Comprehensive review on twin support vector machines
Twin support vector machine (TWSVM) and twin support vector regression (TSVR) are newly
emerging efficient machine learning techniques which offer promising solutions for …
emerging efficient machine learning techniques which offer promising solutions for …
Deep learning for brain age estimation: A systematic review
Abstract Over the years, Machine Learning models have been successfully employed on
neuroimaging data for accurately predicting brain age. Deviations from the healthy brain …
neuroimaging data for accurately predicting brain age. Deviations from the healthy brain …
[HTML][HTML] Brain-age prediction: A systematic comparison of machine learning workflows
S More, G Antonopoulos, F Hoffstaedter, J Caspers… - NeuroImage, 2023 - Elsevier
The difference between age predicted using anatomical brain scans and chronological age,
ie, the brain-age delta, provides a proxy for atypical aging. Various data representations and …
ie, the brain-age delta, provides a proxy for atypical aging. Various data representations and …
KNN weighted reduced universum twin SVM for class imbalance learning
In real world problems, imbalance of data samples poses major challenge for the
classification problems as the data samples of a particular class are dominating. Problems …
classification problems as the data samples of a particular class are dominating. Problems …
A robust deep learning framework based on spectrograms for heart sound classification
Heart sound analysis plays an important role in early detecting heart disease. However,
manual detection requires doctors with extensive clinical experience, which increases …
manual detection requires doctors with extensive clinical experience, which increases …
A review of neuroimaging-driven brain age estimation for identification of brain disorders and health conditions
Background: Neuroimage analysis has made it possible to perform various anatomical
analyses of the brain regions and helps detect different brain conditions/disorders. Recently …
analyses of the brain regions and helps detect different brain conditions/disorders. Recently …
Association of white matter volume with brain age classification using deep learning network and region wise analysis
Structural magnetic resonance imaging (sMRI) has been used to examine age-related
neuroanatomical changes in the human brain. In the present work, a pre-trained deep …
neuroanatomical changes in the human brain. In the present work, a pre-trained deep …
Tumor localization and classification from MRI of brain using deep convolution neural network and Salp swarm algorithm
Early diagnosis of brain tumors is crucial for treatment planning and increasing the survival
rates of infected patients. In fact, brain tumors exist in a range of different forms, sizes, and …
rates of infected patients. In fact, brain tumors exist in a range of different forms, sizes, and …
Conv-ervfl: Convolutional neural network based ensemble RVFL classifier for Alzheimer's disease diagnosis
As per the latest statistics, Alzheimer's disease (AD) has become a global burden over the
following decades. Identifying AD at the intermediate stage became challenging, with mild …
following decades. Identifying AD at the intermediate stage became challenging, with mild …
DDCNN: A deep learning model for AF detection from a single-lead short ECG signal
With the popularity of the wireless body sensor network, real-time and continuous collection
of single-lead electrocardiogram (ECG) data becomes possible in a convenient way. Data …
of single-lead electrocardiogram (ECG) data becomes possible in a convenient way. Data …