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of affective computing. The project aims to develop reproducible machine learning approaches for automatic emotion recognition in music, with stronger theoretical grounding, transparent model
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of affective computing. The project aims to develop reproducible machine learning approaches for automatic emotion recognition in music, with stronger theoretical grounding, transparent model
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increase in data volume, it is necessary to adopt parallelism techniques to achieve reasonable query response time. The main focus will be on parallel query processing, which is the main driver for Big Data
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truck. Computing the location and time of these connections remains an unsolved computational challenge. This project will create new algorithms for calculating cost-effective and time-efficient routes
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truck. Computing the location and time of these connections remains an unsolved computational challenge. This project will create new algorithms for calculating cost-effective and time-efficient routes
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will commence with, in parallel, the survey for collecting the data and a comparison of machine learning methods on artificial pseudo-randomly generated data. The world will see all too many changes
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will commence with, in parallel, the survey for collecting the data and a comparison of machine learning methods on artificial pseudo-randomly generated data. The world will see all too many changes
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NMT models where sufficient parallel corpora are not available (eg [1,2]). This project investigates methods to enable high performing NMT in low-resource scenarios. [1] Neural Machine Translation for
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NMT models where sufficient parallel corpora are not available (eg [1,2]). This project investigates methods to enable high performing NMT in low-resource scenarios. [1] Neural Machine Translation for
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[Malaysia Campus- VPSSP] An AI-Informed Planetary Health Framework for Equitable AMR Risk Mitigation
modelling from Project 1, we will conduct focus groups and interviews with communities in high- and low-risk zones to capture local knowledge, behaviours, and risk perceptions related to AMR. In parallel