518 citations
- Dalian University of TechnologyCN24 papers
- Deakin UniversityAU19 papers
- RMIT UniversityAU18 papers
- Monash UniversityAU8 papers
- Australian National UniversityAU7 papers
- Zhejiang University of TechnologyCN7 papers
- Swinburne University of TechnologyAU6 papers
- Czech Academy of Sciences, Institute of Information Theory and AutomationCZ5 papers
- Johannes Kepler University of LinzAT5 papers
- UNSW SydneyAU5 papers
- XLIMFR5 papers
- Can Tho UniversityVN4 papers
154 papers
Sloshing reduction in a swaying tank with porous baffles using scaled boundary finite element method
Pramod, Aln, Choudhury +5
Sloshing is an inevitable phenomenon in an ocean-going vessel that can have adverse effects. In this work, the mitigation of sloshing is investigated using multiple thin porous baf…
ForCM: Forest Cover Mapping from Multispectral Sentinel-2 Image by Integrating Deep Learning with Object-Based Image Analysis
Maisha Haque, Israt Jahan Ayshi, Sadaf M. Anis +8
This research proposes "ForCM", a novel approach to forest cover mapping that combines Object-Based Image Analysis (OBIA) with Deep Learning (DL) using multispectral Sentinel-2 ima…
Absolute indices for determining compactness, separability and number of clusters
Adil M. Bagirov, Ramiz M. Aliguliyev, Nargiz Sultanova +1
Finding "true" clusters in a data set is a challenging problem. Clustering solutions obtained using different models and algorithms do not necessarily provide compact and well-sepa…
Graph Learning
Feng Xia, Ciyuan Peng, Jing Ren +5
Graph learning has rapidly evolved into a critical subfield of machine learning and artificial intelligence (AI). Its development began with early graph-theoretic methods, gaining…
Refined Causal Graph Structure Learning via Curvature for Brain Disease Classification
Falih Gozi Febrinanto, Adonia Simango, Chengpei Xu +4
Graph neural networks (GNNs) have been developed to model the relationship between regions of interest (ROIs) in brains and have shown significant improvement in detecting brain di…
EAGLE: Contrastive Learning for Efficient Graph Anomaly Detection
Jing Ren, Mingliang Hou, Zhixuan Liu +1
Graph anomaly detection is a popular and vital task in various real-world scenarios, which has been studied for several decades. Recently, many studies extending deep learning-base…