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Detecting Pattern Efficiently with Don’t Cares

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Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference (EANN 2020)

Part of the book series: Proceedings of the International Neural Networks Society ((INNS,volume 2))

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Abstract

In this paper, we introduce our efficient simple method which can locate all occurrences of pattern P of k subpatterns with “don’t cares” of length m in text S of length n. Our algorithm employs advanced data structure and the Kangaroo method, which can be applied to selected suffixes of the suffix tree of S to answer subsequent queries in O(k) time using a predefined computational method to find all occurrences of pattern P with “don’t cares” in text S in a fast and effective manner.

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Author information

Authors and Affiliations

  1. Department of Informatics, King’s College London, London, UK

    Hayam Alamro & Costas Iliopoulos

  2. Department of Information Systems, Princess Nourah Bint Abdulrahman University, Riyadh, Kingdom of Saudi Arabia

    Hayam Alamro

Authors
  1. Hayam Alamro
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  2. Costas Iliopoulos
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Corresponding author

Correspondence to Hayam Alamro .

Editor information

Editors and Affiliations

  1. School of Engineering, Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Lancaster University, Lancaster, UK

    Plamen Parvanov Angelov

  3. School of Computing and Digital Technologies, Teesside University, Middlesbrough, UK

    Chrisina Jayne

  4. University of the West of England, Bristol, UK

    Elias Pimenidis

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Alamro, H., Iliopoulos, C. (2020). Detecting Pattern Efficiently with Don’t Cares. In: Iliadis, L., Angelov, P., Jayne, C., Pimenidis, E. (eds) Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. EANN 2020. Proceedings of the International Neural Networks Society, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-48791-1_35

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  • DOI: https://doi.org/10.1007/978-3-030-48791-1_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-48790-4

  • Online ISBN: 978-3-030-48791-1

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