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Md Zubeda Banu, Dr. A. Ganesh Head · International Conference Computing Methodologies and Communication · 2026

Early diagnosis of Alzheimer disease (AD) is a critical issue because of complex interplay between clinical and temporal as well as neuroimaging parameters. The research proposed a Cross-Attention Based Multimodal Transformer (CAMT-AD) model that is trained on magnetic resonance imaging (MRI) and structured clinical data to diagnose AD correctly and with explanations. The model combines a Vision Transformer (ViT) to extract features of MRI and a Transformer encoder to extract clinical time-series data, integrated with a cross-attention mechanism to learn the inter-modal relationships. The framework us…

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Oluwatoyin Kode, Mitch Hong, Long Nguyen 等 1 人 · ET Journal · 2026

Dementia-related disorders, particularly Alzheimer's disease (AD), represent a growing global health challenge, increasing the need for early and reliable detection. Electroencephalography (EEG), a non-invasive and cost-effective neurophysiological modality, has emerged as a promising tool for identifying neural signatures associated with cognitive decline. Recent advances in machine learning (ML) and deep learning (DL) have enabled more effective analysis of complex EEG signals for automated dementia prediction. This survey provides a comprehensive synthesis of EEG-based dementia studies published between 2020…

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Haiteng Wang, Jing Yan, Xiaokang Wang 等 1 人 · 2026

Industrial time series serve as the foundation for Prognostics and Health Management (PHM) to ensure the reliability and safety of industrial equipment such as aero-engines. However, existing approaches are constrained by single-modality inputs and struggle to exploit the semantic priors carried by maintenance logs and inspection imagery…

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