Fuzzy logic and bio-inspired Ant Colony Algorithm-based technique to find relative desirability in IoT-based healthcare system
A. Firos · 2024
In the context of Internet of Things (IoT)-based healthcare systems, a novel approach to assessing the relative desirability of health-related data emerges through the integration of Fuzzy Logic and a Bio-Inspired Ant Colony Algorithm. This hybrid technique leverages the adaptability of Fuzzy Logic to handle uncertainty and imprecision in healthcare data while drawing inspiration from the collective intelligence of ant colonies for optimization. Fuzzy Logic enables a nuanced understanding of diverse health parameters, considering linguistic variables and membership functions. Concurrently, the Ant Colony Algorithm introduces a bio-inspired optimization mechanism, mimicking the collaborative decision-making process observed in ant colonies. This integrated technique enhances the efficiency of evaluating and prioritizing healthcare data within the IoT framework. It empowers healthcare systems to make informed decisions based on a comprehensive analysis of patient information, treatment outcomes, and other critical factors. By combining the strengths of Fuzzy Logic and bio-inspired algorithms, this approach contributes to creating more adaptive, intelligent, and effective IoT-based healthcare systems.