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insufficient. Recently our research group has developed deep neural networks to enhance underwater communications. Our preliminary study shows that deep learning models are able to improve accuracies
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This project will explore different deep learning techniques for Android app analyses, e.g., to detect Android malware, to identify common vulnerabilities, or to pinpoint repackaged Android apps
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biodiversity and sustainability research. The initial objective is to use deep learning techniques to perform acoustic species identification in real-time on low-cost sensing devices coupled to cloud-based
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Although deep learning has produces state of the art results on many problems, it is a data hungry technology requiring a lot of human supervision in the form of annotated data. Potential PhD topic
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machine learning applications, both at the development phase and release phase. Required knowledge Strong programming skills Deep learning knowledge is a plus
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Over the past decades, we have witnessed the emergence and rapid development of deep learning. DL has been successfully deployed in many real-life applications, including face recognition, automatic
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process by leveraging deep learning including automated app functionality summarization [1], UI design generation [2], and front-end code generation [3]. We hope to explore more in this direction including
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propose a model to proactively locate accessibility issues and recommend potential fixes to their app based on deep learning models. On the other hand, we will also propose a new approach based on
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on leveraging advanced computer vision (CV) and deep learning (DL) techniques to develop a state-of-the-art computer-aided detection and diagnosis (CAD) system for early-stage Alzheimer's disease (AD) detection
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) before the human eye can see them. The principal aim of this PhD research program is to develop methods to improve the hyperspectral image classification using deep learning techniques. The developed