Extending Networking Curriculum with Applied Artificial Intelligence

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F19%3APU134165" target="_blank" >RIV/00216305:26230/19:PU134165 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9000455" target="_blank" >https://ieeexplore.ieee.org/document/9000455</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/EAEEIE46886.2019.9000455" target="_blank" >10.1109/EAEEIE46886.2019.9000455</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Extending Networking Curriculum with Applied Artificial Intelligence

Popis výsledku v původním jazyce

Artificial Intelligence (AI) and related technologies like data mining, machine learning or neural networks became very popular in recent years. Many IT companies today require graduated students to understand and be able to apply these technologies. Application potential of AI is not limited only to robotics, image processing or intelligent agents but also in engineering areas like computer networking and communication. However, on most universities, networking courses focus mainly on transmission protocols, network services and hardware design only while AI, machine learning or neural networks are taught separately. This causes a gap that emerges between AI theory and engineering approach. Thus, teachers of engineering courses are challenged how to introduce their students to an application of AI in the engineering areas, e.g., electronics, communication, embedded systems, power grids, etc. This paper shows how selected AI techniques presently used in computer networks can be incorporated into networking curriculum and demonstrated to students which extends student competencies and prepares them better into future jobs. We also present two case studies where AI techniques are applied on networking data in order to solve typical engineering problems.

Název v anglickém jazyce

Extending Networking Curriculum with Applied Artificial Intelligence

Popis výsledku anglicky

Artificial Intelligence (AI) and related technologies like data mining, machine learning or neural networks became very popular in recent years. Many IT companies today require graduated students to understand and be able to apply these technologies. Application potential of AI is not limited only to robotics, image processing or intelligent agents but also in engineering areas like computer networking and communication. However, on most universities, networking courses focus mainly on transmission protocols, network services and hardware design only while AI, machine learning or neural networks are taught separately. This causes a gap that emerges between AI theory and engineering approach. Thus, teachers of engineering courses are challenged how to introduce their students to an application of AI in the engineering areas, e.g., electronics, communication, embedded systems, power grids, etc. This paper shows how selected AI techniques presently used in computer networks can be incorporated into networking curriculum and demonstrated to students which extends student competencies and prepares them better into future jobs. We also present two case studies where AI techniques are applied on networking data in order to solve typical engineering problems.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20206 - Computer hardware and architecture

Projekt

<a href="/cs/project/TF03000029" target="_blank" >TF03000029: Monitorování a digitální forenzní analýza prostředí IoT (IRONSTONE)</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of EAEEIE 2019

ISBN

978-1-7281-3222-8

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

11-16

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

Ruse

Místo konání akce

Ruse

Datum konání akce

4. 9. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000719758500043