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.
Highlights
With the current inclination towards “smart technology”, data are being generated in symmetric large quantum, resulting in the concept of big data
A detailed discussion about the types of the unsupervised machine learning algorithms is given in the following subsections
This study addresses the supervised and unsupervised machine learning techniques that are considered the main pillars of the Internet of Things (IoT) smart data analysis
Summary
With the current inclination towards “smart technology”, data are being generated in symmetric large quantum, resulting in the concept of big data. The objective of this type of learning is to identify hidden patterns of the analogous samples input data The key contributions of this study are the presentation of a comprehensive analysis of the related literature on contributions of unsupervised this study are machine the presentation a comprehensive analysis the related literature supervised and learningof techniques considered as theofmain pillars of the IoT on supervised and unsupervised machine learning techniques as the main pillarsas ofwell the smart data analysis. This article well as advantages and limitations to achieving a open precise, concrete, and concise conclusion This addresses current research trends in IoT smart data, issues being pursued in this area.
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