Since installing solid oxide fuel cells (SOFCs)-based systems suffers from high expenses,
accurate and reliable modeling is heavily demanded to detect any design issue prior to the …

The purpose of this study is to investigate entropy optimization in the magneto-
hydrodynamic and electro-magneto-hydrodynamic flow of a Casson hybrid nanofluid over a …

Variable electrical load and ever-increasing load demand need to be predicted or
forecasted to avoid the energy crisis. In this paper, machine learning based ANN is explored …

Deep Learning is the newest and the current trend of the machine learning field that paid a
lot of the researchers' attention in the recent few years. As a proven powerful machine …

Rainfall is one of the most critical parameters in a hydrological model. A few models have
been created to investigate and predict the rainfall conjecture. in recent years, soft …

In this research, forecast of Photovoltaic (PV) rooftop power output has been carried out.
Forecast is using machine learning Artificial Neural Network (ANN). ANN is precisely used …

Short term electric load forecasting is an important aspect of power system planning and
operation for utility companies. Short Term Load Forecasting (STLF) has always been one of …

Increase in the number of population in the lowelevation coastal zone has increase the
importanceto reduce the risk of coastal and nuisance flooding, especially during high tide …

This water management system enhances irrigated agriculture usage. We implemented an
open loop fuzzy logic based scheme in this system. Inside the field, the inputs to the fuzzy …