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    Prioritizing barriers for the adoption of industry 4.0 technologies
    ( 2022) Pedro Senna ; Miguel D. F. Ferreira,L ; Barros,AC ; Bonnín Roca,J ; Magalhães,V ; 7330
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    Predicting throughput in IEEE 802.11 based wireless networks using directional antenna
    ( 2019) Ricardo Morla ; Manuel Ricardo ; Patrícia Ramos ; Saravanan Kandasamy ; 4957 ; 651 ; 5154 ; 3645
    In IEEE 802.11 based wireless networks interference increases as more access points are added. A metric helping to quantize this interference seems to be of high interest. In this paper we study the relationship between the (Formula presented.) metric, which captures interference, and throughput for IEEE 802.11 based network using directional antenna. The (Formula presented.) model was found to best represent the relationship between the interference metric and the network throughput. We use this model to predict the performance of similar networks and decide the best configuration a network operator could use for planning his network. © 2017 Springer Science+Business Media, LLC
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    Integrated planning of inbound and outbound logistics with a Rich Vehicle Routing Problem with Backhauls
    ( 2019) Ricardo Ferreira Soares ; Marques,A ; Santos,MJ ; Amorim,P ; 6917
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    Discovering a taste for the unusual: exceptional models for preference mining
    ( 2018) de Sa,CR ; Knobbe,A ; Carlos Manuel Soares ; Alípio Jorge ; Paulo Jorge Azevedo ; Duivesteijn,W ; 4981 ; 5606 ; 5001
    Exceptional preferences mining (EPM) is a crossover between two subfields of data mining: local pattern mining and preference learning. EPM can be seen as a local pattern mining task that finds subsets of observations where some preference relations between labels significantly deviate from the norm. It is a variant of subgroup discovery, with rankings of labels as the target concept. We employ several quality measures that highlight subgroups featuring exceptional preferences, where the focus of what constitutes ‘exceptional’ varies with the quality measure: two measures look for exceptional overall ranking behavior, one measure indicates whether a particular label stands out from the rest, and a fourth measure highlights subgroups with unusual pairwise label ranking behavior. We explore a few datasets and compare with existing techniques. The results confirm that the new task EPM can deliver interesting knowledge. © 2018 The Author(s)
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