Selecting relevant feature subsets is vital in machine learning, and multiclass feature
selection is harder to perform since most classifications are binary. The feature selection …

Nowadays, Human Activity Recognition (HAR) is being widely used in a variety of domains,
and vision and sensor-based data enable cutting-edge technologies to detect, recognize …

Advancements in medical technology have created numerous large datasets including
many features. Usually, all captured features are not necessary, and there are redundant …

Human activity recognition (HAR) is a very active yet challenging and demanding area of
computer science. Due to the articulated nature of human motion, it is not trivial to detect …

In the era of smart life, tracking human activities and motion can play a significant role in the
advanced modern applications, such as the Internet of things (IoT), Internet of healthcare …

Optimization algorithms have shown significant advantages in solving diverse several real-
world problems, especially where are limitations in computations and hardware …

The Grey Wolf Optimizer (GWO) has emerged as one of the most captivating swarm
intelligence methods, drawing inspiration from the hunting behavior of wolf packs. GWO's …

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 …

Since most metaheuristic algorithms for continuous search space have been developed, a
number of transfer functions have been proposed including S-shaped, V-shaped, linear, U …

Moth–flame optimization (MFO) is a prominent swarm intelligence algorithm that
demonstrates sufficient efficiency in tackling various optimization tasks. However, MFO …