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Bio
Dr. Rahman is the leader of ‘Sensor Data Analytics’ team in CSIRO. He leads a team of data scientists who specializes in development of machine learning algorithms for transforming sensor data into decisions by integrating cross-disciplinary knowledge. His key research area is Applied Machine Learning with strong focus on Agriculture and Mining applications. Some key research work by his team involves:
Livestock behavior modelling using machine learning algorithms from on-body sensors. This useful for understanding feed intake, feed conversion ratios, birth difficulty, and health issues that drives efficient management practices. The works are done in collaboration with CSIRO Ag & Food
Data driven decision support for aquaculture. A range of works under this umbrella involves development of machine learning algorithms to understand key water quality factors affecting harvest, factors affecting closure in prawn ponds, and environmental impact of oyster physiology. The works are done in collaboration with other Data61 teams and CSIRO Ag & Food
Integration and exploration of multi modal data sources for effective data mining to identify mineral deposits. The works are done in collaboration with CSIRO Mineral Resources
Adoption of Neural Network on volumetric data for grade engineering. The works are done in collaboration with CSIRO Mineral Resources
Livestock behavior modelling using machine learning algorithms from on-body sensors. This useful for understanding feed intake, feed conversion ratios, birth difficulty, and health issues that drives efficient management practices. The works are done in collaboration with CSIRO Ag & Food
Data driven decision support for aquaculture. A range of works under this umbrella involves development of machine learning algorithms to understand key water quality factors affecting harvest, factors affecting closure in prawn ponds, and environmental impact of oyster physiology. The works are done in collaboration with other Data61 teams and CSIRO Ag & Food
Integration and exploration of multi modal data sources for effective data mining to identify mineral deposits. The works are done in collaboration with CSIRO Mineral Resources
Adoption of Neural Network on volumetric data for grade engineering. The works are done in collaboration with CSIRO Mineral Resources
Research Interests
Papers共 159 篇Author StatisticsCo-AuthorSimilar Experts
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QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY (2024)
ADVANCES IN ARTIFICIAL INTELLIGENCE, AI 2023, PT I (2024): 367-378
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMSno. 3 (2024): 1438-1450
CoRR (2023): 1-8
arXiv (Cornell University) (2023)
Research Square (Research Square) (2023)
IJCNNpp.1-8, (2023)
CoRR (2023)
Research Square (Research Square) (2023)
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