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Pattern Mining—FTISPAM Using Hybrid Genetic Algorithm

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Advanced Soft Computing Techniques in Data Science, IoT and Cloud Computing

Part of the book series: Studies in Big Data ((SBD,volume 89))

Abstract

An innovative approach is elegantly launched to effectively identify the medical behavioural changes of the patients. With this end in view, the sequential change patterns are extracted at two diverse time intervals, with the help of the fuzzy time interval sequential change pattern mining employing the HGA technique. However, the pattern mining at two diverse time intervals is likely to yield further superfluous data. With an eye on averting the generation of the corresponding superfluous data, an optimized method such as the hybrid genetic algorithm (HGA) based fuzzy time interval sequential pattern mining is envisaged for the purpose of attaining the patterns. The sequential pattern detection algorithm effectively segments the located change patterns into four diverse types such as the perished patterns, added patterns, unexpected changes, and the emerging patterns. When the pattern categorization comes to an end, the changed patterns are harmonized by means of the Similarity Computation Index (SCI) values. At last, the significant patterns are estimated and employed to categorize the change in the conduct of the patient. The imaginative system is performed in the working stage of the MATLAB and its execution is surveyed and appeared differently in relation to that of the advanced strategy like the genetic algorithm.

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Authors and Affiliations

  1. Sathyabama Institute of Science and Technology, Chennai, India

    L. Mary Gladence & E. Brumancia

  2. M.I.E.T Engineering College, Trichy, India

    S. Shanmuga Priya

  3. St. Joseph’s College of Engineering, Chennai, India

    A. Shane Sam

  4. Velammal Institute of Technology, Chennai, India

    Gladis Pushparathi

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  1. L. Mary Gladence
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  2. S. Shanmuga Priya
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  3. A. Shane Sam
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  4. Gladis Pushparathi
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  5. E. Brumancia
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Application, North Orissa University, Bhubaneswar, India

    Sujata Dash

  2. School of Computer Science and Engineering, Orissa Engineering College, Bhubaneswar, Odisha, India

    Subhendu Kumar Pani

  3. Faculty of Electrical Engineering and Computer science, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  4. Department of Family and Community Health, University of Maryland, Baltimore, MD, USA

    Yulan Liang

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Mary Gladence, L., Shanmuga Priya, S., Shane Sam, A., Pushparathi, G., Brumancia, E. (2021). Pattern Mining—FTISPAM Using Hybrid Genetic Algorithm. In: Dash, S., Pani, S.K., Abraham, A., Liang, Y. (eds) Advanced Soft Computing Techniques in Data Science, IoT and Cloud Computing. Studies in Big Data, vol 89. Springer, Cham. https://doi.org/10.1007/978-3-030-75657-4_16

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  • DOI: https://doi.org/10.1007/978-3-030-75657-4_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-75656-7

  • Online ISBN: 978-3-030-75657-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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