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Produktbild: PRICAI 2024: Trends in Artificial Intelligence
Band 15281

PRICAI 2024: Trends in Artificial Intelligence 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024, Kyoto, Japan, November 18–24, 2024, Proceedings, Part I

Aus der Reihe Lecture Notes in Computer Science

Fr. 112.00

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

The five-volume proceedings set LNAI 15281-15285, constitutes the refereed proceedings of the 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024, held in Kyoto, Japan, in November 18–24, 2024.

The 145 full papers and 35 short papers included in this book were carefully...

Beschreibung

The five-volume proceedings set LNAI 15281-15285, constitutes the refereed proceedings of the 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024, held in Kyoto, Japan, in November 18–24, 2024.


The 145 full papers and 35 short papers included in this book were carefully reviewed and selected from 543 submissions. 


The papers are organized in the following topical sections:


Part I: Machine Learning, Deep Learning


Part II: Deep Learning, Federated Learning, Generative AI, Natural Language Processing, Large Language Models, 


Part III: Large Language Models, Computer Vision


Part IV: Computer Vision, Autonomous Driving, Agents and Multiagent Systems, Knowledge Graphs, Speech Processing, Optimization


Part V: Optimization, General Applications, Medical Applications, Theoretical Foundations of AI

Produktdetails

Einband

Taschenbuch
Erscheinungsdatum

17.11.2024
Abbildungen

XXIV, 489 p. 127 illus., 113 illus. in color.
Herausgeber Rafik Hadfi + weitere

Produktdetails

Einband

Taschenbuch
Erscheinungsdatum

17.11.2024
Abbildungen

XXIV, 489 p. 127 illus., 113 illus. in color.
Herausgeber
Verlag Springer Singapore
Seitenzahl

489
Maße (L/B/H)

23.5/15.5/2.8 cm
Gewicht

774 g
Auflage

1. Auflage
Sprache

Englisch
ISBN

978-981-9601-15-8
Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: PRICAI 2024: Trends in Artificial Intelligence

  • .- Machine Learning.



    .- Quantitative Analysis of Training Methods, Data Size, and User-Specific Effectiveness in DL-Based Personalized Aesthetic Evaluation.



    .- EQUISCALE: Equitable Scaling for Abstention Learning.



    .- Unsupervised Clustering Using a Variational Autoencoder with Constrained Mixtures for Posterior and Prior.



    .- UTBoost: Gradient Boosted Decision Trees for Uplift Modeling.



    .- CodeMosaic Patch: Physical Adversarial Attacks Against Infrared Aerial Object Detectors.



    .- Sequential Clustering for Real-world Datasets.



    .- Dual-mode Contrastive Learning-Enhanced Knowledge Tracing.



    .- Leveraging Information Consistency in Frequency and Spatial Domain for Adversarial Attacks.



    .- Characterization of Similarity Metrics in Epistemic Logic.



    .- A Relaxed Symmetric Non-negative Matrix Factorization Approach for Community Discovery.



    .- Enhanced Cognitive Distortions Detection and Classification through Data Augmentation Techniques.



    .- Enhancing Music Genre Classification using Augmented Features Ensemble Learning Technique.



    .- A Multi-Layer Network Community Detection Method via Network Feature Augmentation and Contrastive Learning.



    .- Scene Text Recognition Based on Corner Point and Attention Mechanism.



    .- A Comprehensive Framework for Debiased Sample Selection across All Noise Types.



    .- A Traffic Flow Prediction Model Integrating Dynamic Implicit Graph Information.



    .- A Recursive Learning Algorithm for the Least Squares SVM.



    .- BDEL: A Backdoor Attack Defense Method Based on Ensemble Learning.



    .- Customizing Spatial-Temporal Graph Mamba Networks for Pandemic Forecasting.



    .- Distribution-aligned Sequential Counterfactual Explanation with Local Outlier Factor.



    .- T-FIA: Temporal-Frequency Interactive Attention Network for Long-term Time Series Forecasting.



    .- Multi-modal Food Recommendation using Clustering andSelf-supervised Learning.



    .- A quality assessment method of few-shot datasets based on the fusion of quantity and quality.




    .- Deep Learning.



    .- CSDCNet: A Semantic Segmentation Network for Tubular Structures.



    .- Neural Network Surrogate based on Binary Classification for Assisting Genetic Programming in Searching Scheduling Heuristic.



    .- HN-Darts:Hybrid Network Differentiable Architecture Search for Industrial Scenarios.



    High-Order Structure Enhanced Graph Clustering.



    .- CAFGO: Confidence-Adaptive Factor Graph Optimization Algorithm for Fusion Localization.



    .- MFNAS: Multi-Fidelity Exploration in Neural Architecture Search with Stable Zero-shot Proxy.



    .- DyAGL: A Dynamic-aware Adaptive Graph Learning Network for Next POI Recommendation.



    .- Acoustic classification of bird species using improved pre-trained models.



    .- Aspect Term Extraction via Dynamic Attention and a Densely Connected Graph Convolutional Network.



    .- NLDF: Neural Light Dynamic Fields for 3D Talking Head Generation.



    .- Enhanced Knowledge Tracing via Frequency Integration and Order Sensitivity.



    .- Position-Aware Dynamic Graph Convolutional Recurrent Network for Traffic Forecasting.



    .- Pose Preserving Landmark Guided Neural Radiation Fields for Talking Portrait Synthesis.



    .- Adaptive Optimisation of PyTorch Memory Pools for DNNs.



    .- Detaching Range from Depth: Personalized Recommendation Meets Personalized PageRank.



    .- Context-Aware Structural Adaptive Graph Neural Networks.



    .- multi-GAT: Integrative Analysis of scRNA-seq and scATAC-seq Data Using Graph Attention Networks for Cell Annotation.