Big Data

11th CCF Conference, BigData 2023, Nanjing, China, September 8–10, 2023, Proceedings

AvEnhong Chen,Yang Gao

Häftad, Engelska, 2023

Del i serien Communications in Computer and Information Science

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2023-12-15
Mått:155 x 235 x 12 mm
Vikt:330 g
Format:Häftad
Språk:Engelska
Serie:Communications in Computer and Information Science
Antal sidor:195
Förlag:Springer Verlag, Singapore
ISBN:9789819989782

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Databaser inom Data och IT
Artificiell intelligens inom Data och IT

Innehållsförteckning

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.