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Knowledge Science, Engineering and Management: 17th International Conference, KSEM 2024, Birmingham, UK, August 1618, 2024, Proceedings, Part V 2024 ed. [Mīkstie vāki]

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  • Formāts: Paperback / softback, 328 pages, height x width: 235x155 mm, 83 Illustrations, color; 16 Illustrations, black and white; XIII, 328 p. 99 illus., 83 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Artificial Intelligence 14888
  • Izdošanas datums: 27-Jul-2024
  • Izdevniecība: Springer Nature
  • ISBN-10: 9819754887
  • ISBN-13: 9789819754885
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Knowledge Science, Engineering and Management: 17th International Conference, KSEM 2024, Birmingham, UK, August 1618, 2024, Proceedings, Part V 2024 ed.Palielināt
  • Formāts: Paperback / softback, 328 pages, height x width: 235x155 mm, 83 Illustrations, color; 16 Illustrations, black and white; XIII, 328 p. 99 illus., 83 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Artificial Intelligence 14888
  • Izdošanas datums: 27-Jul-2024
  • Izdevniecība: Springer Nature
  • ISBN-10: 9819754887
  • ISBN-13: 9789819754885
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9789819754885.html
The five-volume set LNCS 14884, 14885, 14886, 14887 & 14888 constitutes the refereed deadline proceedings of the 17th International Conference on Knowledge Science, Engineering and Management, KSEM 2024, held in Birmingham, UK, during August 1618, 2024.





The 160 full papers presented in these proceedings were carefully reviewed and selected from 495 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)





Volume IV: Emerging Technology





Volume V: Special Tracks
.- Special Track.



.- Adversary and Attention Guided Knowledge Graph Reasoning based on
Reinforcement Learning.



.- Evaluating GPTs Programming Capability through CodeWars Katas.



.- An Online Portfolio Selection Algorithm with Dynamic Coreset
Construction.



.- Interval-valued Fuzzy Portfolio decision Model with Transaction Cost and
Liquidity Constraint.



.- Active Learning for Low-Resource Project-Specific Code Summarization.



.- A Survey of Game-Theoretic Methods for Controlling COVID-19.



.- ComPAT: a Compiler Principles course AssisTant.



.- Tram Air Conditioning Fault Prediction Using Machine learning.



.- Lexicon Graph Adapter based BERT Model For Chinese Named Entity
Recognition.



.- Insider Threat Defense Strategies: Survey and Knowledge Integration.



.- GA-MEPS: Multiple Experts Portfolio Selection Based on Genetic Algorithm.



.- Deep Learning and Machine Learning-Based Approaches to Inferring Social
Media Network Users Interests from a Missing Data Issus.



.- Customer Segmentation for Telecommunication using Machine Learning.



.- DP-MFRNN: Difficulty Prediction for Examination Questions Based on Neural
Network Framework.



.- Causal Relationship Extraction Combined Boundary Detection and Information
Interaction.



.- Profit Maximization in Edge-enabled Multimedia Data Market: A Game-based
Pricing Approach.



.- Reinforcement learning for scientific application: A survey.



.- Zunna: A New Browser Extension for Protecting Personal Data.



.- HRTC:A Triple Joint Extraction Model Based on Cyber Threat Intelligence.



.- Personalized Image Aesthetics Assessment based onTheme and Personality.



.- A Spatio-temporal Neural Network for Medical Insurance Fraud Detection.



.- Exploring Language Diversity to Improve Neural Text Generation.



.- Diffusion Review-based Recommendation.



.- IntellectSeeker: A Personalized Literature Management System with the
Probabilistic Model and Large Language Model.



.- A novel network intrusion detection method for unbalanced data in open
scenarios.



.- Explainable Knowledge-Based Learning For Online Medical Question
Answering.



.- Energy consumption prediction method for refrigeration systems based on
adversarial networks and Transformer networks.



.- P-Vit: A simplified Vision Transformer model based on FFN and Simple
Attention.