A Project Management Framework for Implementing AI-Driven Fault Mitigation Systems

Isaac Buyondo; Konstantinos Kiousis

doi:doi:10.11648/j.mlr.20251002.15

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A Project Management Framework for Implementing AI-Driven Fault Mitigation Systems

Isaac Buyondo^*

, Konstantinos Kiousis

Published in Machine Learning Research (Volume 10, Issue 2)

Received: 22 September 2025 Accepted: 9 October 2025 Published: 12 November 2025

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Abstract

The research seeks to establish a Project Management Framework for the Implementation of AI-Driven Fault Mitigation Systems by utilizing quantitative methodologies such as machine learning, in conjunction with insights from telecommunications engineers. This study integrates empirical data with practical expertise. The research was conducted using data obtained from Savanna Fibre Limited, resulting in a dataset comprising fault logs. Hybrid AI models, specifically a combination of CNN-LSTM with certain physics-based modifications, were trained and tested to detect faults at an early stage, identify the nature of the problems, locate the source of the issues, and assess the potential severity of the faults. Data preprocessing pipelines were developed to tackle challenges including imbalanced classes and sparse records of submarine cable faults, while domain knowledge also played a crucial role in guiding feature engineering and model interpretation. The framework demonstrates impressive performance: it achieves a 94.3% F1-score in fault classification, forecasts issues up to 72 hours ahead with a 92% confidence level, and accurately identifies fault locations within ±25 meters. To enhance its practicality, a versatile deployment configuration integrates model outputs into real-world workflows through CI/CD pipelines and even utilizes AR tools to assist in field repairs, resulting in a 42% reduction in repair times during actual tests. This research indicates that AI-driven, proactive maintenance is not merely theoretical; it is achievable with the appropriate data, interdisciplinary collaboration, and practical testing. Looking forward, there is significant potential to expand this approach for 5G and IoT networks or to refine our management of uncertainty in critical systems.

Published in	Machine Learning Research (Volume 10, Issue 2)
DOI	10.11648/j.mlr.20251002.15
Page(s)	137-150
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Mitigation, Fibre, Optical, Transmitters, Networks, Telecommunication, Techniques

References

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Buyondo, I., Kiousis, K. (2025). A Project Management Framework for Implementing AI-Driven Fault Mitigation Systems. Machine Learning Research, 10(2), 137-150. https://doi.org/10.11648/j.mlr.20251002.15

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ACS Style

Buyondo, I.; Kiousis, K. A Project Management Framework for Implementing AI-Driven Fault Mitigation Systems. Mach. Learn. Res. 2025, 10(2), 137-150. doi: 10.11648/j.mlr.20251002.15

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AMA Style

Buyondo I, Kiousis K. A Project Management Framework for Implementing AI-Driven Fault Mitigation Systems. Mach Learn Res. 2025;10(2):137-150. doi: 10.11648/j.mlr.20251002.15

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@article{10.11648/j.mlr.20251002.15,
  author = {Isaac Buyondo and Konstantinos Kiousis},
  title = {A Project Management Framework for Implementing AI-Driven Fault Mitigation Systems
},
  journal = {Machine Learning Research},
  volume = {10},
  number = {2},
  pages = {137-150},
  doi = {10.11648/j.mlr.20251002.15},
  url = {https://doi.org/10.11648/j.mlr.20251002.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20251002.15},
  abstract = {The research seeks to establish a Project Management Framework for the Implementation of AI-Driven Fault Mitigation Systems by utilizing quantitative methodologies such as machine learning, in conjunction with insights from telecommunications engineers. This study integrates empirical data with practical expertise. The research was conducted using data obtained from Savanna Fibre Limited, resulting in a dataset comprising fault logs. Hybrid AI models, specifically a combination of CNN-LSTM with certain physics-based modifications, were trained and tested to detect faults at an early stage, identify the nature of the problems, locate the source of the issues, and assess the potential severity of the faults. Data preprocessing pipelines were developed to tackle challenges including imbalanced classes and sparse records of submarine cable faults, while domain knowledge also played a crucial role in guiding feature engineering and model interpretation. The framework demonstrates impressive performance: it achieves a 94.3% F1-score in fault classification, forecasts issues up to 72 hours ahead with a 92% confidence level, and accurately identifies fault locations within ±25 meters. To enhance its practicality, a versatile deployment configuration integrates model outputs into real-world workflows through CI/CD pipelines and even utilizes AR tools to assist in field repairs, resulting in a 42% reduction in repair times during actual tests. This research indicates that AI-driven, proactive maintenance is not merely theoretical; it is achievable with the appropriate data, interdisciplinary collaboration, and practical testing. Looking forward, there is significant potential to expand this approach for 5G and IoT networks or to refine our management of uncertainty in critical systems.
},
 year = {2025}
}

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TY  - JOUR
T1  - A Project Management Framework for Implementing AI-Driven Fault Mitigation Systems

AU  - Isaac Buyondo
AU  - Konstantinos Kiousis
Y1  - 2025/11/12
PY  - 2025
N1  - https://doi.org/10.11648/j.mlr.20251002.15
DO  - 10.11648/j.mlr.20251002.15
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 137
EP  - 150
PB  - Science Publishing Group
SN  - 2637-5680
UR  - https://doi.org/10.11648/j.mlr.20251002.15
AB  - The research seeks to establish a Project Management Framework for the Implementation of AI-Driven Fault Mitigation Systems by utilizing quantitative methodologies such as machine learning, in conjunction with insights from telecommunications engineers. This study integrates empirical data with practical expertise. The research was conducted using data obtained from Savanna Fibre Limited, resulting in a dataset comprising fault logs. Hybrid AI models, specifically a combination of CNN-LSTM with certain physics-based modifications, were trained and tested to detect faults at an early stage, identify the nature of the problems, locate the source of the issues, and assess the potential severity of the faults. Data preprocessing pipelines were developed to tackle challenges including imbalanced classes and sparse records of submarine cable faults, while domain knowledge also played a crucial role in guiding feature engineering and model interpretation. The framework demonstrates impressive performance: it achieves a 94.3% F1-score in fault classification, forecasts issues up to 72 hours ahead with a 92% confidence level, and accurately identifies fault locations within ±25 meters. To enhance its practicality, a versatile deployment configuration integrates model outputs into real-world workflows through CI/CD pipelines and even utilizes AR tools to assist in field repairs, resulting in a 42% reduction in repair times during actual tests. This research indicates that AI-driven, proactive maintenance is not merely theoretical; it is achievable with the appropriate data, interdisciplinary collaboration, and practical testing. Looking forward, there is significant potential to expand this approach for 5G and IoT networks or to refine our management of uncertainty in critical systems.

VL  - 10
IS  - 2
ER  -

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Author Information

Isaac Buyondo

Faculty of Computer Science, Berlin School of Business & Innovation, Barlin, Germany

Contact Email

http://orcid.org/0009-0008-3209-3455
Konstantinos Kiousis

Faculty of Computer Science, Berlin School of Business & Innovation, Barlin, Germany

Contact Email

http://orcid.org/0000-0003-3007-0950

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APA Style

Buyondo, I., Kiousis, K. (2025). A Project Management Framework for Implementing AI-Driven Fault Mitigation Systems. Machine Learning Research, 10(2), 137-150. https://doi.org/10.11648/j.mlr.20251002.15

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ACS Style

Buyondo, I.; Kiousis, K. A Project Management Framework for Implementing AI-Driven Fault Mitigation Systems. Mach. Learn. Res. 2025, 10(2), 137-150. doi: 10.11648/j.mlr.20251002.15

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AMA Style

Buyondo I, Kiousis K. A Project Management Framework for Implementing AI-Driven Fault Mitigation Systems. Mach Learn Res. 2025;10(2):137-150. doi: 10.11648/j.mlr.20251002.15

Copy | Download

@article{10.11648/j.mlr.20251002.15,
  author = {Isaac Buyondo and Konstantinos Kiousis},
  title = {A Project Management Framework for Implementing AI-Driven Fault Mitigation Systems
},
  journal = {Machine Learning Research},
  volume = {10},
  number = {2},
  pages = {137-150},
  doi = {10.11648/j.mlr.20251002.15},
  url = {https://doi.org/10.11648/j.mlr.20251002.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20251002.15},
  abstract = {The research seeks to establish a Project Management Framework for the Implementation of AI-Driven Fault Mitigation Systems by utilizing quantitative methodologies such as machine learning, in conjunction with insights from telecommunications engineers. This study integrates empirical data with practical expertise. The research was conducted using data obtained from Savanna Fibre Limited, resulting in a dataset comprising fault logs. Hybrid AI models, specifically a combination of CNN-LSTM with certain physics-based modifications, were trained and tested to detect faults at an early stage, identify the nature of the problems, locate the source of the issues, and assess the potential severity of the faults. Data preprocessing pipelines were developed to tackle challenges including imbalanced classes and sparse records of submarine cable faults, while domain knowledge also played a crucial role in guiding feature engineering and model interpretation. The framework demonstrates impressive performance: it achieves a 94.3% F1-score in fault classification, forecasts issues up to 72 hours ahead with a 92% confidence level, and accurately identifies fault locations within ±25 meters. To enhance its practicality, a versatile deployment configuration integrates model outputs into real-world workflows through CI/CD pipelines and even utilizes AR tools to assist in field repairs, resulting in a 42% reduction in repair times during actual tests. This research indicates that AI-driven, proactive maintenance is not merely theoretical; it is achievable with the appropriate data, interdisciplinary collaboration, and practical testing. Looking forward, there is significant potential to expand this approach for 5G and IoT networks or to refine our management of uncertainty in critical systems.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - A Project Management Framework for Implementing AI-Driven Fault Mitigation Systems

AU  - Isaac Buyondo
AU  - Konstantinos Kiousis
Y1  - 2025/11/12
PY  - 2025
N1  - https://doi.org/10.11648/j.mlr.20251002.15
DO  - 10.11648/j.mlr.20251002.15
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 137
EP  - 150
PB  - Science Publishing Group
SN  - 2637-5680
UR  - https://doi.org/10.11648/j.mlr.20251002.15
AB  - The research seeks to establish a Project Management Framework for the Implementation of AI-Driven Fault Mitigation Systems by utilizing quantitative methodologies such as machine learning, in conjunction with insights from telecommunications engineers. This study integrates empirical data with practical expertise. The research was conducted using data obtained from Savanna Fibre Limited, resulting in a dataset comprising fault logs. Hybrid AI models, specifically a combination of CNN-LSTM with certain physics-based modifications, were trained and tested to detect faults at an early stage, identify the nature of the problems, locate the source of the issues, and assess the potential severity of the faults. Data preprocessing pipelines were developed to tackle challenges including imbalanced classes and sparse records of submarine cable faults, while domain knowledge also played a crucial role in guiding feature engineering and model interpretation. The framework demonstrates impressive performance: it achieves a 94.3% F1-score in fault classification, forecasts issues up to 72 hours ahead with a 92% confidence level, and accurately identifies fault locations within ±25 meters. To enhance its practicality, a versatile deployment configuration integrates model outputs into real-world workflows through CI/CD pipelines and even utilizes AR tools to assist in field repairs, resulting in a 42% reduction in repair times during actual tests. This research indicates that AI-driven, proactive maintenance is not merely theoretical; it is achievable with the appropriate data, interdisciplinary collaboration, and practical testing. Looking forward, there is significant potential to expand this approach for 5G and IoT networks or to refine our management of uncertainty in critical systems.

VL  - 10
IS  - 2
ER  -

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