Data Science Institute

Most Recent
Most Downloaded

Machine learning prediction to improve operational resilience in managing disinfection for Macarthur Water Supply System

Tian, H

Continue Reading
Domain Generalization by Learning and Removing Domain-specific Features

Ding, Y Wang, L Liang, B Liang, S Wang, Y Chen, F

Continue Reading
Pedestrian Trajectory Prediction for Real-Time Autonomous Systems via Context-Augmented Transformer Networks.

Saleh, K

Forecasting the trajectory of pedestrians in shared urban traffic environments from non-invasive sensor modalities is still considered one of the challenging problems facing the development of auto...

Continue Reading
Water quality prediction in a chloraminated system for drought resilience

Tian, H

Continue Reading
Traffic Accident Risk Forecasting using Contextual Vision Transformers

Saleh, K Grigorev, A Mihaita, A-S

Continue Reading
Traffic disruption modelling with mode shift in multi-modal networks

Zhao, D Mihaita, A-S Ou, Y Shafiei, S Grzybowska, H Qin, AK Tan, G Li, M Dia, H

Continue Reading
Digital Twin Technology Challenges and Applications: A Comprehensive Review

Botín-Sanabria, DM Mihaita, S Peimbert-García, RE Ramírez-Moreno, MA Ramírez-Mendoza, RA Lozoya-Santos, JDJ

A digital twin is a virtual representation of a physical object or process capable of collecting information from the real environment to represent, validate and simulate the physical twin’s presen...

Continue Reading
Energy Saving Optimisation for Water Distribution Networks

Zhao, Y Liang, B Dang, S Taib, R Wang, Y Matthews, G Chen, F Hua, T Doolan, C

Continue Reading
Effective Vehicle-Based Kangaroo Detection for Collision Warning Systems Using Region-Based Convolutional Networks.

Saleh, K Hossny, M Nahavandi, S

Traffic collisions between kangaroos and motorists are on the rise on Australian roads. According to a recent report, it was estimated that there were more than 20,000 kangaroo vehicle collisions t...

Continue Reading
A Lane-based Predictive Model of Downstream Arrival Rates in a Queue Estimation Model Using a Long Short-Term Memory Network

Lee, S Xie, K Ngoduy, D Keyvan-Ekbatani, M Yang, H

In this study, we develop a mathematical framework to predict cycle-based queued vehicles at each individual lane using a deep learning method - the long short-term memory (LSTM) network. The key c...

Continue Reading