Uncertainty-aware multiview deep learning for internet of things applications
As an essential approach in many Internet of Things (IoT) applications, multiview learning
synthesizes multiple features to achieve more comprehensive descriptions of data items …
synthesizes multiple features to achieve more comprehensive descriptions of data items …
Deep learning based pipelines for Alzheimer's disease diagnosis: a comparative study and a novel deep-ensemble method
A Loddo, S Buttau, C Di Ruberto - Computers in biology and medicine, 2022 - Elsevier
Background Alzheimer's disease is a chronic neurodegenerative disease that destroys brain
cells, causing irreversible degeneration of cognitive functions and dementia. Its causes are …
cells, causing irreversible degeneration of cognitive functions and dementia. Its causes are …
EAGA-MLP—an enhanced and adaptive hybrid classification model for diabetes diagnosis
Disease diagnosis is a critical task which needs to be done with extreme precision. In recent
times, medical data mining is gaining popularity in complex healthcare problems based …
times, medical data mining is gaining popularity in complex healthcare problems based …
Effective feature learning and fusion of multimodality data using stage‐wise deep neural network for dementia diagnosis
In this article, the authors aim to maximally utilize multimodality neuroimaging and genetic
data for identifying Alzheimer's disease (AD) and its prodromal status, Mild Cognitive …
data for identifying Alzheimer's disease (AD) and its prodromal status, Mild Cognitive …
Deep learning framework for Alzheimer's disease diagnosis via 3D-CNN and FSBi-LSTM
Alzheimer's disease (AD) is an irreversible progressive neurodegenerative disorder. Mild
cognitive impairment (MCI) is the prodromal state of AD, which is further classified into a …
cognitive impairment (MCI) is the prodromal state of AD, which is further classified into a …
Multi-modal deep learning model for auxiliary diagnosis of Alzheimer's disease
F Zhang, Z Li, B Zhang, H Du, B Wang, X Zhang - Neurocomputing, 2019 - Elsevier
Alzheimer's disease (AD) is one of the most difficult to cure diseases. Alzheimer's disease
seriously affects the normal lives of the elderly and their families. The mild cognitive …
seriously affects the normal lives of the elderly and their families. The mild cognitive …
Tensorized multi-view subspace representation learning
Self-representation based subspace learning has shown its effectiveness in many
applications. In this paper, we promote the traditional subspace representation learning by …
applications. In this paper, we promote the traditional subspace representation learning by …
Rare Category Analysis for Complex Data: A Review
Though the sheer volume of data that is collected is immense, it is the rare categories that
are often the most important in many high-impact domains, ranging from financial fraud …
are often the most important in many high-impact domains, ranging from financial fraud …
CMC: a consensus multi-view clustering model for predicting Alzheimer's disease progression
Abstract Machine learning has been used in the past for the auxiliary diagnosis of
Alzheimer's Disease (AD). However, most existing technologies only explore single-view …
Alzheimer's Disease (AD). However, most existing technologies only explore single-view …
Incomplete multi-modal representation learning for Alzheimer's disease diagnosis
Alzheimers disease (AD) is a complex neurodegenerative disease. Its early diagnosis and
treatment have been a major concern of researchers. Currently, the multi-modality data …
treatment have been a major concern of researchers. Currently, the multi-modality data …