A Secure and Reliable Framework for Explainable Artificial Intelligence (XAI) in Smart City Applications
Received: 29 April 2024 | Revised: 15 May 2024 | Accepted: 17 May 2024 | Online: 2 August 2024
Corresponding author: Mohammad Algarni
Abstract
Living in a smart city has many advantages, such as improved waste and water management, access to quality healthcare facilities, effective and safe transportation systems, and personal protection. Explainable AI (XAI) is called a system that is capable of providing explanations for its judgments or predictions. This term describes a model, its expected impacts, and any potential biases that may be present. XAI tools and frameworks can aid in comprehending and trusting the output and outcomes generated by machine-learning algorithms. This study used XAI methods to classify cities based on smart city metrics. The logistic regression method with LIME achieved perfect accuracy, precision, recall, and F1-score, predicting correctly all cases.
Keywords:
machine learning, explainable artificial intelligence (XAI), smart city, artificial intelligenceDownloads
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