JOURNAL ARTICLE

Development of machine learning based cash forecasting models for automated teller machines.

  • Published In: Cukurova University Journal of Natural & Applied Sciences (CUNAS), 2025, v. 4, n. 1. P. 35 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: Er, Uygar; Ulus, Ceren; Yusufoğlu, Nazlı; Akay, M. Fatih 3 of 3

Abstract

The article focuses on the development of machine learning-based cash forecasting models for automated teller machines (ATMs). Researchers from Innovance and Cukurova University's Department of Computer Engineering utilized dataset generation and model development techniques to create predictive models aimed at optimizing cash management in ATMs. The study details the methodology for building and deploying these models, discusses the results obtained, and concludes with insights on their effectiveness. This work contributes to improving ATM cash forecasting accuracy through advanced computational approaches.

Additional Information

  • Source:Cukurova University Journal of Natural & Applied Sciences (CUNAS). 2025/06, Vol. 4, Issue 1, p35
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2025
  • ISSN:28222938
  • DOI:10.70395/cunas.1680866
  • Accession Number:186565187
  • Copyright Statement:Copyright of Cukurova University Journal of Natural & Applied Sciences (CUNAS) is the property of Cukurova University Journal of Natural & Applied Sciences (CUNAS) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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