A fuzzy-optimized multi-level random forest (FOMRF) model for the classification of the impact of technostress

Authors

  • Gabriel James Department of Computing, Topfaith University, Mkpatak, Nigeria
  • Ifeoma Ohaeri Department of Computing, Topfaith University, Mkpatak, Nigeria
  • David Egete Department of Computer Science, University of Calabar, Calabar, Nigeria
  • John Odey Department of Computer Science, University of Calabar, Calabar, Nigeria
  • Samuel Oyong Department of Computing, Topfaith University, Mkpatak, Nigeria
  • Enefiok Etuk Department of Computer Science, Michael Okpara University of Agriculture, Umudike, Nigeria
  • Imeh Umoren Department of Cyber Security, Federal University of Technology, Ikot Abasi, Nigeria
  • Ubong Etuk Department of Computer Science, Akwa Ibom State University, Ikot Akpaden, Nigeria
  • Aloysius Akpanobong Dept. of Computer Networks, Faculty of Science and Information Technology, UPM, Serdang, Malaysia
  • Anietie Ekong Department of Computer Science, Akwa Ibom State University, Ikot Akpaden, Nigeria
  • Saviour Inyang Department of Computing, Topfaith University, Mkpatak, Nigeria
  • Chikodili Orazulume Department of Electrical/Electronics Engineering, Topfaith University, Mkpatak, Nigeria

Keywords:

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Abstract

Technostress refers to the stress caused by excessive technology use, especially in professional and educational environments. It increasingly affects corporate productivity, well-being, and effectiveness in digital settings. Traditional machine learning models often struggle with the complexity and non-linearity of technostress classification. To address this, this study proposes a Fuzzy-Optimized Multi-Level Random Forest (FOMRF) model that integrates fuzzy logic with machine learning to enhance classification accuracy and interpretability. Data was collected from academic and corporate settings through a structured process. Preprocessing techniques—such as feature extraction, selection, and normalization—were applied to structure and refine the dataset. The FOMRF model uses linguistic variables and expert-defined fuzzy rules to optimize decision boundaries, improving precision and adaptability. The methodology consists of three key stages: preprocessing, fuzzy optimization, and prediction. Trapezoidal membership functions were used to define fuzzy sets for the Random Forest parameters (ntree and mtry), and iterative training ensured robust model evaluation. The model consistently achieved high accuracy (around 99.2%) across all parameter combinations. Benchmarking showed that FOMRF outperformed existing methods in predictive performance, flexibility, and accuracy. These findings emphasize the potential of fuzzy-enhanced machine learning models to effectively detect and mitigate technostress, thereby improving the quality of digital work environments.

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Published

2025-05-28

How to Cite

A fuzzy-optimized multi-level random forest (FOMRF) model for the classification of the impact of technostress. (2025). Journal of the Nigerian Society of Physical Sciences, 7(3), 2699. https://doi.org/10.46481/jnsps.2025.2699

Issue

Volume 7, Issue 3, August 2025

Section

Computer Science

Copyright (c) 2025 Gabriel James, Ifeoma Ohaeri, David Egete, John Odey, Samuel Oyong, Enefiok Etuk, Imeh Umoren, Ubong Etuk, Aloysius Akpanobong, Anietie Ekong, Saviour Inyang, Chikodili Orazulume

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

A fuzzy-optimized multi-level random forest (FOMRF) model for the classification of the impact of technostress. (2025). Journal of the Nigerian Society of Physical Sciences, 7(3), 2699. https://doi.org/10.46481/jnsps.2025.2699

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