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Knowledge Science, Engineering and Management -

Knowledge Science, Engineering and Management

17th International Conference, KSEM 2024, Birmingham, UK, August 16–18, 2024, Proceedings, Part I
Buch | Softcover
449 Seiten
2024 | 2024 ed.
Springer Verlag, Singapore
978-981-97-5491-5 (ISBN)
CHF 109,95 inkl. MwSt
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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

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 16-18, 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

.- Knowledge Science with Learning and AI (KSLA).
.- A Deep Correlation Feature Extraction Network: Intelligent Description of Bearing Fault Knowledge for Zero-Sample Learning.

.- Elastic Filter Prune in Deep Neural Networks using Modified Weighted Hybrid Criterion.

.- EE LCE: An Event Extraction Framework Based on LLM Generated CoT Explanation.

.- Attention and Learning Features enhanced Knowledge Tracing.

.- An MLM Decoding Space Enhancement for Legal Document Proofreading.

.- Meta Pruning: learning to prune on few shot learning.

.- Knowledge informed Molecular Learning: A Survey on Paradigm Transfer.

.-GenFlowchart: Parsing and Understanding Flowchart Using Generative AI.

.- DSCVSR: A Lightweight Video Super-Resolution for Arbitrary Magnification.

.- Programming Knowledge Tracing with Context and Structure Integration.

.- An Konwledge-Based Semi-supervised Active Learning Method for Precision Pest Disease Diagnostic.

.- Multi-Label Feature Selection with Adaptive Subspace Learning.

.- User Story Classification with Machine Learning and LLMs.

.- PTMA: Pre-trained Model Adaptation for Transfer Learning.

.- Optimization Strategies for Knowledge Graph Based Distractor Generation.

.- Reinforced Subject-aware Graph Neural Network for Related Work Generation.

.- EFCC IeT: Cross-modal Electronic File Content Correlation via Image-enhanced Text.

.- Multi relation Neural Network Recommendation Model Based on Knowledge Graph Embedding Algorithm.

.- Link prediction based on deep global information in heterogeneous graph.

.- Subject Knowledge Entity Relationship Extraction Based on Multi-Feature Fusion and Relation Specific Horns Tagging.

.- A Human Computer Negotiation Model Based on Q-Learning.

.- Affine Transformation-Based Knowledge Graph Embedding.

.- Integrating Prior Scenario Knowledge for Composition Review Generation.

.- Distant supervised relation extraction on pre-train model with improved multi-label attention mechanism.

.- sEMG-based Multi-View Feature-Constrained Representation Learning.

.- Vicinal Data Augmentation for Classification Model via Feature Weaken.

.- STM an Improved Peak Price Tracking-Based Online Portfolio Selection Algorithm.

.- Spatiotemporal Dependence Learning with Meteorological Context for Transportation Demand Prediction.

.- Automatic Meter Pointer Reading Based on Knowledge Distillation.

.- Multi-Table Question Answering Method Based on Correlation Evaluation and Precomputed Cube.

.- A Joint Multi-task Learning Model for Web Table-to-Knowledge Graph Matching.

.-  An In Context Schema Understanding Method for Knowledge Base Question Answering.

.- Performance Enhancement Strategies for Node Classification Based on Graph Community Structure Recognition.

Erscheinungsdatum
Reihe/Serie Lecture Notes in Artificial Intelligence
Lecture Notes in Computer Science
Zusatzinfo 124 Illustrations, color; 12 Illustrations, black and white
Verlagsort Singapore
Sprache englisch
Maße 155 x 235 mm
Themenwelt Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Informatik Weitere Themen Hardware
Schlagworte Adaptation • artificial intelligentce • Blockchain • Conceptual Modelling • Construction • Dissemination • Embedding • Knowledge Discovery • Knowledge Engineering • knowledge graphs • knowledge management • knowledge metrics • Knowledge Representation • knowledge science • knowledge verification • machine learning • Ontologies • Reasoning • resource optimizatio • Smart Knowledge
ISBN-10 981-97-5491-7 / 9819754917
ISBN-13 978-981-97-5491-5 / 9789819754915
Zustand Neuware
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