Cloud Computing, Big Data and Emerging Topics (eBook, PDF)

12th Conference, JCC-BD&ET 2024, La Plata, Argentina, June 25-27, 2024, Revised Selected Papers
Redaktion: Naiouf, Marcelo; Libutti, Leandro; Chichizola, Franco; de Giusti, Laura

• Format: PDF

Produktbeschreibung

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 follows: Parallel and Distributed Computing, Machine and Deep Learning, Smart Cities and E-Government, Visualization, Emerging Topics, Innovation in Computer Science Education, Computer Security.

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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 176
• Erscheinungstermin: 10. Oktober 2024
• Englisch
• ISBN-13: 9783031708077
• Artikelnr.: 72243324

Inhaltsangabe

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.