Artificial Intelligence for the European Open Science Cloud

Informazioni relative al progetto

AI4EOSC

ID dell’accordo di sovvenzione: 101058593

DOI

10.3030/101058593

Progetto chiuso

Data della firma CE 11 Maggio 2022

Data di avvio 1 Settembre 2022

Data di completamento 31 Agosto 2025

Finanziato da

• Research infrastructures

Costo totale

€ 4 997 125,00

Contributo UE

€ 4 997 125,00

Coordinato da

AGENCIA ESTATAL CONSEJO SUPERIOR DE INVESTIGACIONES CIENTIFICAS
Spain

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Documents, reports (9)

Communication, training, outreach, dissemination and collaboration/liaison and exploitation plans

This document will contain the plans for all dissemination communication collaboration and collaborationliaison activities exploitation including the basic metrics to measure the viability of the presented plan

WP4 Implementation plan

This document describes the implementation plan for the first implementation and integration phase

Initial plan for Quality Assurance and data FAIRness

Definition and plan of the Software Services and Applications Quality Assurance and the automatic assessment of data FAIRness Plan of infrastructure for the services Software and application development

Data Management Plan

Will define the project DMP as expected in the GA

Updated Communication, training, outreach, dissemination and collaboration/ liaison and exploitation plans and results

This document will contain an update and review of D21

Implementation plan

This document describes the implementation plan for the first implementation and integration phase

State of the art landscaping and initial platform requirements specification

This document provides an overview of the most relevant technologies available together with the key requirements of the AI platform and the codesign approach employed

Initial high-level architecture specification

This document provides the first architecture specification of the AI platform

Analysis of user applications, collection of requirements

This deliverable can be combined with D31 State of the art landscaping and initial platform requirements and provides a prioritised list of user requirements and the implementation roadmap

Pubblicazioni

Peer reviewed articles (14)

Detecting district heating leaks in thermal imagery: Comparison of anomaly detection methods

Autori: Vollmer, Elena; Ruck, Julian; Volk, Rebekka; Schultmann, Frank
Pubblicato in: Automation in Construction, 2024, ISSN 1872-7891
Editore: Elsevier
DOI: 10.5445/ir/1000175054

Enhancing radiomics and Deep Learning systems through the standardization of medical imaging workflows

Autori: Miriam Cobo; Pablo Menéndez Fernández-Miranda; Gorka Bastarrika; Lara Lloret Iglesias
Pubblicato in: Scientific Data, 2023, ISSN 2052-4463
Editore: Springer Nature
DOI: 10.1038/S41597-023-02641-X

Frouros: A Python library for drift detection in machine learning systems

Autori: Céspedes-Sisniega, Jaime; López-García, Álvaro
Pubblicato in: SoftwareX, 2022, ISSN 2352-7110
Editore: Elsevier
DOI: 10.1016/J.SOFTX.2024.101733

Secure Multi-Party Computation for Magnetic Resonance Imaging Classification

Autori: Giang Nguyen, Oleksandr Lytvyn
Pubblicato in: Procedia Computer Science, 2023, ISSN 1877-0509
Editore: Elsevier B.V.
DOI: 10.1016/j.procs.2023.03.006

Infrastructure Manager: A TOSCA-Based Orchestrator for the Computing Continuum

Autori: Caballer, Miguel; Moltó, Germán; Calatrava, Amanda; Blanquer, Ignacio
Pubblicato in: Journal of Grid Computing, 2023, ISSN 1572-9184
Editore: Springer Nature B.V.
DOI: 10.1007/S10723-023-09686-7

IEEE Access

Autori: Giang, Nguyen; Lytvyn, Oleksandr
Pubblicato in: IEEE Access, Numero 12, 2024, ISSN 2169-3536
Editore: Institute of Electrical and Electronics Engineers Inc.
DOI: 10.1109/ACCESS.2024.3486810

Future Generation Computer Systems

Autori: Sebastián, Risco; Caterina, Alarcón; Sergio, Langarita; Caballer, Miguel; Moltó, Germán
Pubblicato in: Future Generation Computer Systems, Numero 153, 2023, ISSN 0167-739X
Editore: Elsevier
DOI: 10.1016/J.FUTURE.2023.12.015

A Python library to check the level of anonymity of a dataset

Autori: Judith Sáinz-Pardo Díaz, Álvaro López García
Pubblicato in: Scientific Data, 2022, ISSN 2052-4463
Editore: Springer Nature
DOI: 10.1038/S41597-022-01894-2

Network security AIOps for online stream data monitoring

Autori: Giang Nguyen; Stefan Dlugolinsky; Viet Tran; Álvaro López García
Pubblicato in: Neural Computing and Applications, 2024, ISSN 1433-3058
Editore: Springer Nature
DOI: 10.1007/S00521-024-09863-Z

An Improved Sea Lion Optimization for Workload Elasticity Prediction with Neural Networks

Autori: Giang Nguyen, Binh Minh Nguyen; Trung Tran; Thieu Nguyen; Giang Nguyen
Pubblicato in: nternational Journal of Computational Intelligence Systems, 2022, ISSN 1875-6883
Editore: Springer Nature
DOI: 10.1007/s44196-022-00156-8

Study of the performance and scalability of federated learning for medical imaging with intermittent clients

Autori: Sáinz-Pardo Díaz, Judith; López García, Álvaro
Pubblicato in: Neurocomputing, Numero 518, 2023, ISSN 1872-8286
Editore: Elsevier
DOI: 10.1016/J.NEUCOM.2022.11.011

A retrospective study of deep learning generalization across two centers and multiple models of X-ray devices using COVID-19 chest-X rays

Autori: Fernández-Miranda, P.M., Fraguela, E.M., de Linera-Alperi, M.Á. et al.
Pubblicato in: Nature Scientific Data, 2024, ISSN 2045-2322
Editore: Springer Nature
DOI: 10.1038/S41598-024-64941-5

Future Generation Computer Systems

Autori: Céspedes Sisniega, Jaime; Rodríguez, Vicente; Moltó, Germán; López García, Álvaro
Pubblicato in: Future Generation Computer Systems, Numero 161, 2024, ISSN 0167-739X
Editore: Elsevier
DOI: 10.1016/j.future.2024.07.010

Personalized federated learning for improving radar based precipitation nowcasting on heterogeneous areas

Autori: Sinz-Pardo Daz, Judith; Castrillo, Mara; Bartok, Juraj; Heredia, Ignacio; Ondk, Irina Malkin; Martynovskyi, Ivan; Alibabaei, Khadijeh; Berberi, Lisana; Kozlov, Valentin; Lpez Garca, lvaro
Pubblicato in: Earth Science Informatics, 2024, ISSN 1865-0481
Editore: Springer Nature
DOI: 10.5445/ir/1000174167

Conference proceedings (4)

Data Partitioning Effects in Federated Learning

Autori: Ahmadzai, Mirwais; Nguyen, Giang
Pubblicato in: Software Quality Analysis, Monitoring, Improvement, and Applications 2023, 2023, ISSN 1613-0073
Editore: CEUR Workshop Proceedings (CEUR-WS.org)
DOI: 10.5281/zenodo.10378239

Making Federated Learning Accessible to Scientists: The AI4EOSC Approach

Autori: Sáinz-Pardo Díaz, Judith; Heredia Canales, Andres; Heredia, Ignacio; Viet, Tran; Nguyen, Giang; Alibabaei, Khadijeh; Obregón Ruiz, Marta; Rebolledo Ruiz, Susana; López García, Álvaro
Pubblicato in: IH&MMSec '24: Proceedings of the 2024 ACM Workshop on Information Hiding and Multimedia Security, 2024, ISBN 979-8-4007-0637-0
Editore: Association for Computing MachineryNew YorkNYUnited States
DOI: 10.1145/3658664.3659642

Enhancing AI development in the EOSC context: AI4EOSC and iMagine

Autori: Alibabaei, Khadijeh; Kozlov, Valentin; Lopez Garcia, Alvaro; Sipos, Gergely
Pubblicato in: IBERGRID2024, 2024
Editore: IBERGRID2024
DOI: 10.5281/zenodo.14056054

Comparison of machine learning models applied on anonymized data with different techniques

Autori: Sinz-Pardo Daz, Judith; Lpez Garca, lvaro
Pubblicato in: 2023 IEEE International Conference on Cyber Security and Resilience (CSR), 2023
Editore: IEEE