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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Recolector de Cienci...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Recolector de Ciencia Abierta, RECOLECTA
Bachelor thesis . 2022
License: CC BY NC ND
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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Tècniques d’Intel·ligència artificial per calcular la robustesa de xarxes

Authors: Madrenys Masferrer, Martí;

Tècniques d’Intel·ligència artificial per calcular la robustesa de xarxes

Abstract

La robustesa d’una xarxa de transport (telecomunicacions, aigua, electricitat, etc) ens dona un indicatiu per saber quina resistència tenen front diversos tipus d’atac (atacs dirigits, aleatoris, en cascada, etc.). Per calcular aquesta mètrica es necessita d’uns càlculs computacionalment costosos. En aquest TFG s’estudiaran i aplicaran diverses tècniques d’aprenentatge supervisat amb l’objectiu d’obtenuir un model que pugui pronosticar el valor de la robustesa d’una xarxa

The robustness of a transport network (telecommunications, water, electricity, etc) gives us an indicator to know what resistance different types have of attack (targeted, random, cascading attacks, etc.). To calculate this metric computationally expensive calculations are needed. In this TFG, various supervised learning techniques will be studied and applied with the objective to obtain a model that can predict the value of the robustness of a network

Country
Spain
Related Organizations
Keywords

Artificial intelligence, Xarxes punt a punt (Xarxes d'ordinadors), Peer-to-peer architecture (Computer networks), Intel·ligència artificial

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
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