Fuzzy logic, genetic algorithms, and artificial neural networks applied to cognitive radio networks: A review

Abstract

Cognitive radios are expected to play an important role in capturing the constantly growing traffic interest on remote networks. To improve the usage of the radio range, a cognitive radio hub detects the weather, evaluates the open-air qualities, and then makes certain decisions and distributes the executives’ space assets. The cognitive radio works in tandem with artificial intelligence and artificial intelligence methodologies to provide a flexible and intelligent allocation for continuous production cycles. The purpose is to provide a single source of information in the form of a survey research to enable academics better understand how artificial intelligence methodologies, such as fuzzy logics, genetic algorithms, and artificial neural networks, are used to various cognitive radio systems. The various artificial intelligence approaches used in cognitive radio engines to improve cognition capabilities in cognitive radio networks are examined in this study. Computerized reasoning approaches, such as fuzzy logic, evolutionary algorithms, and artificial neural networks, are used in the writing audit. This topic also covers cognitive radio network implementation and the typical learning challenges that arise in cognitive radio systems.

Current Trends in Incubator Control for Premature Infants with Artificial Intelligence Based on Fuzzy Logic Control: Systematic Literature Review

“…The NHS and other organizations deploy AI implementations in many healthcare services; however, these systems lack the precision of more algorithmic systems based on machine learning [51][52][53]. These rule-based clinical decision support systems are challenging to keep up with as medical knowledge evolves and frequently are unable to handle the explosion of information and knowledge based on genetics, proteomics, metabolism and other "omics-based" approaches to care.…”