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2020 | OriginalPaper | Chapter

Standard Library Tool Set for Rough Set Theory on FPGA

Authors : Vanita Agarwal, Rajendrakumar A. Patil

Published in: Advances in Data and Information Sciences

Publisher: Springer Singapore

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Abstract

Rough Set Theory is a powerful Artificial Intelligence based tool used for data analysis and mining Inconsistent Information Systems. In the presence of inconsistent, incomplete, imprecise or vague data, normal statistical-based data analytic techniques lag behind. The various software used for the analysis of inconsistent data using Rough Set Theory runs on x86 kind of processors for various operating systems. Unlike the other software implementations, the main objective of undertaking this experimentation is to describe a new and standard library tool set for the computation of inconsistent data using Rough Set Theory which is completely synthesizable on FPGA. Further, the authors have also studied the effect of implemented design on Zybo FPGA for understanding the area, timing, and power efficiency criteria. A Rough Set Theory based Data Analytic Engine can be used as a potential candidate for knowledge discovery and data mining of inconsistent data in IoT applications at fog and/or edge interfaces. This paper defines the standard library tool for Rough Set Theory on FPGA.

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Metadata
Title
Standard Library Tool Set for Rough Set Theory on FPGA
Authors
Vanita Agarwal
Rajendrakumar A. Patil
Copyright Year
2020
Publisher
Springer Singapore
Book
Advances in Data and Information Sciences

Print ISBN: 978-981-15-0693-2

Electronic ISBN: 978-981-15-0694-9

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-15-0694-9_23