Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery
Proceedings of the ICNC-FSKD 2022
Published on 29. January 2023
XXVII, 1508 pages
978-3-031-20738-9 (ISBN)
Description
This book consists of papers on the recent progresses in the state of the art in natural computation, fuzzy systems, and knowledge discovery. The book is useful for researchers, including professors, graduate students, as well as R & D staff in the industry, with a general interest in natural computation, fuzzy systems, and knowledge discovery. The work printed in this book was presented at the 2022 18th International Conference on Natural Computation, Fuzzy Systems, and Knowledge Discovery (ICNC-FSKD 2022), held from 30 July to 1 August 2022, in Fuzhou, China. All papers were rigorously peer-reviewed by experts in the areas.
More details
Series
Edition
1st ed. 2023
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
XXVII, 1508 p. 650 illus., 454 illus. in color.
File size
133,80 MB
ISBN-13
978-3-031-20738-9 (9783031207389)
DOI
10.1007/978-3-031-20738-9
Schweitzer Classification
Other editions
Additional editions
Ning Xiong | Maozhen Li | Kenli Li
Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery
Proceedings of the ICNC-FSKD 2022
Book
01/2023
Springer
€299.59
Shipment within 15-20 days
Persons
Content
Multiple Layers Global Average Pooling Fusion.- Han Dynasty Clothing Image Classification Model Based on KNN-Attention and CNN.- Self-attention SSD Network Detection Method of X-ray Security Images.- Cross Architecture Function Similarity Detection with Binary Lifting and Neural Metric Learning.- Pain Expression Recognition Based on Dual-channel Convolutional Neural Network.- A Noval Air Quality Index Prediction Scheme Based On Long Short-Term Memory Technology.- Code Summarization through Learning Linearized AST Paths with Transformer.- Function Level Cross-Modal Code Similarity Detection with Jointly Trained Deep Encoders.- Ease Solidity Smart Contract Compilation through Version Pragma Identification.- Towards Robust Similarity Detection of Smart Contracts with Masked Language Modelling.- DeSG: Towards Generating Valid Solidity Smart Contracts with Deep Learning.- Combining AST Segmentation and Deep Semantic Extraction for Function Level Vulnerability Detection.- A Novel Variational-Mode-Decomposition-Based Long Short-Term Memory for Foreign Exchange Prediction.
