Decoding cognitive health using machine learning: A comprehensive evaluation for diagnosis of significant memory concern
The timely identification of significant memory concern (SMC) is crucial for proactive
cognitive health management, especially in an aging population. Detecting SMC early …
cognitive health management, especially in an aging population. Detecting SMC early …
Granular ball twin support vector machine with pinball loss function
Alzheimer's disease (AD) and Schizophrenia (SCZ) are prominent neurodegenerative
conditions and leading causes of dementia, resulting in progressive cognitive decline and …
conditions and leading causes of dementia, resulting in progressive cognitive decline and …
Advancements in Alzheimer's disease classification using deep learning frameworks for multimodal neuroimaging: A comprehensive review
P Upadhyay, P Tomar, SP Yadav - Computers and Electrical Engineering, 2024 - Elsevier
Over the past years, Alzheimer's disease has emerged as a serious concern for people's
health. Researchers are facing challenges in effectively categorizing and diagnosing the …
health. Researchers are facing challenges in effectively categorizing and diagnosing the …
Deep evidential fusion with uncertainty quantification and reliability learning for multimodal medical image segmentation
Single-modality medical images generally do not contain enough information to reach an
accurate and reliable diagnosis. For this reason, physicians commonly rely on multimodal …
accurate and reliable diagnosis. For this reason, physicians commonly rely on multimodal …
GB-RVFL: Fusion of randomized neural network and granular ball computing
The random vector functional link (RVFL) network is a prominent classification model with
strong generalization ability. However, RVFL treats all samples uniformly, ignoring whether …
strong generalization ability. However, RVFL treats all samples uniformly, ignoring whether …
Multiview random vector functional link network for predicting DNA-binding proteins
The identification of DNA-binding proteins (DBPs) is a critical task due to their significant
impact on various biological activities. Understanding the mechanisms underlying protein …
impact on various biological activities. Understanding the mechanisms underlying protein …
Soft sensing modeling of penicillin fermentation process based on local selection ensemble learning
F Huang, L Li, C Du, S Wang, X Liu - Scientific Reports, 2024 - nature.com
In the process of penicillin fermentation, there is a strong nonlinear relationship between the
input eigenvector and multiple output vectors, which makes the prediction accuracy of the …
input eigenvector and multiple output vectors, which makes the prediction accuracy of the …
Brain Age Estimation Using Universum Learning-Based Kernel Random Vector Functional Link Regression Network
Brain age serves as a vital biomarker for detecting neurological ailments like Alzheimer's
disease (AD) and Parkinson's disease (PD). Magnetic resonance imaging (MRI) has been …
disease (AD) and Parkinson's disease (PD). Magnetic resonance imaging (MRI) has been …
An Ensemble of InceptionNet and MobileNet Pretrained Deep Learning Models for Classifying Stages of Dementia
M Pandiyarajan, RS Valarmathi, GM Eda… - 2024 15th …, 2024 - ieeexplore.ieee.org
Dementia is a growing brain disorder, with Alzheimer's disease accounting for 60-70% of
cases. By 2050, nearly 10 million people may be affected. To help early detection, we …
cases. By 2050, nearly 10 million people may be affected. To help early detection, we …
Bridging the Gap: Integrating Machine Learning With Biomarkers for Enhanced Alzheimer's Detection and Tracking
R Ravi, TP Sridevi, NN Devi… - Deep Generative Models …, 2025 - igi-global.com
Alzheimer's Disease (AD) is a relentless neurodegenerative disorder that profoundly affects
cognitive abilities. Early detection and precise tracking of AD progression are pivotal for …
cognitive abilities. Early detection and precise tracking of AD progression are pivotal for …