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

Symmetry

ShehzadAshraf Chaudhry¹  AnabiHilary Kelechi²  MohammedH. Alsharif³  Khalid Yahya⁴ 

[1] Department of Computer Engineering, Faculty of Engineering and Architecture, Istanbul Gelisim University, Avcılar, 34310 İstanbul, Turkey;Department of Electrical Engineering and Information Engineering, College of Engineering, Covenant University, Canaanland, Ota P.M.B 1023, Ogun State, Nigeria;Department of Electrical Engineering, College of Electronics and Information Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea;Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Istanbul Gelisim University, Avcılar, 34310 İstanbul, Turkey;

关键词: machine learning;    artificial intelligence;    supervised learning;    unsupervised learning;    big data;    internet of things;   

DOI  :  10.3390/sym12010088

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown