time is less than 5 sec.for low and medium dimensional datasets and less than 10 sec for high dimensional datasets.

Nanofluid Flows Within Porous Enclosures Using Non-Linear Boussinesq Approximation

In this paper,the Galerkin finite element method(FEM)together with the characteristic-based split(CBS)scheme are applied to study the case of the non-linear Boussinesq approximation within sinusoidal heating inclined enclosures filled with a non-Darcy porous media and nanofluids.The enclosure has an inclination angle and its side-walls have varying sinusoidal temperature distributions.The working fluid is a nanofluid that is consisting of water as a based nanofluid and Al2O3 as nanoparticles.The porous medium is modeled using the Brinkman Forchheimer extended Darcy model.The obtained results are analyzed over wide ranges of the non-linear Boussinesq parameter 0≤ζ≤1,the phase deviation 00≤Φ≤1800,the inclination angle 00≤γ≤900,the nanoparticles volume fraction 0%≤φ≤4%,the amplitude ratio 0≤a≤1 and the Rayleigh number 104≤Ra≤106.The results revealed that the average Nusselt number is enhanced by 0.73%,26.46%and 35.42%at Ra=104,105 and 106,respectively,when the non-linearBoussinesq parameter is varied from 0 to 1.In addition,rate of heat transfer in the case of a non-uniformly heating is higher than that of a uniformly heating.Non-linear Boussinesq parameter rises the flow speed and heat transfer in an enclosure.Phase deviation makes clear changes on the isotherms and heat transfer rate on the right wall of an enclosure.An inclination angle varies the flow speed and it has a slight effect on heat transfer in an enclosure.