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Knowledge Science, Engineering and Management: 15th International Conference, KSEM 2022, Singapore, August 68, 2022, Proceedings, Part III 1st ed. 2022 [Mīkstie vāki]

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  • Formāts: Paperback / softback, 753 pages, height x width: 235x155 mm, weight: 1163 g, 240 Illustrations, color; 42 Illustrations, black and white; XVI, 753 p. 282 illus., 240 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 13370
  • Izdošanas datums: 31-Jul-2022
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031109880
  • ISBN-13: 9783031109881
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Knowledge Science, Engineering and Management: 15th International Conference, KSEM 2022, Singapore, August 68, 2022, Proceedings, Part III 1st ed. 2022Palielināt
  • Formāts: Paperback / softback, 753 pages, height x width: 235x155 mm, weight: 1163 g, 240 Illustrations, color; 42 Illustrations, black and white; XVI, 753 p. 282 illus., 240 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 13370
  • Izdošanas datums: 31-Jul-2022
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031109880
  • ISBN-13: 9783031109881
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783031109881.html
Keywords:
The three-volume sets constitute the refereed proceedings of the 15th International Conference on Knowledge Science, Engineering and Management, KSEM 2022, held in Singapore, during August 6–8, 2022. 

The 169 full papers presented in these proceedings were carefully reviewed and selected from 498 submissions. The papers are organized in the following topical sections:

Volume I:
Knowledge Science with Learning and AI (KSLA)

Volume II:
Knowledge Engineering Research and Applications (KERA)

Volume III:
Knowledge Management with Optimization and Security (KMOS)

Knowledge Management with Optimization and Security (KMOS).- Study on
Chinese Named Entity Recognition Based on  Dynamic Fusion and Adversarial
Training.- Spatial Semantic Learning for Travel Time Estimation.- A
Fine-Grained Approach for Vulnerabilities Discovery using Augmented
Vulnerability Signatures.- PPBR-FL: a Privacy-preserving and Byzantine-robust
Federated Learning System.- GAN-Based Fusion Adversarial Training.- MAST-NER:
A Low-Resource Named Entity Recognition Method based on Trigger Pool.- Fuzzy
information measures feature selection using descriptive statistics data.-
Prompt-Based Self-Training Framework for Few-Shot Named Entity Recognition.-
Learning Advisor-Advisee Relationship from Multiplex Network Structure.-
CorefDRE: Coref-aware Document-level Relation Extraction.- Single Pollutant
Prediction Approach by Fusing MLSTM and CNN.- A Multi-objective Evolutionary
Algorithm Based on Multi-layer Network Reduction for Community Detection.-
Detection DDoS of attacks based on federated learning with Digital Twin
Network.- A Privacy-Preserving Subgraph-Level Federated Graph Neural Network
via Differential Privacy.