Ossowski / Sitek / Analide Distributed Computing and Artificial Intelligence, 20th International Conference

1. Auflage 2023
ISBN: 978-3-031-38333-5
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 370 Seiten

Reihe: Lecture Notes in Networks and Systems

The present book brings together experience, current work, and promising future trends associated with distributed computing, artificial intelligence, and their application in order to provide efficient solutions to real problems. DCAI 2023 is a forum to present applications of innovative techniques for studying and solving complex problems in artificial intelligence and computing areas. This year’s technical program presents both high quality and diversity, with contributions in well-established and evolving areas of research. Specifically, 108 papers were submitted, by authors from 31 different countries representing a truly “wide area network” of research activity. The DCAI 23 technical program has selected 36 full papers in the main track and, as in past editions, there will be special issues in ranked journals. This symposium is organized by the LASI and Centro Algoritmi of the University of Minho (Portugal). The authors like to thank all the contributing authors, the members of the Program Committee, National Associations (AEPIA, APPIA), and the sponsors (AIR Institute).

Zielgruppe

Research

Autoren/Hrsg.

Ossowski, Sascha

Sitek, Pawel

Analide, Cesar

Marreiros, Goreti

Chamoso, Pablo

Rodríguez, Sara

Inhaltsverzeichnis

Time-Series Modeling for Intrusion Detection Systems.- Estimation of occlusion region using image completion by network model consisting of Transformer and U-Net.- Operation of a Genetic Algorithm Using an Adjustment Function.- CUBA: An Evolutionary Consortium Oriented Distributed Ledger Byzantine Consensus Algorithm.- From Data to Action: Exploring AI and IoT-driven Solutions for Smarter Cities.-  Adaptive Learning from Peers for Distributed Actor-Critic Algorithms.- Detection of infostealer variants through graph neural networks.