Big Data

11th CCF Conference, BigData 2023, Nanjing, China, September 8¿10, 2023, Proceedings
Herausgegeben:Chen, Enhong; Gao, Yang; Cao, Longbing; Xiao, Fu; Cui, Yiping; Gu, Rong; Wang, Li; Cui, Laizhong; Yang, Wanqi

• Broschiertes Buch

Produktbeschreibung

This book constitutes the refereed proceedings of the 11th CCF Conference on BigData 2023, which took place in Nanjing, China, in September 2023.
The 14 full papers presented in this volume were carefully reviewed and selected from 69 submissions. The topics of accepted papers include theories and methods of data science, algorithms and applications of big data.

Produktdetails

• Communications in Computer and Information Science 2005
• Verlag: Springer / Springer Nature Singapore / Springer, Berlin
• Artikelnr. des Verlages: 978-981-99-8978-2
• 1st ed. 2023
• Seitenzahl: 212
• Erscheinungstermin: 15. Dezember 2023
• Englisch
• Abmessung: 235mm x 155mm x 12mm
• Gewicht: 330g
• ISBN-13: 9789819989782
• ISBN-10: 9819989787
• Artikelnr.: 69357039

Inhaltsangabe

Long-term and Short-term Perception in Knowledge Tracing.- A Transfer Learning Enhanced Decomposition-Based Hybrid Framework for Forecasting Multiple Time-Series.- Dataset Search over Integrated Metadata from China's Public Data Open Platforms.- Integrating DCNNs with Genetic Algorithm for Diabetic Retinopathy Classification.- The Convolutional Neural Network Combing Feature-aligned and Attention Pyramid for Fine-Grained Visual Classification.- OCWYOLO:A Road Depression Detection Method.- Explicit Exploring Geometric Modality for Shape-enhanced Single-view 3D Face Reconstruction.- Fine edge and texture prior guided super resolution reconstruction network.- UD-GCN: Uncertainty-Based Semi-Supervised Deep GCN for Imbalanced Node Classification.- Twin Support Vector Regression with Privileged Information.- Detecting Social Robots Based on Multi-View Graph Transformer.- Scheduling Containerized Workflow in Multi-Cluster Kubernetes.- A Study of Electricity Theft Detection Method Based on Anomaly Transformer.- Application and Research on a Large Model Training Method Based on Instruction Fine-Tuning in Domain-Specific Tasks.