Cognitive Computing - ICCC 2023
7th International Conference Held as Part of the Services Conference Federation, SCF 2023 Shenzhen, China, December 17-18, 2023 Proceedings
Published on 3. January 2024
XIII, 133 pages
978-3-031-51671-9 (ISBN)
Description
This book constitutes the refereed proceedings of the 7th International Conference on Cognitive Computing, ICCC 2023, held in Shenzhen, China, during December 17-18, 2023.
The 9 full papers in this book were carefully reviewed and selected from 14 submissions. They are organized in topical sections as follows: Cognitive Computing Technologies and Infrastructure, Cognitive Computing Applications, Sensing Intelligence, Cognitive Analysis, Mobile Services, Cognitive Computing on Smart Home, and Cognitive Computing on Smart City.
More details
Series
Edition
1st ed. 2024
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Product notice
Reflowable
Illustrations
XIII, 133 p. 41 illus., 32 illus. in color.
File size
9,32 MB
ISBN-13
978-3-031-51671-9 (9783031516719)
DOI
10.1007/978-3-031-51671-9
Schweitzer Classification
Other editions
Additional editions
Xiuqin Pan | Ting Jin | Liang-Jie Zhang
Cognitive Computing - ICCC 2023
7th International Conference Held as Part of the Services Conference Federation, SCF 2023 Shenzhen, China, December 17-18, 2023 Proceedings
Book
01/2024
Springer
€58.84
Shipment within 15-20 days
Content
Research Track
.- High-Precision Detection of Suicidal Ideation on Social Media Using Bi-LSTM and BERT Models.- P-Reader: A Clue-inspired Model for Machine Reading Comprehension.- An Unsupervised Method for Sarcasm Detection with Prompts.- ENER: Named Entity Recognition Model for Ethnic Ancient Books Based on Entity Boundary Detection.- An Enhanced Opposition-Based Golden-Sine Whale Optimization Algorithm.- T4S: Two-stage Screenplay Synopsis Summary Generation with Turning Points.-
A
pplication Track
.- Multi-factor Water Level Prediction Based on InnRNN-Attention.- Ethereum Public Opinion Analysis Based on Attention Mechanism.- Prompt Tuning Models on Sentiment-Aware for Explainable Recommendation.
