Effective deep learning classification for kidney stone using axial computed tomography (CT) images.

Introduction: Stone formation in the kidneys is a common disease, and the high rate of recurrence and morbidity of the disease worries all patients with kidney stones. There are many imaging options for diagnosing and managing kidney stone disease, and CT imaging is the preferred method.

Objectives: Radiologists need to manually analyse large numbers of CT slices to diagnose kidney stones, and this process is laborious and time-consuming.

Predicting the spread of COVID-19 with a machine learning technique and multiplicative calculus.

This paper aims to generate a universal well-fitted mathematical model to aid global representation of the spread of the coronavirus (COVID-19) disease. The model aims to identify the importance of the measures to be taken in order to stop the spread of the virus. It describes the diffusion of the virus in normal life with and without precaution.

Optimal control of a fractional order model for the COVID - 19 pandemic.

In this paper a fractional optimal control problem was formulated for the outbreak of COVID-19 using a mathematical model with fractional order derivative in the Caputo sense. The state and co-state equations were given and the best strategy to significantly reduce the spread of COVID-19 infections was found by introducing two time-dependent control measures, (which represents the awareness campaign, lockdown, and all other measures that reduce the possibility of contacting the disease in susceptible human population) and (which represents quarantine, monitoring and treatment of infected humans). Numerical simulations were carried out using RK-4 to show the significance of the control functions.