Brunei Darussalam

Related Research:

My primary research area is in clustering algorithms. I have spent more than 10 years studying algorithms in the domains of data mining and artificial intelligence, with specialisation in semi-supervised and unsupervised clustering algorithms and evolutionary algorithms. In addition, I am venturing into other areas to apply data science and artificial intelligence such as in geology (characterisation of source rock, use of deep learning for rock facies identification, water quality analysis, characterisation of peatland), traffic driving behaviours (identifying driver profiles), human activity discovery, patent analytics and applying analytics on LiDaR data. I have been working on applying computational intelligence algorithms and data analysis to medical data for modelling since 2010 and continue to do so with colleagues from the medical as well as more recently, geology domains.

Relation to Climate Resilience and Adaptation Theme:

We apply data science and artificial intelligence to domains relating to Climate Resilience and Adaptation such as Forest monitoring, Water Quality Analysis and Green Energy. In the forest monitoring domain, we have built a novel comprehensive geological dataset built from data extracted from literatures on 52 peatlands in Southeast Asia and applied data science on the dataset to understand the characteristics and similarity of peatlands in the region in relation to their geolocation and geology. These peatlands are important carbon sinks and their decline will affect global climate fluctuations. In an on-going project initially funded by UK-ASEAN Institutional Links Early Career Researchers Scheme” and in collaboration with Birmingham City University (BCU), UK, we developed a system applying artificial intelligence to automatically identify and locate the Acacia Mangium trees, a species that can contribute to forest fires, from geolocation information extracted from Geoinformation Information System software and human annotated RGB images collected by drones. We are also collecting other remote sensing data automatically extract information about the forest such as forest canopy structure detection, land use identification and forest change detection. By monitoring our forests, we hope to understand and devise methods of management and control to prevent or regulate forest loss which can impact climate change, and in turn endanger human lives. Under a changing climate, the management of water resources is of key importance. We investigated whether the current water quality changes in the Brunei River is related to climatic change, collecting 16 water quality parameters. Through this study, we can quantify and identify the impacts of climate change and pollution and plan actions for policy changes and remedy. We have applied data science and/or natural language processing in analysing literatures in energy-related studies such as biomass-based carbon supercapacitors, methanation catalysts and hydrothermal biomass processing to generate insights without having to conduct extensive resource-expensive experiments, contributing to green energy initiatives. Another project is our study of wave prediction to determine the feasibility of harnessing green energy from Brunei waves. Such green energy efforts help to promote resilience and adaptation climate change.

Affiliated Organisation:

School of Digital Science, Universiti Brunei Darussalam.