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This paper investigates the use of Artificial Intelligence (AI) technologies for identifying faults in Internet of Things (IoT) cloud systems. By utilizing machine learning and deep learning models, the suggested method seeks to improve fault detection accuracy, minimize downtime, and enhance resource allocation in IoT-enabled cloud settings. The research reviews a range of AI models, assesses their effectiveness on IoT cloud data, and introduces an optimized hybrid model. The findings indicate significant improvements in fault detection rates and management of cloud resources. The study also discusses the implications for the robustness of cloud systems and the monitoring of real-time IoT applications.
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