AIMC Topic: Support Vector Machine

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Support vector machine-based classification of first episode drug-naïve schizophrenia patients and healthy controls using structural MRI.

Schizophrenia research
Although regional brain deficits have been demonstrated in schizophrenia patients by structural MRI studies, one important question that remains largely unanswered is whether the complex and subtle deficits revealed by MRI could be used as objective ...

A novel fuzzy rough selection of non-linearly extracted features for schizophrenia diagnosis using fMRI.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Schizophrenia is a severe brain disorder primarily diagnosed through externally observed behavioural symptoms due to the dearth of established clinical tests. Functional magnetic resonance imaging (fMRI) can capture the dis...

The application of artificial neural networks and support vector regression for simultaneous spectrophotometric determination of commercial eye drop contents.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
In the present study, artificial neural networks (ANNs) and support vector regression (SVR) as intelligent methods coupled with UV spectroscopy for simultaneous quantitative determination of Dorzolamide (DOR) and Timolol (TIM) in eye drop. Several sy...

Protein structure modeling and refinement by global optimization in CASP12.

Proteins
For protein structure modeling in the CASP12 experiment, we have developed a new protocol based on our previous CASP11 approach. The global optimization method of conformational space annealing (CSA) was applied to 3 stages of modeling: multiple sequ...

Computer-aided diagnosis of contrast-enhanced spectral mammography: A feasibility study.

European journal of radiology
OBJECTIVE: To evaluate whether the use of a computer-aided diagnosis-contrast-enhanced spectral mammography (CAD-CESM) tool can further increase the diagnostic performance of CESM compared with that of experienced radiologists.

Cancer Classification Based on Support Vector Machine Optimized by Particle Swarm Optimization and Artificial Bee Colony.

Molecules (Basel, Switzerland)
Intelligent optimization algorithms have advantages in dealing with complex nonlinear problems accompanied by good flexibility and adaptability. In this paper, the FCBF (Fast Correlation-Based Feature selection) method is used to filter irrelevant an...

Multi-class parkinsonian disorders classification with quantitative MR markers and graph-based features using support vector machines.

Parkinsonism & related disorders
BACKGROUND AND PURPOSE: In this study we attempt to automatically classify individual patients with different parkinsonian disorders, making use of pattern recognition techniques to distinguish among several forms of parkinsonisms (multi-class classi...

isGPT: An optimized model to identify sub-Golgi protein types using SVM and Random Forest based feature selection.

Artificial intelligence in medicine
The Golgi Apparatus (GA) is a key organelle for protein synthesis within the eukaryotic cell. The main task of GA is to modify and sort proteins for transport throughout the cell. Proteins permeate through the GA on the ER (Endoplasmic Reticulum) fac...

B-factor profile prediction for RNA flexibility using support vector machines.

Journal of computational chemistry
Determining the flexibility of structured biomolecules is important for understanding their biological functions. One quantitative measurement of flexibility is the atomic Debye-Waller factor or temperature B-factor. Most existing studies are limited...

An EEG-based functional connectivity measure for automatic detection of alcohol use disorder.

Artificial intelligence in medicine
BACKGROUND: The abnormal alcohol consumption could cause toxicity and could alter the human brain's structure and function, termed as alcohol used disorder (AUD). Unfortunately, the conventional screening methods for AUD patients are subjective and m...