Data Science and Machine Learning
21st Australasian Conference, AusDM 2023, Auckland, New Zealand, December 11-13, 2023, Proceedings
(Sprache: Englisch)
This book constitutes the proceedings of the 21st Australasian Conference on Data Science and Machine Learning, AusDM 2023, held in Auckland, New Zealand, during December 11-13, 2023.
The 20 full papers presented in this book were carefully reviewed and...
The 20 full papers presented in this book were carefully reviewed and...
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Produktinformationen zu „Data Science and Machine Learning “
Klappentext zu „Data Science and Machine Learning “
This book constitutes the proceedings of the 21st Australasian Conference on Data Science and Machine Learning, AusDM 2023, held in Auckland, New Zealand, during December 11-13, 2023.The 20 full papers presented in this book were carefully reviewed and selected from 50 submissions. The papers are organized in the following topical sections: research track and application track. They deal with topics around data science and machine learning in everyday life.
Inhaltsverzeichnis zu „Data Science and Machine Learning “
Research Track: Random Padding Data Augmentation.- Unsupervised Fraud Detection on Sparse Rating Networks.- Semi-Supervised Model-Based Clustering for Ordinal Data.- Damage GAN: A Generative Model for Imbalanced Data.- Text-Conditioned Graph Generation Using Discrete Graph Variational Autoencoders.- Boosting QA Performance through SA-Net and AA-Net with the Read+Verify Framework.- Anomaly Detection Algorithms: Comparative Analysis and Explainability Perspectives.- Towards Fairness and Privacy: A Novel Data Pre-processing Optimization Framework for Non-binary Protected Attributes.- MStoCast: Multimodal Deep Network for Stock Market Forecast..- Few Shot and Transfer Learning with Manifold Distributed Datasets.- Mitigating The Adverse Effects of Long-tailed Data on Deep Learning Models.- Shapley Value Based Feature Selection to Improve Generalization of Genetic Programming for High-Dimensional Symbolic Regression.- Hybrid Models for Predicting Cryptocurrency Price Using Financial and Non-Financial Indicators.- Application Track: Multi-Dimensional Data Visualization for Analyzing Materials.- Law in Order: An Open Legal Citation Network for New Zealand.- Enhancing Resource Allocation in IT Projects: The Potentials of Deep Learning-Based Recommendation Systems and Data-Driven Approaches.- A Comparison of One-Class versus Two-Class Machine Learning Models for Wildfire Prediction in California.- Skin Cancer Detection with Multimodal Data: A Feature Selection Approach Using Genetic Programming.- Comparison of Interpolation Techniques for Prolonged Exposure Estimation: A Case Study on Seven years of Daily Nitrogen Oxide in Greater Sydney.- Detecting Asthma Presentations from Emergency Department Notes:An Active Learning Approach.
Bibliographische Angaben
- 2023, 1st ed. 2024, XII, 300 Seiten, 83 farbige Abbildungen, Maße: 15,5 x 23,5 cm, Kartoniert (TB), Englisch
- Herausgegeben: Diana Benavides-Prado, Sarah Erfani, Philippe Fournier-Viger, Yee Ling Boo, Yun Sing Koh
- Verlag: Springer, Berlin
- ISBN-10: 9819986958
- ISBN-13: 9789819986958
Sprache:
Englisch
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