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Context-Aware Machine Learning System: Applications and Challenging Issues

In: Context-Aware Machine Learning and Mobile Data Analytics

Author

Listed:
  • Iqbal H. Sarker

    (Swinburne University of Technology
    Chittagong University of Engineering & Technology)

  • Alan Colman

    (Swinburne University of Technology)

  • Jun Han

    (Swinburne University of Technology)

  • Paul Watters

    (Macquarie University
    Cyberstronomy Pty Ltd)

Abstract

Context-awareness has recently received much attention in academia and industry for a variety of applications. Due to its intelligence in technologies and availability in various real-world applications, there has been a lot of development in the domain of context-aware computing systems in recent years. However, building a context-aware machine learning system still poses a variety of genuine challenges. This chapter addresses the most important and vital issues, ranging from contextual data collection to decision-making, that has been thoroughly explored in the earlier chapters of this book. In terms of new research perspective, future advances in industries or academia, or smart solutions in context-aware technology, prospective research works and challenges in the field of context-aware computing have been addressed in this chapter. Before discussing the challenging issues, we have summarized several real-world context-aware applications that intelligently assist individual smartphone users in their everyday activities as well as motivates to work in this area.

Suggested Citation

  • Iqbal H. Sarker & Alan Colman & Jun Han & Paul Watters, 2021. "Context-Aware Machine Learning System: Applications and Challenging Issues," Springer Books, in: Context-Aware Machine Learning and Mobile Data Analytics, chapter 0, pages 147-157, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-88530-4_10
    DOI: 10.1007/978-3-030-88530-4_10
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