Home – Teaching – Research – Publications – Activities
Dat Tran – Research
Grants
2007
CIs: Prof Simon Hawkins, Dr Alice
Richardson, Dr Brett Lidbury
AIs: A/Prof Dharmendra Sharma, Mr
Gus Koerbin, Prof John Fulcher,
Dr Dat Tran
Grant: University Interdisciplinary Research Grant
Abstract: In today’s health care
environment, the amount of electronically available clinical pathology data has
grown as hospital information systems become more commonplace. This data can
provide the basis for analyzing risk factors for many diseases. This project
will apply data mining techniques to clinical pathology databases to discover
new numerical rules that will identify small groups in the population that are
at high relative risk of certain viral diseases. Once rules have been
discovered, they can be used to develop a system of electronic alerts that will
identify patterns of pathology results that place a patient at a high relative
risk of a viral disease. A secondary aim
of the project is to enrich the established and thriving UC research program in
human virology
2006
CIs: Dr Dat Tran, Dr Wanli Ma,
A/Prof Dharmendra Sharma, Dr Shuangzhe Liu
Grant: BLIS Research Grant
Abstract: We propose fuzzy pattern
recognition methods for intrusion detection and spam emails filtering
systems. The project aims to build up
reliable intrusion detection spam emails filtering systems for activities over
computer networks and telephone lines. These systems will be parts of a
multi-agent system which is developing at School of Information Sciences and
Engineering. The proposed methods including fuzzy hidden Markov modelling, fuzzy normalisation,
fuzzy Gaussian mixture modelling and fuzzy entropy
clustering that have shown good performance in biometric authentication and
bioinformatics. The systems have wide applications in e-commerce, defence, human society and computer security operations,
high-tech crime investigation, and current battle against terrorism.
CIs: Dr
Wanli Ma, Dr Dat Tran
Grant: BLIS Research Grant
Abstract: An intrusion detection system monitors the computer network for possible
intrusions. There are many proposals, yet the simple requirement of being
efficient and effective is far from achievable. Most proposed IDS ignored the
time context when interpreting the network traffic record data. We propose to
interpret the data in the context of time. We propose 2 approaches: (1) using
data aggregation to associate time information and clustering engine of data
mining for intrusion detection; (2) threading network traffic records based on
network session activities along the time axis and using (Hidden) Markov Model
for intrusion detection. The former will be more efficient to perform but less
accurate, and vice versa for the later. Both should yield better detection
rate. An intrusion detection system is an important for ICT security. We have
been working in this area for some times and laid a broad foundation. Our next
step is to build the depth and produce quality research outcomes. This research
work is also in line with Australia National Research Priorities - Safeguarding
Australia.
2005
CI: Dr Dat Tran
Grant: BLIS Completion Research Grant
Abstract: This project aims to develop
an innovative and comprehensive application of statistical and fuzzy pattern
recognition for the computerized classification of cell nuclei in different
mitotic phases. We are interested in applying several advanced computational,
probabilistic, and fuzzy-set methods we have proposed in speech, speaker and
image recognition for the computerized classification of cell nuclei in
different mitotic phases.
CIs: Dr Dat Tran, A/Prof
Dharmendra Sharma, Dr Wanli Ma, Dr Shuangzhe Liu
Grant: University Multidisciplinary Research Grant
Abstract: We propose new statistical modelling methods that can be applied to biometric
authentication, intrusion detection and bioinformatics systems. The project aims to build up a reliable
authentication and intrusion detection system for activities over computer
network, telephone lines, and closed-circuit TV (CCTV) footages. The proposed
methods including quasi-likelihood estimation, temporal hidden Markov models,
and background modelling-based authentication can
avoid limitations of current systems and therefore enhance the system
performance and accuracy. The system has a wide application in e-commerce, defence, human society and computer security operations,
high-tech crime investigation, and current battle against terrorism.
CIs: A/Prof. Dharmendra Sharma,
Dr. Wanli Ma, Dr. Dat Tran, and Dr. Shuangzhe Liu,
Grant: BLIS Completion Research Grant
Abstract:.
CIs: Dr. Shuangzhe Liu, A/Prof.
Dharmendra Sharma, Dr. Dat Tran, Dr. Wanli Ma
Grant: BLIS Completion Research Grant
Abstract:.
CIs: Dr Shuangzhe
Liu, Dr Dat Tran, Dr Wanli Ma, A/Prof Dharmendra Sharma
Grant: BLIS Research Grant
Abstract: In this project, we shall
study likelihood related ideas, methods and other data mining techniques. We
shall obtain efficiency comparison and model selection results with
applications to pattern recognition, classification and evidence specification
and interpretation. The concept of likelihood plays an essential role in data
mining and statistics. Yet it is only recently recognized by researchers for
several areas. For example ideas and methods based on likelihood ratio and
quasi likelihood and their applications to speaker recognition and evidence
specification are still at an early but promising stage of research. The
project is significant also in the sense that the issues addressed are so
important and multidisciplinary of statistics, IT, forensics and security
issues, and are shared in the research areas of colleagues across disciplines
and schools at the University
2003
CI: Dr Dat Tran
Grant: Divisional Research Institute Grant
Abstract: We propose a new method for
person authentication that can achieve a considerably higher level of security.
The voice characteristics and the information content of spoken phrases will be
investigated for person authentication by combining speaker verification (SV)
and verbal information verification (VIV). VIV is the process of verifying
spoken utterances against personal information, such as the date of birth or
the mother’s maiden name, which is stored in a given personal data profile. The
system involves two phases, enrolment and verification, which are described in
the following subsections
Enrolment
phase: A key code, such as an account number, is assigned to each
”client” of the system. The client is then asked to provide a set of
personal information items, such as date of birth, address, home telephone
number, etc. A microphone is set up to collect the speech waveforms. Speech
data after feature extraction are used to train speaker hidden Markov models (HMMs) for the SV sub-system and sub-word HMMs for the VIV sub-system.
Verification
phase: An identity claim is made by an unknown person. The speech data obtained
from the unknown person are sent to the SV sub-system to verify the speaker and
to the VIV sub-system to verify the content of the spoken utterance. The
results from the two sub-systems are similarity scores, which are fused and
compared with a given threshold to either accept or reject the unknown person’s
identity claim
2001
CI: Dr Dat Tran
Grant: ARC Small Grant
Abstract: The project investigates novel ensemble methods in machine learning and
fuzzy pattern recognition approaches to speech and speaker recognition. The
aims are the further developments of Bagging and AdaBoost
methods and fuzzy hidden Markov models to speech and speaker recognition
systems. AdaBoost by re-weighting using fuzzy hidden
Markov models is also the alternative aim of the project. The expected outcomes
of the research project are new leading-edge methodologies for speech and
speaker recognition using ensemble methods and fuzzy hidden Markov models. The
significance of the project is the combined fuzzy and ensemble methodology has
the potential to develop into a new technology for speech and speaker
recognition.