AI 2024: Advances in Artificial Intelligence : 37th Australasian Joint Conference on Artificial Intelligence, AI 2024, Melbourne, VIC, Australia, November 25-29, 2024, Proceedings, Part I (Lecture Notes in Computer Science)

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AI 2024: Advances in Artificial Intelligence : 37th Australasian Joint Conference on Artificial Intelligence, AI 2024, Melbourne, VIC, Australia, November 25-29, 2024, Proceedings, Part I (Lecture Notes in Computer Science)

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  • 製本 Paperback:紙装版/ペーパーバック版/ページ数 395 p.
  • 言語 ENG
  • 商品コード 9789819603473

Full Description

This two-volume set LNAI 15442-15443 constitutes the refereed proceedings of the 37th Australasian Joint Conference on Artificial Intelligence, AI 2024, held in Melbourne, VIC, Australia, during November 25-29, 2024.

The 59 full papers presented together with 3 short papers were carefully reviewed and selected from 108 submissions.

Part 1: Knowledge Representation and NLP; Trustworthy and Explainable AI; Machine Learning and Data Mining.

Part 2: Reinforcement Learning and Robotics; Learning Algorithms; Computer Vision; AI for Healthcare.

Contents

.- Knowledge Representation and NLP.

.- DELA: Dual Embedding Using LSTM and Attention for Asset Tag Inference in Industrial Automation Systems.

.- Combined Change Operators for Trust and Belief.

.- Highlighting Case Studies in LLM Literature Review of Interdisciplinary System Science.

.- Legal Judgment Prediction through Argument Analysis.

.- Conditional Prototypical Optimal Transport for Enhanced Clue Identification in Multiple Choice Question Answering.

.- REFINE on Scarce Data: Retrieval Enhancement through Fine-Tuning via Model Fusion of Embedding Models.

.- Leveraging LLM in Genetic Programming Hyper-Heuristics for Dynamic Microservice Deployment.

.- Bidirectional Dependency Representation Disentanglement for Time Series Classification.

.- SCODA - A Framework for Software Capability Representation and Inspection.

.- Some Considerations for the Preservation of Endangered Languages Using Low-Resource Machine Translation.

.- Trustworthy and Explainable AI.

.- Improving Intersectional Group Fairness Using Conditional Generative Adversarial Network and Transfer Learning.

.- GPT-4 Attempting to Attack AI-Text Detectors.

.- Charting a Fair Path: FaGGM Fairness-aware Generative Graphical Models.

.- Shedding Light on Greenwashing: Explainable Machine Learning for Green Ad Detection.

.- Beyond Factualism: A Study of LLM Calibration through the Lens of Conversational Emotion Recognition.

.- Ensuring Fairness in Stochastic Multi-Armed Bandit Problems for Effective Group Recommendations.

.- Human Decision-Making Concepts with Goal-Oriented Reasoning for Explainable Deep Reinforcement Learning.

.- Towards Explainable Deep Learning for Non-melanoma Skin Cancer Diagnosis.

.- Machine Learning and Data Mining.

.- Localization System Enhanced with CDLPE: A Low-Cost, Resilient Map-Matching Algorithm.

.- FocDepthFormer: Transformer with latent LSTM for Depth Estimation from Focal Stack.

.- TSI: A Multi-View Representation Learning Approach for Time Series Forecasting.

.- Climate Downscaling Monthly Coastal Sea Surface Temperature Using Convolutional Neural Network and Composite Loss.

.- DBSSM: Deep BERT-based Semantic Skill Matching from Resumes to a Public Skill Taxonomy.

.- Designing an Adaptive AI System for Operation on Board the SpIRIT Nano-satellite.

.- LSTM Autoencoder-based Deep Neural Networks for Barley Genotype-to-Phenotype Prediction.

.- An Improved Prescriptive Tree-based Model for Stochastic Parallel Machine Scheduling.

.- Economic Graph Lottery Ticket: A GNN based Economic Forecasting Model.

.- Pattern-based Trading by Continual Learning of Price and Volume Patterns.

.- An Experimental Study on Decomposition-Based Deep Ensemble Learning for Traffic Flow Forecasting.

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