Knowledge Science, Engineering and Management (eBook, PDF)

17th International Conference, KSEM 2024, Birmingham, UK, August 16-18, 2024, Proceedings, Part II
Redaktion: Cao, Cungeng; Wang, Yonghao; Asyhari, Taufiq; Arshad, Junaid; Zhao, Liang; Chen, Huajun

• Format: PDF

Produktbeschreibung

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

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Produktdetails

• Verlag: Springer Nature Singapore
• Seitenzahl: 464
• Erscheinungstermin: 25. Juli 2024
• Englisch
• ISBN-13: 9789819754953
• Artikelnr.: 72240705

Inhaltsangabe

.- Knowledge Engineering Research and Applications (KERA).

.- Research on Node Cluster Analysis in Brain Connection Data.

.- A New Emotion Classification Method Based on JAN-VMD.

.- Neuro-Genetic System: A Hybrid System of CNN-BiLSTM Optimized by Genetic Algorithm for Road Accident Severity Prediction.

.- MoveFormer: Spatial Graph Periodic Injection Network for Next POI Recommendation.

.- Bio-Inspired Feature Selection via An Improved Binary Golden Jackal Optimization Algorithm.

.- Dynamic Reliability-optimised and Energy-efficient Scheduling Algorithms in Heterogeneous Multi-core Systems.

.- A Human-Computer Negotiation Model Based on Sentiment Analysis and Big Data.

.- A novel online sequential learning algorithm for ELM based on optimal control.

.- DICES: Diffusion-Based Contrastive Learning with Knowledge Graphs for Recommendation.

.- Variational Loss of Random Sampling for Searching Cluster Number.

.- DVDNER: Dual-view Learning Named Entity Recognition via Diffusion.

.- Achieving Universal Fairness in Machine Learning: A Multi-objective Optimization Perspective.

.- SSNF: Optimizing Entity Alignment with a Novel Structural and Semantic Neighbor Filtering.

.- Visual Analytics of Learning Behavior Based on the Dendritic Neuron Model.

.- Feature Matching Based Heterogeneous Transfer Learn-ing for Student Performance Prediction.

.- Weighted Multiple Source-Free Domain Adaptation Ensemble Network in Intelligent Machinery Fault Diagnosis.

.- Logarithm of Maximum Posterior Evidence: Advanced Model Selection for Text Classification.

.- A Hybrid Method Combing Reinforcement Learning and Heuristics in Solving Two-Echelon Vehicle Routing Problem with Backhauls.

.- AgriBERT: A Joint Entity Relation Extraction Model Based on Agricultural Text.

.- Research on Key Node Cluster Identification Algorithm based on Louvain and Cycle Ratio.

.- Uncertain $k$ center Clustering, Revisited: Point Assignment.

.- DPSPC: A Density Peak-based Statistical Parallel Clustering Algorithm for Big Data.

.- Insert Commonsense Knowledge through Semantics for Dialogue Generation.

.- Entity Set Expansion based on Category Prompts in MOOCs.

.- ViT Hybrid Channel Fit Pruning Algorithm for Co-Optimization of Hardware and Software for Edge Device.

.- Collaborative Adversarial Learning for Unsupervised Federated Domain Adaptation.

.- Improving Image Captioning with Image Concepts of Words.

.- M HGN: Multi information Enhanced Heterogeneous Graph Network for Multi-party Dialogue Reading Comprehension.

.- A Student Performance Prediction Model Based on Feature Factor Transfer.

.- A Binary Multi-objective Grey Wolf Optimization for Feature Selection.

.- CS Net: A Coarse-to-fine-grained Summarization Network for Community-based Question Answering Summarization.

.- AutoIE: An Automated Framework for Information Extraction from Scientific Literature.

.- Adaptive Density Peak Clustering with Optimized Border-peeling.

.- Efficient Affinity Propagation Clustering Based on Szemer´edi's Regularity Lemma.