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| Link zu diesem Datensatz | https://d-nb.info/1344807224 |
| Art des Inhalts | Konferenzschrift |
| Titel | Cloud Computing, Big Data and Emerging Topics : 12th Conference, JCC-BD&ET 2024, La Plata, Argentina, June 25–27, 2024, Revised Selected Papers / edited by Marcelo Naiouf, Laura De Giusti, Franco Chichizola, Leandro Libutti |
| Person(en) |
Naiouf, Marcelo (Herausgeber) De Giusti, Laura (Herausgeber) Chichizola, Franco (Herausgeber) Libutti, Leandro (Herausgeber) |
| Organisation(en) | SpringerLink (Online service) (Sonstige) |
| Ausgabe | 1st ed. 2025 |
| Verlag | Cham : Springer Nature Switzerland, Imprint: Springer |
| Zeitliche Einordnung | Erscheinungsdatum: 2025 |
| Umfang/Format | Online-Ressource, XII, 176 p. 80 illus., 60 illus. in color. : online resource. |
| Andere Ausgabe(n) |
Printed edition:: ISBN: 978-3-031-70806-0 Printed edition:: ISBN: 978-3-031-70808-4 |
| Inhalt | Parallel and Distributed Computing -- Fast genomic data compression on multicore machines -- Machine and Deep Learning -- Deep Learning based instance segmentation of Neural Progenitor Cell nuclei in fluorescence microscopy images -- Deep Learning based instance segmentation of Neural Progenitor Cell nuclei in fluorescence microscopy images -- CB-RISE Improving the RISE interpretability method through Convergence Detection and Blurred Perturbations -- Wavelength Calibration of Historical Spectrographic Plates With Dynamic Time Warping -- An Empirical Method for Processing IO Traces to Analyze the Performance of DL Application -- Smart Cities and E-Government -- Industry 5.0. Digital Twins in the process industry A bibliometric análisis -- Visualization -- An ABMS COVID19 Propagation Model for Hospital Emergency Departments -- Emerging Topics -- QuantumUnit A proposal for classic multi qubit assertion development -- Tool for quantum classical software lifecycle -- Innovation in Computer Science Education -- Strategies to predict students exam attendance -- Computer Security -- Prediction of TCP Firewall Action Using Different Machine Learning Models |
| Persistent Identifier |
URN: urn:nbn:de:101:1-2410140408568.253345950004 DOI: 10.1007/978-3-031-70807-7 |
| URL | https://doi.org/10.1007/978-3-031-70807-7 |
| ISBN/Einband/Preis | 978-3-031-70807-7 |
| Sprache(n) | Englisch (eng) |
| Beziehungen | Communications in Computer and Information Science ; 2189 |
| DDC-Notation | 005.74 (maschinell ermittelte DDC-Kurznotation) |
| Sachgruppe(n) | 004 Informatik |
| Online-Zugriff | Archivobjekt öffnen |
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