Machine Learning Algorithms for Smart Data Analysis in Internet of Things Environment: Taxonomies and Research Trends

MOHAMMED, H. ALSHARIF and Anabi, Hilary Kelechi and Khalid, Yahya and Shehzad, Ashraf Chaudhry (2020) Machine Learning Algorithms for Smart Data Analysis in Internet of Things Environment: Taxonomies and Research Trends. Symmetry, 12.

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Abstract

Machine learning techniques will contribution towards making Internet of Things (IoT) symmetric applications among the most significant sources of new data in the future. In this context, network systems are endowed with the capacity to access varieties of experimental symmetric data across a plethora of network devices, study the data information, obtain knowledge, and make informed decisions based on the dataset at its disposal. This study is limited to supervised and unsupervised machine learning (ML) techniques, regarded as the bedrock of the IoT smart data analysis. This study includes reviews and discussions of substantial issues related to supervised and unsupervised machine learning techniques, highlighting the advantages and limitations of each algorithm, and discusses the research trends and recommendations for further study.

Item Type:	Article
Uncontrolled Keywords:	machine learning; artificial intelligence; supervised learning; unsupervised learning; big data; internet of things
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User:	Mrs Patricia Nwokealisi
Date Deposited:	29 Jan 2020 11:06
Last Modified:	29 Jan 2020 11:06
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/13073

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