Deep Learning Based Data Management using Optimization Term Memory Neural Network in IOT Sector

Authors

  • J. Shafiq Mansoor
  • S. Syed Jamaesha

Abstract

In the IoT age, a huge number of sensing tools collects and / or produce different data behind time for a range of applications. Depending on the design, these devices can issue real-time data streams. In this article, we give away a review of how to use advanced technology for analysis and training in IoT, namely profound learning. They describe the characteristics of IoT information and identify two important IoT data: big data analytics, and IoT data streaming analytics, beginning from the machine-learning perspective.In this type of data and applications we also discuss a deep learning method that is promising to achieve the desired study. It then discusses, and introduces expectations and obstacles, the significance of the use of new deep learning technologies for IoT data analysis. We provide a long history of various deep learning systems and algorithms.We also address a deep learning approach that promises the necessary analysis in this type of data and applications. This addresses the importance of emerging deeper learning technology for IoT data analysis and presents goals and obstacles. We have a long history of several programs and algorithms with deep training.

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Published

2020-04-01

Issue

Section

Articles