Knowledge Science, Engineering and Management

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

Klappentext

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

Inhalt

.- 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.