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Article
Employing RNN and Petri Nets to Secure Edge Computing Threats in Smart Cities
The Industrial Internet of Things (IIoT) revolution has led to the development a potential system that enhances communication among a city's assets. This system relies on wireless connections to numerous limit...
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Chapter and Conference Paper
Cross-Modal Transformer GAN: A Brain Structure-Function Deep Fusing Framework for Alzheimer’s Disease
Cross-modal fusion of different types of neuroimaging data has shown great promise for predicting the progression of Alzheimer’s Disease(AD). However, most existing methods applied in neuroimaging can not effi...
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Chapter and Conference Paper
Fusing Structural and Functional Connectivities Using Disentangled VAE for Detecting MCI
Brain network analysis is a useful approach to studying human brain disorders because it can distinguish patients from healthy people by detecting abnormal connections. Due to the complementary information fro...
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Chapter and Conference Paper
Multiscale Autoencoder with Structural-Functional Attention Network for Alzheimer’s Disease Prediction
The application of machine learning algorithms to the diagnosis and analysis of Alzheimer’s disease (AD) from multimodal neuroimaging data is a current research hotspot. It remains a formidable challenge to le...
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Chapter and Conference Paper
Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer’s Disease Prediction
Multimodal neuroimage can provide complementary information about the dementia, but small size of complete multimodal data limits the ability in representation learning. Moreover, the data distribution inconsi...
