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International Conference on Big Data, Cloud and Applications

BDCA 2018: Big Data, Cloud and Applications pp 16–28Cite as

A MapReduce-Based Adjoint Method to Predict the Levenson Self Report Psychopathy Scale Value

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 872))

Abstract

The Levenson Self Report Psychopathy serves as a measure to spot persons with psychopathic disorders able to commit crime or offend others. Indeed, predicting the Levenson Self Report Psychopathy factors would help investigator and even psychologist to spot offenders. In this paper, a statistical model is performed with the aim of predicting the Levenson Self Report Psychopathy scale value. For this purpose, the multiple regression statistical method is used. In addition, a parallelized algebraic adjoint method is performed to solve the least square problem. The MapReduce framework is used for this purpose. The Apache implementation of Mapreduce developed in Java untilled Hadoop 2.6.0 is deployed to tackle experiments.

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Author information

Authors and Affiliations

  1. Informatics, Systems and Optimization Laboratory, Department of Computer Science, Faculty of Science, Ibn Tofail University, Kenitra, Morocco

    Manal Zettam, Jalal Laassiri & Nourdddine Enneya

Authors
  1. Manal Zettam
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  2. Jalal Laassiri
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  3. Nourdddine Enneya
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Corresponding author

Correspondence to Manal Zettam .

Editor information

Editors and Affiliations

  1. Abdelmalek Essaâdi University, Tétouan, Morocco

    Youness Tabii

  2. Abdelmalek Essaâdi University, Tétouan, Morocco

    Mohamed Lazaar

  3. Abdelmalek Essaâdi University, Tétouan, Morocco

    Mohammed Al Achhab

  4. Université Ibn-Tofail, Tétouan, Morocco

    Nourddine Enneya

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Zettam, M., Laassiri, J., Enneya, N. (2018). A MapReduce-Based Adjoint Method to Predict the Levenson Self Report Psychopathy Scale Value. In: Tabii, Y., Lazaar, M., Al Achhab, M., Enneya, N. (eds) Big Data, Cloud and Applications. BDCA 2018. Communications in Computer and Information Science, vol 872. Springer, Cham. https://doi.org/10.1007/978-3-319-96292-4_2

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  • DOI: https://doi.org/10.1007/978-3-319-96292-4_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-96291-7

  • Online ISBN: 978-3-319-96292-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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