Ghent University Academic Bibliography

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Sparse random neural networks for online anomaly detection on sensor nodes

Sam Leroux (UGent) and Pieter Simoens (UGent)

(2023) FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE. 144. p.327-343
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FLAGS : a methodology for adaptive anomaly detection and root cause analysis on sensor data streams by fusing expert knowledge with machine learning

Bram Steenwinckel (UGent) , Dieter De Paepe, Sander Vanden Hautte, Pieter Heyvaert (UGent) , Mohamed Bentefrit, Pieter Moens (UGent) , Anastasia Dimou (UGent) , Bruno Van Den Bossche (UGent) , Filip De Turck (UGent) , Sofie Van Hoecke (UGent) , et al.

(2021) FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE. 116. p.30-48
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Implementation-independent function reuse

Ben De Meester (UGent) , Tom Seymoens, Anastasia Dimou (UGent) and Ruben Verborgh (UGent)

(2020) FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE. 110. p.946-959
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A tale of three systems : case studies on the application of architectural tactics for cyber-foraging

Grace Lewis, Patricia Lago, Sebastian Echeverria and Pieter Simoens (UGent)

(2019) FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE. 96. p.119-147
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Multi-fidelity deep neural networks for adaptive inference in the internet of multimedia things

Sam Leroux (UGent) , Steven Bohez (UGent) , Elias De Coninck, Pieter Van Molle (UGent) , Bert Vankeirsbilck (UGent) , Tim Verbelen (UGent) , Pieter Simoens (UGent) and Bart Dhoedt (UGent)

(2019) FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE. 97. p.355-360

Academic Bibliography

Sparse random neural networks for online anomaly detection on sensor nodes

FLAGS : a methodology for adaptive anomaly detection and root cause analysis on sensor data streams by fusing expert knowledge with machine learning

Implementation-independent function reuse

A tale of three systems : case studies on the application of architectural tactics for cyber-foraging

Multi-fidelity deep neural networks for adaptive inference in the internet of multimedia things

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