ML-DEECo: a Machine-Learning-Enabled Framework for Self-organizing Components
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10453434" target="_blank" >RIV/00216208:11320/22:10453434 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/ACSOSC56246.2022.00033" target="_blank" >https://doi.org/10.1109/ACSOSC56246.2022.00033</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACSOSC56246.2022.00033" target="_blank" >10.1109/ACSOSC56246.2022.00033</a>
Alternative languages
Result language
angličtina
Original language name
ML-DEECo: a Machine-Learning-Enabled Framework for Self-organizing Components
Original language description
Machine learning has already proven itself in many scientific domains, most notably by improving classifications or value-predictions (regression). However, its wide deployment in the field of adaptive systems has yet to come. There are several reasons for this cautious adoption, such as the lack of sufficient data for training the models or reluctance to incorporate black-box models into the adaptation processes of existing systems. To gap these limitations, we are often required to perform detailed simulations to generate training data or verify the feasibility of adopting ML models in our systems. We present the ML-DEECo framework that should simplify the design of ML-enabled simulations, which removes repetitive code such as gathering specific data from the simulation, using these data to train models, and applying these models in the simulations as predictors. We also provide two case studies as an example and an evaluation of the usability of the proposed framework.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GC20-24814J" target="_blank" >GC20-24814J: FluidTrust – Enabling trust by fluid access control to data and physical resources in Industry 4.0 systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2022 IEEE INTERNATIONAL CONFERENCE ON AUTONOMIC COMPUTING AND SELF-ORGANIZING SYSTEMS COMPANION (ACSOS-C 2022)
ISBN
978-1-66545-142-0
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
66-69
Publisher name
IEEE COMPUTER SOC
Place of publication
LOS ALAMITOS
Event location
virtual
Event date
Sep 19, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000886623600016