Cognitive and Connectionist Systems Lab

Artificial Vision and Perception

Vision is indispensable. Building automated machines with human vision capabilities is a formidable challenge in both industry and academia. Much of human learning can be viewed as an unsupervised incremental learning process. Investigating this gives a significant contribution to machine learning. In this project our main focus is to investigate how humans accumulate knowledge incrementally by a series of objects, induce a hierarchy of concepts that summarize and organize such observations and use them in classifying future experiences.

 

Antepartum Fetus Health Investigations

Pre-labour fetal health is vital for a successful birth. Despite the maternal involvement in assuring fetal health, there are the risky deliveries where intervention is mandatory. The only external measurement that relates directly to fetal health is fetal heart rate patterns. Analyses of this property can be used to determine ominous instances. However, these pattens are largely misinterpreted by human observers leading to unnecessary surgical procedures. This project presents a cognitive approach to fetal heart rate interpretation. Artificial knowledge acquisition means are revamped with emphasis on understanding rather than enforcing knowledge.

 

Evolving Music

To be added.

Bioinformatics

Bioinformatics research involves retrieval and analysis of a large amount of biological data such as gene expressions, DNA, RNA, and proteins. Biological data is characterised by its high volume, high dimensionality and also complexity -in terms of structure, evolving nature and biological processes it involves in. Machine learning algorithms have been widely used for solving problems in bioinformatics such as sequence alignment, gene finding and protein domain analysis. This research is an attempt to develop a connectionist system that identifies dimensional changes in biological data.

 

Multi-modal Knowledge Discovery for Crime Profiling

Different modals of crime data are being produced at an accelerated pace on par with the escalating crime rate. This has led law enforcement agencies into many difficulties such as data explosion, data warehousing and distribution of data. Data mining in such a multi-modal environment provides a way out to obtain insights into the massive amount of crime data accumulated. Interesting patterns thus exposed can be used by law enforcement authorities to create profiles for each aspect of the crime such as the crime scene, the criminals and victims. Such an analysis would ultimately lead to the identification of significant pre-crime information. This project investigates the application of unsupervised connectionist mechanisms to visualisation, analysis and fusion of multi-modal criminal data. The applicability of a an incremental learning model is also explored.