Xiong / Li / Wang | Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery | Buch | 978-3-031-20737-2 | sack.de

Buch, Englisch, Band 153, 1508 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 2324 g

Reihe: Lecture Notes on Data Engineering and Communications Technologies

Xiong / Li / Wang

Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery

Proceedings of the ICNC-FSKD 2022
1. Auflage 2023
ISBN: 978-3-031-20737-2
Verlag: Springer International Publishing

Proceedings of the ICNC-FSKD 2022

Buch, Englisch, Band 153, 1508 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 2324 g

Reihe: Lecture Notes on Data Engineering and Communications Technologies

ISBN: 978-3-031-20737-2
Verlag: Springer International Publishing


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.

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Zielgruppe


Research

Weitere Infos & Material


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.



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