Proposal of new models for prediction of the cost of agricultural raw materials in a business intelligence and machine learning context

  • Authors

    • Kanga Koffi Ecole Supérieure Africaine des TIC (ESATIC)
    • Kamagate beman Hamidja Ecole supérieure Africaine des TIC (ESATIC)
    • Brou Aguié Pacôme Bertrand Ecole supérieure Africaine des TIC (ESATIC), Laboratory : LASTIC
    2024-03-06
    https://doi.org/10.14419/qrbc9m52
  • In this paper, we propose a data model for prediction of the cost of raw materials in a business intelligence context. Our contribution focuses initially on the implementation of a model with a star representation. This model highlights the fact (cost) to be predicted according to the axes linked to it. Secondly, from this basic model, our contribution is based on sub-models enabling us to carry out mono-dimensional anal-yses of the 'cost' fact. Thirdly, from these sub-models we establish associated mathematical models that allow us to deduce a global mathe-matical model from our basic model using linear regression and artificial neural network techniques. The implementation of these mono-dimensional sub-models in a machine learning database management system 'Minds DB', produces results that allow the prediction of raw material costs. Also, the predictions made by “Minds DB” are computationally validated by linear regression techniques which give better results than those of artificial neural networks.

    Author Biographies

    • Kamagate beman Hamidja, Ecole supérieure Africaine des TIC (ESATIC)

      Mr KAMAGATE is a computer science teacher at esatic (Republic of Ivory Coast).
      I am now submitting this paper to your journal for publication after reviewing the ideas presented

    • Brou Aguié Pacôme Bertrand , Ecole supérieure Africaine des TIC (ESATIC), Laboratory : LASTIC

      mR BROU PACOME is a computer science teacher at esatic (Republic of Ivory Coast).
      I am now submitting this paper to your journal for publication after reviewing the ideas presented .

      His laboratory is LASTIC

  • References

    1. Baumann, P., Misev, D., Merticariu, V., & Huu, B. P. (2021). Array databases: concepts, standards, implementations. Journal of Big Data, 8(1), 1-61 https://doi.org/10.1186/s40537-020-00399-2.
    2. Chee, T., Chan, L. K., Chuah, M. H., Tan, C. S., Wong, S. F., & Yeoh, W. (2009). Business intelligence systems: state-of-the-art review and contemporary applications. In Symposium on progress in information & communication technology (Vol. 2, No. 4, pp. 16-30).
    3. Majumdar, J., Naraseeyappa, S., & Ankalaki, S. (2017). Analysis of agriculture data using data mining techniques : application of big data. Journal of Big data, 4(1), 1-15. https://doi.org/10.1186/s40537-017-0077-4.
    4. Sajid, S., Haleem, A., Bahl, S., Javaid, M., Goyal, T., & Mittal, M. (2021). Data science applications for predictive maintenance and materials science in context to Industry 4.0. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2021.01.357.
    5. Tripathy, A. K., Adinarayana, J., Sudharsan, D., Merchant, S. N., Desai, U. B., Vijayalakshmi, K., ... & Tanaka, K. (2011, December). Data mining and wireless sensor network for agriculture pest/disease predictions. In 2011 World Congress on Information and Com-munication Technologies (pp. 1229-1234). IEEE. https://doi.org/10.1109/WICT.2011.6141424.
    6. Baptiste, J. L. (2009). Merise Guide pratique : Modélisation des données et des traitements, langage SQL. Editions ENI.
    7. He, B., & Yin, L. (2021). Prediction Modelling of cold chain logistics demand based on data mining algorithm. Mathematical Problems in Engineering, 2021. https://doi.org/10.1155/2021/3421478.
    8. Cui, Y. (2021). Intelligent recommendation system based on mathematical modeling in personalized data mining. Mathematical Prob-lems in Engineering, 2021. https://doi.org/10.1155/2021/6672036.
    9. Cortez, P., Cerdeira, A., Almeida, F., Matos, T., & Reis, J. (2009). Modeling wine preferences by data mining from physicochemical properties. Decision support systems, 47(4), 547-553. https://doi.org/10.1016/j.dss.2009.05.016.
    10. Delen, D., & Demirkan, H. (2013). Data, information and analytics as services. Decision Support Systems, 55(1), 359-363. https://doi.org/10.1016/j.dss.2012.05.044.
    11. Liao, S. H., Chu, P. H., & Hsiao, P. Y. (2012). Data mining techniques and applications–A decade review from 2000 to 2011. Expert systems with applications, 39(12), 11303-11311. https://doi.org/10.1016/j.eswa.2012.02.063.
    12. Saggi, M. K., & Jain, S. (2018). A survey towards an integration of big data analytics to big insights for value-creation. Information Processing & Management, 54(5), 758-790. https://doi.org/10.1016/j.ipm.2018.01.010.
    13. Goswami, S., Chakraborty, S., Ghosh, S., Chakrabarti, A., & Chakraborty, B. (2018). A review on application of data mining tech-niques to combat natural disasters. Ain Shams Engineering Journal, 9(3), 365-378. https://doi.org/10.1016/j.asej.2016.01.012.
    14. Nam, K., Kim, S. S., Park, C. S., Nam, T. Y., & Lee, T. Designing ML-based Approximate Query Processing Services on Time-Varying Large Dataset for Distributed Systems.
    15. https://mindsdb.com/integrations
    16. https://www.verteego.com/technologie accessed on 10/10/20212.
    17. https://ledatascientist.com/arima accessed on 10/10/2022
    18. Vagropoulos, S. I., Chouliaras, G. I., Kardakos, E. G., Simoglou, C. K., & Bakirtzis, A. G. (2016, avril). Comparaison des modèles SARIMAX, SARIMA, SARIMA modifié et ANN pour la prévision à court terme de la production photovoltaïque. En2016, Conférence internationale sur l’énergie de l’IEEE (ENERGYCON) (pp. 1-6). IEEE.
  • Downloads

  • How to Cite

    Koffi, K., beman Hamidja, K., & Aguié Pacôme Bertrand , B. (2024). Proposal of new models for prediction of the cost of agricultural raw materials in a business intelligence and machine learning context. International Journal of Engineering & Technology, 13(1), 102-111. https://doi.org/10.14419/qrbc9m52