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This book offers a clear understanding of the concept of contextaware machine learning including an automated rulebased framework within the broad area of data science and analytics, particularly, with the aim of datadriven intelligent decision making. Thus, we have bestowed a comprehensive study on this topic that explores multidimensional contexts in machine learning modeling, context discretization with timeseries modeling, contextual rule discovery and predictive analytics, recentpattern or rulebased behavior modeling, and their usefulness in various contextaware intelligent applications and services. The presented machine learningbased techniques can be employed in a wide range of realworld application areas ranging from personalized mobile services to security intelligence, highlighted in the book. As the interpretability of a rulebased system is high, the automation in discovering rules from contextual raw data can make this book more impactful for the applicationdevelopers as well as researchers. Overall, this book provides a good reference for both academia and industry people in the broad area of data science, machine learning, AIDriven computing, humancentered computing and personalization, behavioral analytics, IoT and mobile applications, and cybersecurity intelligence.
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