The whole world is embroiling the pandemic situation caused by COVID-19, which is spreading across all countries. As of mid-May, COVID-19 continues to increase the number of people affected and the number of deaths in each country. Each country's administrations concerned are making endless efforts to maintain public health, mental health and to regulate the rate of illness of COVID-19. Analysis of COVID-19 data using the machine learning paradigm is becoming a major interest of the researcher in these situations. Several researchers analyzed data from COVID-19 to predict infection, death, cured persons in the future, which may lead to the planning of each country's regulatory authority to maintain the public health of its people. The machine learning algorithm provides more accurate results when the data size is large due to the lower number of data sets available to COVID-19, making the most accurate predictions a challenging task to implement the machine learning algorithm. This paper was essentially designed to predict the active rate, the death rate, and the cured rate in India by analyzing the data of COVID-19. There are three models of machine learning Support Vector Machine (SVM), Prophet Forecasting Model, and Linear Regression Model for predicting active rate, death rate and cured rate. Prophet Forecasting Model has been shown to be the best predictive method for predicting active rate, death rate and cured rate compared to SVM and Linear Regression when the vast uncertain and small data sets.

Cylindrically symmetric inhomogeneous magnetized string cosmological model is investigated. The source of the magnetic field is due to an electric current produced along x-axis. F 23 is the only non-vanishing component of electromagnetic field tensor. To get the deterministic solution, it has been assumed that the expansion (θ ) in the model is proportional to the eigen value σ 1 1 of the shear tensor σ i j . The physical and geometric properties of the model are also discussed in presence and absence of magnetic field.

PurposePresent investigation based on the flow of electrically conducting Williamson nanofluid embedded in a porous medium past a linearly horizontal stretching sheet. In addition to that, the combined effect of thermophoresis, Brownian motion, thermal radiation and chemical reaction is considered in both energy and solutal transfer equation, respectively.Design/methodology/approachWith suitable choice of nondimensional variables the governing equations for the velocity, temperature, species concentration fields, as well as rate shear stress at the plate, rate of heat and mass transfer are expressed in the nondimensional form. These transformed coupled nonlinear differential equations are solved semi-analytically using variation parameter method.FindingsThe behavior of characterizing parameters such as magnetic parameter, melting parameter, porous matrix, Brownian motion, thermophoretic parameter, radiation, Lewis number and chemical particular case present result validates with earlier established results and found to be in good agreement. Finally reaction parameter is demonstrated via graphs and numerical results are presented in tabular form.Originality/valueThe said work is an original work of the authors.

The flow of magneto-micropolar nanofluid, that is, the composition of TiO 2 nanoparticles in an organic solvent, kerosene, and the normal water past a stretchable surface has been considered. With effectiveness idea on the application in several areas, the Darcy-Forchheimer inertial drag and the second-order velocity slip approach are vital for the current investigation. The influence of viscous, Joule and Darcy dissipations on the energy transfer cannot be neglected due to the interaction of the body forces characterized by magnetic and porosity of the medium. The dissipative heat energy with the heat generation/absorption is useful for the enhancement in the fluid temperature. Due to the complexity of the problem, a numerical solution is implemented using the inbuilt code bvp5c with the help of MATLAB software. The physical properties abide by the characterizing parameters that appeared in the flow profiles are presented via graphs and the computed results for the rate coefficients are also displayed through table both for waterand kerosene-based nanofluids. Finally, the main findings of the results are: the growth in the shear rate coefficient is marked due to the inclusion of second-order slip, and an attenuation in the fluid velocity is rendered with an increase in the volume fraction whereas impact is reversed in the case of nanofluid temperature.

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