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Presenting a Flexible and Adaptive Machine Learning Layer Architecture for IoT

EasyChair Preprint no. 4908

9 pagesDate: January 18, 2021


Machine learning has customarily been exclusively performed on servers and superior machines. In any case, propels in chip innovation have given us smaller than expected libraries that fit in our pockets and portable processors have limitlessly expanded in capacity narrowing the tremendous hole between the basic processors implanted in such things and their increasingly complex cousins in PCs. Accordingly, with the present headway in these gadgets, as far as processing power, vitality stockpiling, and memory limit, the open door has emerged to extract incredible incentive in having on-gadget machine learning for Internet of Things (IoT) gadgets. Machine learning can likewise help machines, a large number of machines, get together to comprehend what individuals need from the information made by people. Likewise, machine learning assumes a fundamental job in the IoT angle to deal with the immense measure of information produced by those machines.

Keyphrases: Advancement, learning, machine, Performance

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Abhay Shukla},
  title = {Presenting a Flexible and Adaptive Machine Learning Layer Architecture for IoT},
  howpublished = {EasyChair Preprint no. 4908},

  year = {EasyChair, 2021}}
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