Using machine learning techniques to predict the cost of repairing hard failures in underground fiber optics networks

Nyarko‐Boateng, Owusu; Adekoya, Adebayo Felix; Weyori, Benjamin Asubam

doi:10.1186/s40537-020-00343-4

Cited by 9 publications

(5 citation statements)

References 25 publications

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“…The values obtained for the evaluation matrix MSE and MAE were 0.061291 and 0.080143, respectively, indicating the predictive model is good for the prediction of the exact distance of faults in underground fiber cable cut. Identifying and repairing the fault in the underground optical networks significantly reduce the cost of repairing the faults 41 and then overcome the challenges identified by Reference 42.…”

Section: Resultsmentioning

confidence: 99%

Predicting the actual location of faults in underground optical networks using linear regression

Nyarko‐Boateng

Adekoya

Weyori

2020

Engineering Reports

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Optical cables are enormous transmission media that carry high‐speed data across transatlantic, intercontinental, international boundaries, and cities. The optical cable is essential in data communication. The cable has become an indispensable component in optical communications infrastructure; hence, conscious efforts are always adopted to prevent or minimize faults in the optical network infrastructure. Typically, tracing fault in the underground optical network has been difficult even though the optical time‐domain reflectometer (OTDR) has been used to measure the distance of faults in the underground fiber cable. The methodologies deployed in the reviewed literature indicate a vast gap between the fault distance measured by the OTDR and the actual distance of fault. This paper observed the difficulties involved in tracing the actual spot of fault in the underground optical networks. The difficulty of tracing these underground faults mostly result in an undue delay and loss of revenue. This research presents a machine learning approach to predict the actual location of a fiber cable fault in an underground optical transmission link. Linear regression in the python sci‐kit learn library was used to predict the actual location of a fault in an underground optical network. The mean square error and MAE evaluation matrix used provided good accuracy results of 0.061291 and 0.080143, respectively. The result obtained in this paper indicates that faults in underground optical networks can be found quickly to avoid the delays in the fault tracing process, which leads to an excessive revenue loss.

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Section: Resultsmentioning

confidence: 99%

Predicting the actual location of faults in underground optical networks using linear regression

Nyarko‐Boateng

Adekoya

Weyori

2020

Engineering Reports

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“…Some of the studies have linked software defects with hardware failures in existing era and introduces a solution for this as reported in work of Boateng et al [34]. According to this study model, feedforward neural network and linear regression method has been used for investigating the cost associated with hardware failures for optic networks.…”

Section: Existing Studies Deploying Machine Learning Approachesmentioning

confidence: 99%

Insights of effectivity analysis of learning-based approaches towards software defect prediction

Rai,

Arcot Prashant

2024

IJECE

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Software defect prediction is one of the essential sets of operation towards mitigating issues of risk management in software development known to contribute towards enhancing the quality of software. There is evolution of various methodologies towards resolving this issue while learning-based methodology is witnessed to be the most dominant contributor. The problem identified is that there are yet many unsolved queries associated with practical viability of such learning-based approach adoption in software quality management. Proposed approaches discussed in this paper contributes towards mitigating this challenge by introducing a simplified, compact, and crisp analysis of effectiveness associated with learning-based schemes. The paper presents its major findings of effectivity analysis of machine learning, deep learning, hybrid, and other miscellaneous approaches deployed for fault prediction followed by highlighting research trend. The major findings infer that feature selection, data imbalance, interpretability, and in adequate involvement of context are prime gaps in existing methods. The paper also contributes towards research gap as well as essential learning outcomes of present review work.

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“…It goes beyond descriptive analytics, which focuses on understanding past events, to provide insights into what is likely to happen in the future. In satellite telecommunications, predictive analytics can be used to forecast equipment failures, optimize maintenance schedules, and improve overall infrastructure reliability (Nyarko-Boateng et al, 2020). Data collection is a crucial aspect of predictive analytics, as the quality and quantity of data can significantly impact the accuracy of predictions (Tomasevic et al, 2020).…”

Section: Key Concepts Of Predictive Analyticsmentioning

confidence: 99%

“…This conceptual review explores the strategies and technological advancements driving the adoption of predictive analytics in this sector. Predictive analytics involves the use of statistical algorithms and machine learning techniques to analyze historical data and predict future outcomes (Namoun and Alshanqiti, 2020). In the context of satellite telecommunications, predictive analytics enables companies to anticipate potential issues and proactively address them before they escalate into costly downtime or service disruptions (Alqahtani and Kumar, 2024).…”

Section: Introductionmentioning

confidence: 99%

Predictive Analytics in the Maintenance and Reliability of Satellite Telecommunications Infrastructure: A Conceptual Review of Strategies and Technological Advancements

Nneka Adaobi Ochuba,

Favour Oluwadamilare Usman,

Enyinaya Stefano Okafor

et al. 2024

Eng. sci. technol. j.

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Predictive analytics is transforming the maintenance and reliability of satellite telecommunications infrastructure, offering proactive solutions to prevent downtime and enhance operational efficiency. This conceptual review explores key strategies and technological advancements driving the adoption of predictive analytics in this field. The integration of IoT devices and sensors enables real-time monitoring, providing valuable data on equipment performance and environmental conditions. Advanced algorithms, such as AI and ML, analyze this data to predict equipment failures and optimize maintenance schedules. These technologies improve the accuracy of predictive models, allowing companies to reduce downtime and improve overall infrastructure reliability. Challenges include data privacy and security concerns, as well as the integration of predictive analytics into existing maintenance processes. Companies must invest in specialized skills and expertise to implement predictive analytics successfully. Looking ahead, emerging technologies like real-time analytics and AI will continue to shape the future of predictive analytics in satellite telecommunications. Standardized practices, collaboration with industry partners, and a focus on data quality are essential for companies to harness the full potential of predictive analytics. In conclusion, predictive analytics is a game-changer for the maintenance and reliability of satellite telecommunications infrastructure. By adopting predictive analytics, companies can optimize maintenance processes, reduce downtime, and improve overall infrastructure reliability. Keywords: Predictive Analytics, Infrastructure, Telecommunications, Satellite, Reliability.

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Using machine learning techniques to predict the cost of repairing hard failures in underground fiber optics networks

Cited by 9 publications

References 25 publications

Predicting the actual location of faults in underground optical networks using linear regression

Predicting the actual location of faults in underground optical networks using linear regression

Insights of effectivity analysis of learning-based approaches towards software defect prediction

Predictive Analytics in the Maintenance and Reliability of Satellite Telecommunications Infrastructure: A Conceptual Review of Strategies and Technological Advancements

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