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Recent Advances in Next-Generation Data Science de Henry Han

Recent Advances in Next-Generation Data Science: Communications in Computer and Information Science, cartea 2158

Editat de Henry Han
en Limba Engleză Paperback – 9 aug 2024
This book constitutes the refereed proceedings of the Third Southwest Data Science Conference, on Recent advances in next-generation data science, SDSC 2024, held in Waco, TX, USA, in March 22, 2024.
The 15 full papers presented were carefully reviewed and selected from 59 submissions. These papers focus on AI security in next-generation data science and address a range of challenges, from protecting sensitive data to mitigating adversarial threats.
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Specificații

ISBN-13: 9783031678707
ISBN-10: 3031678702
Pagini: 248
Ilustrații: XX, 235 p.
Dimensiuni: 155 x 235 x 14 mm
Greutate: 0.38 kg
Ediția:2024
Editura: Springer
Colecția Communications in Computer and Information Science
Seria Communications in Computer and Information Science

Locul publicării:Cham, Switzerland

Cuprins

.- Wasserstein graph convolutional network with attention for imbalanced scRNA-seq data knowledge discovery.
.- Toward a Unified Cybersecurity Knowledge Graph: Leveraging Ontologies and Open Data Sources.
.- How does normalization impact clustering?.
.- Quantitative Stock Market Modeling Using Multivariate Geometric Random Walk.
.- Disease Similarity and Disease Clustering.
.- Composition analysis and identification of ancient glass products.
.- Data entropy-based imbalanced learning.
.- Analysis of the changes in microbial community during the fermentation of Feng-flavour Baijiu.
.- EnhanciGraph: Visualizing Enhancer-Gene Interactions.
.- Event-triggered Control for Synchronization of Chaotic Delayed Lur’e Systems with Stochastic cyber-attacks.
.- In-game Win Prediction Models for Cricket.
.- Finite-time outer average synchronization between two coupled heterogeneous complex dynamical
networks and its application in secure communication.
.- Classification of in-situ Solar Wind Data Measured by Solar Orbiter/SWA-PAS and HIS using
Machine Learning.
.- Node classification with Multi-hop Graph Convolutional Network.
.- PyDaskShift: Automatically Convert Loop-Based Sequential Programs to Distributed Parallel Programs.