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Semantic IoT Interoperability and Data Analytics Using Machine Learning in Healthcare Sector

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Part of the Studies in Computational Intelligence book series (SCI, volume 941)

Abstract

With the exponential growth of data in electronic form, it becomes a complex and tedious task to extract meaningful information. The vast collection of data has resulted in big data that may be in indeterminate form. The challenge is to extract meaningful data from internet sources that are spreading across multiple domains and to enable consistent resource sharing, interoperability on multiple IoT platforms. The use of emerging technologies like Machine Learning and IoT is realized on multiple platforms, systems, and service applications. The introduction of predefined libraries on Natural Language Processing in Machine learning platforms has emphasized on the semantic web technologies and its IoT future directions. In this chapter, authors have discussed the role of the semantic web, three layered framework for IoT interoperability, and have framed a web ontology structure for semantic interoperability in IoT for the healthcare sector. Authors have also proposed the text analytics model for the healthcare sector and performed semantic data classification on synthesized healthcare dataset to predict the patient diagnosis using Machine learning techniques.

Keywords

Healthcare IoT Learning Machine Python Semantic Structured 

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Copyright information

© Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.NIELIT ShimlaShimlaIndia
  2. 2.Department of Computer ScienceHimachal Pradesh UniversityShimlaIndia

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