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Knowledge Management and Acquisition for Intelligent Systems 20th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2024, Kyoto, Japan, November 18–19, 2024, Proceedings

Aus der Reihe Lecture Notes in Computer Science

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Beschreibung

This book constitutes the refereed proceedings of the 20th International Conference on Knowledge Management and Acquisition for Intelligent Systems, PKAW 2024, held in Kyoto, Japan, during November 18–19, 2024. 

The 15 full papers and 9 short papers presented in this volume were carefully reviewed and...

Beschreibung

This book constitutes the refereed proceedings of the 20th International Conference on Knowledge Management and Acquisition for Intelligent Systems, PKAW 2024, held in Kyoto, Japan, during November 18–19, 2024. 


The 15 full papers and 9 short papers presented in this volume were carefully reviewed and selected from 52 submissions. These papers demonstrate advanced research on machine learning, natural language processing, computer vision, and intelligent systems.

Produktdetails

Einband

Taschenbuch
Erscheinungsdatum

16.11.2024
Abbildungen

XI, 300 p. 121 illus., 100 illus. in color.
Herausgeber Shiqing Wu + weitere

Produktdetails

Einband

Taschenbuch
Erscheinungsdatum

16.11.2024
Abbildungen

XI, 300 p. 121 illus., 100 illus. in color.
Herausgeber
Verlag Springer Singapore
Seitenzahl

300
Maße (L/B/H)

23,5/15,5/1,7 cm
Gewicht

476 g
Auflage

1. Auflage
Sprache

Englisch
ISBN

978-981-9600-25-0
Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Knowledge Management and Acquisition for Intelligent Systems
  • Produktbild: Knowledge Management and Acquisition for Intelligent Systems

  • .- Mining Prevalent Co-location Patterns with Multiple Minimum Prevalence Thresholds.



    .- Computable Relations Mapping by Horn Clauses for Inductive Program Synthesis.



    .- Aspect-Adaptive Knowledge-based Opinion Summarization.



    .- Towards Responsible Decisions with Limited Training Data Using Human-in-the-Loop.



    .- Intent-Spectrum BotTracker: Tackling LLM-based Social Media Bots through an Enhanced BotRGCN Model with Intention and Entropy Measurement.



    .- Improving User Satisfaction through Approaches that Balance Recommendation Accuracy and Serendipity Tailored to Individual Preferences.



    .- kNN-Res: Residual Neural Network with kNN-Graph Coherence for Point Cloud Registration.



    .- Revolutionizing Organic Product Supply Chains: Blockchain, RSA-Encrypted NFTs, and IPFS for Ethical and Transparent Supply Chains.



    .- Efficient Redundancy Elimination To Discovering Concise Prevalent Co-location Patterns.



    .- EBcGAN: An Edge-Based Conditional Generative Adversarial Network for Image Fusion.



    .- A Variational Approach to Personalized Federated Learning and its Improvement.



    .- Natural Language Integration for Multimodal Few-Shot Class Incremental Learning: Image Classification Problem.



    .- Multi-Target Contrastive Objective for Learning Property-Aware Vision-Language Representation.



    .- Low Cost Active Learning Framework for Short Answer Scoring.



    .- Fast and Robust Differential Private Stochastic Gradient Descent with Preconditioner.



    .- The Integration of Federated Learning Techniques in Predictive Aircraft Maintenance using Cloud Services.



    .- Precision 3D Motion Capture Using Pose Estimation Techniques: Application in Sports Video Analysis.



    .- A Cross-Chain Analysis of NFT-Based Personal Data Marketplaces: Evaluating EVM-Supported Platforms for Transparent of Data Trading.



    .- Optimizing Resource Distribution Towards Energy Justice in Resilient Smart Grids.



    .- A Novel Adaptive Multi-channel Fusion Network Based on Deep Learning for Diabetes Diagnosis and Readmission Prediction.



    .- Category-Aware Keypoint Masking to Address Biases in Semi Supervised 2D Pose Estimation.



    .- Seq2Seq RNNs for Bus Arrival Time Prediction.



    .- Virtual Learning Machine for Tiny Devices.



    .- Distributed Dataset Framework for Large Language Models Pre-training.