Network Slicing and Deep Reinforcement Learning (DRL) are vital enablers for achieving
5G and 6G networks. A 5G/6G network can comprise various network slices from unique or …

An exhaustive review of adaptive signal processing schemes which are robust, sparsity-
aware and robust as well as sparsity-aware has been carried out in this paper. Conventional …

Numerous optimization problems can be addressed using metaheuristics instead of
deterministic and heuristic approaches. This study proposes a novel population-based …

In this paper, we innovatively propose the Arctic Puffin Optimization (APO), a metaheuristic
optimization algorithm inspired by the survival and predation behaviors of the Arctic puffin …

Per-example gradient clip** is a key algorithmic step that enables practical differential
private (DP) training for deep learning models. The choice of clip** threshold $ R …

The prediction of heart failure survivors is a challenging task and helps medical
professionals to make the right decisions about patients. Expertise and experience of …

This book was written in response to the growing demand for a text that provides a unified
treatment of linear and nonlinear complex valued adaptive filters, and methods for the …

Abstract Evaluation of operator Mental Workload (MW) levels via ongoing
electroencephalogram (EEG) is quite promising in Human-Machine (HM) collaborative task …

Numerous variable step-size normalized least mean-square (VSS-NLMS) algorithms have
been derived to solve the dilemma of fast convergence rate or low excess mean-square …

In this paper a new class of nonlinear adaptive filters, consisting of a linear combiner
followed by a flexible memory-less function, is presented. The nonlinear function involved in …