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the shortcomings of these techniques, deep learning is more and more involved in static vulnerability localization and improving fuzzing efficiency. This project aims to deliver a smart software vulnerability
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‘dynamic graphs’. Although recently many studies on extending deep learning approaches for graph data have emerged, there is still a research gap on extending deep learning approaches for identifying
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. The specific research areas we will explore are + Adaptive scientific deep learning methods for mathematical physics problems governed by partial differential equations (domain decomposition, adaptive quadrature
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Project description: On behalf of the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR), we will establish the role of artificial intelligence (AI) deep learning to improve the prediction
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segmentation and classification; for example, segmenting tumour from the medical images, and then classify the grade of the tumour. We will use various Deep Learning techniques, such as CNN, and will experiment
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to the camera about your views on the same topic. Get ready to bring your ideas to life! Consider these pointers for your essay and video: Dive into how AI, machine learning, and automation jazz up moving
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Discovery Project, this research aims to develop highly novel physics-informed deep learning methods for Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) and applications in image
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HDR courses. Eligibility criteria The applicant will have a computing background in deep learning and familiar with psychology / human centred computing or biomedical engineering approaches. Some
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for intelligent systems to guard their execution and evolution. Required knowledge - deep learning - software development lifecycle
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In recent years, AI techniques such as GANs and associated deep learning neural networks have become popular tools applied to the production and creation of works of art. In 2018, AI Art made