References of "Omar, Yamila 50023645"
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See detailA Survey of Information Entropy Metrics for Complex Networks
Omar, Yamila UL; Plapper, Peter UL

in Entropy (2020)

Information entropy metrics have been applied to a wide range of problems that were abstracted as complex networks. This growing body of research is scattered in multiple disciplines, which makes it ... [more ▼]

Information entropy metrics have been applied to a wide range of problems that were abstracted as complex networks. This growing body of research is scattered in multiple disciplines, which makes it difficult to identify available metrics and understand the context in which they are applicable. In this work, a narrative literature review of information entropy metrics for complex networks is conducted following the PRISMA guidelines. Existing entropy metrics are classified according to three different criteria: whether the metric provides a property of the graph or a graph component (such as the nodes), the chosen probability distribution, and the types of complex networks to which the metrics are applicable. Consequently, this work identifies the areas in need for further development aiming to guide future research efforts. [less ▲]

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See detailMaximum flow of complex manufacturing networks
Omar, Yamila UL; Plapper, Peter UL

in Procedia CIRP (2019)

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See detailLessons from social network analysis to Industry 4.0
Omar, Yamila UL; Minoufekr, Meysam UL; Plapper, Peter UL

in Manufacturing Letters (2018), 15B

With the advent of Industry 4.0, a growing number of sensors within modern production lines generate high volumes of data. This data can be used to optimize the manufacturing industry in terms of complex ... [more ▼]

With the advent of Industry 4.0, a growing number of sensors within modern production lines generate high volumes of data. This data can be used to optimize the manufacturing industry in terms of complex network topology metrics commonly used in the analysis of social and communication networks. In this work, several such metrics are presented along with their appropriate interpretation in the field of manufacturing. Furthermore, the assumptions under which such metrics are defined are assessed in order to determine their suitability. Finally, their potential application to identify performance limiting resources, allocate maintenance resources and guarantee quality assurance are discussed. [less ▲]

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