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State and Trends of Machine Learning Approaches in Business: An Empirical Review

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Artificial Intelligence and Applied Mathematics in Engineering Problems (ICAIAME 2019)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 43))

Abstract

Strong competition is imposing to enterprises an incessant need for extracting more business values from collected data. The business value of contemporary volatile data derives from the meanings mainly for market tendencies, and overall customer behaviors. With such continuous urge to mine valuable patterns from data, analytics have skipped to the top of research topics. One main solution for the analysis in such context is ‘Machine Learning’ (ML). However, Machine Learning approaches and heuristics are plenty, and most of them require outward knowledge and deep thoughtful of the context to learn the tools fittingly. Furthermore, application of prediction in business has certain considerations that strongly affects the effectiveness of ML techniques such as noisy, criticality, and inaccuracy of business data due to human involvement in an extensive number of business tasks. The objective of this paper is to inform about the trends and research trajectory of Machine Learning approaches in business field. Understanding the vantages and advantages of these methods can aid in selecting the suitable technique for a specific application in advance. The paper presents a comprehensively review of the most relevant academic publications in the topic carrying out a review methodology based on imbricated nomenclatures. The findings can orient and guide academics and industrials in their applications within business applications.

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Author information

Authors and Affiliations

  1. EM Strasbourg Business School, University of Strasbourg, HuManiS (EA 7308), Strasbourg, France

    Samia Chehbi-Gamoura & Ridha Derrouiche

  2. Department of Industrial Engineering, Süleyman Demirel University, Isparta, Turkey

    Halil-Ibrahim Koruca

  3. Department of Industrial Engineering, Antalya Bilim University, Antalya, Turkey

    Umran Kaya

Authors
  1. Samia Chehbi-Gamoura
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  2. Ridha Derrouiche
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  3. Halil-Ibrahim Koruca
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  4. Umran Kaya
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Corresponding author

Correspondence to Samia Chehbi-Gamoura .

Editor information

Editors and Affiliations

  1. Department of ECE, Karunya University, Coimbatore, Tamil Nadu, India

    D. Jude Hemanth

  2. Department of Computer Engineering, Faculty of Engineering, Suleyman Demirel University, Isparta, Isparta, Turkey

    Utku Kose

Appendix

Appendix

Table 9. Abbreviations table of journals (with editors) included in the literature review.
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Chehbi-Gamoura, S., Derrouiche, R., Koruca, HI., Kaya, U. (2020). State and Trends of Machine Learning Approaches in Business: An Empirical Review. In: Hemanth, D., Kose, U. (eds) Artificial Intelligence and Applied Mathematics in Engineering Problems. ICAIAME 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 43. Springer, Cham. https://doi.org/10.1007/978-3-030-36178-5_1

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