Wind power prediction is a critical goal for power engineers, aimed at forecasting the power
output for applicable power plants. However, the complex, nonlinear, non-stationary, and …

In current study innovative approach is presented to improve the accuracy and efficiency of
machine learning models on the dataset for each of the 31 US states, solely comprising a …

Abstract We have used Bayesian Regularization Back Propagation based deep neural
networks to analyze the flow and heat transfer behavior for the flow of a nanofluid (aluminum …

During the takeoff phase, aircraft engines reach maximum speed and temperature to
achieve the required thrust. Due to these harsh operating conditions, the performance of …

In this investigation, a comprehensive study has been made to reveal electro osmosis flow
through a tapered ciliated symmetric porous channel. The flow is initiated due to …

Current study aims to numerically investigate the bi-directional flow of nanofluids in the
presence of Cattaneo–Christov double diffusion for two heat sources, namely, prescribed …

In this study, a stochastic computing structure is provided for the numerical solutions of the
SIRC epidemic delay differential model, ie SIRC-EDDM using the dynamics of the COVID …

This research implements an evolutionary optimized finite differences scheme (FDS) for
nonlinear electrohydrodynamics ion drag flow dynamics in a cylindrical conduit (EHD …

The analysis of images using deep learning techniques makes it possible to detect
anomalous or dangerous situations in different fields of application. This work aims to …

The study addresses the formidable challenge of calculating atomic coordinates for carbon
nanotubes (CNTs) using density functional theory (DFT), a process that can endure for days …