To determine the applications of machine learning (ML) techniques used for the primary
prevention of work-related musculoskeletal disorders (WMSDs), a scoping review was …

This paper introduces a comprehensive survey of a new population-based algorithm so-
called gradient-based optimizer (GBO) and analyzes its major features. GBO considers as …

Stochastic optimization methods have gained significant prominence as effective techniques
in contemporary research, addressing complex optimization challenges efficiently. This …

Energy forecasting plays an important role in effective power grid management. The
widespread adoption of emerging technologies and the increased reliance on renewable …

Background An appropriate sample size is essential for obtaining a precise and reliable
outcome of a study. In machine learning (ML), studies with inadequate samples suffer from …

The area of a Microgrid (μ G) is a very fast-growing and promising system for overcoming
power barriers. This paper examines the impacts of a microgrid system considering Electric …

Meta-heuristic algorithms are an essential way to solve realistic optimization problems.
Developing effective, accurate, and stable meta-heuristic algorithms has become the goal of …

A multicollinearity-aware multi-objective optimisation (MA-MOO) framework was developed
to minimise the main environmental issues and the cost of production of green concrete …

This survey compiles ideas and recommendations from more than a dozen researchers with
different backgrounds and from different institutes around the world. Promoting best practice …

One of the greatest challenges for widespread utilization of solar energy is the low
conversion efficiency, motivating the needs of developing more innovative approaches to …