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Data Science: Foundations and Applications: 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, Sydney, NSW, Australia, June 10-13, 2025, Proceedings, Part VII [Mīkstie vāki]

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  • Formāts: Paperback / softback, 441 pages, height x width: 235x155 mm, 122 Illustrations, color; 13 Illustrations, black and white; XXX, 441 p. 135 illus., 122 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Artificial Intelligence 15876
  • Izdošanas datums: 15-Jun-2025
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 9819682975
  • ISBN-13: 9789819682973
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Data Science: Foundations and Applications: 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, Sydney, NSW, Australia, June 10-13, 2025, Proceedings, Part VIIPalielināt
  • Formāts: Paperback / softback, 441 pages, height x width: 235x155 mm, 122 Illustrations, color; 13 Illustrations, black and white; XXX, 441 p. 135 illus., 122 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Artificial Intelligence 15876
  • Izdošanas datums: 15-Jun-2025
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 9819682975
  • ISBN-13: 9789819682973
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9789819682973.html
The two-volume set LNAI 15875 + 15876 constitutes the proceedings of the 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025 Special Session, held in Sydney, NSW, Australia, during June 1013, 2025.



The 68 full papers included in this set were carefully reviewed and selected from 696 submissions. They were organized in topical sections as follows: survey track; machine learning; trustworthiness; learning on complex data; graph mining; machine learning applications; representation learning; scientific/business data analysis; and special track on large language models.
.- Graph Mining.
.- MuCo-KGC: Multi-Context-Aware Knowledge Graph Completion.
.- Tensor-Fused Multi-View Graph Contrastive Learning.
.- FOG: Interpretable Feature-Oriented Graph Neural Networks for Tabular Data
 Prediction.
.- High Resolution Image Classification with Rich Text Information Based on
Graph Convolution Neural Network.
.- Time Interval Aware Graph Neural Networks for Session-Based
Recommendation.
.- SSGNN: Structure-aware Scoring Graph Neural Network for Molecular
Representation.
.- Mint: An Efficient and Robust In-Place Update Approach for Graph-based
Vector Index.
.- Machine Learning Applications.
.- Advancing Comprehensive Aspect-Based Sentiment Analysis with Generative
Models.
.- A Systematic Evaluation of Generative Models on Tabular Transportation
Data.
.- SDF-Guided Multi-modal Big Data Road Extraction.
.- Player Movement Predictions Using Team and Opponent Dynamics for Doubles
Badminton.
.- Representation Learning.
.- Late Fusion Ensembles for Speech Recognition on Diverse Input Audio
Representations.
.- Text Enhancement-based Multimodal Fusion for Video Sentiment Analysis.
.- Advancing Rubric-based Automated Essay Scoring with Multi-View BERT: A
Case Study in New Zealand.
.- A Script Event Prediction Method Based on Multi-Level Joint Pretraining
and Prompt Fine-Tuning.
.- Scientific/Business Data Analysis.
.- A Multimodal Fusion Model Leveraging MLP Mixer and Handcrafted
Features-based Deep Learning Networks for Facial Palsy Detection.
.- Using Pseudo-Synonyms to Generate Embeddings for Clinical Terms.
.- Corporate Carbon Emission Prediction: Combining Structured and
Unstructured Data.
.- GDCK: Efficient Large-Scale Graph Distillation utilizing a Model-free
Kernelized Approach.
.- Efficient DNA fragment assembly based on Discrete Slime Mould Algorithm.
.- Multi-Scale Control Model for Network Group Behavior.
.- Can Self Supervision Rejuvenate Similarity-Based Link Prediction?.
.- Managing Data Uncertainty in Automatic Mapping of Clinical Classification
Systems.
.- Insomnia Detection Based on Brain State Sleep Trajectories.
.- MCA: Multimodal Contrastive Augmentation for Medical Report Generation.
.- Special Track on Large Language Models.
.-Adapting Large Language Models for Parameter-Efficient Log Anomaly
Detection.
.- Bot Wars Evolved: Orchestrating Competing LLMs in a Counterstrike Against
Phone Scams.
.- Large Language Models with Multi-Faceted Relation Alignment for User Novel
Interest Discovery.
.- Estimating Impact of Behavior Change Messages Using Large Language
Models.
.- A Meta-Thinking Approach to Mitigating Linguistic Sycophancy in
Vision-Language Models.
.- VisCon-100K: Leveraging Contextual Web Data for Fine-tuning Vision
Language Models.
.- TRAWL: Tensor Reduced and Approximated Weights for Large Language Models.
.- DAG-Think-Twice: Causal Structure Guided Elicitation of Causal Reasoning
in Large Language Model.
.- GRL-Prompt: Towards Prompts Optimization via Graph-empowered Reinforcement
Learning using LLMs Feedback.