Big Data (eBook, PDF)

8th CCF Conference, BigData 2020, Chongqing, China, October 22-24, 2020, Revised Selected Papers
Redaktion: Mei, Hong; Wang, Li; Gao, Yang; Bu, Jiajun; Huang, Yihua; Zhang, Zili; Fan, Wenfei; Zhang, Weiguo

• Format: PDF

Produktbeschreibung

This book constitutes the proceedings of the 8th CCF Conference on Big Data, BigData 2020, held in Chongqing, China, in October 2020.

The 16 full papers presented in this volume were carefully reviewed and selected from 65 submissions. They present recent research on theoretical and technical aspects on big data, as well as on digital economy demands in big data applications.

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Produktdetails

• Verlag: Springer Nature Singapore
• Seitenzahl: 243
• Erscheinungstermin: 31. März 2021
• Englisch
• ISBN-13: 9789811607059
• Artikelnr.: 62291980

Inhaltsangabe

A Short Text Classification Model Based on Cross-Layer Connected Gated Recurrent Unit Capsule Network.- Image Compressed Sensing using Neural Architecture Search.- Discovery of Sparse Formula Based on Elastic Network Method and its Application in Identification of Turbulent Boundary Layer Wall Function.- Rotation-Dpeak: Improving Density Peaks Selection for Imbalanced Data.- Introducing MDPSD, a Multimodal Dataset for Psychological Stress Detection.- Small-Scale Data Classification Based Deep Forest.- An Answer Sorting Method Combining Multiple Neural Networks and Attentional Mechanisms.- Optimal Subspace Analysis based on Information-Entropy Increment.- Link Prediction of Attention Flow Network Based on Maximum Entropy Model.- Graph Representation Learning using Attention Network.- Food Pairing Based on Generative Adversarial Networks.- Comparisons of Deep Neural Networks in Multi-Label Classification for Chinese Recipes.- Improving Word Alignment With Contextualized Embedding and Bilingual Dictionary.- Hypernetwork Model Based on Logistic Regression.- Multi Dimensional Evaluation of Middle School Students' Physical and Mental Quality and Intelligent Recommendation of Exercise Programs Based on Big Data Analysis.- Diversity-Aware Top-N Recommendation: A Deep Reinforcement Learning way.