The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2024.0151240
PDF

A Hybrid Machine Learning Approach for Continuous Risk Management in Business Process Reengineering Projects

Author 1: RAFFAK Hicham
Author 2: LAKHOUILI Abdallah
Author 3: MANSOURI Moahmed

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 12, 2024.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: This study proposes a hybrid machine learning approach for continuous risk management in Business Process Reengineering (BPR) projects. This approach combines supervised and unsupervised learning techniques, integrating feature selection and preprocessing through Principal Component Analysis (PCA), clustering with K-means, and visualization with t-SNE. The labeled data are then used as input for predictive modeling with XGBoost, optimized using Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Grid Search algorithms.PCA reduces data dimensionality, simplifying analysis and improving model performance. K-means and t-SNE are employed for data clustering and visualization, enabling the identification of risk segments and uncovering hidden patterns. XGBoost, a powerful boosting algorithm, is utilized for predictive modeling due to its efficiency, accuracy, and ability to handle missing values. Optimization techniques further enhance XGBoost's performance by fine-tuning its hyperparameters. The approach was applied to a risk database from the automotive sector, demonstrating its practical applicability. Results show that PSO achieves the lowest mean squared error (MSE) and root mean squared error (RMSE), followed by GWO and Grid Search. Mahalanobis distance yields more accurate clustering results compared to Euclidean, Manhattan, and Cosine distances. This hybrid machine learning approach significantly enhances risk detection, evaluation, and mitigation in BPR projects, offering a robust framework for proactive decision-making.

Keywords: BPR; Risk management; PCA; K-means; XGBoost; PSO; GWO

RAFFAK Hicham, LAKHOUILI Abdallah and MANSOURI Moahmed, “A Hybrid Machine Learning Approach for Continuous Risk Management in Business Process Reengineering Projects” International Journal of Advanced Computer Science and Applications(IJACSA), 15(12), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151240

@article{Hicham2024,
title = {A Hybrid Machine Learning Approach for Continuous Risk Management in Business Process Reengineering Projects},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151240},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151240},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {12},
author = {RAFFAK Hicham and LAKHOUILI Abdallah and MANSOURI Moahmed}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org