Skip to main content
Springer Nature Link
Log in

Data Analytics and Machine Learning for Smart Decision Making in Automotive Sector

  • Conference paper
  • First Online:
Enterprise Design, Operations, and Computing. EDOC 2022 Workshops (EDOC 2022)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 466))

  • 443 Accesses

Abstract

The objective of this thesis is to conduct scientific research on the use of data science and artificial intelligence techniques in the practices of automotive dealership companies to assist them in their decision-making processes and to use data-driven methods with modeling approaches for computing these enterprises. By proposing algorithms capable of continuously extracting relevant information from a diverse and multi-structured automotive environment. Due to the large amount of data available within these companies, we will develop algorithms to correctly assess the situation, suggest recommendations for decision-making, develop marketing strategies, and automate manual tasks that cost time, effort, and money.

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

Access this chapter

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

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Pai, P.F., Liu, C.H.: Predicting vehicle sales by sentiment analysis of Twitter data and stock market values. IEEE Access 6, 57655–57662 (2018)

    Article  Google Scholar 

  2. Wijnhoven, F., Plant, O.: Sentiment analysis and Google trends data for predicting car sales (2017)

    Google Scholar 

  3. Gao, J., Xie, Y., Gu, F., Xiao, W., Hu, J., Yu, W.: A hybrid optimization approach to forecast automobile sales of China. Adv. Mech. Eng. 9(8), 1687814017719422 (2017)

    Article  Google Scholar 

  4. Wang, F.K., Chang, K.K., Tzeng, C.W.: Using adaptive network-based fuzzy inference system to forecast automobile sales. Expert Syst. Appl. 38(8), 10587–10593 (2011)

    Article  Google Scholar 

  5. Qin, Y., Song, D., Chen, H., Cheng, W., Jiang, G., Cottrell, G.: A dual-stage attention-based recurrent neural network for time series prediction (2017). arXiv preprint arXiv:1704.02971

  6. Zhao, K., Wang, C.: Sales forecast in E-commerce using convolutional neural network (2017). arXiv preprint arXiv:1708.07946

  7. Elsworth, S., Güttel, S.: Time series forecasting using LSTM networks: A symbolic approach (2020). arXiv preprint arXiv:2003.05672

  8. Waqas, U., Akram, N., Kim, S., Lee, D., Jeon, J.: Vehicle damage classification and fraudulent image detection including moiré effect using deep learning. In: 2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1–5. IEEE (2020)

    Google Scholar 

  9. Kyu, P.M., Woraratpanya, K.: Car damage detection and classification. In: Proceedings of the 11th international conference on advances in information technology, pp. 1–6 (2020 July)

    Google Scholar 

  10. Bandi, H., Joshi, S., Bhagat, S., Deshpande, A.: Assessing car damage with convolutional neural networks. In: 2021 International Conference on Communication information and Computing Technology (ICCICT), pp. 1–5. IEEE (2021 June)

    Google Scholar 

  11. Singh, R., Ayyar, M.P., Pavan, T.V.S., Gosain, S., Shah, R.R.: Automating car insurance claims using deep learning techniques. In: 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM), pp. 199–207. IEEE (2019 September)

    Google Scholar 

  12. Hsu, M.W., Lessmann, S., Sung, M.C., Ma, T., Johnson, J.E.: Bridging the divide in financial market forecasting: machine learners vs. financial economists. Expert Syst. Appl. 61, 215–234 (2016)

    Article  Google Scholar 

  13. Wang, J., Wang, J., Fang, W., Niu, H.: Financial time series prediction using elman recurrent random neural networks. Computational intelligence and neuroscience (2016)

    Google Scholar 

  14. Sapankevych, N.I., Sankar, R.: Time series prediction using support vector machines: a survey. IEEE Comput. Intell. Mag. 4(2), 24–38 (2009)

    Article  Google Scholar 

  15. Koochakpour, K., Tarokh, M.J.: Sales budget forecasting and revision by adaptive network fuzzy base inference system and optimization methods. Journal of Computer & Robotics 9(1), 25–38 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LIB Laboratory, University of Burgundy, Dijon, France

    Hamid Ahaggach

Authors
  1. Hamid Ahaggach
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hamid Ahaggach .

Editor information

Editors and Affiliations

  1. University of Twente, Enschede, The Netherlands

    Tiago Prince Sales

  2. TU Wien, Vienna, Austria

    Henderik A. Proper

  3. University of Twente, Enschede, The Netherlands

    Giancarlo Guizzardi

  4. Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

    Marco Montali

  5. Free University of Bozen-Bolzano, Bolzano, Italy

    Fabrizio Maria Maggi

  6. University of Twente, Enschede, The Netherlands

    Claudenir M. Fonseca

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ahaggach, H. (2023). Data Analytics and Machine Learning for Smart Decision Making in Automotive Sector. In: Sales, T.P., Proper, H.A., Guizzardi, G., Montali, M., Maggi, F.M., Fonseca, C.M. (eds) Enterprise Design, Operations, and Computing. EDOC 2022 Workshops . EDOC 2022. Lecture Notes in Business Information Processing, vol 466. Springer, Cham. https://doi.org/10.1007/978-3-031-26886-1_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-26886-1_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26885-4

  • Online ISBN: 978-3-031-26886-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics