AIMC Topic: Support Vector Machine

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Resting state fMRI feature-based cerebral glioma grading by support vector machine.

International journal of computer assisted radiology and surgery
PURPOSEĀ : Tumor grading plays an essential role in the optimal selection of solid tumor treatment. Noninvasive methods are needed for clinical grading of tumors. This study aimed to extract parameters of resting state blood oxygenation level-dependen...

Experimental analysis and mathematical prediction of Cd(II) removal by biosorption using support vector machines and genetic algorithms.

New biotechnology
We investigated the bioremoval of Cd(II) in batch mode, using dead and living biomass of Trichoderma viride. Kinetic studies revealed three distinct stages of the biosorption process. The pseudo-second order model and the Langmuir model described wel...

Dealing with heterogeneous classification problem in the framework of multi-instance learning.

Talanta
To deal with heterogeneous classification problem efficiently, each heterogeneous object was represented by a set of measurements obtained on different part of it, and the heterogeneous classification problem was reformulated in the framework of mult...

Predictors of schizophrenia spectrum disorders in early-onset first episodes of psychosis: a support vector machine model.

European child & adolescent psychiatry
Identifying early-onset schizophrenia spectrum disorders (SSD) at a very early stage remains challenging. To assess the diagnostic predictive value of multiple types of data at the emergence of early-onset first-episode psychosis (FEP), various suppo...

Kernel methods for large-scale genomic data analysis.

Briefings in bioinformatics
Machine learning, particularly kernel methods, has been demonstrated as a promising new tool to tackle the challenges imposed by today's explosive data growth in genomics. They provide a practical and principled approach to learning how a large numbe...

Detection of temporal lobe epilepsy using support vector machines in multi-parametric quantitative MR imaging.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The detection of MRI abnormalities that can be associated to seizures in the study of temporal lobe epilepsy (TLE) is a challenging task. In many cases, patients with a record of epileptic activity do not present any discernible MRI findings. In this...

Computer-aided diagnosis of malignant mammograms using Zernike moments and SVM.

Journal of digital imaging
This work is directed toward the development of a computer-aided diagnosis (CAD) system to detect abnormalities or suspicious areas in digital mammograms and classify them as malignant or nonmalignant. Original mammogram is preprocessed to separate t...

A multiresolution clinical decision support system based on fractal model design for classification of histological brain tumours.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Tissue texture is known to exhibit a heterogeneous or non-stationary nature; therefore using a single resolution approach for optimum classification might not suffice. A clinical decision support system that exploits the subbands' textural fractal ch...

Epileptic seizure prediction using relative spectral power features.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: Prediction of epileptic seizures can improve the living conditions for refractory epilepsy patients. We aimed to improve sensitivity and specificity of prediction methods, and to reduce the number of false alarms.

Rule extraction from support vector machines using ensemble learning approach: an application for diagnosis of diabetes.

IEEE journal of biomedical and health informatics
Diabetes mellitus is a chronic disease and a worldwide public health challenge. It has been shown that 50-80% proportion of T2DM is undiagnosed. In this paper, support vector machines are utilized to screen diabetes, and an ensemble learning module i...