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Machine learning algorithms for smart data analysis in the Internet of things: an overview

Machine learning algorithms for smart data analysis in the Internet of things: an overview

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Machine learning (ML) techniques will benefit immensely from the avalanche of data readily available from various (IoT) applications considered as the major contributor of new data for future intelligent network. Based on this new concept, network systems will further magnify their capacity to exploit variety of experimental data across a plethora of network devices, study the data information, obtain knowledge and make informed decisions based on the dataset at their disposal. Smart IoT data analysis are performed utilizing supervised learning, unsupervised learning and reinforced learning. This study is limited to supervised and unsupervised ML techniques. In other to achieve the set objectives, reviews and discussions of substantial issues related to supervised or unsupervised machine learning techniques were executed, highlighting the advantages and limitations of each algorithm as well presenting the recent research trends and recommendations for future study.

Chapter Contents:

  • 12.1 Introduction
  • 12.2 Taxonomies of supervised and unsupervised ML algorithms
  • 12.2.1 Supervised ML algorithm
  • 12.2.1.1 Classification tasks
  • 12.2.1.2 Regression tasks
  • 12.2.1.3 Combining classification and regression tasks
  • 12.2.2 Unsupervised ML algorithm
  • 12.2.2.1 Clustering
  • 12.2.2.2 Feature extraction
  • 12.2.3 Neural networks approach
  • 12.3 Research trends and open issues
  • 12.3.1 Privacy and security
  • 12.3.2 Real-time implementation and data analysis
  • 12.4 Conclusions and recommendations
  • References

Inspec keywords: Internet of Things; unsupervised learning; intelligent networks; supervised learning; data analysis

Other keywords: informed decisions; network systems; supervised machine learning techniques; network devices; smart IoT data analysis; data information; supervised ML techniques; smart data analysis; unsupervised machine learning techniques; unsupervised ML techniques; Internet of Things

Subjects: Mobile, ubiquitous and pervasive computing; Unsupervised learning; Other topics in statistics; Supervised learning; Data handling techniques; Neural nets

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