Machine Learning Powered IoT for Smart Applications
Zhala Jameel Hamad and
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Zhala Jameel Hamad: Information System Engineering, Erbil Polytechnic University, Erbil, Iraq
International Journal of Science and Business, 2021, vol. 5, issue 3, 92-100
With the coming of fast advancements, with the assistance of IoT, a great percentage of heterogeneous devices can be connected with each other. The technology with the relationship of different devices through the internet is named the internet of things (IoT), makes a wide number of different characteristics and qualities of data. IoT and Machine learning (ML) guarantees the widespread advancement to grow the insights of the IoT devices and applications. Over the final few years, artificial intelligence and machine learning have advanced very significantly. It allows a machine or system to learn more effectively than people learn on their own. When we learn some kind of system about the concept of our trial or the knowledge obtained after evaluating it. Combining IoT with rapidly advancing ML technologies can make ‘smart machines’ that mimic smart action to do well-informed resolve with little or no human involvement. There are at least two fundamental reasons, why machine learning is a suitable solution for the IoT world? The primary has got to do with the volume of data and the automation openings. The second is related to the prescient investigation. Therefore, this paper focuses on ML in different techniques and different domains that motivate and support IoT applications. Many previous works related to this subject and examples have been addressed, explained in detail. The results showed that ML plays a vital role in monitoring, processing, systematic investigation, and smart use of the expansive measure of data in several fields. It was also beneficial for helping users’ process massive data.
Keywords: Machine Learning; Internet of things; SDN; Smart Application (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:aif:journl:v:5:y:2021:i:3:p:92-100
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