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Data Science and Machine Learning: 23rd Australasian Conference, AusDM 2025, Brisbane, QLD, Australia, November 2628, 2025, Proceedings [Pehme köide]

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  • Formaat: Paperback / softback, 562 pages, kõrgus x laius: 235x155 mm, 129 Illustrations, color; 8 Illustrations, black and white
  • Sari: Communications in Computer and Information Science
  • Ilmumisaeg: 13-Feb-2026
  • Kirjastus: Springer Verlag, Singapore
  • ISBN-10: 9819567858
  • ISBN-13: 9789819567850
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  • Formaat: Paperback / softback, 562 pages, kõrgus x laius: 235x155 mm, 129 Illustrations, color; 8 Illustrations, black and white
  • Sari: Communications in Computer and Information Science
  • Ilmumisaeg: 13-Feb-2026
  • Kirjastus: Springer Verlag, Singapore
  • ISBN-10: 9819567858
  • ISBN-13: 9789819567850
This book constitutes the proceedings of the 23rd Australasian Conference on Data Science and Machine Learning, AusDM 2025, held in Brisbane, Australia, during November 26-28, 2025.



The 37 full papers presented in this book were carefully reviewed and selected from 99 submissions. The papers are organized in the following topical sections: (1) Federated, Adaptive and Trustworthy Machine Learning; (2) Environment, Information Security and Productivity; (3) Deep Learning Fusion and Vision; (4) Health and Social Good and (5) Knowledge-Driven and Domain Specific AI. They deal with topics around data science, machine learning and also AI in everyday life.
.- Federated, Adaptive, and Trustworthy Machine Learning.
.- DAARA: Divergence-Aware Attention for Robust Aggregation in Federated
Learning Against Poisoning Attacks.
.- Understanding the Asymmetric Impact of Forecast Accuracy on Decision
Quality.
.- WaveFSL: Wave Interference-Based Meta-Learning for Few-Shot Cross-Modality
Traffic Forecasting.
.- FedMOAR: Multi-Objective Adaptive Regularization for Fair and Efficient
Federated Learning.
.- Unveiling Reliability in Multi-Omics Classification:Fusion, Calibration,
and Dynamic Scaling.
.- Stability Evaluation of Clusterings Across Time.
.- DriftSense: Adaptive Drift Detection with Incremental Hoeffding Trees for
Real-Time Spatial Crowdsourcing.
.- Dynamic Meta-Learning Ensemble for Financial Forecasting.
.- Environment, Information Security and Productivity.
.- Effective Missing-Data Imputation for Time Series with Seasonality and
Causality.
.- UniCausal: A Unified Approach to Causal Discovery from Hybrid Industrial
Time Series and Events.
.- Dynamic Source Code Vulnerability Characteristics Selection for Enhanced
Vulnerability Discover.
.- Modelling Financial Time Series of Returns and Covariance Matrices Using
Time-Space Transformers.
.- Temporal Fusion of Biophysical and Climate Data: A Data-Driven Hybrid
Learning Approach for Short-Term Aboveground Biomass Forecasting.
.- Precision to Costing: Budgeted Modelling for Customer Contact Prediction.
.- Defining Responsible AI: Contextual Insights Powered by LLMs.
.- Deep Learning Fusion and Vision.
.- Fusing Deep Object Detectors via Spatial Heatmap-Based Relevance
Modeling.
.- CarDamageEval: Benchmark Evaluation of Car Damage Assessment Using Vision
Language Models.
.- Regularizing StyleGAN with Inter-Resolution Residual Pattern Consistency
via a Laplacian Pyramid.
.- Mixup and Local-FOMA based Two-Phase Manifold Augmentation in Image
Classification.
.- BARE: Boundary-Aware with Resolution Enhancement for Tree Crown
Delineation.
.- Integrating Vision Transformers and Autoencoders for Interpretable Cancer
Risk Assessment.
.- LightSkinNet: Lightweight CNN with Attention for Accurate,Mobile-Efficient
Multiclass Skin Lesion Classification.
.- A DenseNet-YOLOv8 Fusion Model for Intelligent Parasite Egg Detection and
Classification.
.- Health and Social Good.
.- An AI-Driven Framework for Real-Time Reporting and Identification of Lost
Cats.
.- Benchmarking Preprocessing and Integration Methods in Single-Cell
Genomics.
.- Towards Automated Differential Diagnosis of Skin Diseases Using Deep
Learning and Imbalance-Aware Strategies.
.- Causal Recommendation Method for Personalised Chemotherapy Optimisation in
Breast Cancer.
.- Machine Learning for Traffic Accident Prediction: Integrating Spatial and
Behavioral Data for Road Safety
Insights.
.- Visionary: Enhancing Visual Context for the Visually Impaired.
.- Knowledge-Driven and Domain Specific AI.
.- Advancing Atayal Language Preservation with AI-Driven Multimodal Speech
and Text Processing.
.- ETCOD: Embedding-Based Anomaly Detection and LLM-Driven Validation
Framework for Knowledge Graphs.
.- Top-k Ranking with Exact Positional Fairness.
.- Evaluating Structural Preprocessing in RAG for Academic Curriculum
Applications.
.- Evaluating Cross-Lingual Classification Strategies EnablingTopic Discovery
for Multilingual Social Media Data.
.- From Burst to Routine: Mining Time-Compact Patterns from Sequential
Dataset.
.- A Parameter-free Method Tuning for Multi-scale Wildfire Images Retrieval
Task.
.- NeuroPhysNet: A FitzHugh-Nagumo-Based Physics-InformedNeural Network
Framework for Electroencephalograph (EEG)Analysis and Motor Imagery
Classification.