Cloud Computing, Big Data and Emerging Topics 12th Conference, JCC-BD&ET 2024, La Plata, Argentina, June 25–27, 2024, Revised Selected Papers /

This volume CCIS 2189 constitutes the refereed proceedings of the 12th Conference, JCC-BD&ET 2024, held in La Plata, Argentina during June 25–27, 2024. The 12 full papers presented were carefully reviewed and selected from 37 submissions. They were categorized under the topical sections as follo...

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Detalhes bibliográficos
Autor Corporativo: SpringerLink (Online service)
Outros Autores: Naiouf, Marcelo (Editor), De Giusti, Laura (Editor), Chichizola, Franco (Editor), Libutti, Leandro (Editor)
Formato: Recurso Eletrônico livro eletrônico
Idioma:inglês
Publicado em: Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
Edição:1st ed. 2025.
coleção:Communications in Computer and Information Science, 2189
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Acesso em linha:https://doi.org/10.1007/978-3-031-70807-7
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505 0 |a 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. 
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