Feature Engineering and Machine Learning for IoT-based Applications: An Overview of Algorithms

This paper presents an overview of machine learning algorithms for feature valuation in IoT-based applications. The increasing use of IoT devices has led to a vast amount of data generated from sensors, which require analysis and interpretation. Machine learning algorithms can help extract useful information from this data and enable IoT devices to make intelligent decisions. The paper begins with an introduction to IoT-based applications and the importance of feature engineering for these applications. It then provides a brief overview of various machine learning algorithms that are commonly used for feature estimation, including classification and regression models. For each algorithm, we discuss the strengths and weaknesses and highlight the applications for which they are well-suited. The paper also examines the challenges associated with feature interpretation in IoT-based applications, such as data variability and resource constraints. We discuss the strategies to overcome these challenges, such as data preprocessing, feature selection, and model optimization. Additionally, we present a comparative analysis of these algorithms in terms of accuracy, speed, and efficiency, which can help guide the selection of the appropriate algorithm for a given application. Overall, this paper provides a comprehensive overview of machine learning algorithms for feature estimation in IoT-based applications. It highlights the potential of these algorithms to extract meaningful insights from IoT data and improve the performance of monitoring IoT devices.