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An intelligent outlier detection with machine learning empowered big data analytics for mobile edge computing

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Abstract

In recent times, the Internet of Things and big data analytics have become a hot research topic in mobile edge computing (MEC) desires wide-ranging research works for intelligent decision making. In this view, this paper designs an intelligent outlier detection with machine learning empowered big data analytics (IODML-BDA) for MEC. The proposed model involves an adaptive synthetic sampling-based outlier detection technique to eradicate the existence of outliers. Besides, the oppositional swallow swarm optimization (OSSO) based feature selection technique is used to choose an effective set of features. Finally, long short-term memory based classification model is employed to identify different class labels. The design of OSSO algorithm for feature selection with ADASYN technique for big data analytics show the novelty of the work. A comprehensive experimental analysis is carried out on GPS trajectories, movement prediction, water treatment plant, hepatitis, and Twitter datasets to confirm the experimental results. The experimentation outcomes pointed out that the proposed IODML-BDA model achieves the higher accuracy of 0.9735, 0.9816, 0.9798, 0.9896, and 0.9912 on the applied datasets.

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Data sharing not applicable to this article as no datasets were generated during the current study.

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Acknowledgements

Taif University Researchers Supporting Project Number (TURSP-2020/154), Taif University, Taif, Saudi Arabia.

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Authors and Affiliations

  1. Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, Egypt

    Romany F. Mansour

  2. Department of Mathematics and Statistics, College of Science, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia

    S. Abdel-Khalek

  3. Computer Science and IT Department, College of Community, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia

    Inès Hilali-Jaghdam

  4. College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, P.O. Box 151, Alkharj, 11942, Saudi Arabia

    Jamel Nebhen

  5. Department of Software Convergence, Daegu Catholic University, Gyeongsan, 38430, South Korea

    Woong Cho

  6. Department of Computer Science and Engineering, Sejong University, Seoul, 05006, Korea

    Gyanendra Prasad Joshi

Authors
  1. Romany F. Mansour
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  2. S. Abdel-Khalek
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  3. Inès Hilali-Jaghdam
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  4. Jamel Nebhen
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  5. Woong Cho
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  6. Gyanendra Prasad Joshi
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Contributions

Conceptualization, RFM; formal analysis, RFM; funding acquisition, GPJ and WC; investigation, SAK; methodology, RFM and SAK; Project administration, WC; Resources, GPJ and WC; Software, IHJ and JN; supervision, GPJ; visualization, WC, IHJ and JN; writing—original draft, RFM; writing—review and editing, GPJ.

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Correspondence to Gyanendra Prasad Joshi.

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The authors declare that they have no conflict of interest. The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

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Mansour, R.F., Abdel-Khalek, S., Hilali-Jaghdam, I. et al. An intelligent outlier detection with machine learning empowered big data analytics for mobile edge computing. Cluster Comput 26, 71–83 (2023). https://doi.org/10.1007/s10586-021-03472-4

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  • DOI: https://doi.org/10.1007/s10586-021-03472-4

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