Machine Learning-Driven Smart Discovery in IoT-Enabled Environments

Abstract

The application of the Internet of Things (IoT) and machine learning to enhance intelligent discovery procedures in IoT-enabled environments has been covered in this chapter. It draws attention to how machine learning algorithms would address these issues and shows how traditional approaches are inefficient when dealing with the massive amounts of data created by IoT devices and sensors. It also highlights how crucial feature engineering, model selection, and assessment metrics are to the development of machine learning-driven intelligent discovery systems. The data privacy, algorithm bias, and security flaws have been described. The chapter also covers the real-world use of ML algorithms in sectors including manufacturing, transportation, healthcare, and agriculture. IoT and ML integration with infrastructure are discussed to address the new possibilities for innovation, optimization, and decision-making.