Machine Learning for Predictive Analytics in Smart IoT Ecosystems: A Comprehensive Review
Abstract
The convergence of Machine Learning (ML) and Internet of Things (IoT) has enabled intelligent predictive systems across domains such as healthcare, agriculture, and smart cities. This paper presents a comprehensive review of ML techniques applied to IoT-generated data, including supervised, unsupervised, and deep learning approaches. It evaluates real-time data processing, edge computing integration, and scalability challenges. Key applications such as predictive maintenance, anomaly detection, and energy optimization are examined. The paper also discusses data privacy concerns and communication bottlenecks in IoT networks. Finally, it outlines emerging trends such as federated learning and TinyML for resource-constrained environments.
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