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Self-Supervised Learning for Near-Wild Cognitive Workload Estimation
Feedback on cognitive workload may reduce decision-making mistakes. Machine learning-
based models can produce feedback from physiological data such as …
based models can produce feedback from physiological data such as …
Ml-powered handwriting analysis for early detection of Alzheimer's disease
Alzheimer's disease (AD) is a progressive, incurable condition leading to decline of nerve
cells and cognitive functions over time. Early detection is essential for improving quality of …
cells and cognitive functions over time. Early detection is essential for improving quality of …
Ensemble of vision transformer architectures for efficient Alzheimer's Disease classification
Transformers have dominated the landscape of Natural Language Processing (NLP) and
revolutionalized generative AI applications. Vision Transformers (VT) have recently become …
revolutionalized generative AI applications. Vision Transformers (VT) have recently become …
Exploring intervention techniques for Alzheimer's disease: Conventional methods and the role of AI in advancing care
Alzheimer's disease (AD) is a neurodegenerative condition characterized by cognitive
decline and functional impairment. This study compares conventional intervention …
decline and functional impairment. This study compares conventional intervention …
A combinatorial deep learning method for Alzheimer's disease classification-based merging pretrained networks
Introduction Alzheimer's disease (AD) is a progressive neurodegenerative disorder
characterized by cognitive decline, memory loss, and impaired daily functioning. Despite …
characterized by cognitive decline, memory loss, and impaired daily functioning. Despite …
VisTAD: A Vision Transformer Pipeline for the Classification of Alzheimer's Disease
In recent times, the Visual Transformer (VT) has emerged as a powerful alternative to the
conventional Convolutional Neural Networks (CNNs) for their superior attention mechanism …
conventional Convolutional Neural Networks (CNNs) for their superior attention mechanism …
AlzONet: a deep learning optimized framework for multiclass Alzheimer's disease diagnosis using MRI brain imaging
HA Alahmed, GA Al-Suhail - The Journal of Supercomputing, 2025 - Springer
Abstract Alzheimer's disease (AD), characterized by progressive neurological degeneration
and cognitive decline, necessitates early detection for effective intervention before symptom …
and cognitive decline, necessitates early detection for effective intervention before symptom …
[HTML][HTML] DenseIncepS115: a novel network-level fusion framework for Alzheimer's disease prediction using MRI images
One of the most prevalent disorders relating to neurodegenerative conditions and dementia
is Alzheimer's disease (AD). In the age group 65 and older, the prevalence of Alzheimer's …
is Alzheimer's disease (AD). In the age group 65 and older, the prevalence of Alzheimer's …
Revolutionizing Brain Disease Diagnosis: The Convergence of AI, Genetic Screening, and Neuroimaging
L Wang, S Li, X ** - Proceedings of the 2024 International Conference …, 2024 - dl.acm.org
The integration of artificial intelligence (AI), genetic screening, and neuroimaging heralds a
revolutionary advance in the diagnosis and understanding of brain diseases. This review …
revolutionary advance in the diagnosis and understanding of brain diseases. This review …