Leveraging Explainable AI (XAI) Across Diverse Domains: IoT, Biomedicine, and Cyber security

This paper discusses the growing importance of Explainable AI (XAI), as it relates to making machine learning models more transparent and trustworthy in various applications and fields. In this regard, XAI addresses the need to understand to trust the most complex titles. In biomedicine, XAI assists clinicians in understanding the decisions made by machine learning-based models in the course of diagnosis, thus enabling improved patient management. In cyber security, it supports generating and situating further actions once the AI has flagged an anomaly, which increases overall security. In the Internet of Things (IoT), XAI assists in identifying the integrity of the system by spotting irregularities in the uniformity of interactions from various sources. In particular, this research investigates core XAI methods including but not limited to LIME, SHAP, and the application of interpretable models in decision-making, particularly focusing on decision trees and their implementation in practice in these fields. A comparative evaluation of XAI strategies is also presented to provide the information that is useful about implementation. Hence providing with an adequate theoretical basis for XAI, as well as practical recommendations on incorporating explain ability into the models of AI for biomedicine, cyber security, and IoT.