Z
Zaharah Allah Bukhsh
Researcher at University of Twente
Publications - 28
Citations - 280
Zaharah Allah Bukhsh is an academic researcher from University of Twente. The author has contributed to research in topics: Computer science & Asset (computer security). The author has an hindex of 7, co-authored 20 publications receiving 145 citations. Previous affiliations of Zaharah Allah Bukhsh include Eindhoven University of Technology.
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Predictive maintenance using tree-based classification techniques: A case of railway switches
TL;DR: P predictive models based on the decision tree, random forest, and gradient boosted trees are developed and a detail explanation of models’ predictions by features importance analysis and instance level details is provided to facilitate in models interpretability.
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A systematic literature review on requirement prioritization techniques and their empirical evaluation
TL;DR: The results show that the newly proposed requirements prioritization methods tend to use as basis fuzzy logic and machine learning algorithms, which means that the Analytical Hierarchy Process is the most accurate and extensively used requirement prioritization method in industry.
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Network level bridges maintenance planning using Multi-Attribute Utility Theory
TL;DR: This study is presenting a proof-of-concept on how MAUT provides a systematic approach to improve the decision-making of maintenance planning by making use of available data, accommodating multiple performance goals, their uncertainty, and preferences of infrastructure managers.
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Maintenance intervention predictions using entity-embedding neural networks
TL;DR: It is shown that the historical and operational data, readily available at the agencies, is of vital importance and can be used effectively for the recommendations of maintenance advises for bridges by developing a machine learning system trained on the past asset management data.
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Damage detection using in-domain and cross-domain transfer learning
TL;DR: A combination of in-domain and cross-domain transfer learning strategies for damage detection in bridges and visual explanations of predictive models to enable algorithmic transparency and provide insights to experts about the intrinsic decision-logic of typically black-box deep models are provided.