This paper is intended to contribute to both the dialogue around the use of AI in the ADF, as well as to provide a useful resource for ADF members to enhance their education and understanding about the technologies of artificial intelligence, with a particular focus on deep learning.

This study investigated information exchange and processing under the pressures and limitations of first respondents at the tactical edge to provide insight into transitioning reference information to a dynamic 'real-time' paradigm.

This report thus investigates the subject of factor screening, for stochastic simulation models, and overviews several solutions to this problem. In particular, sequential bifurcation will be shown to provide a very efficient approach to the problem of factor screening, in comparison to standard one factor at a time classication methods.

This work investigated a method for predicting future trends in peer-reviewed publication data related to science and technology. The developed method use non-linear regression to fit a particular type of s-shaped curve

There has been some work in recent years on investigating the use of machine learning (ML) techniques in order to assist software vulnerability discovery. Motivated by the spectacular success of deep learning (DL) approaches in fields such as computer vision and natural language processing, we propose to study the application of DL techniques to software vulnerability discovery. 

A suitable model of the return ground clutter is required to help assess the performance of over-the-horizon radar. Currently, models for the clutter reflected from the sea exist but there are no models for the backscatter from land. The backscatter coefficient, which characterises the backscattered power, can be determined by considering the difference between observed backscatter ionograms and synthesised ionograms.