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 …
[HTML][HTML] Twin support vector machine: a review from 2007 to 2014
Abstract Twin Support Vector Machine (TWSVM) is an emerging machine learning method
suitable for both classification and regression problems. It utilizes the concept of …
suitable for both classification and regression problems. It utilizes the concept of …
FDN-ADNet: Fuzzy LS-TWSVM based deep learning network for prognosis of the Alzheimer's disease using the sagittal plane of MRI scans
Alzheimer's disease (AD) is the most pervasive form of dementia, resulting in severe
psychosocial effects such as affecting personality, reasoning, emotions, and memory …
psychosocial effects such as affecting personality, reasoning, emotions, and memory …
A framework of human detection and action recognition based on uniform segmentation and combination of Euclidean distance and joint entropy-based features …
Human activity monitoring in the video sequences is an intriguing computer vision domain
which incorporates colossal applications, eg, surveillance systems, human-computer …
which incorporates colossal applications, eg, surveillance systems, human-computer …
Dual-tree complex wavelet transform and twin support vector machine for pathological brain detection
(Aim) Classification of brain images as pathological or healthy case is a key pre-clinical step
for potential patients. Manual classification is irreproducible and unreliable. In this study, we …
for potential patients. Manual classification is irreproducible and unreliable. In this study, we …
Magnetic resonance brain image classification based on weighted‐type fractional Fourier transform and nonparallel support vector machine
To classify brain images into pathological or healthy is a key pre‐clinical state for patients.
Manual classification is tiresome, expensive, time‐consuming, and irreproducible. In this …
Manual classification is tiresome, expensive, time‐consuming, and irreproducible. In this …
[HTML][HTML] An improved multi-modal based machine learning approach for the prognosis of Alzheimer's disease
Alzheimer's disease (AD) is the most common type of neurological disorder that leads to the
brain's cell death overtime. It is one of the major important causes of memory loss and …
brain's cell death overtime. It is one of the major important causes of memory loss and …
An implementation of optimized framework for action classification using multilayers neural network on selected fused features
In video sequences, human action recognition is a challenging problem due to motion
variation, in frame person difference, and setting of video recording in the field of computer …
variation, in frame person difference, and setting of video recording in the field of computer …
Detection of Alzheimer's disease by displacement field and machine learning
Aim. Alzheimer's disease (AD) is a chronic neurodegenerative disease. Recently, computer
scientists have developed various methods for early detection based on computer vision …
scientists have developed various methods for early detection based on computer vision …
Detection of Alzheimer's disease by three-dimensional displacement field estimation in structural magnetic resonance imaging
Background: Within the past decade, computer scientists have developed many methods
using computer vision and machine learning techniques to detect Alzheimer's disease (AD) …
using computer vision and machine learning techniques to detect Alzheimer's disease (AD) …