Data Science and Machine Learning : 21st Australasian Conference, AusDM 2023, Auckland, New Zealand, December 11–13, 2023, Proceedings / edited by Diana Benavides-Prado, Sarah Erfani, Philippe Fournier-Viger, Yee Ling Boo, Yun Sing Koh.

Format:

Book

Contributor:

Benavides-Prado, Diana., editor.

Erfani, Sarah. M, editor.

Fournier-Viger, Philippe, editor.

Boo, Yee Ling., editor.

Koh, Yun Sing, 1978- editor.

Xue, Bing (Senior lecturer in computer science)

Series:

Communications in Computer and Information Science, 1865-0937 ; 1943

Language:

English

Subjects (All):

Data mining.

Artificial intelligence.

Image processing--Digital techniques.

Image processing.

Computer vision.

Education--Data processing.

Education.

Computer science--Mathematics.

Computer science.

Data Mining and Knowledge Discovery.

Artificial Intelligence.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Computers and Education.

Mathematics of Computing.

Local Subjects:

Data Mining and Knowledge Discovery.

Artificial Intelligence.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Computers and Education.

Mathematics of Computing.

Physical Description:

1 online resource (310 pages)

Edition:

1st ed. 2024.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2024.

Summary:

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. .

Contents:

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.

Notes:

Includes bibliographical references and index.

Other Format:

Print version: Benavides-Prado, Diana Data Science and Machine Learning

ISBN:

981-9986-96-6