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Anomaly Detection with Machine Learning Technique to Support Smart Logistics

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Computational Science and Its Applications – ICCSA 2019 (ICCSA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11619))

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Abstract

Accurate planning and cost-effective management on product delivery are among key factors leading to the success of most manufacturing sectors in the current era of the fourth industrial revolution, or Industry 4.0. In this work, we focus our study on the development of a model based on machine learning technique to support smart logistics by detecting anomaly events on the big stream of electronic orders obtained from ubiquitous customers. We use the data-driven approach to build the order-anomaly model and present the built model as the classification and regression tree. Our model in a tree formalism is to be used as an automatic detector for unusual events inherent in the customers’ order stream. The anomaly events should invoke special care in the smart logistics environment that delivery tasks are performed in an automatic manner. Early detection of anomaly ordering events is expected to improve accuracy on delivery planning.

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Acknowledgment

This work was financially supported by grants from the National Research Council of Thailand and Suranaree University of Technology through the funding of the Data and Knowledge Engineering Research Unit.

Author information

Authors and Affiliations

  1. Data and Knowledge Engineering Research Unit, Nakhon Ratchasima, Thailand

    Nittaya Kerdprasop & Kittisak Kerdprasop

  2. School of Computer Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand

    Nittaya Kerdprasop, Kacha Chansilp & Kittisak Kerdprasop

  3. Department of Environmental Science, Thammasat University, Pathumthani, Thailand

    Paradee Chuaybamroong

Authors
  1. Nittaya Kerdprasop
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  2. Kacha Chansilp
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  3. Kittisak Kerdprasop
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  4. Paradee Chuaybamroong
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Corresponding author

Correspondence to Nittaya Kerdprasop .

Editor information

Editors and Affiliations

  1. Covenant University, Ota, Nigeria

    Sanjay Misra

  2. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  3. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Saint Petersburg State University, Saint Petersburg, Russia

    Vladimir Korkhov

  6. Polytechnic University of Bari, Bari, Italy

    Carmelo Torre

  7. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  8. Monash University, Clayton, VIC, Australia

    David Taniar

  9. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  10. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

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Kerdprasop, N., Chansilp, K., Kerdprasop, K., Chuaybamroong, P. (2019). Anomaly Detection with Machine Learning Technique to Support Smart Logistics. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11619. Springer, Cham. https://doi.org/10.1007/978-3-030-24289-3_34

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  • DOI: https://doi.org/10.1007/978-3-030-24289-3_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24288-6

  • Online ISBN: 978-3-030-24289-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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