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Advances in Data and Information Sciences pp 293–305Cite as

Code Profiling Analysis of Rough Set Theory on DSP and Embedded Processors for IoT Application

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 94))

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. This paper discusses the code profiling for rough set theory on DSP and ARM processors. This work was undertaken to understand the performance of rough set theory on existing processors for mining/analyzing inconsistent nature of IoT application at fog/edge interface.

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References

  1. Pawlak, Z. (1982). Rough sets. International Journal of Computer and Information science, 11, 341–356.

    Article  Google Scholar 

  2. Pawlak, Z. (1984). Rough classifications. International Journal of Man Machine studies (No. 20)

    Google Scholar 

  3. Pawlak, Z. (1991). Rough Sets: Theoretical aspects and reasoning about data. Dordrecht: Kluwer Academic.

    Google Scholar 

  4. Jiye, Li, & Cercone, Nick. (2006). Assigning missing attribute values based on rough sets theory. In Proceedings of IEEE International Conference on Granular Computing, GrC, (Vol. 2006(May), pp. 10–12).

    Google Scholar 

  5. Riza, L. S., et al. (2014). Implementing algorithms of rough set theory and fuzzy rough set theory in the R package “RoughSets”. Information Sciences, 287, 68–89.

    Article  Google Scholar 

  6. Hassan, Y. F. (2017). Deep learning architecture using rough sets and rough neural networks. Kybernetes, 46(4), 693–705. https://doi.org/10.1108/K-09-2016-0228.

    Article  Google Scholar 

  7. Zhang, Q., Xie, Q., & Wang, G. (2016). A survey on rough set theory and its applications. CAAI Transactions on Intelligence Technology, 1, 323–333.

    Article  Google Scholar 

  8. Bello, R., & Falcon, R. (2017). “Rough Sets in machine learning: A review”, chapter in studies in computational. Intelligence. https://doi.org/10.1007/978-3-319-54966-8_5.

  9. Jiang, H. Study on the application of rough sets theory in machine learning. In Proceedings of Second International Symposium on Intelligent Information Technology Application. https://doi.org/10.1109/IITA.2008.154

  10. Mahajan, P., Kandwal, R., & Vijay, R. (2012). Rough set approach in machine learning: a review. International Journal of Computer Applications, (0975-8887) 56(10)

    Google Scholar 

  11. ROSE 2 User guide. (2017). Retrieved June 25, 2017, from http://idss.cs.put.poznan.pl/site/fileadmin/projects-images/rosemanual.pdf.

  12. Abbas, Z., & Burney, A. (2016). A survey of software packages used for rough set analysis. Journal of Computer and Communications, 4, 10–18.

    Article  Google Scholar 

  13. Agarwal, V., Patil, R. A., Patki, A. B. Architectural considerations for next generation iot processors. Accepted for Publication in IEEE Systems Journal. https://doi.org/10.1109/JSYST.2018.2890571

  14. Jyoti, A. (2017). Code profiling for RST algorithm on DSP and embedded processors. M.Eng thesis, College of Engineering Pune, India.

    Google Scholar 

  15. Munakata, T. (2008). Rough sets. In Springer: Fundamentals of the new artificial intelligence neural, evolutionary, Fuzzy and More (2nd ed., pp. 162–202).

    Google Scholar 

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Acknowledgements

The authors would also like to thank Mr. A. B. Patki, Ex-Senior Director/Scientist G and HoD, Ministry of Electronics and Information Technology, Government of India for his valuable suggestions and guidance. Authors also acknowledge the help and support of College of Engineering Pune for carrying out this work.

Author information

Authors and Affiliations

  1. Electronics and Telecommunication Department, College of Engineering Pune, Pune, Maharashtra, India

    Vanita Agarwal & Rajendrakumar A. Patil

  2. Centre for VLSI and Nanotechnology, VNIT, Nagpur, Maharashtra, India

    Jyoti Adwani

Authors
  1. Vanita Agarwal
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  2. Rajendrakumar A. Patil
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  3. Jyoti Adwani
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Corresponding author

Correspondence to Vanita Agarwal .

Editor information

Editors and Affiliations

  1. Smart Grid and Renewable Energy, University of Agder, Kristiansand, Norway

    Mohan L. Kolhe

  2. Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, Uttar Pradesh, India

    Shailesh Tiwari

  3. Department of Computer Science and Engineering, NIT Agartala, Tripura, India

    Munesh C. Trivedi

  4. Computer Science and Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, India

    Krishn K. Mishra

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Agarwal, V., Patil, R.A., Adwani, J. (2020). Code Profiling Analysis of Rough Set Theory on DSP and Embedded Processors for IoT Application. In: Kolhe, M., Tiwari, S., Trivedi, M., Mishra, K. (eds) Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 94. Springer, Singapore. https://doi.org/10.1007/978-981-15-0694-9_28

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  • DOI: https://doi.org/10.1007/978-981-15-0694-9_28

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0693-2

  • Online ISBN: 978-981-15-0694-9

  • eBook Packages: EngineeringEngineering (R0)

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