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Intelligence Test Case Based-Approach for Crude Oil Prediction System

  • Conference paper
Intelligent Systems'2014

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 323))

Abstract

The intelligent system has the ability to predict the future depending on dataset and rules relations. Petroleum prediction using computational intelligence techniques aims at enhancing the petroleum industry. Using test cases processes, we are able to discover information, more effective for different classes of and prove strictness prediction results. In the case of crude oil prediction, the prediction results are going to be so conservative that it is often felt useless for decision-making, using test cases and clustering functions of the predicted results and empirical values prove to have more precision and efficiency. In this paper, the computational intelligence technique (Fuzzy), test case and clustering functions are used to achieve overlap Strictness Crude Oil Prediction System (SCOPS). The dataset sources are extracted from distinct oilfields sources. The proposed prediction intelligent system manipulates petroleum vagueness data, retesting predicted results and reduces system failures to achieve idealistic results.

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Author information

Authors and Affiliations

  1. Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Senan A. Ghallab, N. L. Badr & M. F Tolba

Authors
  1. Senan A. Ghallab
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  2. N. L. Badr
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  3. M. F Tolba
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Corresponding author

Correspondence to Senan A. Ghallab .

Editor information

Editors and Affiliations

  1. Ford Motor Company, Research & Advanced Engineering, Dearborn, Mississippi, USA

    D. Filev

  2. Industrial Research Institute for Automation and Measurements (PIAP), Warsaw, Poland

    J. Jabłkowski

  3. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    J. Kacprzyk

  4. Polish Academy of Sciences and WIT - Warsaw School of Information Technology, Systems Research Institute, Warsaw, Poland

    M. Krawczak

  5. Bulgarian Academy of Sciences, Institute of Information and Communication, Sofia, Bulgaria

    I. Popchev

  6. Department of Computer Engineering, Częstochowa University of Technology, Częstochowa, Poland

    L. Rutkowski

  7. Bulgarian Academy of Sciences, Institute of Information and Communication Technologies, Sofia, Bulgaria

    V. Sgurev

  8. Department of Computer and Information Technologies, “Prof. Assen Zlatarov" University Faculty of Technical Sciences, Bourgas, Bulgaria

    E. Sotirova

  9. Industrial Research Institute for Automation and Measurements (PIAP), Warsaw, Poland

    P. Szynkarczyk

  10. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    S. Zadrozny

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Ghallab, S.A., Badr, N.L., Tolba, M.F. (2015). Intelligence Test Case Based-Approach for Crude Oil Prediction System. In: Filev, D., et al. Intelligent Systems'2014. Advances in Intelligent Systems and Computing, vol 323. Springer, Cham. https://doi.org/10.1007/978-3-319-11310-4_78

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  • DOI: https://doi.org/10.1007/978-3-319-11310-4_78

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11309-8

  • Online ISBN: 978-3-319-11310-4

  • eBook Packages: EngineeringEngineering (R0)

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