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Rough Sets for Intelligence on Embedded Systems

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Foundations of Intelligent Systems (ISMIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13515))

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Abstract

In the continuing effort to run artificial intelligence (AI) on resource-constrained embedded devices, we present experiments that test the viability of enabling an embedded system to run Rough Set Theory. We first show that the Fuzzy-Rough Nearest Neighbor (FRNN) algorithm, a classic Rough Sets methodology, can indeed be effectively run on an embedded device. Next we compare 10 iterations of four algorithms in terms of power consumption, speed, accuracy, and model size. Specifically, we analyze on an embedded system, KNN, Fuzzy-Rough Nearest Neighbour (FRNN) without weights, FRNN with weights and Fuzzy Rough One-Versus-One (FROVOCO). Herein, we present a step towards the goal of designing an AI microprocessor, without software, based entirely on Rough Sets.

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Authors and Affiliations

  1. University of Colorado at Colorado Springs, Colorado Springs, CO, 80918, USA

    Katrina Nesterenko & Rory Lewis

Authors
  1. Katrina Nesterenko
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  2. Rory Lewis
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Correspondence to Katrina Nesterenko or Rory Lewis .

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Editors and Affiliations

  1. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  2. Università della Calabria, Rende, Italy

    Sergio Flesca

  3. Università Federico II di Napoli, Naples, Italy

    Elio Masciari

  4. ICAR-CNR, Rende, Italy

    Giuseppe Manco

  5. University of North Carolina, Charlotte, NC, USA

    Zbigniew W. Raś

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Nesterenko, K., Lewis, R. (2022). Rough Sets for Intelligence on Embedded Systems. In: Ceci, M., Flesca, S., Masciari, E., Manco, G., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2022. Lecture Notes in Computer Science(), vol 13515. Springer, Cham. https://doi.org/10.1007/978-3-031-16564-1_22

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  • DOI: https://doi.org/10.1007/978-3-031-16564-1_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16563-4

  • Online ISBN: 978-3-031-16564-1

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