297 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"U.S" positions at Monash University
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
motif, hence renders the identification of the binding protein difficult. Here we propose for the first time to apply the Bayesian information-theoretic Minimum Message Length (MML) principle to optimise
-
Anomaly detection is an important task in data mining. Traditionally most of the anomaly detection algorithms have been designed for ‘static’ datasets, in which all the observations are available
-
new approaches for a playful human-computer integration future. For more information see http://exertiongameslab.org The result will be a thesis in the field of interaction design.
-
While deep learning has shown remarkable performance in medical imaging benchmarks, translating these results to real-world clinical deployment remains challenging. Models trained on data from one
-
I supervise computational projects in electron microscopy imaging for investigating materials at atomic resolution. Some projects centre on analysing experimental data acquired by experimental
-
Methods of balancing model complexity with goodness of fit include Akaike's information criterion (AIC), Schwarz's Bayesian information criterion (BIC), minimum description length (MDL) and minimum
-
This project examines how films produced in Asian markets perform in terms of commercial success and critical recognition using real-world industry data. Students will compile a dataset of films
-
) to support surgeons, operating room technicians, and other professionals in and around operating room activities. Particular areas that may be explored are: Immersive OR analytics: using XR to analyse data
-
to avoid system bottlenecks, and ensuring low-latency performance. Energy-Efficient Operations with Carbon-Aware Scheduling: Example: For non-urgent data processing, SmartScaleSys (S3) could prioritize tasks
-
Minimum Message Length (MML) is an elegant information-theoretic framework for statistical inference and model selection developed by Chris Wallace and colleagues. The fundamental insight of MML is