Data science and machine learning : 23rd Australasian Conference, AusDM 2025, Brisbane, QLD, Australia, November 26-28, 2025, proceedings /
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 org...
| Corporate Author: | |
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| Other Authors: | , , , , , |
| Format: | Conference Proceeding eBook |
| Language: | English |
| Published: |
Singapore :
Springer,
[2026]
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| Series: | Communications in computer and information science ;
2765. |
| Subjects: |
Table of Contents:
- 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.