Photovoltaics (PV) output power is highly sensitive to many environmental parameters and
the power produced by the PV systems is significantly affected by the harsh environments …

The presented paper aims to establish a strong basis for utilizing machine learning (ML)
towards the prediction of the overall insulation health condition of medium voltage …

Abstract “Solar photovoltaic (PV) energy in GCC”-the term seems convincing to many solar
PV industries due to high solar exposure in GCC region. However, long-term effects such as …

Context Bio-inspired feature selection algorithms got the attention of the researchers in the
domain of Software Development Effort Estimations (SDEE) because they can improve the …

Real-world software systems are becoming larger, more complex, and much more
unpredictable. Software systems face many risks in their life cycles. Software practitioners …

Given high-dimensional software measurement data, researchers and practitioners often
use feature (metric) selection techniques to improve the performance of software quality …

One factor that affects the success of machine learning is the presence of irrelevant or
redundant information in the training data set. Filter-based feature ranking techniques …

This study aims developing customized novel data acquisition for photovoltaic systems
under extreme climates by utilizing off-the-shelf components and enhanced with data …

Despite the general acceptance that software engineering datasets often contain noisy,
irrelevant or redundant variables, very few benchmark studies of feature subset selection …

Feature selection helps improve the accuracy and computational time of solar forecasting.
However, FS is often passed by or conducted with methods that do not suit the solar …