Skip to main content
Springer Nature Link
Log in

Addressing Missing Data and Data Competitiveness Issues: Transforming Tacit Knowledge into Explicit Form by Fuzzy Inference Learning System

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

Although we are living in the era of big data, in many real-world applications, being able to access the right set and quantity of data is still a challenging task. One solution to address this drawback is to transform the existing information and knowledge from its tacit form back to data which can be used to simulate and regenerate the required knowledge in different scenarios for further analysis in explicit form. In this paper, we present our developed fuzzy inference-based learning system to achieve this objective. Our proposed framework is based on both conventional fuzzy-based modelling and the adaptive network-based fuzzy inference system (ANFIS) that first transforms the existing tacit information and knowledge into a fuzzy form which is then fed into ANFIS to develop a trained model that regenerates them for analysis purposes. We validate our proposed model and demonstrate its accuracy to estimate the fuel efficiency of heavy duty trucks using real-world data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. This report can be accessed through the following link: http://www-cta.ornl.gov/cta/Publications/Reports/ORNL_TM_2011_471.pdf.

  2. This report can be accessed through the following link:http://www-cta.ornl.gov/cta/Publications/Reports/ORNL_TM_2008-122.pdf.

References

  1. Witten, I.H., Frank, E., Hall, M.A.: Data Mining Practical Machine Learning Tools and Techniques, 3rd edn. Elsevier, Amsterdam (2011)

    Google Scholar 

  2. Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. The Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann, Burlington (2012)

  3. Yin, Y., Jiang, D.: Research and application on intelligent parking solution based on internet of things. In: Fifth International Conference on Intelligent Human-Machine Systems and Cybernetics (20130

  4. Davenport, T.H., Redman, T.C.: Build data quality into the internet of things. Wall Str. J. (2015)

  5. GS1 UK, IBM, C. S. of M. (KTP project) The Institute of Grocery Distribution, and Value Chain Vision, “Data crunch report: The impact of bad data on profits and customer service in the UK grocery industry,” 2009

  6. Jamshidi, A., Yazdani-chamzini, A., Haji, S.: Developing a new fuzzy inference system for pipeline risk assessment. J. Loss Prev. Process Ind. 26(1), 197–208 (2013)

    Article  Google Scholar 

  7. Zakeri, A., Saberi, M., Hussain, O.K., Chang, E.: A heuristic machine learning based approach for uti-lizing scarce data in estimating fuel consumption of heavy duty trucks. In: The 9-th International Conference on Intelligent Networking and Collaborative Systems (INCoS-2017) (2017)

  8. Meadow, C.T.: Text Information Retrieval Systems. Academic Press, San Diego (1992)

    Google Scholar 

  9. Babuska, R., Sousa, J., Verbruggen, H.B.: Fuzzy Prédictive Control Based on Human Reasoning, pp. 1–6. Delft University of Technology, Delft (1995)

    Google Scholar 

  10. Banks, W.: Linguistic Variables: Clear Thinking with Fuzzy Logic, Byte Craft Limited, IEEE Toronto Section, Toronto (2008)

  11. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning-I. Inf. Sci. (Ny) 8, 199–249 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  12. Zhou, M., Jin, H., Wang, W.: A review of vehicle fuel consumption models to evaluate eco-driving and eco-routing. Transp. Res. Part D 49(5), 203–218 (2016)

    Article  Google Scholar 

  13. Ribeiro, V., Rodrigues, J., Aguiar, A.: Mining geographic data for fuel consumption estimation. In: 16th International IEEE Conference on Intelligent Transportation Systems—(ITSC) (2013)

  14. Pelkmans, L., Debal, P., Hood, T., Hauser, G.: Development of a simulation tool to calculate fuel consumption and emissions of vehicles operating in dynamic conditions. SAE Technical Paper, vol. 2004-01–18 (2004)

  15. Franzese, O.: Effect of Weight and Roadway Grade on the Fuel Economy of Class-8 Freight Trucks, no. October 2011. http://cta.ornl.gov/cta/Publications/Reports/ORNL_TM_2011_471.pdf (2011)

  16. Capps, G., Franzese, O., Knee, B., Lascurain, M.B., Otaduy, P.: Class-8 Heavy Truck Duty Cycle Project Final Report. http://www-cta.ornl.gov/cta/Publications/Reports/ORNL_TM_2008-122.pdf (2008)

Download references

Author information

Authors and Affiliations

  1. University of New South Wales, Canberra, Australia

    Atefe Zakeri, Morteza Saberi, Omar Khadeer Hussain & Elizabeth Chang

Authors
  1. Atefe Zakeri
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Morteza Saberi
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Omar Khadeer Hussain
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Elizabeth Chang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Atefe Zakeri.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zakeri, A., Saberi, M., Hussain, O.K. et al. Addressing Missing Data and Data Competitiveness Issues: Transforming Tacit Knowledge into Explicit Form by Fuzzy Inference Learning System. Int. J. Fuzzy Syst. 20, 1224–1239 (2018). https://doi.org/10.1007/s40815-017-0419-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-017-0419-6

Keywords