Intuitionistic fuzzy generalized eigenvalue proximal support vector machine
Generalized eigenvalue proximal support vector machine (GEPSVM) has attracted
widespread attention due to its simple architecture, rapid execution, and commendable …
widespread attention due to its simple architecture, rapid execution, and commendable …
Multi-view intuitionistic fuzzy support vector machines with insensitive pinball loss for classification of noisy data
C Lou, X Xie - Neurocomputing, 2023 - Elsevier
Multi-view support vector machines (MvSVMs) have been widely used to solve multi-view
classification problems. However, the conventional MvSVMs often overlook the presence of …
classification problems. However, the conventional MvSVMs often overlook the presence of …
Functional iterative approach for Universum-based primal twin bounded support vector machine to EEG classification (FUPTBSVM)
Due to the increasing popularity of support vector machine (SVM) and the introduction of
Universum, many variants of SVM along with Universum such as Universum support vector …
Universum, many variants of SVM along with Universum such as Universum support vector …
Universum twin support vector machine with truncated pinball loss
For classification problems, twin support vector machine with pinball loss (Pin-GTSVM) is
noise insensitive and has better performance than twin support vector machine (TWSVM) …
noise insensitive and has better performance than twin support vector machine (TWSVM) …
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 …
OISVM: Optimal Incremental Support Vector Machine-based EEG Classification for Brain-computer Interface Model
The brain-computer interface (BCI) is a field of computer science where users can interact
with devices in terms of brain signals. The brain signals are mimicked from the motor cortex …
with devices in terms of brain signals. The brain signals are mimicked from the motor cortex …
Classifying unstable and stable walking patterns using electroencephalography signals and machine learning algorithms
Analyzing unstable gait patterns from Electroencephalography (EEG) signals is vital to
develop real-time brain-computer interface (BCI) systems to prevent falls and associated …
develop real-time brain-computer interface (BCI) systems to prevent falls and associated …
Spectral analysis and Bi-LSTM deep network-based approach in detection of mild cognitive impairment from electroencephalography signals
A Said, H Göker - Cognitive Neurodynamics, 2024 - Springer
Mild cognitive impairment (MCI) is a neuropsychological syndrome that is characterized by
cognitive impairments. It typically affects adults 60 years of age and older. It is a noticeable …
cognitive impairments. It typically affects adults 60 years of age and older. It is a noticeable …
Universum parametric -support vector regression for binary classification problems with its applications
Universum data sets, a collection of data sets that do not belong to any specific class in a
classification problem, give previous information about data in the mathematical problem …
classification problem, give previous information about data in the mathematical problem …
Seizure detection via deterministic learning feature extraction
Z Zhang, W Wu, C Sun, C Wang - Pattern Recognition, 2024 - Elsevier
Epileptic seizures have a significant impact on the well-being of a large number of
individuals worldwide. Utilizing electroencephalographic (EEG) signals for automatic …
individuals worldwide. Utilizing electroencephalographic (EEG) signals for automatic …