Intelligent Systems Research Group




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Intelligent Systems Research @ Murdoch University

The Intelligent Systems Research Group (ISRG) is established within the School of Information Technology in Murdoch University to identify and nurture promising research areas in the disciplines of intelligent and thinking systems. The ISRG serves as the focal point for intelligent data analysis and data mining related research. We also focus on development activities among the school, university, industry, and business. Research in ISRG is conducted by academic members, postgraduate students, and undergraduate students from the School of Information Technology in Murdoch University.

If you would like to know more information or explore research collaborations, please do not hesitate to contact us.

If you have any queries or comments, please feel free to contact any member of the research group.





Featured Research Projects

Intelligent Processing of Ancient Palm Leaves 

This project aims to create an intelligent system to extract Thai text information from Thai ancient palm leaves. The information that can be extracted from these ancient Thai palm leaves range from history, culture to medical information.
Estimation of Missing Precipitation Records using Computational Intelligence Techniques 

Estimation of missing precipitation records is one of the important tasks in hydrological study. The completeness of precipitation data leads to more accurate results from the hydrological models. This study investigate the use of modular computational intelligence technique to estimate missing monthly rainfall data.

 Timetabling and Scheduling with Memetic Algorithm 

Memetic Algorithms (MAs) attempt to improve evolutionary process by incorporating problem-specific knowledge into their framework. Consequently, MAs may deal well with the highly constrained combinatorial problem such as timetabling. Knowledge from the problem that is included in the chosen representation, search operators or evaluation function may improve the performance of MAs in solving timetabling problem. However, careful consideration must be taken into account when embedding the problem-knowledge into the components of MAs.








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