Vol. 5 No. 1 (2025): Issue 5
Articles

Sustainable Digital Transformation through Gradient Boosting Machines

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  • Dimitris Papadopoulos,
  • Elena Stavrou,
  • Nikos Georgiou
Dimitris Papadopoulos
Department of Digital Innovation, Technological Educational Institute of Central Greece, Karpenisi, 36100, Greece
Elena Stavrou
Center for Sustainable Computing, University of Western Macedonia, Kozani, 50100, Greece
Nikos Georgiou
Institute of Data Science and Artificial Intelligence, University of the Peloponnese, Tripoli, 22100, Greece
DOI: https://doi.org/10.71070/jcbm.v5i1.94

Published 2025-01-28

Keywords

  • Digital Transformation,
  • Technological Advancements,
  • Machine Learning,
  • Gradient Boosting Machines,
  • Sustainable Innovation

How to Cite

Papadopoulos, D., Stavrou, E., & Georgiou, N. (2025). Sustainable Digital Transformation through Gradient Boosting Machines. Journal of Computational Biology and Medicine, 5(1). https://doi.org/10.71070/jcbm.v5i1.94

Abstract

Digital transformation has become a critical aspect for organizations to thrive in the era of rapid technological advancements. However, achieving sustainable digital transformation remains a challenge due to the complexity and dynamism of digital ecosystems. Current research efforts primarily focus on utilizing traditional machine learning techniques for digital transformation, but face limitations in capturing the non-linear and intricate relationships within digital data. This paper addresses this gap by proposing a novel approach utilizing Gradient Boosting Machines (GBM) to enhance the sustainability of digital transformation initiatives. The study demonstrates the effectiveness of GBM in optimizing digital processes, identifying patterns, and predicting future trends with high accuracy and efficiency. By incorporating GBM into the digital transformation framework, this research contributes to advancing the field by providing a more robust and adaptive solution for sustainable digital innovation.

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